40results about How to "Implement soft sensing" patented technology

The invention relates to a soft sensing method for load parameters of a ball mill. The method is that a hardware supporting platform is used to obtain vibration signals, vibration sound signals and current signals of a ball mill cylinder to soft sense ball mill internal parameters (ratio of material to ball, pulp density and filling ratio) characterizing ball mill load. The method comprises the following steps that: the vibration, the vibration sound, the current data and the time-domain filtering of the ball mill cylinder are acquired, time frequency conversion is conducted to the vibration and the vibration sound data, kernel principal component analysis based nonlinear features of the sub band of the vibration and the vibration sound data in frequency domain are extracted, nonlinear features of the time domain current data are extracted, feature selection is conducted to the fused nonlinear feature data and a soft sensing model based on a least squares support vector machine is established. The soft sensing method of the invention has the advantages that the sensitivity is high, the sensed results are accurate, the practical value and the popularization prospect are very good, and the realization of the stability control, the optimization control, the energy saving and the consumption reduction of the grinding production process is facilitated.

The invention discloses a supercritical boiler fire coal heat value self-balance control loop distributed control system implementation method. The method comprises the steps of outputting a final coal feeder rotation speed command after PID adjustment is carried out on a received coal feed quantity command and a total coal quantity command with a heat value corrected, wherein the total coal quantity command with the heat value corrected is a total coal quantity command output by multiplying a dynamically-measured total coal feed quantity by a heat value balance correction coefficient, and the heat value balance correction coefficient is a delayed heat value balance correction coefficient. According to the method, soft measurement of a fire coal heat value can be achieved based on a unit DCS platform, additional investment is not needed, and a soft measurement signal of the fire coal heat value makes it convenient for operating personnel to monitor the quality of fired coal in real time and guides boiler combustion adjustment. A fire coal heat value self-balance control loop is designed, and therefore a unit coordinated control system has the ability to adapt to various types of coal, and the operation stability and the economy of a unit are improved.

The invention relates to a robust random-weight neural network-based molten-iron quality multi-dimensional soft measurement method which belongs to the blast-furnace smelting automatic control field, in particular to a Cauchy distribution weighted M-estimation random-weight neural network (M-RVFLNs) based method for multi-dimensional parameter-dynamic soft measurement of the molten-iron quality in the blast-furnace smelting process. According to the method of the invention, the principal component analysis (PCA) method is adopted to chose main parameters which affect the blast-furnace molten iron quality as model input variables, a molten-iron quality multi-dimensional dynamic prediction model which has an output self-feedback structure and takes into account input-output data at different moments is constructed, and it is possible to carry out multi-dimensional dynamic soft measurement of the main parameters Si content, P content, S content and molten iron temperature which represent the blast-furnace molten iron quality. The method of the invention comprises the following steps of (1) choosing auxiliary variables and determining model input variables and (2) training and using the M-RVFLNs soft measurement model.

The invention provides a method for soft measurement of the effluent total phosphorus (TP) in the sewage disposal process based on the neural network, and belongs to the field of sewage disposal field. The mechanism is complex in the sewage disposal process, and to enable a sewage disposal system to be in a good running working condition and to obtain the higher effluent quality, the procedure parameters and the water quality parameters in the sewage disposal system need to be detected. The invention provides a soft measurement model established based on the self-organization radial-based neural network to solve the problem that the effluent total phosphorus of a current sewage disposal plant cannot be obtained in real time. The initial structure and the initial parameters of the neural network are determined according to the self-organization method, the structure of the neural network is simplified, and real-time soft measurement is carried out on the effluent TP. According to the soft measurement result, the related control link in the sewage disposal process and materials in the biochemical reaction are adjusted, the quality of the effluent obtained after sewage disposal is improved, and a theoretical support and a technological guarantee are provided for safe and stable running in the sewage disposal process.

The invention discloses a soft measuring method of product quality of an industrial fluidized bed gas-phase polyethylene device. The method comprises the following steps: selecting a plurality of key variables influencing product quality to set up a process detection variable set; applying the projection principle of multivariate statistics to establish a soft measuring model between product resin melt index and density and process detection variables; adopting a nonlinear partial least square method of embedded Taylor series approximation (used for carrying out process nonlinear characteristic representation) as core technology; and determining the number of optimal pivot elements through cross check technology. The soft measuring method avoids complex process mechanism analysis and has convenient site implementation and high precision of soft measuring; therefore, the method is particularly suitable for nonlinear industrial occasions with high dimension and abundant progress data similar to the industrial fluidized bed gas-phase polyethylene production device. Moreover, the soft measuring method can be used for real-time monitoring or production guidance to improve product quality, increase output and ensure smooth device operation, thereby bringing greater economic benefits.

The invention discloses a traveling-wave tube internal temperature soft-measurement method based on a finite element model. With multi-point temperature values of the shell of a traveling-wave tube as auxiliary variables, the finite element soft-measurement thermal model of the traveling-wave tube is created; with heat source distribution as independent variables, a target function of an error sum of squares between the multi-point temperature measurement values of the shell and corresponding point temperature simulation values of the finite element thermal model of the traveling-wave tube is derived, and is solved by an iterative algorithm, so that an optimal heat source distribution solution is obtained, finally, the optimal heat source distribution solution is loaded into the finite element thermal model of the traveling-wave tube, and an internal temperature soft-measurement value of the traveling-wave tube is obtained by finite element stimulation calculation. The traveling-wave tube internal temperature soft-measurement method based on the finite element model breaks through the limitation of conventional detection, and prevents a series of complex problems brought about by temperature sensors placed in traveling-wave tubes, measurement is convenient, the implementation cost is low, and the traveling-wave tube internal temperature soft-measurement method based on the finite element model can be applied in the mass testing of traveling-wave tubes.

The invention provides a method for distinguishing the temperature of a submersible motor based on a lumped parameter model, belonging to the technical field of an application base of combination of thermodynamics modeling and motor control. The method is used for solving the problem that acquisition of temperature information for the submersible motor by using a temperature sensor is restricted by subsurface environments. The method comprises the following steps: firstly, collecting the three-phase stator voltage and three-phase stator current of the submersible motor; then, respectively processing the three-phase stator voltage and the three-phase stator current by a signal conditioning circuit, wherein the processed signals as original current input signals of a stator and original voltage input signals of the stator; calculating the gross calorific power u of the submersible motor by an industrial personal computer (IPC) in accordance with the original current input signals of the stator and the original voltage input signals of the stator; and allocating the gross calorific power u to each part of the submersible motor, combining equivalent circuits of the submersible motor, constructing a lumped parameter heat network model and solving the lumped parameter heat network model to obtain a temperature value of each part of the submersible motor. The method is applied to distinguishing the temperature of the submersible motor.

The invention discloses an oxygen content detecting method for boiler fuel gas of a coal-fired power station. The oxygen content detecting method includes: step 1, acquiring and preprocessing primary data; step 2, screening variables; step 3, selecting training samples; step 4, establishing at least square supported vector machine model; step 5, comparing relation between an output value of a soft-measuring model and a measured value of a zirconia sensor, and selecting one of the output value and the measured value as a detecting value of a fuel gas oxygen content detecting system. The oxygen content detecting method has the advantages of no added hardware equipment, good reliability, sufficient precision and the like, and can realize precise measurement for oxygen content of the fuel gas of large boilers.

The invention discloses a disc mill control method. The disc mill control method comprises the following steps: a neural network is established, and a BP neural network and an additional momentum learning rule are used to train the neural network according to existing historical monitoring data; the optimal input parameter of the neural network is solved with particle swarm optimization; through estimated and actually measured error data of the neural network, the additional momentum learning rule is used to update the trained neural network. The disc mill control method solves the problems that the beating degree of a pulp mill is mainly determined by manual assay and far lags behind a beating process and a disc mill cannot be controlled precisely and effectively, soft measurement of the beating degree is realized, and on-line detection signals are provided for real-time control of the disc mill.

The invention discloses an energy-saving control-oriented hybrid electric vehicle energy and heat integrated model, and belongs to the technical field of hybrid electric vehicle modeling. The invention aims to provide the energy-saving-control-oriented hybrid electric vehicle energy and heat integrated model of each power transmission node module in a power chain from a power driving device such as an engine and a motor to wheels. A model establishing method comprises the steps of determining power chain input, describing an energy flow state in the power chain, establishing a battery model, establishing an engine energy consumption and temperature model, establishing an air conditioner heat transfer model, receiving a heat chain input instruction and establishing a cockpit temperature model. According to the method, the coupling relation among the cab heat supply requirement, the driver power requirement and the vehicle energy consumption under the high and cold weather condition is established, the soft measurement of part of important thermodynamic variables is achieved, and support is provided for driver comfort evaluation.

The invention provides a method for detecting the length of a calcium carbide furnace electrode. The method comprises the steps: obtaining a non-linear prediction model of electrode consumption according to a model training sample set, wherein electrode temperature T, electrode power P, a furnace charge ratio R and calcium carbide output G are sample input variables, and the electrode consumption is a sample output variable; determining electrode consumption in a current working condition according to the non-linear prediction model of electrode consumption; and determining the length of an electrode according to the electrode consumption in the current working condition. The method can monitor the position changes of an electrode in real time and then guide pressing and releasing for electrodes, so the pressing and releasing amount for electrodes can be accurately controlled, and electrodes are made to work in a balance manner. The method guarantees stable production and energy-saving operation of a calcium carbide furnace.

The invention relates to a method for detecting hydroquinone in compost on line, which comprises putting a biosensor in measured solution, utilizing an electrochemical analyzer to analyze current variation data which are adopted by the biosensor, getting response current change characteristic values, response current stabilization time and steady-state current values, outputting the parameter values in a neural network analysis apparatus, and getting the concentration value of hydroquinone after analyzing and calculating. The invention also relates to an on-line detection system which is used in the detecting method, which comprises a three-electrode device, an electrochemical analyzer and a neural network analysis apparatus, wherein the three-electrode device is connected with the electrochemical analyzer, and the electrochemical analyzer is connected with the neural network analysis apparatus, the three-electrode device utilizes a carbon paste electrode which decorates a laccase-Fe3O4 magnetic nano-particle cross-linking body to a working electrode. The one-line detection method and the on-line detection system have the advantages of convenience, high effective, sensitivity, strong anti-interference capability, low production cost and the like.

The invention provides a metal material forged microstructure soft measurement method based on a self-adaptive expert system. The metal material forged microstructure soft measurement method comprises the following steps of (1) building an initial expert system according to historic forging technological parameters and metal material microstructure data, wherein the initial expert system mainly comprises a database, an inference engine and a learning machine; (2) comparing actual forging technological parameters with technological parameters in the database, thus obtaining a technological parameter error vector; computing the technological parameter matching degree according to the inference engine; (3) estimating a current microstructure by utilizing the technological parameter matching degree and the microstructure data in the database; (4) carrying out self-learning on the expert system, and storing new technological parameters and the microstructure data in the database if the estimated current microstructure is a new microstructure; ending if the estimated current microstructure is not the new microstructure. The metal material forged microstructure soft measurement method based on the self-adaptive expert system, provided by the invention, is capable of accurately measuring a metal material forged microstructure online, and the technical problem that the metal material forged microstructure is difficult to measure online is solved.

The invention provides a knowledge-based robust effluent ammonia nitrogen soft measurement method, and belongs to the field of sewage treatment. As the urban sewage treatment process is complex in mechanism and serious in uncertain interference, and it is difficult to detect the current effluent ammonia nitrogen concentration and it is hard to establish an accurate mathematical model, a robust soft measurement method urgently needs to be researched. A fuzzy neural network based on modeling error probability density function distribution is utilized to establish a soft measurement model of effluent ammonia nitrogen concentration: to begin with, establishing a robustness criterion based on modeling error probability density function distribution; and then, adjusting parameters of the fuzzy neural network based on an adaptive gradient descent algorithm until the model meets the requirement of information processing. The method realizes accurate measurement of the effluent ammonia nitrogenconcentration, helps to improve the monitoring level of the effluent ammonia nitrogen concentration in the urban sewage treatment process, and provides necessary guarantee for stable and efficient operation of the sewage treatment process.

The invention relates to a blasting bead wall material capable of regulating and controlling bursting pressure and a method for measuring the bursting pressure of a blasting bead, which belong to thetechnical field of blasting bead production. The wall material is prepared from the following raw materials in parts by weight: 100 parts of deionized water, 1 to 4 parts of colloid, 4 to 10 parts oftoughening agent, 0.05 to 1 part of filling agent and 0.1 to 1 part of edible pigment, wherein the colloid is selected from one or a mixture of more of carrageenan, gelatin, Arabic gum and pectin; andthe toughening agent is selected from one or a mixture of more of glycerol and sorbitol. The bursting bead prepared from the wall material has the advantages that under the condition of ensuring theflowability, the bursting pressure of the bursting bead can be controlled within the range of 6-10 N / mm, and the bursting bead has stronger environmental adaptability and better stability; meanwhile,soft measurement of the bursting pressure of the blasting bead is realized, and experiments prove that the fitting degree of the actual bursting pressure and the bursting pressure calculated by utilizing the pressure index is high, so that the method can be applied to prediction of the bursting pressure for preparing the blasting bead.

The invention discloses a steel mill gas sulfur content soft measurement method. The theoretical dry flue gas amount generated by unit volume of gas combustion is directly calculated through the gas heat value, the actual dry flue gas amount generated by unit volume of gas combustion is further obtained by combining with flue gas oxygen content calculation, and finally the sulfur content of the gas is calculated by combining with the SO2 emission concentration in the flue gas. The measuring method is particularly suitable for gas combustion equipment such as a gas boiler. The sulfur content ofthe coal gas can be deduced reversely by means of online monitoring SO2 emission concentration, coal gas heat value configured by combustion equipment, flue gas oxygen content and other online measurement data, and therefore online soft measurement of the sulfur content of the coal gas is achieved. Compared with a traditional measurement method, the calculation precision is not reduced, but the solving process is greatly simplified.

The invention discloses a coal-fired boiler NOX soft measurement method based on a whale algorithm for optimizing a long-short term memory network. The method comprises the following steps: analyzing and selecting related auxiliary variables according to an NOX generation mechanism and a boiler combustion process; extracting data corresponding to the auxiliary variables from a DCS (Distributed Control System) of the coal-fired power plant and carrying out data preprocessing; carrying out secondary screening on the auxiliary variables by adopting a principal component analysis method, and solving each principal component and the contribution rate thereof; adding a whale optimization algorithm, and obtaining a proper hyper-parameter value through optimization; substituting the hyper-parameter values into the network, and constructing a coal-fired power plant boiler NOX soft measurement model of the whale algorithm optimized long-short term memory neural network; taking the obtained data of the auxiliary variables as input, obtaining a soft measurement value of the NOX content through a coal-fired power plant boiler NOX soft measurement model, and assisting hard measurement equipment by establishing the soft measurement model. The defects of traditional equipment are overcome, and the economic cost of production monitoring is reduced.

The invention belongs to the technical field of petrochemical engineering, and provides an automatic control method for hydrogen supplementing of an intermittent polypropylene device based on soft measurement. A programmable controller is used for calculating a hydrogen dissolution amount according to temperature and pressure of a high pressure propylene storage tank, the responded dissolution concentration in the tank according to built-in hydrogen-propylene phase balance data, and the total flow of high-pressure recovered propylene into a next kettle, finally, the amount of fresh hydrogen needed to be supplemented is calculated according to the required amount of hydrogen, and the corresponding amount of hydrogen is automatically supplemented. According to the method, human experience factors in operation can be reduced, produced polypropylene products are more stable, and the polypropylene products with the corresponding grades can be produced more accurately.

The invention provides a rotor revolving speed and displacement soft measurement method of a bearingless permanent magnet synchronous motor, which creates conditions for realizing real-time online control of torque and radial suspension force of the bearingless permanent magnet synchronous motor and is suitable for high-performance control of the bearingless permanent magnet synchronous motor. Inorder to construct a relation among the rotor revolving speed and displacement and other easy-to-measure auxiliary variables, a soft measurement model which is established based an adaptive BP neuralnetwork is provided, and being different from the conventional parameter self-adaption, the method performs adaptive control for the number of hidden layers of the BP neural network, directly determines least square approximation weight and network precision of the neural network through a weight direct determination method, and can realize adaptive design of a hidden layer structure of the neuralnetwork through comparison with expected precision. The neural network model is not dependent on motor parameters, and thus, detection precision and system reliability are improved.

The invention discloses a solid state fermentation control method based on a neural network and a particle swarm optimization and aims to solve the problems that the fermented material data cannot be detected in real time and cannot be controlled in real time in the conventional solid state fermentation technology. The method comprises the following steps: 1, initializing training data, training the neural network, and starting the fermentation process; 2, solving proper external input parameters based on the neural network through the particle swarm optimization; 3, training the neural network through the real-time data; 4, judging whether manual sampling measurement is performed, if so, performing sampling measurement and training the neural network through the measured data; and 5, judging whether the fermentation process is ended, if so, stopping cycling, otherwise returning to the step 2. The manual sampling frequency needed by solid state fermentation is far less than the manual regular sampling frequency of the traditional solid state fermentation, the solid state fermentation production efficiency is improved, and the problem that pollution is easily caused due to repeated sampling is solved.

The invention relates to an energy-saving control-oriented hybrid electric vehicle energy and heat integration model, which belongs to the technical field of hybrid electric vehicle modeling. The purpose of the present invention is an energy-saving control-oriented hybrid electric vehicle energy and heat integration model of each power transmission node module in the power chain from the engine, the motor and other power drive devices to the wheels. The steps of the invention are: determining the power chain input, describing the energy flow state in the power chain, establishing a battery model, establishing an engine energy consumption and temperature model, establishing an air-conditioning heat transfer model, receiving heat chain input instructions, and establishing a cockpit temperature model. The invention establishes the coupling relationship between the heating demand of the cockpit, the power demand of the driver and the energy consumption of the vehicle under the alpine climate condition, realizes the soft measurement of some important thermodynamic variables, and provides support for the evaluation of the driver's comfort.

The invention discloses an online monitoring method for the contact resistance of multiple key contact points in a medium-voltage switch cabinet, and relates to the technical field of power equipment.Finite element software is adopted to analyze and solve the temperature rise under different contact resistance distributions. During simulation calculation, the influence of the contact resistance of each point location on the temperature rise can be fully considered by reasonably setting the contact resistance distribution of different point locations. And neural network training is performed by taking data obtained by simulation as a sample, so the accuracy of a neural network prediction result can be ensured. And finite element software is adopted to analyze and calculate the relationshipbetween the temperature rise of each temperature measurement point and the effective value of the load current. Temperature rise data under different loads are calculated through finite element simulation, and the result obtained through the method is accurate. And fitting is performed according to the obtained data, and the obtained relational expression is accurate and reliable. By adopting theprediction method of the neural network, the contact resistance of multiple key points in the switch cabinet can be predicted at the same time, the prediction result is accurate, the process is rapid, and the problem that the contact condition in the switch cabinet cannot be monitored online is solved.

The invention discloses a soft measurement method for the internal temperature of a traveling wave tube based on a finite element model. The multi-point temperature values ​​of the shell of the traveling wave tube are used as auxiliary variables to establish a finite element soft measurement thermal model of the traveling wave tube. variables, derive the objective function of the sum of the squares of errors between the multi-point temperature measurement values ​​of the tube shell and the temperature simulation values ​​of the corresponding points of the TWT finite element thermal model, solve it with an iterative algorithm, and obtain the optimal solution of heat source distribution, and finally optimize the heat source distribution The solution is loaded into the finite element thermal model of the traveling wave tube, and the soft sensor value of the internal temperature of the traveling wave tube is obtained through finite element simulation calculation. The invention breaks through the traditional detection limitation, avoids a series of complicated problems caused by placing a temperature sensor inside the traveling wave tube, has convenient measurement, low implementation cost, and can be applied to the detection of a large number of traveling wave tubes.

The invention discloses a soft measuring method of product quality of an industrial fluidized bed gas-phase polyethylene device. The method comprises the following steps: selecting a plurality of key variables influencing product quality to set up a process detection variable set; applying the projection principle of multivariate statistics to establish a soft measuring model between product resin melt index and density and process detection variables; adopting a nonlinear partial least square method of embedded Taylor series approximation (used for carrying out process nonlinear characteristic representation) as core technology; and determining the number of optimal pivot elements through cross check technology. The soft measuring method avoids complex process mechanism analysis and has convenient site implementation and high precision of soft measuring; therefore, the method is particularly suitable for nonlinear industrial occasions with high dimension and abundant progress data similar to the industrial fluidized bed gas-phase polyethylene production device. Moreover, the soft measuring method can be used for real-time monitoring or production guidance to improve product quality, increase output and ensure smooth device operation, thereby bringing greater economic benefits.

The invention discloses a measurement method of aluminum liquid interface fluctuation. The method comprises the following steps: step one, collecting anode current of each anode in an aluminum electrolysis cell and calculating an anode current fluctuation coefficient of each anode; step two, obtaining a theoretical aluminum liquid interface height and a position coordinate corresponding to each anode by using a CFD current field simulation technology, and correcting the theoretical aluminum liquid interface height by using the anode current fluctuation coefficient to obtain an aluminum liquidinterface actual height of each anode; step three, obtaining an aluminum liquid interface actual height and a coordinate position of each sample point in the whole electrolysis cell by using a Krigingspatial interposition method according to the aluminum liquid interface actual height and the position coordinate of each anode; and step four, building a three-dimensional coordinate system throughthe position coordinate and the aluminum liquid interface actual height of each sample point in the electrolysis cell and drawing a three-dimensional diagram and a two-dimensional contour diagram of the aluminum liquid interface. The three-dimensional diagram and the two-dimensional contour diagram obtained by the method are capable of visually reflecting the aluminum liquid interface fluctuationcondition.

The invention discloses a method for realizing a decentralized control system of a self-balancing control loop for coal-fired calorific value of a supercritical boiler. The machine speed command; the total coal volume command after the calorific value correction is the total coal volume command output after the dynamic actual measured coal supply and the calorific value balance correction coefficient are multiplied; the calorific value balance correction coefficient is a delay Calorific value balance correction coefficient of . Based on the DCS platform of the unit, the present invention can realize the soft measurement of the calorific value of coal without adding additional capital investment. The soft measurement signal of the calorific value of coal can facilitate the real-time monitoring of the coal quality of the coal into the furnace by the operator, guide the adjustment of the boiler combustion, and design based on this A coal-fired calorific value self-balancing control loop enables the unit coordination control system to have the ability to adapt to changes in multiple coal types, improving the unit's operational stability and economy.

The invention discloses a soft measurement method for a pichia pastoris fermentation process based on ONLINE-RLSSVM. The soft measurement method comprises the following steps of firstly, analyzing a pichia pastoris fermentation process technology mechanism, selecting appropriate auxiliary variables, and establishing a training sample database according to historical tank batch data; performing pretreatment on sample data, performing normalizing treatment, performing dimension reduction on the auxiliary variables by a principal component analysis method, and using several main component aggregative variables as a training sample database of ONLINE-RLSSVM; and then, using a firefly-fruit fly hybrid optimization algorithm for on-line optimizing a punishment coefficient c and a breadth factorsigma 2, using the training sample database for training the ONLINE-RLSSVM, establishing a soft measurement model, and finally, obtaining an inulase concentration predicted value. The firefly-fruit fly hybrid optimization algorithm is adopted, so that the overall optimization capacity and the prediction precision of the soft measurement model are improved, and the method can be effectively used for guiding the production of inulase.

The invention relates to a soft sensing method for load parameters of a ball mill. The method is that a hardware supporting platform is used to obtain vibration signals, vibration sound signals and current signals of a ball mill cylinder to soft sense ball mill internal parameters (ratio of material to ball, pulp density and filling ratio) characterizing ball mill load. The method comprises the following steps that: the vibration, the vibration sound, the current data and the time-domain filtering of the ball mill cylinder are acquired, time frequency conversion is conducted to the vibration and the vibration sound data, kernel principal component analysis based nonlinear features of the sub band of the vibration and the vibration sound data in frequency domain are extracted, nonlinear features of the time domain current data are extracted, feature selection is conducted to the fused nonlinear feature data and a soft sensing model based on a least squares support vector machine is established. The soft sensing method of the invention has the advantages that the sensitivity is high, the sensed results are accurate, the practical value and the popularization prospect are very good, and the realization of the stability control, the optimization control, the energy saving and the consumption reduction of the grinding production process is facilitated.

The invention discloses a wireless detection method and device for contact resistance of a busbar joint. A busbar penetrates a groove opening formed by an upper iron core and a lower iron core in a combined and surrounding mode, a flow and temperature measurement module is buckled on the surface of the busbar joint in a surrounding mode, a temperature sensor is directly contacted with the busbar joint and is connected with the flow and temperature measurement module, the highest surface temperature and the lowest surface temperature of the busbar joint are simulated through a finite element analysis method, the highest temperature and the lowest temperature are selected as BP neural network input variables, the contact resistance of the busbar serves as an output variable, and BP neural network training is finished. Alternating current is generated in a coil when current passes through the busbar, power supply by taking energy or current collection is selected through a selector switch, the temperature sensor detects the highest temperature and the lowest temperature of the surface of the busbar joint, current and temperature digital signals pass through an MCU processing unit and are sent to an upper computer through a wireless communication module, and the upper computer inputs the current and temperature digital signals into a BP neural network to realize the measurement of the contact resistance. The wireless detection method and device for the contact resistance of the busbar joint effectively resolve the problem that the contact resistance of the busbar in the operation process is difficult to measure.