54 results about "Degree function" patented technology

The invention discloses an automatic instrument fault prediction method based on fuzzy logic, and belongs to the technical field of industrial automation and equipment fault diagnosis. Comprising the following steps: generating a preprocessed sensor data set; constructing a feature space fusing multi-dimensional information; constructing a three-layer fuzzy reasoning architecture comprising an input layer, a reasoning layer and an output layer; mapping membership function parameters and rule weights in the three-layer fuzzy reasoning architecture into a parameter optimization space comprising a parameter adjustment action, a rule weight optimization action and a reasoning structure adjustment action; self-adaptive adjustment of the membership function parameters is achieved; and calculating the fault probability according to the current optimized membership function parameter, and evaluating the optimization effect of the current parameter configuration in combination with the multi-target reward function. According to the method, through combination of multi-time window dynamic monitoring, multi-type membership function design and a strict hard-soft constraint mechanism, accurate evaluation and stable prediction of the equipment fault probability are realized.

The invention discloses a software quality evaluation method based on a fuzzy theory. The method comprises the following steps: obtaining a quality factor set according to a Meiger software quality model; determining an evaluation grade set, and dividing the evaluation grade set into five evaluation grades; constructing a membership function by adopting a kernel density estimation method; constructing a judgment matrix through the triangular fuzzy number and calculating a defuzzification weight; and converting the score into a fuzzy synthesis value through fuzzy synthesis operation, and generating a fuzzy comprehensive evaluation score through weighted average and normalization processing. According to the method, the fuzzy theory and the Meicole model are combined innovatively, and uncertainty in subjective and objective evaluation is processed by applying fuzzy mathematics. The objectivity, scientificity and repeatability of an evaluation result are effectively improved through a membership function and a weight set constructed based on historical data of similar software. The method is suitable for quality evaluation and optimization decision making of various complex software systems.

The invention relates to the technical field of avionics, and discloses a friend behavior prediction and multi-platform autonomous decision-making method and system during communication interruption, and the method comprises the steps: constructing a multi-level fuzzy inference tree, and carrying out the fuzzy processing of multi-source heterogeneous input information, and forming a plurality of fuzzy sets; decomposing the complex input relation into a plurality of subsystems based on a hierarchical fuzzy reasoning mechanism, and obtaining a subtask response benefit value through layer-by-layer reasoning; input variables are fuzzified based on a triangular membership function, and a potential task allocation scheme of the friend formation is predicted in combination with a hierarchical rule reasoning mode. According to the method, centerless, low-dependence and high-robustness multi-platform autonomous task allocation can be realized, and the collaborative decision-making capability and task execution efficiency of formation members in a complex environment under an incomplete information condition are improved.

The invention provides a pipeline transaction bilateral contract matching method and system, belongs to the field of crossing of metering economics and computer algorithms, and is used for solving the problems of multi-parameter coupling, high research and development uncertainty and low matching efficiency of pipeline transaction contracts in related technologies. A core term is converted into a tensor model, a Markov chain is combined to quantify expected utility, multi-round intelligent matching is realized based on a fuzzy satisfaction function and a sub-scene convex optimization algorithm, a key parameter dynamic calibration mechanism is matched, a complete technical closed loop is formed, the matching accuracy and negotiation efficiency are objectively improved, and the negotiation efficiency is improved. And reliable technical support is provided for complex pipeline transactions.

The present application relates to a kind of video enhancement inference method, device, equipment and medium based on fuzzy control, method includes: initialization multiple different scale pre-training video inference model;Define the fuzzy semantic set of input variable and output variable and membership function;Based on prior knowledge, construct fuzzy rule table;Real-time acquisition input data and complete fuzzy inference, and accurate score is obtained by solving fuzzy output by barycenter method;According to the score value range and anti-shake strategy, the optimal video inference model of next frame is dynamically selected.This method makes full use of the continuity characteristics of target between video frames, realizes the dynamic balance of hardware resources and inference accuracy, the utilization rate of GPU is obviously improved, the target detection rate is also obviously improved, and the temperature is stable within the safety threshold, suitable for real-time video scene such as traffic monitoring, unmanned aerial vehicle inspection.

The invention discloses a multi-AGV formation keeping control method based on IPSO-fuzzy PID. The multi-AGV formation keeping control method is characterized by comprising the following steps: (1) establishing a dynamic self-adaptive multi-AGV pilot following formation control model; (2) constructing a fuzzy PID controller, setting a fuzzy domain of input and output variables, a membership function and a fuzzy control rule, and obtaining the correction of PID parameters through fuzzy reasoning and defuzzification; (3) constructing an improved particle swarm optimization algorithm with dynamic balance of global search and local optimization, improving the inertia weight and learning factor of a particle swarm, and setting a fitness function; and (4) optimizing the input quantization factor and the output scaling factor of the fuzzy PID controller by using an IPSO algorithm, assigning optimized parameters to the controller, and adjusting the linear velocity and the angular velocity of the following AGV. The formation keeping precision, the response speed and the anti-interference capability can be improved.

The application discloses a mechanical arm fuzzy PID control method based on quantum pigeon group optimization, first motion trajectory planning is carried out, and a mechanical arm is controlled to move according to the planned motion trajectory through a fuzzy PID controller; a QAPIO algorithm is used to optimize fuzzy PID control parameters, and each pigeon can be used as a PID control parameter of each joint of the mechanical arm. The QAPIO algorithm of the application modifies a compass operator update equation, a probability density function in quantum representation of real number coding and a quantum rotation gate in the original algorithm, so that the algorithm has a continuous updating capability, can continuously update an optimal value, improves global optimization capability, avoids premature local optimization, and solves the problem that the original algorithm is prone to falling into a [0, 0,..., 0, 0]T point, causing the optimization algorithm to fail. The application effectively realizes optimization of fuzzy PID controller parameters of the mechanical arm, and has good practicability.

This invention relates to a vehicle differential lock control method, terminal device, and storage medium. The method includes: S1: constructing a first fuzzy membership function, a second fuzzy membership function, and a fuzzy rule inference table; S2: real-time acquisition of environmental values, road grade, and road curvature values, obtaining the maximum value of road curvature; S3: obtaining all corresponding first fuzzy intervals and fuzzy values ​​from the first fuzzy membership function based on the road curvature ratio; S4: obtaining all corresponding second fuzzy intervals and fuzzy values ​​from the second fuzzy membership function based on the environmental values; S5: matching the first fuzzy intervals with the second fuzzy intervals to obtain a combined fuzzy value of the fuzzy interval combination; S6: taking the fuzzy interval combination corresponding to the maximum value as the fuzzy decision combination, and obtaining the decision result from the fuzzy rule inference table; S7: controlling the differential lock based on the decision result. This invention achieves timely differential locking of the vehicle in weather conditions such as rain and snow when slight slippage occurs during normal driving.

The invention relates to a video enhanced reasoning method and device based on fuzzy control, equipment and a medium. The method comprises the following steps: initializing a plurality of pre-training video reasoning models of different calculation scales; defining a fuzzy semantic set and a membership function of the input variable and the output variable; constructing a fuzzy rule table based on prior knowledge; collecting input data in real time, completing fuzzy reasoning, and defuzzifying through a gravity center method to output an accurate score; and dynamically selecting the optimal video reasoning model of the next frame according to the score value range and the anti-shake strategy. According to the method, the target continuity characteristic between video frames is fully utilized, dynamic balance between hardware resources and reasoning precision is achieved, the GPU utilization rate is obviously increased, the target detection rate is also obviously increased, the temperature is stabilized within a safety threshold value, and the method is suitable for real-time video scenes such as traffic monitoring and unmanned aerial vehicle inspection.

This invention relates to the field of control, and in particular to a method and system for controlling the operation of a reactor used in resin production. The method involves acquiring real-time operating parameters of the reactor, iteratively training a fuzzy neural network model using a hierarchical collaborative hybrid optimization strategy. The antecedent membership function of the fuzzy neural network model is composed of a Gaussian mixture model, and the consequent is a nonlinear polynomial function of the input variables. The training aims to minimize the comprehensive objective function. The optimization strategy involves using an improved quantum particle swarm optimization algorithm for global search to determine the Gaussian mixture model parameters, and using a recursive least squares algorithm for local search after each iteration to determine the consequent coefficients. During training, importance indices are calculated based on the average activation degree of the fuzzy rules and their contribution to the prediction error. Rules with importance consistently below a preset threshold are removed. After training, control commands for controlling the heating system power and material feed rate of the reactor at the next moment are generated and executed based on the real-time parameters.

The embodiment of the application discloses a kind of based on FG-DBN's multi-electric aircraft airborne system reliability analysis method, it is related to reliability design field, can be in fuzzy uncertain condition, to the complex polymorphic system in multi-electric aircraft airborne equipment is carried out dynamic reliability analysis.This application includes: the DBN model of complex polymorphic system in airborne equipment is established, the node in DBN model corresponds the failure state of component in complex polymorphic system;Fuzzy gradient function model containing fuzzy support radius is established, fuzzy gradient function model includes: fuzzy membership model of node in corresponding time slice and state transition matrix corresponding to failure mode;The conditional probability table is established for the failure relationship between nodes in DBN model;The output result of fuzzy gradient function model containing fuzzy support radius and the conditional probability table established are used, the node of weak link in complex polymorphic system is identified and reliability is analyzed.

The application belongs to the technical field and discloses a control method and equipment of a high-speed motor and an electromagnetic bearing system, the method comprising the following steps: selecting input variables and output variables of a fuzzy controller; fuzzifying the input variables and the output variables, determining a quantization factor, a basic domain and a fuzzy domain; defining fuzzy subsets and membership functions in the fuzzy domain of the input variables and the output variables; calculating the membership degrees through the membership functions; substituting the membership degrees into a fuzzy value formula to obtain fuzzy values of the output variables, obtaining accurate control amounts of the output variables through defuzzification; and outputting the accurate control amounts of the output variables to an electromagnetic bearing model for control. The method can effectively reduce the oscillation and vibration of the system, improve the stability of the overall system, and adapt to large disturbances or complex environments.

The invention relates to the technical field of intelligent control of rail transit vehicle-mounted equipment, in particular to a quantum fuzzy control method and system for a rail transit vehicle-mounted air conditioner, and the method comprises the steps: collecting the operation data of the vehicle-mounted air conditioner in real time, and carrying out the Kalman filtering noise reduction and EMC anti-interference compensation preprocessing; performing four-dimensional variable quantum bit mapping coding on the preprocessed operation data, and constructing a quantum superposition state including disturbance compensation; a fuzzy membership function is optimized based on quantum probability distribution, a quantum fuzzy rule base is called, and an air conditioner control instruction is generated through quantum annealing algorithm iterative reasoning; an execution mechanism is driven to execute the air conditioner control instruction, the adjusted operation data, the comfort index and the state of the solid state contactor are collected, and deviation is calculated; and if the deviation meets a preset threshold value, maintaining the current parameter, otherwise, returning to re-execute quantum mapping coding and quantum fuzzy reasoning. According to the invention, accurate control of the vehicle-mounted air conditioner of the rail transit vehicle can be realized.

The invention relates to the technical field of rectifier power dynamic allocation methods, in particular to a CC-Dickson fusion rectifier power dynamic allocation method based on self-evolution fuzzy control, which comprises the following steps of: acquiring actual operation parameters of a rectification system; determining a load power dynamic distribution coefficient; establishing a load characteristic model; constructing a fuzzy control rule; generating a membership function relation matrix; establishing a proportional-integral control algorithm model; a multi-level self-adaptive control algorithm based on a Riemann-Zeta function is executed; adjusting the working mode and parameters of the CC-Dickson fusion rectifier based on the output of the multi-level adaptive control algorithm; the system performance is continuously monitored, the multi-level self-adaptive control algorithm is executed circularly, and the projection of the Riemann-Zeta function is matched with the mapping of transformation, so that the calculation complexity is reduced, and the key nonlinear characteristics of the system are kept.

The invention relates to a routing method and device based on fuzzy logic and logarithm accumulation, equipment and a medium. The method comprises the following steps: acquiring preprocessed node state parameters; inputting the node state parameters into a preset fuzzy logic system, and respectively generating corresponding energy weight, queue weight and link weight through membership function mapping, fuzzy rule reasoning and defuzzification operation; performing weighted calculation on the node state parameters according to the energy weight, the queue weight and the link weight to obtain node reliability; performing logarithmic operation on the node reliability, and performing hop-by-hop accumulation on a logarithmic operation result based on a node sequence to generate path logarithmic reliability; calculating the path logarithm reliability according to a preset path hop count calculation mode, and determining a corresponding path comprehensive routing degree; and comparing the candidate paths according to the path comprehensive routing degree, and determining a corresponding optimal path as a target route. The method has the effect of improving the routing reliability.

The invention provides a multi-factor fuzzy comprehensive evaluation method for the emergency degree of a collapse scene area, and belongs to the field of rescue area evaluation. Establishing a factor set influencing the importance degree of the collapse scene area; key parameter assignment of a continuous membership function and enumeration mapping of a discrete membership function are realized; constructing an initial fuzzy rule base; the initial fuzzy rule weight is optimized, and an updated fuzzy rule base is obtained based on the optimized weight; and carrying out comprehensive evaluation on the input collapse scene area information by utilizing the updated fuzzy rule base to obtain a result. According to the method, a fuzzy system is constructed to comprehensively analyze multiple factors such as casualty conditions, building danger degrees, collapse structures and rescue operation conditions in different regions in a collapse scene, accurate identification of regional emergency rescue emergency degrees is realized, and a scientific basis is provided for rescue personnel to formulate a rescue strategy.

The invention provides a phosphine production equipment control method and device, equipment and a medium, and belongs to the technical field of automatic control, and the method comprises the steps: mapping a real-time operation parameter into a corresponding fuzzy linguistic variable based on a preset membership function; based on the fuzzy linguistic variable of the refining unit, determining a target adjusting unit according to a preset top fuzzy rule; determining a first adjustment instruction corresponding to the target adjustment unit according to a preset middle-layer fuzzy rule based on the target adjustment unit and the fuzzy linguistic variable of each production equipment unit; based on the target adjustment unit, the first adjustment instruction and the fuzzy linguistic variable of the reaction unit, determining a second adjustment instruction of the reaction unit according to a preset bottom fuzzy rule; and issuing the first adjustment instruction and the second adjustment instruction to the corresponding production equipment units so as to realize control of each production equipment unit. According to the phosphine production equipment control method and device, the equipment and the medium provided by the invention, the equipment adjustment accuracy can be improved.

The application discloses a damaged ancient Zhuang character detection and recognition method based on fuzzy logic and belongs to the technical field of ancient character intelligent recognition. First, the damaged ancient Zhuang character image is collected, and preprocessing is completed through adaptive denoising, stroke loss detection and completion; then, multi-dimensional fuzzy features such as stroke integrity, contour similarity, structural connectivity and texture consistency are extracted, corresponding membership functions are constructed, and membership values are calculated; fuzzy processing, rule matching and defuzzification processing are completed based on a fuzzy inference rule base, and the damage degree grade and candidate recognition confidence are output; then, high-dimensional semantic features are extracted through a deep learning model, and the fuzzy inference result is weighted and fused to obtain the final recognition result; finally, the modern character mapping database of ancient Zhuang characters is queried to output the interpretation. The application combines fuzzy logic and deep learning, effectively processes uncertain problems such as stroke loss and contour fuzziness, significantly improves the recognition accuracy and robustness of damaged ancient Zhuang characters, is suitable for damaged scenes such as epigraphy, ancient books and cliff inscriptions, and provides a reliable technical scheme for the digital protection of ancient Zhuang character cultural heritage.

The application discloses a kind of suitable for multi-modal physiological signal emotion recognition method, device and equipment, belong to man-machine interaction technical field.Method includes: randomly select d features from the original feature space with feature dimension D, and repeat t times, obtain t feature subspaces, wherein, d < D;Cluster analysis is carried out to each feature subspace, and the cluster center and corresponding membership function are obtained;The information entropy of all membership functions is calculated, and the feature subspace corresponding to the first several membership functions with minimum entropy value is screened out;Fuzzy rule is constructed using the cluster center and membership function of the screened feature subspace, and emotion recognition model based on fuzzy rule is established.The application combines newly proposed subspace FCM to carry out feature dimension reduction processing, prevents gradient vanishing and other problems in training process.Also, the algorithm introduces the concept of uniform regularization in deep learning, accelerates training, and reduces the risk of overfitting.

An arithmetic apparatus includes an arithmetic unit configured to adjust an intensity schedule function in quantum annealing of a constraint term expressed by many-body interactions in an LHZ model by a hybrid algorithm that uses a value of the intensity schedule function at one time point or each of a plurality of time points as a variational parameter.

This invention discloses a fuzzy multi-attribute decision-making evaluation method and system. The method includes defining a hesitant fuzzy language terminology set; determining the evaluation object and its influencing factors based on the hesitant fuzzy language terminology set to form a factor set, and assigning weights to each influencing factor to determine the membership function of each factor; performing fuzzy synthesis operations on the membership function of each factor and its corresponding weights to obtain a comprehensive membership value; and performing fuzzy evaluation on the evaluation object based on the comprehensive membership value to obtain a fuzzy comprehensive evaluation result of the evaluation object. This invention's multi-attribute decision-making method, capable of handling incomplete and imprecise fuzzy information, plays a crucial role in ensuring the rationality and accuracy of investment risk assessment.

The application discloses a servo press motion control method and system, and relates to the technical field of servo press control. The method comprises the following steps: acquiring displacement, velocity, acceleration and jerk boundary parameters of stamping section start and end points, and constructing a seven-degree polynomial function; determining zero to third order low order term coefficients based on start point parameters; adopting an intelligent optimization algorithm to iteratively solve fourth to seventh order high order term coefficients to minimize a multi-objective fitness function; pre-storing the coefficient set in a controller register; in each interpolation period, the controller calls the coefficients to calculate displacement instructions and outputs, simultaneously collects actual slider displacement, calculates deviation and adjusts the low order term coefficients in the register in real time, which are used for instruction calculation at the next moment. By adjusting only the low order term coefficients and keeping the high order term unchanged in real-time control, the application effectively avoids curve oscillation caused by online adjustment of high order coefficients, and improves reliability.

The application relates to an intermittent sampling and forwarding interference threat evaluation method and relates to the field of radar anti-interference. The application is aimed at solving the problem that the existing interference threat evaluation method has slow recognition speed and low accuracy when applied to intermittent sampling and forwarding interference threat evaluation. The application comprises the following steps: establishing an evaluation parameter index set; fuzzy processing the evaluation parameter index set based on the membership function of the evaluation parameter index to obtain an evaluation parameter index intuitionistic fuzzy subset; establishing a reasoning rule base based on the mapping relationship between the intermittent sampling and forwarding interference and the interference threat grade; obtaining a total intuitionistic fuzzy relationship by using an intuitionistic fuzzy reasoning synthesis algorithm, and obtaining the relationship between the interference threat grade intuitionistic fuzzy subset, the total intuitionistic fuzzy relationship and the evaluation parameter index; and de-fuzzifying the interference threat grade intuitionistic fuzzy subset to obtain the clear quantity of the intermittent sampling and forwarding interference threat. The application is used for evaluating the intermittent sampling and forwarding interference threat.

The present invention discloses a servo hydraulic clamping control method and system based on fuzzy backward time domain control; the present invention relates to the field of numerical control electromechanical technology. A fuzzy controller receives the control axis variable X(t) transmitted by the CNC control system at the current time step t; a membership function is used to convert the control axis variable X(t)' into a fuzzy variable. The membership of the displacement error e1 and the displacement error change rate e2 is calculated; the fuzzy set of the displacement error e1 and the displacement error change rate e2 is divided into three fuzzy subsets: negative N, zero Z and positive P; the present invention introduces a fuzzy controller, and utilizes the membership function and the fuzzy rule base to achieve accurate modeling and intelligent control of the state of the hydraulic clamping mechanism. Fuzzy control effectively overcomes the limitations of traditional control methods by fuzzifying precise input parameters, performing reasoning using fuzzy logic, and then defuzzifying to obtain precise control signals.

The embodiment of the invention provides a fuzzy query method based on an exponential distribution probability density function and a processor. The method comprises the steps of obtaining sample information in a database; the sample information is an attribute value of an object associated with the user, and the attribute value obeys the index distribution; dividing a fuzzy interval according to the sample information and the discourse domain; calculating a maximum value of a membership function corresponding to each fuzzy interval according to the threshold value and the weight of each fuzzy interval and an inverse function of the exponential distribution probability density function; according to the maximum value of the membership function corresponding to each fuzzy interval and the discourse domain boundary, matching the membership function corresponding to each fuzzy interval; and performing fuzzy query in the database according to a preset fuzzy query condition, and performing defuzzification processing based on the matched membership function to obtain a target query result. According to the scheme, an accurate query result can be obtained during fuzzy query.

This application provides a data processing method, electronic device, storage medium, and product. The method includes acquiring multiple attribute data corresponding to data to be processed; calculating the comprehensive fuzzy proximity degree corresponding to each attribute data based on the multiple attribute data of the data to be processed; and calculating the attribute fusion result corresponding to the data to be processed based on the comprehensive fuzzy proximity degree. The method obtains the comprehensive fuzzy proximity degree of multiple attribute data corresponding to any position of the data to be processed in the data matrix through a membership function, then calculates the weight value of each attribute data based on the comprehensive fuzzy proximity degree, and finally performs data fusion to obtain a comprehensive attribute fusion result. Although the attribute fusion result loses the physical meaning inherent in the original attribute data, it retains the commonalities and advantages of each attribute data, enabling a more accurate, comprehensive, and reliable description of the same target. Therefore, it improves the accuracy of describing and characterizing underground geological structures.

This invention discloses a software quality evaluation method based on fuzzy theory. The method includes: obtaining a set of quality factors based on the McCall software quality model; determining an evaluation level set and dividing the evaluation level set into five levels; constructing a membership function using kernel density estimation; constructing a judgment matrix using triangular fuzzy numbers and calculating defuzzification weights; converting the score into a fuzzy composite value through fuzzy synthesis operations, and generating a fuzzy comprehensive evaluation score through weighted averaging and normalization. This method innovatively combines fuzzy theory with the McCall model, using fuzzy mathematics to handle the uncertainty in subjective and objective evaluations. By constructing a membership function and weight set based on historical data of similar software, the objectivity, scientific rigor, and repeatability of the evaluation results are effectively improved. This method is applicable to quality assessment and optimization decisions for various complex software systems.

The embodiment of the invention provides an interestingness prediction method and device, equipment, a storage medium and a program product, and relates to the technical field of financial science and technology or the technical field of artificial intelligence. The method comprises the following steps: acquiring a plurality of fuzzy sets, wherein the plurality of fuzzy sets are related to influence factors of interestingness of a user on a product; according to the user data and the multiple membership functions, first membership degrees are determined, and the first membership degrees comprise multiple membership degrees, corresponding to the multiple fuzzy sets one by one, of the user data; according to the first membership degree, determining a feature vector of the user data; according to the feature vectors, fuzzy C-means clustering is executed, second membership degrees are obtained, and the second membership degrees comprise C membership degrees corresponding to the C clustering clusters one by one by the user; and carrying out weighted sum processing on the second membership degree and preset weights corresponding to the C clustering clusters to obtain the interestingness of the user on the product. According to the method, the accuracy of interestingness prediction is improved.

The invention discloses a multi-modal remote sensing image classification method and device and a storage medium, and the method comprises the steps: constructing a fuzzy enhanced gating fusion Mama network which comprises a feature extraction and fuzzy processing main network, a dual-branch gating fusion unit and a classifier; the feature extraction and fuzzy processing main network comprises a spatial Mama block, a spectrum Mama block and an HSI exclusive fuzzy block; the double-branch gating fusion unit is a gating cross-modal fusion block; the classifier is composed of an average pooling layer, a linear layer and a Softmax layer; wherein the HSI exclusive fuzzy block dynamically models fuzzy distribution of spectral characteristics through a learnable membership function based on a Gaussian fuzzy theory and a fuzzy mathematics principle; calculating the difference between the prediction category and the real category by adopting a cross entropy loss function, and updating network parameters through back propagation iteration by utilizing an Adam optimizer until the loss function converges or reaches a preset number of iterations, so as to complete network training; and classifying the multi-modal remote sensing images based on the trained network.

The invention discloses a target identification basic probability assignment generation method based on an evidence theory, and the method comprises the steps: calculating a membership degree corresponding to an interval endpoint value through employing a triangular fuzzy number according to a target attribute interval value observed by a sensor, representing a membership degree function of a target attribute as a triple, according to the interval endpoint values, the numerical relationship of the triple and the membership degrees corresponding to the interval endpoint values, obtaining the initial interval membership degrees of the target attribute interval values belonging to the targets; performing normalization processing on the initial interval membership degrees of the various types of targets to obtain basic probability assignment; and according to the detection reliability of each sensor under the interference condition, correcting the basic probability assignment to obtain the basic probability assignment of each sensor after correction of various types of targets. According to the embodiment of the invention, the problem of basic probability assignment of how to convert the detection result into the evidence focus element when the target identification detection value is the interval value in the information confrontation environment is solved.