300 results about "Degree function" patented technology

The invention discloses an intelligent control method of a driverless vehicle tracking a desired trajectory. The method is characterized by comprising the following steps of: establishing a body coordinate system, and determining the position information of the vehicle; calculating a membership degree function of a road curvature characteristic according to a radian value, and defining a fuzzy rule to calculate a decision output speed; according to the distance the vehicle runs for 1.5-2.5 seconds, searching for the trajectory points larger than the distance and closest to the vehicle in the coordinate sequence of the desired trajectory; calculating the moving curvature of the vehicle according to an adaptive proportion-differential-integral control algorithm formula; and finally judging whether the target point of the current control cycle is the last point selected from the road point sequence; if so, outputting a signal to a brake servo control system to start the brake to slow down the vehicle; otherwise, outputting a voltage signal by a controller local area network module to keep and control the accelerator opening so that the vehicle continues proceeding. Through adoption of the intelligent control method, the driverless vehicle can realize a function of tracking the desired trajectory, and has certain adaptability to the change of the road curvature characteristic.

The invention provides a multi-robot path planning and coordination collision prevention method based on fuzzy logic. The method comprises the steps of confirming an input variable and an output variable of a fuzzy controller by setting up the fuzzy controller with robots as a system, using related languages to describe the input variable and the output variable of the fuzzy controller, confirming a qualitative reasoning principle according to the fuzzy control theory, setting up fuzzy control rules according to input signals and output signals of the fuzzy controller, selecting a subordinating degree function of each input language variable and a subordinating degree function of each output language variable, finally, conducting defuzzification, conducting voting on the basis of the maximum principle of the subordinating degree functions, conducting corresponding motions on the robots, and completing the task for planning a path of a single robot. On the basis of the method, the other robots are regarded as dynamic front barriers, and the aim of coordination collision prevention of the multiple robots can be achieved.

Aiming at the defects in the prior art, the invention discloses a control method of a speed regulator of a servo system of a flat knitting machine. A system in the prior art has low adaptability and low stability. According to a fuzzy proportional-integral (PI) control algorithm of the control method, the running speed of a transmission mechanism of the computerized flat knitting machine is used as a control object; the difference value between the practical reference speed and the feedback speed and the change rate of the difference value are served as input of a fuzzy controller; the input quantity is fuzzified through selecting an appropriate universe of discourse and an appropriate membership function; appropriate fuzzy rule tables are set by utilizing the practical tuning strategies of PI parameters; and after a Mamdani fuzzy reasoning algorithm and defuzzification processing are adopted, the variable quantities of parameter values of the PI controller are output, thereby realizing on-line correction of the PI parameters. By adopting the control method, the disadvantages of a traditional manual correction method for the PI parameters are overcome, and on-line real time correction of the PI parameters is realized, thereby improving the adaptability and the stability of the system.

A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a suspension system is described. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and / or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual suspension system model of the controlled suspension system. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.

The invention provides a method for correcting navigation deviation and a navigation display adopting the same. The method comprises the steps of: matching with a given electronic map through the moving and running directions of the coordinates of a vehicle, pointing out a currently selected road, obtaining matching degrees of candidate road sections by means of a matching degree function by taking the road section and the vehicle running direction as one parameter and taking an Euclidean distance from a measuring point to the candidate road section as the other parameter, and selecting the matching road section with the maximum matching degree. By means of the invention, the deviation of an inertial system, caused by gyro drift, can be eliminated, and errors can be corrected in real time to obtain the accurate running position and direction of the vehicle.

The invention discloses a tension fuzzy PID (Proportion Integration Differentiation) control method for a recoiling machine. In the method, current ring-speed ring double closed-loop control is adopted, a speed regulator and a current regulator are arranged respectively in the feedback elements of a current ring and a speed ring, six fuzzy controllers are adopted for controlling the PID parameters of the current ring and the speed ring, two inputs and one output are adopted for each fuzzy controller, input quantities are deviation values E and deviation change rates Ec of set tension, and output quantities are P, I and D parameters of a current controller and a speed controller respectively; three corresponding fuzzy controllers are connected in series respectively in front of the current regulator and the speed regulator, a subordinating degree function is regulated continuously in the running process of the recoiling machine, automatic control over the tension of the recoiling machine is realized by controlling the rotating speed of a motor and armature current, and the tension of the recoiling machine is regulated automatically and is always kept constant; and as proved by simulink simulation, the method has the advantages of quick response, high regulating capability, high robustness and effective improvement on the control effect.

The invention discloses an oilfield development evaluation method. The oilfield development evaluation method includes that acquiring development dynamic data and phase permeability experiment data of a researched oil reservoir block; dividing the researched oil reservoir block into a plurality of development stages according to water content; confirming evaluation indexes of each development stage of the researched oil reservoir block; calculating the practical values and theoretical values of each evaluation index of each development stage of the researched oil reservoir block; calculating the threshold of each evaluation index of each development stage; confirming the weight of each evaluation index which influences the researched oil reservoir block development effect, and acquiring an evaluation index weight matrix; acquiring development dynamic data of an oil reservoir block to be evaluated; confirming the development stage of the oil reservoir block to be evaluated, and calculating the practical values of evaluation indexes of the development stage; building evaluation index subordinating degree functions of the oil reservoir block to be evaluated, and acquiring an evaluation index subordinating degree matrix; calculating comprehensive evaluation values of the oil reservoir block to be evaluated. The oilfield development evaluation method is easy to acquire data, the evaluation workload is lowered, and the result is reliable.

The present invention relates to design method of optimizing water quality model parameters in improved mixed genetic algorithm, and belongs to the field of water quality model parameter calibration optimizing method. The design method includes the following steps: preparing data for water quality model parameter calibration optimizing design; establishing and optimizing improved mixed genetic algorithm; improving adaptability degree function; mixing with simplex algorithm serially; running the algorithm program, inspecting the simulating result of different algorithms and completing the optimization after the error reaching the specified standard. The present invention can calibrate model parameters fast accurately, and raise the application efficiency of comprehensive water quality model in various water body researches.

The invention relates to a self-learning mechanism-base fast matching fuzzy reasoning method. The method includes the following steps that: a Gaussian membership degree function method is adopted to construct parameter fuzzification information; a fuzzy rule base is established; external parameters are fuzzificated, so that a fact item can be obtained; the fact item is matched with rules in the fuzzy rule base by adopting a rete algorithm, so that a fuzzy reasoning result can be obtained; the fuzzy reasoning result is subjected to defuzzification, so that a final reasoning result can be obtained; and a sample set is constructed according to the final reasoning result and an actual feedback result, and rule strength self-learning correction is carried out based on the sample set. According to the self-learning mechanism-base fast matching fuzzy reasoning method of the invention, the rete algorithm is adopted, so that the efficiency of fuzzy reasoning can be improved, and the fuzzy reasoning method can be applied to the engineering field with high real-time requirements.

A fuzzy fault classification method of an electric transmission line includes the first step of determining the time of occurrence of a fault, the second step of computing fault input vectors, the third step of constructing fuzzy support vector machine FSVM dichotomy devices, the fourth step of training and optimizing the FSVM dichotomy devices, the fifth step of constructing a banding subsection subordinating degree function of a FSVM higher space, the sixth step of enabling the fault input vectors to be input into each FSVM dichotomy device to obtain a preliminary classification label, a decision function value and an initial subordinating degree of each FSVM dichotomy device, the seventh step of constructing and training a support vector regression (SVR), the eighth step of sending the decision function values and initial subordinating degrees into the SVR to obtain a final fault subordinating degree of a fault sample, and the ninth step of judging the final fault type according to the final subordinating degree. According to the fuzzy fault classification method of the electric transmission line, the fuzzy subordinating degree function is introduced, and therefore influences of noise points and isolated points on a SVM hyperplane structure are reduced; the SVR is adopted to perform correction on the preliminary classification labels obtained by the FSVM, the fault classification label is obtained accurately through fuzzification processing, regressive optimization processing and the like, and therefore the accuracy and fault tolerance for fault classification of the electric transmission line are greatly improved.

The invention discloses a comprehensive microgrid planning and design evaluation system based on an OTFN-AHP (optimized triangular fuzzy number analytic hierarchy process), and belongs to the technical field of electric information planning. An evaluation method comprises the steps that 1) a microgrid planning and design evaluation index system is determined; 2) computation models of microgrid evaluation indexes and reasonable and practical non-linear membership function models of the indexes are provided, so that quantized values of the indexes are obtained; 3) the traditional analytic hierarchy process and a fuzzy mathematic theory are combined and optimized to form the OTFN-AHP; 4) the fuzziness of an ordering vector is removed by utilizing a centroid method variable according to preference of a decision maker; 5) relative weights of the indexes are solved by nonlinear planning according to the ordering vector; and 6) an advantage and disadvantage degree of a microgrid is determined according to the quantized values and the weights of the indexes; and an evaluation report of the micogrid is output. The evaluation system can make scientific and objective evaluation on microgrid planning and design schemes in different structures, so that the evaluation system provides a reasonable reference for the decision maker, and has very high actual instruction significance.

The present invention discloses a system and a method of bus dynamic scheduling based on WiFi detection and GPS positioning. WiFi probes are arranged on buses and bus stations to detect devices opening a WiFi function on the buses and in a detection range near the bus stations and record MAC address data; vehicle GPS devices are employed to record bus real-time GPS data; a core algorithm is employed to perform noise reduction and matching of the MAC address data and estimate real-time passenger capacities of the buses and the number of waiting passengers at the bus stations in real time; passenger waiting time is taken as parameters to set a waiting satisfaction degree function, departure frequencies are taken as parameters to set a bus company satisfaction degree function, and two weightvalues are determined according to actual conditions to obtain a comprehensive satisfaction degree function; and an optimal value of the satisfaction degree function is solved based on the real-time data such as the real-time passenger capacities of departed buses and the real-time number of waiting passengers at each bus station, and a departure frequency suitable for a current line passenger flow condition is dynamically obtained.

The invention provides an L-curve-based hyperspectral unmixing method for estimating a regularized parameter automatically. An expected target is reached by using the method in hyperspectral unmixing. The method comprises the following steps: (1) reading hyperspectral remote sensing image data V with a computer; (2) determining the number of end members in the hyperspectral image by a virtual dimensionality (CD) method; (3) initializing an end member matrix W and an abundance matrix H by a vertex component analysis (VCA) algorithm, and performing projection operations on the W and the H; and (4) determining a regularized item, determining an iteration expression of the regulated parameter alpha, the end member matrix W and the abundance matrix H, and iterating the alpha, the W and the H until simultaneous constringency of a target function and the regularized parameter. The algorithm is characterized by overcoming the unreasonable disadvantages caused by selecting the regularized parameter manually in an NMF in the prior art, utilizing a fixed point algorithm to estimate an optimal regularized parameter, and dynamically weighing a fitting degree function and the regularized item automatically so as to achieve a better unmixing effect.

The invention provides a retrieving and ordering method and system for commodity data. The method comprises the steps that results related to retrieving sample images and retrieving keywords are found from a commodity database according to retrieving conditions submitted by a user, and the similarity between the retrieving conditions input by the user at present and the results is calculated; attribute information of preferential commodities of user is extracted from a user interest database, a user interest preference model is built, and the similarity between the commodities and the user preference is calculated through the user preference model; an ordering degree function of each result is calculated according to the similarities calculated in the front steps; after descending ordering is performed on the results according to the size of function values of the ordering degree functions, an ordering result is returned to the user. The system comprises a retrieving condition similarity calculating module, a user preference similarity calculating module, an ordering degree calculating module and an ordering result displaying module. Accordingly, personalized commodity ordering which is more accordant with the retrieving intention of user is supplied to the user.

The invention discloses a power supply reliability fuzzy comprehensive evaluation method based on an improved membership degree function. The method comprises the following steps: (1) selecting N basic level indexes capable of reflecting a current situation of a power supply reliability of a power distribution network to establish an evaluation index system; (2) dividing quality grades of N evaluation indexes into six quality grades, which respectively are "optimal", "excellent", "good", "mean", "qualified" and "unqualified"; (3) determining a comprehensive weight for each of the evaluation indexes in the evaluation index system by employing subjective and objective analysis methods which combine an improved hierarchical analysis method with a variation coefficient method; and (4) carrying out scoring on each evaluation index to obtain a scoring value of the each evaluation index. According to the method disclosed by the invention, the problem that a relation is established difficultly between two quality grades of the evaluation indexes in power supply reliability fuzzy comprehensive evaluation field is solved; and meanwhile, the problem that when index data are similar, evaluation results are consistent and cannot be distinguished is solved, and more fine power supply reliability fuzzy comprehensive evaluation is realized.

The invention discloses a method and a system for optimizing database parameters. The method comprises the following steps: selecting a plurality of sample values of database performance parameters, and carrying out typical application pressurization operation on a database to obtain server performance index parameter values as teacher values corresponding to the sample values; carrying out fuzzification processing operation on each sample value and the corresponding teacher value through a fuzzy subordinating degree function so as to form a plurality of input samples; constructing a fuzzy neural network, and using the input samples to train the fuzzy neural network; selecting the server performance index parameter values with a preset numeric value of the database as the input of an adjusting and optimizing model according to the adjusting and optimizing model corresponding to the trained fuzzy neural network, and computing the database performance parameter values as optimization results so as to adjust the database performance parameters of the database. Through the technical scheme provided by the invention, automatic optimization of the database is realized, and complicated operation steps caused by empirical value depending on an engineer and manual adjusting and optimizing implementation in the prior art are prevented.

The invention discloses an electric transmission line icing prediction model based on a neural network and a fuzzy logic algorithm. The model comprises the following steps: reading micro meteorological parameters to form a training sample, modifying the weight of a network, introducing a threshold, acquiring the fundamental component of the icing thickness, reading position information of a pole tower, establishing an altitude subordinating degree function and a large area moisture distance subordinating degree function, establishing an error correction subordinating degree function, forming a fuzzy rule bank so as to obtain correction coefficients through defuzzification, and combining the calculation result of the neural network and the fuzzy logic compensation result. The electric transmission line icing prediction model with geographical location information is high in prediction precision when being compared with a conventional global model and a single BP (Back Propagation) neural network, and has a good effect in practical application.

The invention provides a mechanical arm flexible joint control method based on a fuzzy neural network. The method comprises the following steps that a fuzzy neural network model is established, a neural network parameter learning algorithm is developed; the learning algorithm determines a connection weight parameter of a consequent network and a subordinating degree function center value and a width parameter of an antecedent network; and a fuzzy neural network controller is established according to an identification model to overcome the influence of many nonlinear characteristics of a flexible joint. According to the provided technical scheme, two kinds of control methods of fuzzy logic and the neural network are combined, and the fuzzy neural network controller base on an X model is adopted, so that the neural network is provided with a structure of a fuzzy system, each layer and each node of the neural network correspond to a part of the fuzzy system, the network is different fromblack box operation of a general neural network, all parameters are of definite physical significances, and the network can adapt to the characteristics such as time varying of rigidity and nonlinearfriction of the flexible mechanical arm joint.

The invention provides a space mechanical arm structure parameter optimization method based on operability. The method comprises the following steps that (1) the freedom degree layout of a space mechanical arm and an initial parameter of an arm rod are determined; (2) the mechanical arm direct kinematics is applied, and the reachable work space of the space mechanical arm is determined by a numerical value method; (3) an upper half circle part is taken from a symmetrical surface of the reachable work space, and is divided into c*c sub regions, c is a positive integer, then, flexible posture probability coefficients are respectively calculated for each sub region, and a flexible work space chart of the whole reachable work space is obtained; (4) in the reachable work space, M work points are randomly selected, the respective corresponding flexible posture probability coefficients are calculated, in addition, the number m of work points greater than alpha in the posture probability coefficients is counted, and the mechanical arm operability is obtained; (5) the reciprocal number of the operability is used as an adaptability degree function, and the parameters of each arm rod of the space mechanical arm are optimized by a genetic algorithm.

The invention discloses a hybrid electric vehicle hydrogen consumption and load change energy management method. Through measures of model establishment, fuzzy rule introduction, energy management controller design and optimization and the like, a fuzzy energy management control method optimized through a genetic algorithm is established, and the operation efficiency of a fuel cell vehicle is improved. A subordinating degree function and a fuzzy rule base of the subordinating degree function are defined, and expert knowledge can be used for grasping a main output rule. The core of the method is optimization of parameters of the subordinating degree function, and inputs, outputs and fuzzy division of the fuzzy rule base of the inputs and the outputs are optimized. Due to the fact that the structure of a fuzzy rule cannot be directly expressed mathematically, an optimization problem is solved through the genetic algorithm.

The invention requests to protect a fuzzy vertical handoff method for personalized services of a terminal in a heterogeneous network. According to the method, aiming at differences of requirements of different types of applications in the terminal on quality of services (QoS), influence of dynamic variation of a network load state on handoff performance is considered, performance optimization is performed from two aspects: firstly, at a network finding stage, candidate networks are screened by forecasting load states of the candidate networks at handoff time; and then at a handoff judgment stage, different subordinating degree functions are designed according to different requirement ranges of each type of applications on QoS parameters. Experimental results show that the method can effectively reduce a handoff blocking rate and improve throughput of a system, and can also reasonably select the handoff network according to the application type of the terminal, thus meeting requirements on the personalized services of the terminal.

Disclosed is a road traffic state estimation method based on microwave radar data. The method includes the steps that firstly, the maximum traffic capacity of road segments is acquired through statistics, and saturability is estimated; secondly, the mean value and standard difference of cross section speeds of a traffic flow lasting five minutes on each road segment are calculated through statistics; thirdly, an individualized subordinating degree function is automatically generated for each road segment; fourthly, a smoothness constant, a slow moving constant and a congestion constant are given respectively based on fuzzy rules, the strength of each rule is calculated for each set of input data (speed and saturability), three output maximum rule strengths are sequentially recorded as w1, w2 and w3, and then an output value Z is worked out through the weighted average method; fifthly, it is judged that traffic is smooth if the final value Z is the most approximate to the smoothness constant, it is judged that cars need to run slowly if the final value Z is the most approximate to the slow moving constant, and it is judged that the traffic is congested if the final value Z is the most approximate to the congestion constant. A model of the method has good continuity, high adaptive ability and good practicability.

The invention relates to an electric vehicle dual-scale charging guiding method and system considering users' satisfaction degree. According to the method, electric vehicles are divided into an immediate charging cluster and a time-delay charging cluster according to charging time frame requirements from uses, and satisfaction degree functions in different scales are established; in the time scalelayer, a model is established by considering power grid factors and user charging cost factors, and a load curve is adjusted by guiding the start charging time of the electric vehicles; in the spacescale layer, a model is established by considering charging station factors and user traveling cost factors, and the charging station selection of each vehicle is planned, so that the charging stationoperation efficiency is improved; and electric vehicle charging behavior guiding for users on time and space scales is performed according to the solved optimal solution. Compared with the prior art,optimization and balance utilization of the load curve, the traffic condition and charging station equipment are realized while users' satisfaction degree is satisfied.

The invention discloses a method of eliminating ferromagnetic resonances. A sine fitting algorithm is utilized to identify ferromagnetic resonances and one-phase grounding faults, according to the method, zero sequence voltage sampling data are subjected to fitting, whether zero sequence voltages are in fundamental frequency quantity can be judged according to the amplitude of a fitting function, wave distortion characteristics of the zero sequence voltages in the fundamental resonances are utilized to construct a wave distortion degree function, and degrees of wave distortion are calculated to distinguish the fundamental resonances and the one-phase grounding. The method has the advantages of being simple in principle, small in calculation amount, and high in sensitivity, the fundamental resonances and the one-phase grounding are accurately distinguished, and moreover ferromagnetic resonance faults are eliminated.

A plurality of prisms each has side surfaces that forms an angle of 90 degrees functions as a wavelength filter, and the prisms are arranged so that the angles of 90 degrees of adjacent prisms are pointing in opposite directions so as to form one wavelength separation filter by bonding the prisms together. With this configuration, a path of light output from the wavelength separation filter is displaced as an orientation of the wavelength separation filter changes; however, the path of the light after displacement is not inclined to but parallel to the path of the light before displacement.

Disclosed is an image identification method based on difference feature description. The method includes the steps that firstly, the gray values of image pixel points are converted into the subordinate degree through a selected subordinating degree function; on the basis of defining a difference value concept, each set of adjacent pixel points, with the same difference value, of an image serves as an area, and feature vectors of the area are defined; each area is represented through a feature vector point at the mass center; the mass centers of the adjacent areas are connected through a triangle, the inner corners of the triangle serve as the relation angle of the adjacent areas, and features of a target image are extracted; finally, the features of an image to be identified are extracted, target feature matching is carried out on the processed image, and then whether the image contains the target image or not is determined. The difference values and direction of the image area feature points are combined, the novel image identification method is provided, and the image identification method is simple, convenient to implement, easy to understand, complete in feature and wide in application range, and has the better combination property compared with a traditional method.

The invention discloses a method of assessing the health degree of the device running status based on the multi-characteristic adaptive fusion. The method comprises the steps that 1, based on the vibration signals of each measurement surface of the rotor component of the device, the time-domain characteristic parameters and the frequency domain parameters of the rotor component of the device are computed; meanwhile the process characteristic parameters of the current conditions of the device are collected; 2, a model of assessing the health degree of the device status is obtained, the hierarchical structure of the health degree membership relations can be reflected from the model of assessing the health degree of the device status, then an objective set and an indicator set are determined by utilizing the hierarchical structure of the health degree membership relations; 3, the membership degree function corresponding to each indicator in the indicator set is determined; 4, the health degree value of each indicator is calculated according to the membership degree function corresponding to each indicator in the indicator set; 5, the health degree of the device running status is finally obtained through the data fusion of the health degree values of various indicators and the corresponding adjusted weights. The method can accurately achieve the self-adaptive assessments of the health degree of the device running status.

The invention discloses a ship reactor shielding design optimization method based on a neural network and a genetic algorithm. A Monte Carlo method is used for simulating and calculating the reactor neutron and photon transport process; an MCNP (Monte Carlo N Particle Transport Code) program is used for carrying out simulation and calculation on a ship reactor layered shielding model; a neutron and photon flux after the reactor shielding is obtained; the total equivalent dose is worked out; a topological structure of a BP (Back Propagation) neural network is determined according to the input parameter number n1 and the output parameter number n2 of a sample; an adaptation degree function of the genetic algorithm is determined according to a reactor shielding problem objective; the recommended adaptation degree function is selected; and the strong optimization capacity of the genetic algorithm is used and is mutually coupled with the neural network to find the optimum shielding parameter. The ship reactor shielding design optimization method has the advantages that the high fitting capability of the neural network is used; the time consumption of the MCNP program in the particle transport process is reduced; the good optimization capacity of the genetic algorithm is used; and the optimum shielding parameter can be found in few iteration steps.

The invention provides a risk fuzzy synthetic evaluation method for a regional water environment. The method includes the steps that a membership degree function is established according to the hierarchical structure relation of a target level, a system level, a criterion level and an index level, and the membership degrees of indexes of a regional water environment risk evaluation index system to estimation scales are worked out; a structural model is established according to the hierarchical relation of the index system, and a fuzzy relation matrix is established through the relative membership degree function; a final evaluation result is determined based on the maximum membership degree principle. By means of the technical scheme, the regional water environment can be evaluated more accurately to provide an accurate basis for decision making.

The invention discloses a credit evaluation method based on an AHP (analytic hierarchy process). The credit evaluation method based on the AHP includes: confirming a credit evaluation grade set; confirming a multilevel evaluation index, and constructing an index evaluation system of a four hierarchy structure formed by an overall objective layer, a criterion layer, a discrimination layer and an index layer according to the confirmed evaluation index; confirming value ranges of the evaluation index of the index layer according to three modes of using an actual number, a percentage and a status value; using a matrix judgment method to calculate the weight of the evaluation index according to the importance degree of the evaluation index and structural relationships among the four layers, confirming the value of the evaluation index according to information which constitutes an index element, and then using a preset subordinating degree function to calculate the subordinating degree valued of the evaluation index according to the value of the evaluation index; using the AHP to evaluate the credit grade of an evaluated object. The credit evaluation method based on the AHP has the advantages of being objective, scientific and accurate, and can be widely used in the credit evaluation field.