46 results about "Fuzzy petri nets" patented technology

Thousands of process and equipment measurements are gathered by the modern digital process control systems that are deployed in refineries and chemical plants. Several years of these data are historized in databases for analysis and reporting. These databases can be mined for the data patterns that occur during normal operation and those patterns used to determine when the process is behaving abnormally. These normal operating patterns are represented by sets of models. These models include simple engineering equations, which express known relationships that should be true during normal operations and multivariate statistical models based on a variation of principle component analysis. Equipment and process problems can be detected by comparing the data gathered on a minute by minute basis to predictions from these models of normal operation. The deviation between the expected pattern in the process operating data and the actual data pattern are interpreted by fuzzy Petri nets to determine the normality of the process operations. This is then used to help the operator localize and diagnose the root cause of the problem.

Thousands of process and equipment measurements are gathered by the modern digital process control systems that are deployed in refineries and chemical plants. Several years of these data are historized in databases for analysis and reporting. These databases can be mined for the data patterns that occur during normal operation and those patterns used to determine when the process is behaving abnormally.These normal operating patterns are represented by sets of models. These models include simple engineering equations, which express known relationships that should be true during normal operations and multivariate statistical models based on a variation of principle component analysis. Equipment and process problems can be detected by comparing the data gathered on a minute by minute basis to predictions from these models of normal operation. The deviation between the expected pattern in the process operating data and the actual data pattern are interpreted by fuzzy Petri nets to determine the normality of the process operations. This is then used to help the operator localize and diagnose the root cause of the problem.

The invention discloses a gas path fault diagnostic method for a marine gas turbine based on a fuzzy Petri net (FPN), comprising the following steps of: (1) obtaining the operating standard value of the marine gas turbine; (2) based on an actually-measured value, calculating the membership grade of the actual operating parameter deviation belonging to the criterion deviation fuzzy subset by using a membership function; (3) defining the FPN in response to the characteristics of the fault diagnostic Petri net after determining the operating standard value and the deviation; and (4) determining a FPN reasoning algorithm for gas path fault diagnosis for the marine gas turbine based on related definitions of the transitional enabling and igniting rules. Through the gas path fault diagnostic method for the marine gas turbine based on the fuzzy Petri net (FPN), the defects of not strong university, wrong judgment and reasoning of the traditional method are overcome; it is actually proved that the problems of fuzziness and nondeterminacy in an gas path fault can be effectively solved by the gas path fault diagnostic algorithm for the marine gas turbine established by the gas path fault diagnostic method disclosed by the invention.

The invention relates to a transformer fault diagnosis method based on a fuzzy Petri net. The method comprises the steps of firstly, constructing a transformer fault diagnosis model by using the fuzzy Petri net; performing separation by using a gas chromatography to obtain a concentration value of gas in transformer oil; acquiring the increase speed of the gas in the transformer oil through a gas generation rate formula; an obtaining initial identification of a model through combination of the concentration value and the gas generation rate of the gas in the comprehensive transformer oil; and performing rational analysis of transformer fault diagnosis by using the transformer fault diagnosis model to obtain a diagnosis result. According to the transformer fault diagnosis method based on the fuzzy Petri net, weighted sum of values of values on input arcs of the processed transitional range of input places serve as the fault possibility, so that the problems such as missing determination, false determination and incapability of determination of transformer faults are solved, the possibility of each of faults is given quantitatively, and the diagnosis accuracy is improved.

A method for diagnosing the failure of a hydraulic variable-pitch system of a wind turbine generator based on a fuzzy Petri net belongs to the field of technologies for diagnosing the failure of the hydraulic variable-pitch system of the wind turbine generator. The method comprises the following steps of determining a top event, then finding the direct reason of the event, and so forth finding the most basic reason causing the failure of the system to establish a failure Petri net model of the hydraulic variable-pitch system; acquiring the attainable set and the like of all failure libraries of the hydraulic variable-pitch system according to the relationship among all the libraries; and analyzing the immediately attainable set and the like of the failure libraries, determining failure reasons and the credibility of the failure libraries according to fuzzy production rules, transition trigger rules and a fuzzy inference algorithm and realizing the failure diagnosis of the hydraulic variable-pitch system of the wind turbine generator. The method has the advantages that the failure diagnosis of the hydraulic variable-pitch system of the wind turbine generator is realized, leaked judgment, wrong judgment and incapable judgment problems in the diagnosis of the hydraulic variable-pitch system are effectively solved, a failure possibility value can be quantitively given, and the diagnosis correct rate is increased. The method has positive meaning in the failure diagnosis of the hydraulic variable-pitch system of the wind turbine generator.

The invention discloses a GIS fault diagnosis and reliability analysis method based on fuzzy Petri. According to the method, a GIS fault modeling and inference method of a fuzzy Petri net is put forward on the basis of the randomness and diversity of GIS faults and the fuzziness of fault symptoms, the theory of the Petri net and the fuzzy inference rules are organically combined together, and the high-efficiency and accurate parallel inference capacity is achieved. Through the combination of lots of statistics fault cases, the fault diagnosis model of a GIS based on the FPN is established. When the system does not fail, matrix forward reasoning is conducted on the fault state of the GIS by online monitoring and predicting the possible fault symptoms, the credibility of all places can be rapidly and accurately calculated, and the importance degree of the initial place is analyzed; fault reasons are found through backward fuzzy inference rules under the known fault phenomenon.

The invention particularly relates to a modelling and emulation method for enterprise energy consumption process, belonging to the technical field of energy consumption control, wherein the modelling method comprises using fuzzy variation of a fuzzy Petri net model to represent each energy consumption activity, using fuzzy warehouse to represent the warehouse of raw materials and finished products relevant to the energy consumption activities; the number of marks in the fuzzy warehouse represents the number of the raw materials and the finished products, the relevant supply and demand relationship is denoted by connection line between crunodes; the connection intensity of the connection line signifies the loss rate when conveying the raw materials and finished products from the start point to the end point. The emulation method comprises an initial network model and an excitation fuzzy variation which determines the enabling fuzzy variation set at the current moment, disposes the fuzzy variation at certain excitation moment into the future event table and finds out the minimum excitation time. The method of the invention can clearly and completely describe all kinds of interaction activities in the process of the energy consumption of the enterprise, and the invention has high computing efficiency, strong versatility, which can reinforce the management of energy consumption and save the energy for the enterprise.

The invention discloses an improved fuzzy Petri field fault diagnosis method for electric power communication, which comprises the following steps: an improved dynamic fuzzy petri net algorithm is defined; based on the algorithm, a dynamic fuzzy petri net model for fault diagnosis in electric power communication field is constructed; and the constructed dynamic fuzzy petri net model is applied tothe fault diagnosis of electric power communication field. Based on the fuzzy Petri net theory, the present invention proposes a dynamic electric power communication network field fault diagnosis method, effectively avoids the error caused by artificial subjective factors in the fault diagnosis process of the fuzzy Petri net, and improves the accuracy of the reasoning process.

The present invention discloses a layered time fuzzy Petri network based power transmission grid fault diagnosis method and belongs to the technical field of electric power. A fault tolerance capability of a Petri network in the prior art for dealing with uncertain and missing alarm information needs to be strengthened. A layered time fuzzy Prtei network model provided by the present invention comprises a layered sub model and a comprehensive diagnosis model. A correct diagnosis result can always be provided in cases that a protector and a circuit breaker malfunctions or refuses to act, alarm information is missing and multiple faults occur and the like, so that the method has a relatively good application prospect.

The invention relates to a fuzzy Petri net transformer station fault diagnosis method based on a time membership analysis. The method is characterized by: using a fuzzy Petri net with a time membership function to construct a transformer station fault diagnosis model; grouping a set of collected sequence signals according to a fault diagnosis rule, calculating weighted expectation and weighted variance of the each sequence signal set and selecting the signal set with a minimum dispersion degree according to weighted variance minimum correspondingly to carry out fuzzy inference; using a fuzzy time slice function to calculate a time membership degree corresponding to the each sequence signal of the set with the minimum weighted variance; using a fuzzy Petri net model to carry out the inference analysis on the transformer station fault diagnosis and determine a possibility of fault generation. By using the method of the invention, a problem of a time uncertainty in the transformer station fault diagnosis can be effectively solved, and a signal time distribution dispersion degree, a time difference function and influence of the repeated signal on a diagnosis result can be quantitatively analyzed.

Thousands of process and equipment measurements are gathered by the modern digital process control systems that are deployed in refineries and chemical plants. Several years of these data are historized in databases for analysis and reporting. These databases can be mined for the data patterns that occur during normal operation and those patterns used to determine when the process is behaving abnormally. These normal operating patterns are represented by sets of models. These models include simple engineering equations, which express known relationships that should be true during normal operations and multivariate statistical models based on a variation of principle component analysis. Equipment and process problems can be detected by comparing the data gathered on a minute by minute basis to predictions from these models of normal operation. The deviation between the expected pattern in the process operating data and the actual data pattern are interpreted by fuzzy Petri nets to determine the normality of the process operations. This is then used to help the operator localize and diagnose the root cause of the problem.

The invention discloses a power system fault diagnosis method based on a timing order fuzzy Petri net (TOFPN). The TOFPN is defined as an eight-membered group according to the definition of a fuzzy Petri net (FPN) and the information timing order sequence, the reasoning decision of TOFPN and the reasoning decision of an ordinary fuzzy Petri net are the same, a practical uncertain reasoning method, namely an MYCIN confidence coefficient method is adopted, two operators (please see the two operators in the specification) in a maximum algebra are introduced in the method. By means of the power system fault diagnosis method based on the TOFPN, power system fault diagnosis on alarm information time order attributes under the situation of protective circuit breaker maloperation, refusing and information losing is achieved. According to the method, the diagnosis speed is high, precision is high, the action evaluation of a protective circuit breaker can be accurately finished, high adaptive capacity for the power grid topological change is achieved, and the method is suitable for diagnosing faults of a large complex power grid and has good application prospects.

The invention provides a fuzzy Petri net-based distribution network reliability assessment method considering a weather model. The method includes the following steps of step 1, obtaining a logical structure between distribution network lines; step 2, converting a distribution network into a simplified Petri net network diagram; step 3, based on whether there is a component failure in the distribution network, classifying as 'healthy', 'sub-healthy' and 'non-healthy' states and giving them different probability values; step 4, adding the weather model into the credibility value of Petri net transition; step 5, obtaining a weather model-tree diagram based on a fuzzy Petri net. The method utilizes the logic structure between distribution network elements to convert a logic structure of the distribution network into a simplified Petri net network diagram, then classifies different power supply modes of load as 'healthy', 'sub-healthy' and 'non-healthy' states according to whether there isa component failure in the distribution network, and adds the weather model into the credibility of the transition to obtain a weather model based on the fuzzy Petri net.

The invention discloses a fault diagnosis method used for a downhole draining system. Fault information is measured through a draining equipment data monitoring system, and a fault feature vector reflecting the fault information is obtained through preprocessing. Then, fuzzy processing is carried out on the fault feature vector, a fuzzy value adapting to a Petri net input feature is obtained, and a fuzzy Petri net fault diagnosis model of the draining system is established according to the fuzzy value. Then, a BP algorithm in a neural network is adopted on the fault diagnosis model to carry out network optimization training on parameters, such as weight, threshold and confidence, an error is minimized, and the optimized parameters are used as the final running parameters of the fault diagnosis model. By analyzing the embodiment, the method can relatively accurately and rapidly find fault reasons, has great accuracy, rapidness and adaptability, and can be used for diagnosing the fault of the downhole draining system and have a relatively better effect.

The invention discloses a high-voltage circuit breaker fault intelligent diagnosis method based on an improved fuzzy Petri network. The method comprises the following steps of (1), through online monitoring, acquiring a plurality of sets of data samples in normal operation and faulted operation of the high-voltage circuit breaker, and extracting characteristic quantities in various signals; (2), based on a rough set theory, performing continuous characteristic quantity discretization and decision table reduction by means of an improved greedy algorithm for eliminating redundant characteristic quantities, and simplifying a fault diagnosis rule; (3), according to the simplified diagnosis rule, setting a corresponding database and transition, establishing a fuzzy Petri network reasoning model, and obtaining a corresponding MYCIN reasoning equation; and (4), acquiring testing data, preprocessing the testing data, inputting the testing data into the MYCIN equation for performing reasoning, and obtaining a fault conclusion. The high-voltage circuit breaker fault intelligent diagnosis method performs processing for aiming at low accuracy of the sampling data and improves fault diagnosis efficiency through equation reasoning. Furthermore the high-voltage circuit breaker fault intelligent diagnosis method can promote development of intelligent power grid technology and improves reliability and stability of a power system.

The invention discloses a power grid fault diagnosis method, including the steps of: topologically analyzing a power grid with a fault according to a preset topological analysis rule to obtain a power-losing element; establishing a Petri network model of the power-losing element according to the pre-configured protection information and the action information of a circuit breaker corresponding to protection information of the power-losing element, wherein the Petri network model is composed of a library, a transition and a connecting arc between the library and the transition, and the library includes a protection library, a circuit breaker library and a element library; valuating the parameters of the Petri network model to obtain an intuitionistic fuzzy Petri network model; the parameters include a library identification value, a transition threshold value and a connecting arc weight; the library identification value includes a protection library identification value, a circuit breaker library identification value and an element library identification value; obtaining the element library identification value according to the intuitionistic fuzzy Petri network model and a preset fuzzy reasoning rule; obtaining the fault probability of the power-losing element and determining the fault element according to the element library identification value and a preset measure function.

The invention provides a filling control method of semiconductor production lines, using a fuzzy Petri network technology to perform on-line filling control of the production lines. The method comprises the following steps of: establishing a filling fuzzy Petri network inference model, establishing an on-line filling mechanism of the semiconductor production lines, setting parameters of the fuzzy Petri network inference model, confirming a fuzzy inference condition warehouse set of the filling Petri network model according to uncertain events of an actual semiconductor production system, confirming an inference conclusion warehouse set of the filling Petri network according to the conclusion obtained in the confirmed inference condition, simultaneously gathering the inference rule set by abstracting and accumulating filling decision rules, establishing the filing fuzzy Petri network inference model, judging periodicity decisions by using the filing fuzzy Petri network inference model, starting the model once the accidents occur, and judging whether to modify and self-modify the filling plan or not according to the credibility of each conclusion warehouse. The method is simple in design process, easy in realization and good in robustness; meanwhile, the efficiency of the semiconductor production line is improved; the precognition capability is enhanced; and the integrity is correspondingly improved.

The invention discloses a formalized inference method of an autonomous unmanned aerial vehicle on the basis of a weighted fuzzy Petri net, and is used for solving the technical problem of poor practicality of a traditional formalized inference method based on a fuzzy Petri net. The technical scheme of the formalized inference method is that the formalized inference algorithm of the weighted fuzzy Petri net is introduced, the corresponding column element and the corresponding row element of a matrix are multiplied in the inference algorithm, and a maximum value operator is taken to replace a summation operator obtained in a way that the corresponding column element and the corresponding row element of the matrix are multiplied in the background prior so as to adapt to a situation that the output propositions of a plurality of rules are the same in the formalized inference strategy rule set of the autonomous unmanned aerial vehicle. Since the intelligent decision formalized inference method on the basis of the weighted fuzzy Petri net is adopted, the autonomous unmanned aerial vehicle can effectively adapt to the situation that a formalized inference process strategy rule set simultaneously has different input propositions to bring different contributions and influences on a rule conclusion and the output propositions of the plurality of rules are the same when the autonomous unmanned aerial vehicle executes a double-aerial-vehicle or multi-aerial-vehicle search / attack task, and practicality is high.

The invention discloses a network fault diagnosis method for a hierarchical fuzzy petri net based on integrated variable weight. The network fault diagnosis method comprises the following steps that 1, suspicious fault components are searched in a fault area by using a wiring analysis method; 2, double-layer fuzzy petri net diagnostic models are established for the suspicious fault components separately; 3, the suspicious fault components are subjected to the timing information check, and the confidence coefficient of the corresponding library which does not meet the delay constraint in the models is corrected; and 4, the component failure rate is calculated by using the fixed weight and the integrated variable weight separately, and the criteria of four conditions that the components arenormal, the components fail, the components fail but the warning information partially misses and the components are normal but misinformation exists. In view of the fact that when a traditional fixedweighting method performs fault reasoning under the condition that the winding information partially misses, the faulty line is possibly misjudged as the fault of the non-faulty line, the determiningmethod of the information missing and the information misinformation is provided, and the accuracy of the fault diagnosis is improved.

The invention discloses an intelligent power grid fault diagnosis method based on an event origin. According to the method, on the basis of improvement of a bus and line stratification weight fuzzy Petri network model, the fault occurrence time is taken as the event origin, a protection and breaker motion information sequential constraint inspection method based on the event origin is proposed. The time point and time distance constraints can be acquired according to the protection and breaker alarm information, forward sequential reasoning and reverse sequential reasoning are carried out to acquire the event origin and the time point constraints of the warning information, an initial confidence vector of a place is corrected, influence of sequential inconsistency and the error alarm information on a diagnosis result can be effectively eliminated, and diagnosis accuracy is improved. The method is advantaged in that not only can the fault occurrence time of equipment be acquired, through comparing and analyzing the acquisition time point of the alarm information and the time point constraints, protection and breaker error motion / rejection motion and alarm information missing report conditions are evaluated, and a fault development process is explained according to an evaluation result.

Embodiments of the present application provide a fuzzy Petri net parameter optimization method and system, and an electronic device. The method comprises: performing fuzzy processing on a Petri net model by using a fuzzy generation rule to obtain a fuzzy Petri net model, and based on the leapfrog algorithm, creating an initial population based on to-be-optimized parameters in the fuzzy Petri net model; performing optimization processing on the initial population by using the genetic algorithm; performing iterative optimization on the optimized initial population by using the leapfrog algorithm, and jumping out the iteration until the obtained new population meets the preset stopping condition or the number of times of iterations reaches the preset maximum number of times; and obtaining theoptimal solution from the obtained new population. According to the optimization scheme of the embodiments of the present application, the Petri net model is enabled to have a better knowledge expression and knowledge acquisition ability by using the fuzzy generation rule, and optimization processing is performed by using the improved leapfrog algorithm, so that disadvantages caused by that the parameters in the fuzzy Petri net model need to be manually set are avoided, the accuracy of parameter optimization is improved, and the accuracy of the output result of the fuzzy Petri net model is further improved.

The invention discloses an Agent federal design flow modeling method. In the method, three kinds of Agent federations and a blackboard system are used, wherein the three kinds of Agent federations refer to a design Agent federation, a control Agent federation and a domain service Agent federation; the design Agent federation is a core of an entire system and is in charge of an entire process ranging from concept design to design completion, and comprises a design Agent federation of each module, a product design Agent federation and the like; the control Agent federation is used for coordinating the activity of each Agent federation in the system and interacting with a user; and the domain service Agent federation is used for providing resources such as knowledge, data, rules and the like required by each federation. The method comprises the following steps of: modeling and analyzing a task conflict, a design conflict, a resource conflict and a cost conflict at each stage of quick design by using a Petri network; analyzing a conflict coordinating model and reasoning in a coordinating process by using a fuzzy Petri network; and establishing a detailed model of an entire Agent federation rapid design flow on the modeling and analyzing bases, and providing a design frame ranging from task allocation to drawing generation for Agent federation rapid design.

The invention provides a numerical control equipment fault diagnosis method based on a fuzzy Petri net. From a basic principle of a network theory, a characteristic of a diagnosis problem is combined,numerical control machine tool fault diagnosis is taken as an example, and a Petri net model in the field of fault diagnosis is studied. At the same time, the characteristic that the fuzzy Petri netcan be layered is used, relatively complex faults are subdivided step by layer so as to narrow a diagnosis scope, and efficiency and practicability of fault diagnosis of a numerical control equipmentdiagnosis object are finally improved.

The invention provides a filling control method of semiconductor production lines, using a fuzzy Petri network technology to perform on-line filling control of the production lines. The method comprises the following steps of: establishing a filling fuzzy Petri network inference model, establishing an on-line filling mechanism of the semiconductor production lines, setting parameters of the fuzzyPetri network inference model, confirming a fuzzy inference condition warehouse set of the filling Petri network model according to uncertain events of an actual semiconductor production system, confirming an inference conclusion warehouse set of the filling Petri network according to the conclusion obtained in the confirmed inference condition, simultaneously gathering the inference rule set by abstracting and accumulating filling decision rules, establishing the filing fuzzy Petri network inference model, judging periodicity decisions by using the filing fuzzy Petri network inference model,starting the model once the accidents occur, and judging whether to modify and self-modify the filling plan or not according to the credibility of each conclusion warehouse. The method is simple in design process, easy in realization and good in robustness; meanwhile, the efficiency of the semiconductor production line is improved; the precognition capability is enhanced; and the integrity is correspondingly improved.

The invention belongs to the field of energy-saving optimization of the production and manufacturing industry, and discloses a manufacturing system energy-saving operation decision-making method and device based on a fuzzy Petri net. The method comprises the following steps: (1) acquiring real-time state information of a machine and real-time in-process product levels of upstream and downstream buffer areas of the machine as input variables of an energy-saving decision; (2) taking the decision state of the machine as an output variable, establishing a fuzzy reasoning rule base of an energy-saving operation decision, and determining influence weights of an input variable and the output variable; (3) establishing a fuzzy Petri net model of an energy-saving operation decision according to theinput and output variables, the influence weight and a fuzzy reasoning rule base; (4) obtaining the credibility of a conclusion proposition through fuzzy Petri network reasoning calculation; and (5)the decision corresponding to the conclusion proposition with the maximum credibility being the machine decision state at the current moment. While the yield of the production system is effectively maintained, remarkable energy consumption saving can be achieved, the execution process is simple, and the application range is wide.

The invention relates to a fuzzy fault Petri net and a network fault diagnosis method thereof. The technical features are that the fuzzy fault Petri net includes an eleven-element set as follows: Sigma=(S, T, F, D, G, w, Eta, Theta, Gamma, M0, H); for the network fault diagnosis method, a fuzzy Petri net and a fault Petri net are combined to provide the fuzzy fault Petri net (FFPN), a network fault cause model based on the FFPN is established, by combining an algorithm based on the FFPN diagnosis idea, the validity of one factor causing the Ethernet to break down in the model is calculated, and the factor having the highest validity is defined to be the main factor which can cause the Ethernet to break down. The fuzzy fault Petri net is reasonable in design, the problem that the network fault processing speed is slow, and the user experience is affected is solved, the causal relationship between network fault factors can be well described, the main factor causing the Ethernet to breakdown is obtained by means of quantitative analysis, and a reference can be provided for research and exploration in the network fault field.

The invention discloses a formalized inference method of an autonomous unmanned aerial vehicle on the basis of a weighted fuzzy Petri net, and is used for solving the technical problem of poor practicality of a traditional formalized inference method based on a fuzzy Petri net. The technical scheme of the formalized inference method is that the formalized inference algorithm of the weighted fuzzy Petri net is introduced, the corresponding column element and the corresponding row element of a matrix are multiplied in the inference algorithm, and a maximum value operator is taken to replace a summation operator obtained in a way that the corresponding column element and the corresponding row element of the matrix are multiplied in the background prior so as to adapt to a situation that the output propositions of a plurality of rules are the same in the formalized inference strategy rule set of the autonomous unmanned aerial vehicle. Since the intelligent decision formalized inference method on the basis of the weighted fuzzy Petri net is adopted, the autonomous unmanned aerial vehicle can effectively adapt to the situation that a formalized inference process strategy rule set simultaneously has different input propositions to bring different contributions and influences on a rule conclusion and the output propositions of the plurality of rules are the same when the autonomous unmanned aerial vehicle executes a double-aerial-vehicle or multi-aerial-vehicle search / attack task, and practicality is high.

The invention relates to a parallel reasoning method based on a fuzzy Petri net, which is technically characterized by comprising the following steps: converting a fuzzy Petri net model into a matrix form, and providing a parallel reasoning algorithm on the basis. According to the algorithm, the reasoning process is converted into matrix operation, factors such as weights, transition thresholds and rule credibility of preconditions are considered, transition triggering conditions are further strict, some unnecessary repeated triggering of transition is effectively avoided, and the complexity of the algorithm is reduced. Examples show that the inference algorithm is easy to implement, can improve the inference efficiency, and is particularly suitable for a larger and more complex fuzzy Petri net model. The invention is reasonable in design, and based on the parallel reasoning algorithm of the fuzzy Petri net, the reasoning process is converted into the operation process of the matrix. On the premise of considering factors such as weights, transition thresholds and rule credibility, transitions which have been triggered and of which the input intensity is not increased are eliminated. Repeated triggers of transitions that no longer generate new information are avoided. Application examples show that the reasoning algorithm can save reasoning time, and the effect of the reasoning algorithm is more obvious when the fuzzy Petri net model is large and complex.

The invention relates to a large-scale network service system behavior reconstruction method, providing the concept of a user behavior membership degree according to user interaction behavior sequence features. A system behavior reconstruction model and a system behavior reconstruction algorithm for a random fuzzy Petri network with delay are provided according to system interaction behavior classification and delay control based on the system behavior reconstruction model. Therefore, the reconstruction model is triggered to balance a system load when the real-time system load exceeds the maximum load, and thus the system can always keep in a good service state.