Modeling method and device of power business process, computer equipment and storage medium

Abstract

The invention relates to a modeling method and device for a power business process, computer equipment and a storage medium. The method comprises the following steps: acquiring graphical power business process data; converting the power business process data into a formal semantic model based on a token; determining a model state space scale of the formal semantic model, and performing process verification on the formal semantic model by adopting a verification mode matched with the model state space scale to obtain a verification result; and adjusting the formal semantic model according to the verification result, and taking the adjusted formal semantic model as the power business process model. By adopting the method, the modeling operation difficulty of the power business process model can be reduced, and the modeling efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of digital power grid technology, and in particular to a modeling method, apparatus, computer equipment, computer-readable storage medium, and computer program product for power business processes. Background Technology

[0002] Currently, the control mechanisms for power business processes generally rely on manual coding by backend programmers. These process control statements are deeply embedded within the services, lacking a unified modeling language and standardized expression methods. Business personnel find it difficult to directly participate in process modeling and optimization decisions. Furthermore, each process adjustment requires code modification and redeployment, resulting in long development cycles, high risk and costs, and difficulty in quickly responding to changes in business strategies. Summary of the Invention

[0003] Therefore, it is necessary to provide a modeling method, apparatus, computer equipment, computer-readable storage medium, and computer program product for power business processes that can improve efficiency for personnel without programming background, in order to address the above-mentioned technical problems.

[0004] Firstly, this application provides a modeling method for power business processes, including:

[0005] Obtain graphical data of power business processes;

[0006] The power business process data is converted into a token-based semantic model;

[0007] The size of the model state space of the formal semantic model is determined, and the formal semantic model is verified by a verification method that matches the size of the model state space to obtain the verification result.

[0008] The formal semantic model is adjusted based on the verification results, and the adjusted formal semantic model is used as the power business process model.

[0009] In one embodiment, the method further includes:

[0010] Obtain the dependencies between each task node in the power business process model;

[0011] For task nodes whose dependencies meet the preset conditions, add an asynchronous flag; the asynchronous flag is used to set asynchronous attributes.

[0012] Add a parallel gateway to the power business process model;

[0013] Based on the asynchronous flag and the parallel gateway, the sub-processes in the power business process model are translated into multi-threaded tasks;

[0014] Extract the configuration information of the multi-threaded tasks to form the configuration list of the power business process model.

[0015] In one embodiment, the method further includes:

[0016] Obtain the call frequency parameters and communication delay parameters of each task node in the power business process model;

[0017] The traffic disturbance index value is determined using the call frequency parameter and the communication delay parameter;

[0018] Construct a network delay matrix based on the communication delay parameters;

[0019] The microservice deployment method of the power business process model is optimized by using the traffic disturbance index value and the network latency matrix to obtain the target deployment method.

[0020] In one embodiment, the method further includes:

[0021] The power business process model is run based on the target deployment method described above;

[0022] Collect the node load rate during the operation of the power business process model;

[0023] Based on the comparison between the node load rate and the traffic disturbance index value, a corresponding load adjustment strategy is determined.

[0024] In one embodiment, the method further includes:

[0025] Obtain the latency threshold corresponding to the network latency matrix;

[0026] The inter-service communication latency during the operation of the power business process model is collected.

[0027] Based on the comparison between the inter-service communication latency and the latency threshold, a corresponding latency adjustment strategy is determined.

[0028] In one embodiment, the method further includes:

[0029] The task execution time of the power business process model during runtime is collected;

[0030] When the task execution time exceeds a preset time threshold, the task is determined to have failed, and a fault self-healing strategy is triggered.

[0031] When the number of times the fault self-healing strategy is executed reaches a preset threshold, the fault service that failed to execute the task is isolated from the process link of the power business process model.

[0032] Secondly, this application also provides a modeling apparatus for power business processes, comprising:

[0033] The data acquisition module is used to acquire graphical power business process data;

[0034] The model conversion module is used to convert the power business process data into a token-based semantic model.

[0035] The process verification module is used to determine the model state space size of the formal semantic model, and to perform process verification on the formal semantic model using a verification method that matches the model state space size, so as to obtain the verification result.

[0036] The model generation module is used to adjust the formal semantic model based on the verification results, and use the adjusted formal semantic model as the power business process model.

[0037] Thirdly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the power business process modeling method described in any of the embodiments of the first aspect.

[0038] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the power business process modeling method described in any of the embodiments of the first aspect.

[0039] Fifthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the modeling method for the power business process described in any of the embodiments of the first aspect.

[0040] The aforementioned modeling method, apparatus, computer equipment, computer-readable storage medium, and computer program product for power business processes acquire graphical power business process data; convert the power business process data into a token-based formal semantic model; determine the scale of the model state space of the formal semantic model; perform process verification on the formal semantic model using a verification method matching the scale of the model state space to obtain verification results; adjust the formal semantic model based on the verification results; and use the adjusted formal semantic model as the power business process model. This enables business personnel to provide modeling data graphically and provides a process verification method to adjust the formal semantic model corresponding to the modeling data, thereby reducing the operational difficulty of power business process modeling and improving the reliability and accuracy of power business process modeling. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart illustrating a modeling method for a power business process in one embodiment;

[0043] Figure 2 This is a flowchart illustrating the asynchronous parallel configuration steps in one embodiment;

[0044] Figure 3 This is a flowchart illustrating the deployment optimization steps in one embodiment;

[0045] Figure 4 This is a flowchart illustrating the load adjustment steps in one embodiment;

[0046] Figure 5 This is a flowchart illustrating the delay adjustment step in one embodiment;

[0047] Figure 6 This is a flowchart illustrating the fault self-healing steps in one embodiment;

[0048] Figure 7 This is a structural block diagram of a power business process modeling device 700 in one embodiment;

[0049] Figure 8 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the solutions, or any combination of multiple solutions. The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with relevant regulations.

[0052] In one exemplary embodiment, such as Figure 1 As shown, a modeling method for power business processes is provided. This embodiment illustrates the application of this method to a server. It is understood that this method can also be applied to a terminal, and further to a system including both a terminal and a server, and is implemented through interaction between the terminal and the server. In this embodiment, the method includes the following steps S102 to S108. Wherein:

[0053] Step S102: Obtain graphical power business process data.

[0054] For example, the server can be based on a Workflow Model and use BPMN 2.0 as the process modeling language to obtain power business process data in which business personnel describe the process logic in a graphical way. The business process model can include basic components such as task nodes, event nodes, sequence flows, and gateway nodes.

[0055] Optionally, in some implementations, the execution logic of the business process can be changed from "synchronous serial" to "event-based asynchronous scheduling mode" based on the above business process model. That is, by introducing a control channel and an event dispatcher, loosely coupled triggering of dependencies between tasks can be achieved.

[0056] Step S104: Convert the power business process data into a token-based formal semantic model.

[0057] For example, the server can use the formal processing engine BProVe to transform power business process data into a token-based formal semantic model, and define process execution rules on it, including task execution, message passing, control flow logic, and gateway routing rules. Through this semantic mapping, power business process data of any topology can be transformed into a formal representation suitable for subsequent verification.

[0058] Step S106: Determine the size of the model state space of the formal semantic model, and perform process verification on the formal semantic model using a verification method that matches the size of the model state space to obtain the verification result.

[0059] For example, the server can determine the size of the formal semantic model's state space based on the size of various process execution rules added to the formal semantic model. The size of the model's state space is evaluated based on preset size parameters. If the state space is deemed appropriate, an LTL model checker embedded in the MAUDE engine is used to comprehensively verify the temporal attributes defined in the formal semantic model using linear temporal logic (LTL) model checking, such as process completion, correct termination, no dead tasks, and security, to obtain verification results. If the state space is deemed large or contains complex loops, statistical model checking (SMC) is used to verify the formal semantic model's process. Multiple random samplings and probabilistic inferences are performed using tools such as MultiVeStA to verify attribute correctness under controlled confidence and error rates, effectively avoiding the state explosion problem in model checking, and obtaining verification results.

[0060] Optionally, in some implementations, user-defined business rules using linear time-series logic formulas can be embedded into the verification process for specific attributes in the power system business processes. For example, rules such as "if event A occurs, event B must eventually respond" or "no suspended node may wait indefinitely" can be defined, and the model behavior can be automatically checked to ensure it conforms to these constraints during the verification phase.

[0061] Step S108: Adjust the formal semantic model based on the verification results, and use the adjusted formal semantic model as the power business process model.

[0062] For example, the server can optimize and adjust the state transition trajectory and counterexample paths of the formal semantic model based on the verification results to avoid logical defects. The adjusted formal semantic model is then used as the power business process model.

[0063] The aforementioned modeling method for power business processes involves: acquiring graphical power business process data; converting the power business process data into a token-based formal semantic model; determining the model state space size of the formal semantic model; using a verification method matching the model state space size to perform process verification on the formal semantic model and obtain verification results; adjusting the formal semantic model based on the verification results; and using the adjusted formal semantic model as the power business process model. This method enables business personnel to provide modeling data graphically and provides a process verification method to adjust the formal semantic model corresponding to the modeling data, thereby reducing the operational difficulty of power business process modeling and improving the reliability and accuracy of power business process modeling.

[0064] In one exemplary embodiment, such as Figure 2 As shown, it also includes the following steps S202 to S210. Wherein:

[0065] Step S202: Obtain the dependencies between various task nodes in the power business process model.

[0066] Step S204: Add an asynchronous flag to task nodes whose dependencies meet the preset conditions. The asynchronous flag is used to set asynchronous attributes.

[0067] Step S206: Add a parallel gateway to the power business process model.

[0068] Step S208: Translate the sub-processes in the power business process model into multi-threaded tasks based on asynchronous tags and a parallel gateway.

[0069] Step S210: Extract the configuration information of the multi-threaded task to form a configuration list of the power business process model.

[0070] For example, the server can obtain the dependencies between various task nodes in the power business process model. For task nodes whose dependencies meet preset conditions (such as low coupling between task nodes), an asynchronous flag is added. This asynchronous flag sets asynchronous attributes to instruct the process engine to dispatch events without waiting for the previous task to complete, thus achieving non-blocking task triggering. This mechanism, combined with event middleware, transforms the process advancement method from traditional synchronous calls to an event-driven model, significantly reducing the coupling between services and improving scheduling granularity. Simultaneously, to support the parallel startup and merging of multiple tasks, a flowchart structure based on a parallel gateway for the business process model is adopted, automatically translating sub-processes with concurrency capabilities into container-schedulable multi-threaded tasks. Configuration information for multi-threaded tasks is extracted to form a configuration list for the power business process model.

[0071] In this embodiment, by adding asynchronous flags and a parallel gateway, non-blocking triggering of tasks can be achieved, thereby helping to improve process execution efficiency.

[0072] In one exemplary embodiment, such as Figure 3 As shown, the procedure may further include steps S302 to S308. Wherein:

[0073] Step S302: Obtain the call frequency parameters and communication delay parameters of each task node in the power business process model.

[0074] Step S304: Determine the traffic disturbance index value using the call frequency parameter and the communication delay parameter.

[0075] Step S306: Construct a network delay matrix based on communication delay parameters.

[0076] Step S308: Optimize the microservice deployment method of the power business process model using the traffic disturbance index value and the network latency matrix to obtain the target deployment method.

[0077] For example, the power business process model can be instantiated and deployed in a microservice system for execution. The call relationships between the services in the microservice system constitute a directed graph G=(V,E), where V represents the set of service nodes and E represents the dependency edges between services. Each service is treated as a task node. The traffic stress metric is determined using the following formula:

[0078] ,in, This represents the call frequency from service i to service j per unit of time. This represents the average communication latency incurred during the call. This metric reflects the instantaneous traffic pressure on the service at a given moment and can be used to dynamically extract critical paths.

[0079] To further account for network transmission costs between deployment nodes, this invention maintains a network latency matrix in the scheduling engine. Defined as:

[0080] ,in, These represent different physical / virtual host nodes. The average round-trip time between nodes is obtained periodically by the system. This latency matrix is ​​used to assist in performance evaluation during node migration.

[0081] Based on the aforementioned traffic graph and network latency modeling, this invention further constructs an optimal mapping problem from microservice instances to compute nodes, with the objective of minimizing overall communication cost and load imbalance while satisfying resource constraints. This problem is formalized as the following optimization objective function:

[0082] ,in: Indicates whether service i is deployed on node k; Indicates the node where service i resides; Let k be the computing capacity of node k; the first term is used to minimize the latency cost of cross-node communication; the second term controls the load balancing across nodes. This is a regulating term used to balance communication costs and resource utilization.

[0083] The optimization problem is solved using a parallel graph partitioning algorithm, and the optimal solution is delivered to the Kubernetes container orchestration platform for execution during deployment to obtain the target deployment method.

[0084] In this embodiment, an optimization objective function is constructed by setting a traffic disturbance index value and a network latency matrix, thereby optimizing the micro-deployment service method for the power business process model, which can help improve the execution efficiency of the subsequent micro-deployment service of the power business process model.

[0085] In one exemplary embodiment, such as Figure 4 As shown, the procedure may further include steps S402 to S406. Wherein:

[0086] Step S402: Run the power business process model based on the target deployment method.

[0087] Step S404: Collect the node load rate during the operation of the power business process model.

[0088] Step S406: Determine the corresponding load adjustment strategy based on the comparison results of node load rate and traffic disturbance index value.

[0089] For example, when the node load rate exceeds the traffic disturbance index value, it can be determined that a predicted load overload event has occurred, and the corresponding load adjustment strategy is executed: multiple microservice tasks related to load adjustment will be asynchronously triggered by the event dispatcher, including nodes such as "regional load aggregation", "standby capacity analysis", "scheduling strategy recommendation" and "scheduling instruction generation". At this time, the process engine no longer uses a serial synchronous method to call services sequentially, but performs topology parsing based on the task dependency graph, identifies a set of subtasks that can be executed in parallel, and immediately hands them over to the scheduling optimization engine for task mapping and resource allocation.

[0090] In this embodiment, by monitoring the node load, and executing the corresponding load adjustment strategy when the load exceeds the limit, the stability of the power business process can be improved.

[0091] In one exemplary embodiment, such as Figure 5 As shown, the procedure may further include steps S502 to S506. Wherein:

[0092] Step S502: Obtain the delay threshold corresponding to the network delay matrix.

[0093] Step S504: Collect the inter-service communication latency during the operation of the power business process model.

[0094] Step S506: Determine the corresponding delay adjustment strategy based on the comparison result between the inter-service communication delay and the delay threshold.

[0095] For example, the server can use the scheduling engine to call the task stress model in real time during the allocation process, based on the traffic intensity between tasks. and node delay matrix By combining computational resource load and service health status, an optimization objective function is constructed to find a scheduling scheme that minimizes communication cost and load imbalance. The optimization results determine the nodes where each task container should be deployed, and the scheduling and execution are completed through the Kubernetes platform.

[0096] In this embodiment, by monitoring the inter-service communication latency, and executing the corresponding latency adjustment strategy when the inter-service communication latency exceeds the latency threshold, the execution efficiency of the power business process can be improved.

[0097] In one exemplary embodiment, such as Figure 6 As shown, it may also include the following steps S602 to S606. Wherein:

[0098] Step S602: Collect the task execution time when the power business process model is running.

[0099] Step S604: When the task execution time exceeds the preset time threshold, the task execution is determined to have failed, and the fault self-healing strategy is triggered.

[0100] Step S606: When the number of times the fault self-healing strategy is executed reaches a preset threshold, the fault service that failed to execute the task is isolated from the process link of the power business process model.

[0101] For example, during task scheduling, the system allows for elastic scaling of nodes. That is, when resource contention arises for high-priority tasks, the corresponding container replicas can be temporarily expanded to improve execution concurrency. Furthermore, for service nodes that fail to execute, the system automatically triggers a circuit breaker and isolates them according to the set maximum number of retries. and interval time Initiate the fault recovery process to ensure the task is completed. Resume execution within a controllable timeframe to ensure the process moves forward uninterrupted.

[0102] Throughout the process, the execution order is dynamically scheduled by the event-driven control channel, achieving decentralized task triggering and state-driven evolution. Ultimately, the scheduling platform achieves highly reliable concurrent execution of complex processes under sudden load scenarios with optimal resource utilization, minimal communication overhead, and the strongest fault tolerance guarantee, ensuring that power load scheduling tasks can respond and stably close the loop within milliseconds.

[0103] In the embodiments described above, the introduction of asynchronous execution markers and parallel gateway mechanisms supports the structural refactoring of processes in power services that require concurrent response. The business process model flexibly expresses asynchronous and parallel behaviors, compatible with both well-structured and unstructured process structures, breaking the limitations of traditional serial process modeling and significantly improving the modeling and expression capabilities of complex business logic. By combining an event-driven architecture with message middleware, asynchronous triggering and non-blocking execution at the task level are achieved, breaking the strong coupling between services. The system can dynamically schedule process nodes based on business events, significantly shortening task response time and improving system efficiency in high-concurrency scenarios, meeting the time-sensitive business needs of power systems such as fault repair and load adjustment. The introduction of the BProVe tool provides formal verification of the soundness, security, and application-related attributes of the process model, ensuring that the process still meets key process correctness constraints such as "no dead activities" and "reachable final state" under asynchronous execution and concurrent scheduling, thus improving the robustness and reliability of process modeling and deployment.

[0104] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0105] Based on the same inventive concept, this application also provides a modeling apparatus for power business processes to implement the modeling method for power business processes described above. The solution provided by this apparatus is similar to the implementation described in the above method. Therefore, the specific limitations of one or more power business process modeling apparatus embodiments provided below can be found in the limitations of the power business process modeling method described above, and will not be repeated here.

[0106] In one exemplary embodiment, such as Figure 7 As shown, a modeling device 700 for power business processes is provided, including: a data acquisition module 702, a model conversion module 704, a process verification module 706, and a model generation module 708, wherein:

[0107] The data acquisition module 702 is used to acquire graphical power business process data.

[0108] Model conversion module 704 is used to convert power business process data into a token-based formal semantic model.

[0109] The process verification module 706 is used to determine the size of the model state space of the formal semantic model, and to perform process verification on the formal semantic model using a verification method that matches the size of the model state space, so as to obtain the verification result.

[0110] The model generation module 708 is used to adjust the formal semantic model based on the verification results, and use the adjusted formal semantic model as the power business process model.

[0111] In an exemplary embodiment, the power business process modeling device 700 further includes: an asynchronous parallel module, used to obtain the dependency relationships between various task nodes in the power business process model; add asynchronous tags to task nodes whose dependency relationships meet preset conditions, the asynchronous tags being used to set asynchronous attributes; add a parallel gateway in the power business process model; translate the sub-processes in the power business process model into multi-threaded tasks based on the asynchronous tags and the parallel gateway; and extract the configuration information of the multi-threaded tasks to form a configuration list of the power business process model.

[0112] In an exemplary embodiment, the power business process modeling device 700 further includes: a deployment optimization module, used to obtain the call frequency parameters and communication delay parameters of each task node in the power business process model; determine the traffic disturbance index value using the call frequency parameters and communication delay parameters; construct a network delay matrix based on the communication delay parameters; and optimize the microservice deployment method of the power business process model using the traffic disturbance index value and the network delay matrix to obtain the target deployment method.

[0113] In an exemplary embodiment, the power business process modeling device 700 further includes: a load adjustment module, used to run the power business process model based on the target deployment method; collect the node load rate during the operation of the power business process model; and determine the corresponding load adjustment strategy based on the comparison result between the node load rate and the flow disturbance index value.

[0114] In an exemplary embodiment, the power business process modeling apparatus 700 further includes: a delay adjustment module, used to obtain a delay threshold corresponding to the network delay matrix; collect the inter-service communication delay during the operation of the power business process model; and determine a corresponding delay adjustment strategy based on the comparison result between the inter-service communication delay and the delay threshold.

[0115] In an exemplary embodiment, the power business process modeling device 700 further includes: a fault self-healing module, used to collect the task execution time during the operation of the power business process model; when the task execution time exceeds a preset time threshold, the task execution is determined to have failed, and a fault self-healing strategy is triggered; when the number of times the fault self-healing strategy is executed reaches a preset number threshold, the fault service that failed to execute the task is isolated from the process link of the power business process model.

[0116] Each module in the aforementioned power business process modeling device 700 can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0117] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operating system and computer programs stored in the non-volatile storage media. The database stores data such as power business process data and verification results. The I / O interfaces are used for information exchange between the processor and external devices. The communication interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a modeling method for power business processes.

[0118] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0119] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0120] In one exemplary embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above-described method embodiments.

[0121] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0122] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0123] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0124] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method of modeling an electric power business process, characterized by, The method includes: Obtain graphical data of power business processes; The power business process data is converted into a token-based semantic model; The size of the model state space of the formal semantic model is determined, and the formal semantic model is verified by a verification method that matches the size of the model state space to obtain the verification result. The formal semantic model is adjusted based on the verification results, and the adjusted formal semantic model is used as the power business process model.

2. The method according to claim 1, characterized in that, The method further includes: Obtain the dependencies between each task node in the power business process model; For task nodes whose dependencies meet the preset conditions, add an asynchronous flag; the asynchronous flag is used to set asynchronous attributes. Add a parallel gateway to the power business process model; Based on the asynchronous flag and the parallel gateway, the sub-processes in the power business process model are translated into multi-threaded tasks; Extract the configuration information of the multi-threaded tasks to form the configuration list of the power business process model.

3. The method according to claim 1, characterized in that, The method further includes: Obtain the call frequency parameters and communication delay parameters of each task node in the power business process model; The traffic disturbance index value is determined using the call frequency parameter and the communication delay parameter; Construct a network delay matrix based on the communication delay parameters; The microservice deployment method of the power business process model is optimized by using the traffic disturbance index value and the network latency matrix to obtain the target deployment method.

4. The method according to claim 3, characterized in that, The method further includes: The power business process model is run based on the target deployment method described above; Collect the node load rate during the operation of the power business process model; Based on the comparison between the node load rate and the traffic disturbance index value, a corresponding load adjustment strategy is determined.

5. The method according to claim 4, characterized in that, The method further includes: Obtain the latency threshold corresponding to the network latency matrix; The inter-service communication latency during the operation of the power business process model is collected. Based on the comparison between the inter-service communication latency and the latency threshold, a corresponding latency adjustment strategy is determined.

6. The method according to claim 4, characterized in that, The method further includes: The task execution time of the power business process model during runtime is collected; When the task execution time exceeds a preset time threshold, the task is determined to have failed, and a fault self-healing strategy is triggered. When the number of times the fault self-healing strategy is executed reaches a preset threshold, the fault service that failed to execute the task is isolated from the process link of the power business process model.

7. A modeling device for power business processes, characterized in that, The device includes: The data acquisition module is used to acquire graphical power business process data; The model conversion module is used to convert the power business process data into a token-based semantic model. The process verification module is used to determine the model state space size of the formal semantic model, and to perform process verification on the formal semantic model using a verification method that matches the model state space size, so as to obtain the verification result. The model generation module is used to adjust the formal semantic model based on the verification results, and use the adjusted formal semantic model as the power business process model.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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