39results about How to "Help formulate" patented technology

The invention discloses an anisotropic rotor holographic dynamic balancing method based on equivalent initial phase vector. According to the method, the holographic spectrum principle is taken as the basis, vibration data in two directions are taken into account and are subjected to data fusion, so the anisotropic characteristic can be completely formulated, and the balance fault diagnosis is facilitated; furthermore, the rule of angular momentum conservation is utilized, a holographic spectrum ellipse is converted into an equivalent rotating frequency circle, the equivalent initial phase vector is taken as the characteristic for unbalanced vibration, and the requirement on fine balance is met. Through the equivalent initial phase vector, the defects of traditional initial phase vector on describing the amplitude of unbalance and phase position caused by anisotropy can be eliminated, the unbalance characteristic can be accurately characterized, and the spatial dynamic property of a rotor can be truly indicated. Therefore, the anisotropic rotor holographic dynamic balancing method based on the equivalent initial phase vector, provided by the invention, facilitates improving the accuracy and efficiency of on-site dynamic balance, so that the operating precision and service life of a rotary machine can be effectively improved and prolonged.

The invention provides an urban education service facility evaluation optimization method. The method comprises the steps of extracting and integrating basic data and traffic big data of geographicalnational condition elements, calculating and determining a basic evaluation index system of urban education service facilities, performing service capability analysis, accessibility analysis and education equalization analysis, and quantitatively measuring the supply degree of the education facilities in multiple dimensions. Based on the traffic big data, according to the distribution condition ofschools and communities in a city, identifying blind areas of education service facilities; comprehensively evaluating the reasonability of the school zoning by utilizing the consistency index, the school space dominance index, the service blind area, the school district service scale, the service population quantity and the characteristic population quantity; and optimally configuring schools towhich the planning unit belongs according to a majority principle based on the blind area and the traffic big data and the conditions of schools to which all the communities belong in the planning unit. According to the invention, the current situation of urban education service configuration can be automatically evaluated and predicted, and an optimal configuration scheme is provided.

The invention belongs to the technical field of environmental pollutant prevention and control, and particularly relates to a method for diagnosing the concentration level of environmental medium pollutants. The method comprises the following steps: S1, determining an environmental medium; S2, selecting pollutants to be diagnosed; S3, analyzing and diagnosing the concentration of pollutants in theenvironmental medium; S4, concluding: analyzing and summarizing the result of the concentration level of the pollutants in the environmental medium. S3 specifically comprises the following operations: S3.1, setting a data acquisition range; S3.2, determining a data acquisition rule; S3.3, sorting, merging and calculating data; S3.4, sampling data; S3.5 performing pollution level evaluation. According to the method, data with the same investigation range and the same investigation purpose are summarized, the investigation frequency and the number of investigation samples are indirectly increased, sampling errors caused by the small number of investigation samples are avoided, the accuracy of environmental pollution concentration level analysis is improved, and a manager can make a pollution prevention and control strategy easily.

The embodiment of the invention provides a delay duration prediction method and device. According to the invention, the delay duration of the target flight can be predicted; wherein the delay durationof the target flight is predicted by combining delay parameters of multiple dimensions of the target flight; wherein the delay parameters at least comprise weather parameters of the target flight ata predetermined takeoff moment, air traffic control parameters of the target flight, delay duration of a previous flight of the target flight, minimum station passing duration of the target flight, mechanical fault parameters of the target flight and the like; the delay duration of the target flight is predicted by combining a plurality of types of delay parameters and the target prediction model.Therefore, the accuracy of the predicted delay duration is high, flight scheduling, maintenance ground service scheduling and emergency plan making of airlines are facilitated, and the method has important practical significance for improving the service quality of the airlines and improving the competitiveness of the airlines.

The invention discloses an intelligent health examination diagnostic apparatus and belongs to the field of medical diagnostic equipment. The intelligent health examination diagnostic apparatus comprises an information data detecting and collecting system, a data processing system, a transmission system and a palmtop computer. The intelligent health examination diagnostic apparatus is characterized in that the information data detecting and collecting system is composed of a high-definition camera, an infrared detecting device, a blood oxygen detector, a sound image collecting and converting device, an ultrasound generating, accepting and converting device, an electrocardio remote sensing and accepting device and a blood pressure detecting gauge. The in-vivo and in-vitro states are detected through camera shooting/photographing, sounds, sound waves and electrocardio sensing accepting, and the detection information is transmitted to a computer software system of the data processing system through the transmission system to be processed or directly referenced by a doctor or directly transmitted to an electronic medical record platform of the palmtop computer. The intelligent health examination diagnostic apparatus has the advantages of being light, simple, practical, easy and convenient to operate, accurate in diagnosis, standard, efficient and the like.

The invention discloses an output quantity and input quantity recording method, a system, a device and a storage medium. The output quantity and input quantity recording method comprises the followingsteps: performing image recognition processing on a first image and a second image, recognizing types and quantities of excrement and food, determining water contents of the food and the excrement, determining the output quantity and input quantity, and the like. According to the invention, an intelligent terminal is used for recording the output quantity and input quantity of a patient, so thatthe situations of excessive recording, missing recording, disordered recording and the like can be effectively avoided, medical data can be systematically managed by institutions such as hospitals andthe like conveniently, and medical measures for the patient can be formulated. The method is executed through a computer system, part of work of hospital staff in the prior art can be replaced, the workload of medical staff is reduced, and the high error rate caused by much manual participation is avoided. The method provided by the embodiment of the invention can be executed through terminals such as a mobile phone, so that the implementation cost of the output quantity and input quantity recording method provided by the embodiment of the invention is lower. The method is widely applied to the technical field of intelligent terminals.

The invention discloses a method for predicting the total energy consumption on the basis of a fixed base energy consumption elasticity coefficient. According to the method, the research limitation caused by fluctuations and incomparability of traditional energy consumption elasticity coefficients is overcome, the fixed base energy consumption elasticity coefficient is adopted, the relation between the fixed base energy elasticity coefficient and an industrial structure is considered, a prediction model for the fixed base energy consumption elasticity coefficient is built, and then the total energy consumption in the future years can be predicted. The method comprises the steps of obtaining GDP, CPI and total energy consumption data of historical years, defining the fixed base energy consumption elasticity coefficient, and calculating the fixed base energy consumption elasticity coefficient value and the tertiary industrial added value proportion of the historical years; building the prediction model of the fixed base energy consumption elasticity coefficient; calculating the total energy consumption of a predictive period. According to the method, the fixed base energy consumption elasticity coefficient is predicted, then the total energy consumption in the future years is predicted, medium and long energy planning is facilitated, and the energy-saving policy is reasonably formulated.

The invention discloses an Alzheimer's disease pre-diagnosis method based on a 3D convolutional neural network. The method comprises the following steps: S1, selecting a data set: selecting MRI and DTI image data through adopting an ADNI database; S2, performing data preprocessing: preprocessing the selected MRI and DTI image data; S3, adopting a DL method based on the CNN comprising the specific steps: performing 3D convolution; adopting a batch regularization technology, and adopting a linear rectification function as an activation function; and performing 3D pooling; S4, establishing a network system structure; and S5, realizing performance evaluation, adding DTI data to improve diagnosis accuracy, and acquiring the DTI image data by scanning the same parameter from the same system as an MRI image. Therefore, the method also has the characteristics of low cost, no trauma and easiness in obtaining, and is easy to popularize in clinical application.

The invention provides a power load prediction method and device, electronic equipment and a storage medium, and relates to the technical field of micro-grid management, and the method comprises the steps: obtaining the user feature information of a first user in a micro-grid, the power load of the first user at a current date, and the environment information of the first user, wherein the environment information comprises a geographic position, climate information and/or a date type of a current date; and taking the user feature information of the first user, the power consumption load of thefirst user on the current date and the environment information of the first user as a trained power load prediction model to obtain a predicted power consumption load of a specified day output by thepower load prediction model. The problems that in the prior art, the load law is difficult to grasp, and prediction is inaccurate and easy can be solved, and the safety and stability of a microgrid system can be improved.

The invention provides a method for performing quantitative evaluation on a device operation mode. The method comprises the steps of collecting historical operation data which comprise device defect data and production plan data; according to the device defect data and the production plan data, generating an operation work effectiveness basic score and an operation work adjusting coefficient, andobtaining a percentage value of the operation work effectiveness through multiplying the operation work effectiveness basic score by the operation work adjusting coefficient; analyzing production operation plan data, generating operation work manpower cost and an operation work cost adjusting coefficient, and obtaining a total cost number of the operation work through multiplying the manpower costby the operation work cost adjusting coefficient; and obtaining a benefit quantification result of the particular operation work mode of an individual device through dividing the percentage value ofthe operation work effectiveness by the total cost number of the operation work and multiplying by an individual device age coefficient. The method according to the invention can be used for visuallycalculating the effectiveness of the operation work.

The invention provides an intersection traffic state identification method based on a PSO-ELM algorithm. The method comprises the following steps: acquiring a historical traffic flow data set of a labeled intersection connection road section; learning the historical traffic flow data set by using an extreme learning machine (ELM) algorithm, optimizing parameters of the ELM by using a particle swarm optimization (PSO) algorithm, setting a fitness function of the PSO as a mean square error (MSE) of an ELM predicted value and an actual value, taking a parameter at the optimal fitness value as a final parameter of the ELM, and taking a final ELM model as an intersection traffic state recognition model; and identifying the traffic state of the intersection to be identified by using the intersection traffic state identification model. According to the method, the PSO-ELM algorithm is applied to the field of traffic state recognition, and the traffic state of the intersection is recognized by using the traffic state of the intersection connecting road, so that the formulation of traffic control measures of the intersection connecting road is facilitated.