Interaction mechanism modeling method and system based on CPPS

Abstract

The invention discloses a CPPS-based interaction mechanism modeling method and system, and the method comprises the steps: collecting the data of a power grid system and an information system through an intelligent electric meter and a sensor, determining key connection points and paths between a power system and the information system through a graph theory and a complex network theory, carrying out the modeling of a coupling event through a discrete event system, and carrying out the modeling of the coupling event. The dynamic relationship between the information event and the physical event is described, and the stability and toughness of the system are evaluated by simulating and calculating the system response under different scenes. Therefore, comprehensive monitoring of the operation condition of the power grid system is realized, high-quality and real-time data support is provided for subsequent analysis and optimization, and the reliability and timeliness of data-driven decision making are ensured. The structural transparency and predictability of the power grid are enhanced, targeted maintenance and optimization for key nodes and paths are facilitated, and the overall efficiency and safety of the system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of smart grids, and in particular to a CPPS-based interaction mechanism modeling method and system. Background Art

[0002] As a key supporting technology for smart grid, information and communication technology has been increasingly applied in the power sector in recent years. Massive information and communication equipment and systems are increasingly being applied to power systems and play an increasingly important role. They have profoundly changed the operation mode of power systems and promoted the research and construction of smart grids. In the future, smart grids will eventually evolve into a power grid cyber-physical system (CPPS) with a high degree of integration of power systems and cyberspace. CPPS has achieved the deep integration and collaborative work of power systems and cyberspace, which has greatly changed people's lives, but there are still many key technical problems to be solved. Although CPPS has made it possible to achieve the deep integration and collaborative work of cyberspace and power systems, the interaction mechanism between cyberspace and power systems is still unclear, and it is urgent to conduct in-depth research on the cyber-physical interaction mechanism. Although there are several general modeling methods for describing CPPS, such as hybrid system modeling methods, these methods are mostly applicable to embedded systems. For complex power systems, there is still a lack of systematic modeling methods that can adapt to different levels (such as component level, system level) and multiple application scenarios.

[0003] Based on the above-mentioned problems of CPPS, it is necessary to study the interaction mechanism between information space and the physical system of the power grid, reveal the inherent mechanism of CPPS operation, and improve the measurement, perception, analysis and control capabilities of CPPS; establish its micro-component model and system-level fusion model to ensure the safe and stable operation of CPPS. Summary of the invention

[0004] In view of the above-mentioned problems, the present invention is proposed.

[0005] Therefore, the technical problem solved by the present invention is that in traditional power systems, the interaction between cyber systems and physical systems is often not fully understood, resulting in the inability of system design and operation and maintenance to effectively integrate the dynamics and complexity of the two systems. There is a lack of effective strategies to deal with the joint response of cyber systems and physical systems when facing external interference. Existing power cyber-physical system management tools often fail to fully optimize the integrated operation of the two systems, resulting in low energy efficiency and high operating costs.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a CPPS-based interaction mechanism modeling method, comprising:

[0007] Collect data from the power grid system and information system through smart meters and sensors;

[0008] Use graph theory and complex network theory to determine the key connection points and paths between power systems and information systems;

[0009] Use discrete event systems to model coupled events and describe the dynamic relationship between information events and physical events;

[0010] Evaluate system stability and resilience by simulating and calculating system responses under different scenarios.

[0011] As a preferred solution of the CPPS-based interactive mechanism modeling method described in the present invention, the data of the power grid system and the information system collected through smart meters and sensors include power flow data, fault and abnormality records, communication logs, system control commands and feedback, meteorological data, energy policy update data, consumer electricity consumption data, demand response events, cross-system event correlation data, and virtual simulation data.

[0012] As a preferred scheme of the CPPS-based interaction mechanism modeling method described in the present invention, wherein: using graph theory and complex network theory to determine the key connection points and paths between the power system and the information system includes explaining the interaction path between the information space and the physical system of the power grid by analyzing the role of the linkage interface between power secondary equipment in the cross-space interaction process, and identifying the information-physical key interaction path that plays a core role in realizing information-physical interaction among many interaction paths, clarifying the cross-space linkage interface, and optimizing the information-physical key interaction path search method.

[0013] As a preferred solution of the interaction mechanism modeling method based on CPPS described in the present invention, wherein: using graph theory and complex network theory to determine the key connection points and paths between the power system and the information system also includes determining the key information-physical interaction paths, and when trying to cut off an information-physical interaction path, performing the following judgments;

[0014] If CPPS directly becomes a mutually separated cyberspace and grid physical system, then this path is the cyber-physical key interaction path;

[0015] If the CPPS is decomposed into multiple isolated sub-CPPSs due to functional failure of the CPPS, then the path is the critical information-physical interaction path;

[0016] If the CPPS system is neither split nor decoupled, calculate whether other information-physical interaction paths can be used to compensate for the function of the path and further determine its function.

[0017] As a preferred scheme of the CPPS-based interaction mechanism modeling method described in the present invention, wherein: using a discrete event system to model coupling events, describing the dynamic relationship between information events and physical events includes: using a discrete event system to model the electric power information-physical system, and characterizing the bidirectional driving mechanism of the electric power continuous state and the information space discrete events; by analyzing the logic of the bidirectional driving between the discrete information state and the continuous electric process, constructing a discrete state-continuous process driving event chain, revealing the cause of the information-physical coupling event; then performing an operation analysis on the CPPS system, establishing a mathematical model of the information-physical coupling event and quantifying the calculations that affect the operational stability of the CPPS system.

[0018] As a preferred solution of the CPPS-based interaction mechanism modeling method described in the present invention, a discrete event system is used to model coupling events, and the description of the dynamic relationship between information events and physical events also includes constructing a multidimensional mathematical space containing discrete information state variables, continuous power process variables and time multi-class variables, and then establishing a coupling event mathematical model that integrates discrete information state time-varying functions and continuous power process time-varying functions in the space. Finally, the fusion calculation method between the information time-varying function and the power time-varying function inside the coupling event and the quantitative calculation method of the superposition of multiple information-physical coupling events are discussed. The formula is expressed as follows:

[0019]

[0020] Among them, y c represents the output variable of the information system, f c The discrete time-varying function representing the information system converts the input variable x of the information system into c and time t into output y c , x c represents the input variable of the information system, y p represents the output variable of the power system, f p represents the continuous time-varying function of the power system, x p represents the input variable of the power system, t represents the time variable;

[0021] Assume that there is a discrete information time-varying function f in the above formula during the operation of the CPPS system c With the continuous power time-varying function f p ,x c With x p is the input variable of the time-varying function, t is the time factor, y c With y p is the output variable of the time-varying function, then f c With f P The mathematical model of information-physics coupling events has the following calculation methods;

[0022] Finding function mappings Make y c As the input variable x′ of fp p , mapping the changes in information space into changes in the physical system of the power grid, with y p As the calculation result of information-physical coupling events, the formula is expressed as:

[0023]

[0024] y p =f p (x p ,x′ p ,t)

[0025] in, Indicates that this is a function mapping, y c represents the output variable of the information system, x′ p Indicates that the mapping The processed information system output y c , now as an input variable of the power system, x p represents the original power system input variable, y p represents the output variable of the power system, f p represents the time-varying function of the power system, and t represents the time variable;

[0026] Finding function mappings and So that x C With x P All are mapped to x L , and then calculate x L The corresponding y L , with y L As the quantitative calculation result of information-physics coupling events, the formula is expressed as:

[0027]

[0028] in, represents the mapping function from the input space of the information system to the unified input space, represents the mapping function from the input space of the power system to the unified input space, x C represents the input variable of the information system, x L represents the unified system input variable after mapping transformation, y L Represents the uniform input x L The output calculated at time t, f L Indicates that according to the unified input x L and time t to determine the output yL .

[0029] As a preferred solution of the CPPS-based interaction mechanism modeling method described in the present invention, by simulating and calculating the system response in different scenarios, the system stability and resilience are evaluated, including simplifying the driving event chain within the coupling event for each business scenario of the CPPS system operation, and analyzing the mutual triggering conditions and constraints between multiple coupling events;

[0030] Based on the collected data and pre-defined performance indicators, the stability and resilience of the system are evaluated. Stability usually focuses on whether the system can return to a certain ideal state after a disturbance.

[0031] Based on the simulation and analysis results, recommendations for improving system design and operating strategies are made.

[0032] A CPPS-based interactive mechanism modeling system using any of the methods described in the present invention, wherein:

[0033] Data acquisition module, which collects data from the power grid system and information system through smart meters and sensors;

[0034] Path identification module, which uses graph theory and complex network theory to determine the key connection points and paths between the power system and the information system;

[0035] The event modeling module uses discrete event systems to model coupled events and describe the dynamic relationship between information events and physical events;

[0036] The evolution analysis module evaluates system stability and resilience by simulating and calculating system responses under different scenarios.

[0037] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0038] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the steps of any one of the methods of the present invention.

[0039] Beneficial effects of the present invention: The interactive mechanism modeling method based on CPPS provided by the present invention realizes comprehensive monitoring of the operating status of the power grid system, provides high-quality and real-time data support for subsequent analysis and optimization, and ensures the reliability and timeliness of data-driven decision-making. It enhances the structural transparency and predictability of the power grid, helps to carry out targeted maintenance and optimization of key nodes and paths, and improves the overall efficiency and safety of the system. It provides a powerful tool to predict and evaluate the stability and recovery ability of the system in the face of natural disasters, technical failures or malicious attacks, enhances the reliability and safety of the power grid, and thus provides a guarantee for the continuous and stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0041] Figure 1 An overall flow chart of an interaction mechanism modeling method based on CPPS provided in the first embodiment of the present invention;

[0042] Figure 2 A technical architecture diagram of an interaction mechanism modeling method based on CPPS provided for the first embodiment of the present invention. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0044] Example 1

[0045] Reference Figure 1 , as an embodiment of the present invention, provides an interaction mechanism modeling method based on CPPS, comprising:

[0046] S1: Collect data from the power grid system and information system through smart meters and sensors.

[0047] The data collected from the power grid system and information system through smart meters and sensors include power flow data, fault and abnormality records, communication logs, system control commands and feedback, meteorological data, energy policy update data, consumer electricity consumption data, demand response events, cross-system event correlation data, and virtual simulation data.

[0048] S2: Use graph theory and complex network theory to determine the key connection points and paths between the power system and the information system.

[0049] By analyzing the role of the linkage interface between power secondary equipment in the cross-space interaction process, the interaction path between the cyber space and the physical system of the power grid is further explained, and the key information-physical interaction path that plays a core role in realizing information-physical interaction is identified among many interaction paths, the cross-space linkage interface is clarified, and the search method for the key information-physical interaction path is optimized.

[0050] By analyzing the role of spatial linkage interfaces such as power secondary equipment in the cross-space interaction process, the interaction path between the information space and the physical system of the power grid is further explained, and the key information-physical interaction paths that play a core role in realizing information-physical interaction are identified among many interaction paths. Based on the knowledge of graph theory and complex network theory, a search method for key information-physical interaction paths that consider cross-space linkage interfaces is proposed. First, the interaction characteristics of the information space and the physical space are analyzed, including the interaction characteristics of the information-physical interaction topology, the interaction characteristics of the mutual drive of information flow and energy flow, and the interaction characteristics of the evolution of the operating state of the CPPS system. Then, the CPPS system operation model is constructed. After determining the system scale and main functions of the target CPPS, a complex network G(V,A) corresponding to the target CPPS is constructed. The vertex set V includes information nodes (such as computing, storage, control, measurement, etc.), communication nodes, and power nodes (such as traditional power generation, transmission / transformation, distribution, load, energy storage, new energy, etc.), and the type of each edge in the directed edge set A (such as communication edge, data edge, control edge, flow edge, etc.), weight, directed edge capacity threshold and other parameters are set according to the node function.

[0051] Determine the critical information-physical interaction paths and try to cut off a certain information-physical interaction path, then make the following judgment.

[0052] If CPPS directly turns into a mutually separated information space and power grid physical system, then this path is the information-physical key interaction path.

[0053] If the CPPS is decomposed into multiple isolated sub-CPPSs due to functional failure, then this path is the critical information-physical interaction path.

[0054] If the CPPS system is neither split nor decoupled, calculate whether other information-physical interaction paths can be used to compensate for the function of the path and further determine its function.

[0055] S3: Use discrete event systems to model coupled events and describe the dynamic relationship between information events and physical events.

[0056] The coupling events are modeled using discrete event systems to describe the dynamic relationship between information events and physical events, including: using discrete event systems to model the power information-physical system and characterizing the bidirectional driving mechanism of the power continuous state and information space discrete events; by analyzing the logic of bidirectional driving between discrete information states and continuous power processes, a driving event chain of discrete states and continuous processes is constructed to reveal the causes of information-physical coupling events; then, an operation analysis is conducted on the CPPS system, a mathematical model of information-physical coupling events is established, and calculations that affect the operational stability of the CPPS system are quantified.

[0057] A multidimensional mathematical space containing discrete information state variables, continuous power process variables and time multi-class variables is constructed, and then a coupled event mathematical model that integrates discrete information state time-varying functions and continuous power process time-varying functions is established in this space. Finally, the fusion calculation method between the information time-varying function and the power time-varying function within the coupled event and the quantitative calculation method of the superposition of multiple information-physical coupling events are discussed. The formula is expressed as follows:

[0058]

[0059] Among them, y c represents the output variable of the information system, f c The discrete time-varying function representing the information system converts the input variable x of the information system into c and time t into output y c , x c represents the input variable of the information system, y p represents the output variable of the power system, f p represents the continuous time-varying function of the power system, x p represents the input variable of the power system, and t represents the time variable.

[0060] Assume that there is a discrete information time-varying function f in the above formula during the operation of the CPPS system c With the continuous power time-varying function f p ,x c With x p is the input variable of the time-varying function, t is the time factor, y c With y p is the output variable of the time-varying function, then f c With fP The mathematical model of information-physical coupling events is calculated in the following way.

[0061] Finding function mappings Make y c As f p The input variable x′ p , mapping the changes in information space into changes in the physical system of the power grid, with y p As the calculation result of information-physical coupling events, the formula is expressed as:

[0062]

[0063] y p =f p (x p ,x′ p ,t)

[0064] in, Indicates that this is a function mapping, y c represents the output variable of the information system, x′ p Indicates that the mapping The processed information system output y c , now as an input variable of the power system, x p represents the original power system input variable, y p represents the output variable of the power system, f p represents the time-varying function of the power system, and t represents the time variable;

[0065] Finding function mappings and So that x C With x P All are mapped to x L , and then calculate x L The corresponding y L , with y L As the quantitative calculation result of information-physics coupling events, the formula is expressed as:

[0066]

[0067] in, represents the mapping function from the input space of the information system to the unified input space, represents the mapping function from the input space of the power system to the unified input space, x C represents the input variable of the information system, x L represents the unified system input variable after mapping transformation, y L Represents the uniform input x L The output calculated at time t, fL Indicates that according to the unified input x L and time t to determine the output y L . It should be noted that x L With y L It is an artificially constructed information-physics coupling event variable, and its physical meaning needs further discussion.

[0068] S4: Evaluate system stability and resilience by simulating and calculating system responses under different scenarios.

[0069] Aiming at various business scenarios of CPPS system operation, the driving event chain within the coupling event is simplified, and the mutual triggering conditions and constraints between multiple coupling events are analyzed.

[0070] Combined with business scenarios, the mathematical essence of spatiotemporal coordination of multiple coupling events is analyzed, the optimal coordinated evolution mode of multiple coupling events in the evolution of the operating state of the CPPS system is solved, and a multi-stage system operating state evolution model of the CPPS operating state evolution is established.

[0071] Based on the collected data and pre-defined performance indicators, the stability and resilience of the system are evaluated. Stability usually focuses on whether the system can return to a certain ideal state after a disturbance.

[0072] Based on the simulation and analysis results, recommendations for improving system design and operating strategies are made.

[0073] On the other hand, this embodiment also provides an interaction mechanism modeling system based on CPPS, which includes:

[0074] Data acquisition module, which collects data from the power grid system and information system through smart meters and sensors;

[0075] Path identification module, which uses graph theory and complex network theory to determine the key connection points and paths between the power system and the information system;

[0076] The event modeling module uses discrete event systems to model coupled events and describe the dynamic relationship between information events and physical events;

[0077] The evolution analysis module evaluates system stability and resilience by simulating and calculating system responses under different scenarios.

[0078] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0079] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0080] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0081] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0082] Example 2

[0083] The experiment was conducted at two power grid nodes in the city and suburbs. Smart meters and various environmental sensors were installed to monitor power parameters such as current, voltage, and frequency, and information system parameters such as communication delay and data packet loss. In addition, environmental monitoring equipment such as temperature and humidity sensors and anemometers were deployed to capture environmental factors that may affect system behavior.

[0084] Smart meters and sensors are used to continuously collect data for a week, including peak and off-peak periods, to ensure the breadth and representativeness of the data. The data collection of the information system focuses on network status monitoring, including but not limited to network delay and data transmission efficiency.

[0085] The collected data are analyzed by applying graph theory and complex network theory, especially using network analysis tools to identify the key nodes and paths between the power system and the information system. The key connection points of the system are determined by calculating the network characteristics such as the centrality and clustering coefficient of the nodes. A discrete event-based model is established to simulate the interaction between the power and information systems. The model is represented by a state transition diagram, which includes state transitions triggered by specific events. The state and events extracted from the experimental data, such as sudden changes in power load and communication failures, are integrated into the model.

[0086] A variety of simulation scenarios were designed, including natural disasters (such as storms), equipment failures (such as transformer failures), and cyber attacks, to evaluate the stability and resilience of the system under these different conditions. Professional simulation software (such as MATLAB Simulink) and self-developed scripts were used to calculate and analyze the system response. The experimental data are shown in Table 1.

[0087] Table 1 Experimental data table

[0088]

[0089]

[0090] Table 1 shows the dynamic relationship between the power system and the information system and their performance at different times and locations. For example, during peak power demand hours (e.g., 18:00), the network delay and data packet loss rate are relatively low, indicating that the information system can effectively support high power demand, reflecting the optimization and efficiency of the system design. Comparing the data between urban and suburban areas, urban nodes perform more stably in power and information indicators, which may be related to denser infrastructure and more advanced technology in urban areas. Due to the relatively backward infrastructure in suburban areas, network delay and data packet loss rate are relatively high, revealing the potential need to optimize suburban information infrastructure.

[0091] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A CPPS-based interaction mechanism modeling method, characterized in that: include: Collect data from the power grid system and information system through smart meters and sensors; Use graph theory and complex network theory to determine the key connection points and paths between power systems and information systems; Use discrete event systems to model coupled events and describe the dynamic relationship between information events and physical events; Evaluate system stability and resilience by simulating and calculating system responses under different scenarios.

2. The CPPS-based interaction mechanism modeling method according to claim 1, characterized in that: The data collected from the power grid system and information system through smart meters and sensors include power flow data, fault and abnormality records, communication logs, system control commands and feedback, meteorological data, energy policy update data, consumer electricity consumption data, demand response events, cross-system event correlation data, and virtual simulation data.

3. The CPPS-based interaction mechanism modeling method according to claim 2, characterized in that: Using graph theory and complex network theory to determine the key connection points and paths between the power system and the information system includes explaining the interaction path between the information space and the physical system of the power grid by analyzing the role of the linkage interface between power secondary equipment in the cross-space interaction process, and identifying the key information-physical interaction path that plays a core role in realizing information-physical interaction among many interaction paths, clarifying the cross-space linkage interface, and optimizing the search method for key information-physical interaction paths.

4. The CPPS-based interaction mechanism modeling method according to claim 3, characterized in that: Using graph theory and complex network theory to determine the key connection points and paths between the power system and the information system also includes determining the key information-physical interaction paths. When trying to cut off an information-physical interaction path, the following judgments are made; If CPPS directly becomes a mutually separated cyberspace and grid physical system, then this path is the cyber-physical key interaction path; If the CPPS is decomposed into multiple isolated sub-CPPSs due to functional failure of the CPPS, then the path is the critical information-physical interaction path; If the CPPS system is neither split nor decoupled, calculate whether other information-physical interaction paths can be used to compensate for the function of the path and further determine its function.

5. The CPPS-based interaction mechanism modeling method according to claim 4, characterized in that: The coupling events are modeled using discrete event systems to describe the dynamic relationship between information events and physical events, including: using discrete event systems to model the power information-physical system and characterizing the bidirectional driving mechanism of the power continuous state and information space discrete events; by analyzing the logic of bidirectional driving between discrete information states and continuous power processes, a driving event chain of discrete states and continuous processes is constructed to reveal the causes of information-physical coupling events; then, an operation analysis is conducted on the CPPS system, a mathematical model of information-physical coupling events is established, and calculations that affect the operational stability of the CPPS system are quantified.

6. The CPPS-based interaction mechanism modeling method according to claim 5, characterized in that: The coupling event is modeled using a discrete event system. The description of the dynamic relationship between information events and physical events also includes constructing a multidimensional mathematical space containing discrete information state variables, continuous power process variables and time multi-class variables, and then establishing a coupling event mathematical model that integrates discrete information state time-varying functions and continuous power process time-varying functions in the space. Finally, the fusion calculation method between the information time-varying function and the power time-varying function within the coupling event and the quantitative calculation method of the superposition of multiple information-physical coupling events are discussed. The formula is expressed as follows: Among them, y c represents the output variable of the information system, f c The discrete time-varying function representing the information system converts the input variable x of the information system into c and time t into output y c , x c represents the input variable of the information system, y p represents the output variable of the power system, f p represents the continuous time-varying function of the power system, x p represents the input variable of the power system, t represents the time variable; Assume that there is a discrete information time-varying function f in the above formula during the operation of the CPPS system c With the continuous power time-varying function f p ,x c With x p is the input variable of the time-varying function, t is the time factor, y c With y p is the output variable of the time-varying function, then f c With f P The mathematical model of information-physics coupling events has the following calculation methods; Finding function mappings Make y c As f p The input variable x′ p , mapping the changes in information space into changes in the physical system of the power grid, with y p As the calculation result of information-physical coupling events, the formula is expressed as: y p =f p (x p ,x′ p ,t) in, Indicates that this is a function mapping, y c represents the output variable of the information system, x′ p Indicates that the mapping The processed information system output y c , now as an input variable of the power system, x p represents the original power system input variable, y p represents the output variable of the power system, f p represents the time-varying function of the power system, and t represents the time variable; Finding function mappings and So that x C With x P All are mapped to x L , and then calculate x L The corresponding y L , with y L As the quantitative calculation result of information-physics coupling events, the formula is expressed as: in, represents the mapping function from the input space of the information system to the unified input space, represents the mapping function from the input space of the power system to the unified input space, x C represents the input variable of the information system, x L represents the unified system input variable after mapping transformation, y L Represents the uniform input x L The output calculated at time t, f L Indicates that according to the unified input x L and time t to determine the output y L .

7. The CPPS-based interaction mechanism modeling method according to claim 6, characterized in that: By simulating and calculating the system response in different scenarios, the system stability and resilience are evaluated, including various business scenarios for the operation of the CPPS system, simplifying the driving event chain within the coupling event, and analyzing the mutual triggering conditions and constraints between multiple coupling events; Based on the collected data and pre-defined performance indicators, the stability and resilience of the system are evaluated. Stability usually focuses on whether the system can return to a certain ideal state after a disturbance. Based on the simulation and analysis results, recommendations for improving system design and operating strategies are made.

8. A CPPS-based interactive mechanism modeling system using the method according to any one of claims 1 to 7, characterized in that: Data acquisition module, which collects data from the power grid system and information system through smart meters and sensors; Path identification module, which uses graph theory and complex network theory to determine the key connection points and paths between the power system and the information system; The event modeling module uses discrete event systems to model coupled events and describe the dynamic relationship between information events and physical events; The evolution analysis module evaluates system stability and resilience by simulating and calculating system responses under different scenarios.

9. A computer device comprising: Memory and processor; The memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the interaction mechanism modeling method based on CPPS are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the interaction mechanism modeling method based on CPPS are implemented.

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