Bidirectional coupling method and device, storage medium and computer equipment

Abstract

According to the bidirectional coupling method and device, the storage medium and the computer equipment provided by the invention, the corresponding coupling rule base is obtained when the job task is triggered, the service chain and the data chain corresponding to the job task are determined, the coupling rule base is used for automatic examination and approval, and the change of the service event and the data state is continuously monitored after the examination and approval is passed. Real-time linkage of a service chain and a data chain can be realized. When a business event changes, a data chain is automatically updated, the real-time performance and accuracy of data are ensured, meanwhile, when a data state changes, a coupling rule matched with the change of the data state is inquired in a coupling rule base, business intervention is performed on the business chain according to the coupling rule, and dynamic adjustment and optimization of a business process are ensured. The scheme not only reduces the labor cost, but also realizes the deep fusion and real-time interaction of business and data, thereby improving the management efficiency and accuracy of power grid safety production, and enhancing the intelligent level of the power grid production management process.

Description

Technical Field

[0001] This application relates to the field of power grid production technology, and in particular to a bidirectional coupling method, apparatus, storage medium and computer equipment. Background Technology

[0002] Safe production in the power grid is the fundamental guarantee for the continuous and stable operation of power enterprises. Its core management focuses on key elements such as personnel, work plans, safety tools and equipment, and work permits. With the development of information technology, large power enterprises have successively deployed various professional management systems, such as safety production management systems and tool and equipment management systems. Although actual business operations are a continuous whole in the physical world, they are fragmented into different systems in the digital space, making it difficult to fully utilize them.

[0003] The current mainstream solution uses work orders as the main thread, linking together steps such as plan application, personnel qualification verification, and tool status inspection to achieve online operation of the process. However, this technology is essentially still an electronic reproduction of the traditional offline approval process, with each step relying on manual triggering and approval, lacking real-time and in-depth data interaction. Summary of the Invention

[0004] The purpose of this application is to at least address one of the aforementioned technical deficiencies, particularly the technical deficiency in existing technologies that still rely on the electronic reproduction of traditional offline approval processes, with each step depending on manual triggering and approval, and lacking real-time and in-depth data interaction.

[0005] In a first aspect, this application provides a bidirectional coupling method, the method comprising:

[0006] When a task is triggered, retrieve the coupling rule base corresponding to the current task;

[0007] The business chain and data chain corresponding to the task are determined, and the task is approved using the coupling rule base. If the approval is successful, the business event changes in the business chain and the data status changes in the data chain are continuously monitored.

[0008] When a change in a business event is detected in the business chain, the data chain is updated according to the change in the business event.

[0009] When a change in the data state in the data chain is detected, the coupling rule matching the change in the coupling rule base is queried, and the business chain is intervened in accordance with the coupling rule.

[0010] In one embodiment, the construction process of the coupling rule base includes:

[0011] Identify each executable node in the business chain;

[0012] For each executable node, the node tasks of that executable node are analyzed, and multiple risk conditions and corresponding rules are set based on the analysis results.

[0013] A coupled rule base is generated based on the risk conditions and rules of each executable node.

[0014] In one embodiment, after generating the coupling rule base, the method further includes:

[0015] The coupled rule base was simulated and verified to obtain the simulation verification results;

[0016] When the coupling rule base fails the simulation verification, the coupling rule base is adjusted according to the simulation verification results, and the adjusted coupling rule base is used as the final coupling rule base for application.

[0017] In one embodiment, the step of approving the job task using the coupled rule base includes:

[0018] The start conditions for the job task are determined based on the coupling rule base and the business chain;

[0019] Based on the aforementioned activation conditions, each data item in the data chain is verified, and data items that fail the verification process are identified as risky data items.

[0020] When the verification is completed, if there are risky data items in the data chain, a rejection report indicating that the approval has not been passed will be generated based on each risky data item; otherwise, the job task is considered to have been approved.

[0021] In one embodiment, updating the data chain according to the changes in the business event includes:

[0022] The changes in the business events are analyzed to determine the data items affected by the changes.

[0023] In a preset set of update rules, an update rule matching the data item that is affected by the change in the business event is determined, and the data item in the data chain that is affected by the change in the business event is determined as the target data item.

[0024] The target data items in the data chain are updated according to the determined update rules.

[0025] In one embodiment, querying the coupling rule base for a coupling rule that matches the data state change includes:

[0026] Extract semantic information about the changes in the data state;

[0027] Query the target risk conditions that match the semantic information in the coupling rule base;

[0028] Obtain the rules in the coupling rule base that correspond to the target risk condition, and determine the obtained rules as coupling rules.

[0029] In one embodiment, the step of intervening in the business chain according to the coupling rule includes:

[0030] Extract the intervention instructions corresponding to the coupling rules, including allowing continued execution, issuing warnings, forcibly suspending and terminating business flows;

[0031] An intervention command is issued to the business chain to intervene in the business chain.

[0032] Secondly, this application provides a bidirectional coupling device, the device comprising:

[0033] The rule acquisition module is used to acquire the coupled rule library corresponding to the current job task when the job task is triggered;

[0034] The task approval module is used to determine the business chain and data chain corresponding to the job task, and to approve the job task using the coupling rule base. If the approval is successful, the module continuously monitors changes in business events in the business chain and changes in data status in the data chain.

[0035] The data update module is used to update the data chain according to the changes in business events detected in the business chain.

[0036] The business adjustment module is used to query the coupling rule base that matches the data state change when a change in the data state is detected in the data chain, and to intervene in the business chain according to the coupling rule.

[0037] Thirdly, this application provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the bidirectional coupling method as described in any of the above embodiments.

[0038] Fourthly, this application provides a computer device, including: one or more processors, and a memory;

[0039] The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the bidirectional coupling method as described in any of the above embodiments.

[0040] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:

[0041] The bidirectional coupling method, apparatus, storage medium, and computer equipment provided in this application acquire the corresponding coupling rule base when a task is triggered, determine the business chain and data chain corresponding to the task, and use the coupling rule base for automated approval. After approval, it continuously monitors changes in business events and data states, enabling real-time linkage between the business chain and the data chain. When a business event changes, the data chain is automatically updated to ensure data real-time performance and accuracy. Simultaneously, when the data state changes, the coupling rule base is queried to find a coupling rule matching the data state change, and business intervention is performed on the business chain based on the coupling rule, ensuring dynamic adjustment and optimization of business processes. This solution not only reduces labor costs but also achieves deep integration and real-time interaction between business and data, thereby improving the management efficiency and accuracy of power grid safety production and enhancing the intelligence level of power grid production management processes. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 A flowchart illustrating a bidirectional coupling method provided in an embodiment of this application;

[0044] Figure 2 This is an example diagram illustrating the bidirectional coupling between the data chain and the business chain provided in the embodiments of this application.

[0045] Figure 3 This is a schematic diagram of the structure of a bidirectional coupling device provided in an embodiment of this application;

[0046] Figure 4 This is an internal structural diagram of a computer device provided in an embodiment of this application. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] In one embodiment, this application provides a bidirectional coupling method, which is described below using an example of applying the method to a server. It is understood that the bidirectional coupling method can be implemented on a single server or in a server cluster consisting of multiple servers; this application does not impose any specific limitations on this.

[0049] like Figure 1 As shown, this application provides a bidirectional coupling method, the method comprising:

[0050] S101: When a job task is triggered, obtain the coupling rule base corresponding to the current job task.

[0051] Among them, the operation task refers to the on-site power operation that needs to be performed during the operation and maintenance of the power system. The coupling rule base is a database containing various rules and logics, which is used to define the relationship and interaction rules between the business chain and the data chain during the execution of the operation task.

[0052] In this step, when a user submits a job task, the job task is triggered, and the corresponding coupling rule base is retrieved. Specifically, each job task corresponds to a business chain, and each business chain corresponds to a coupling rule base. This coupling rule base is used to store predefined coupling rules, which ensure the reliability and security of the business chain during execution.

[0053] For example, suppose a substation needs to overhaul a transformer. When the work task is triggered, the system automatically retrieves the coupled rule base related to the transformer overhaul task. These rules may include the high-voltage electrical work qualifications that maintenance personnel must possess, rules for checking the availability of required insulating tools, and rules for updating data records during the overhaul process. In this way, the server can ensure that all relevant rules are correctly applied during the execution of the work task, thereby guaranteeing the standardization and safety of the operation.

[0054] S102: Determine the business chain and data chain corresponding to the job task, and use the coupling rule base to approve the job task. If the approval is successful, continuously monitor the changes in business events in the business chain and the changes in data status in the data chain.

[0055] The business chain refers to a series of coherent and orderly business activities in power grid safety production, such as the entire process of "work permit issuance and approval" -> "on-site safety measure deployment" -> "tool and personnel access inspection" -> "work execution and monitoring" -> "work completion and tool recycling". The data chain refers to the collection of all data generated, circulated, and recorded at each stage of the business chain. The data chain includes structured data (such as planned time, personnel ID, tool number, etc.) and unstructured data (such as on-site photos, video surveillance streams, sensor readings, etc.).

[0056] In this step, the corresponding business chain and data chain can be determined based on the task number and other identifying information. Specifically, each task needs to have its corresponding business chain and data chain built upon creation, and some initial relevant data from the business chain is written into the data chain. Then, a mapping relationship is established between the task and its corresponding business chain and data chain for subsequent querying and identification. For example, for a power equipment maintenance task, its business chain can be determined to include stages such as plan application, personnel qualification verification, tool and equipment status inspection, and on-site operation execution. Simultaneously, the data chain can be determined to include equipment operating parameters, tool and equipment status data, and work environment data. Finally, the business chain and data chain for the power equipment maintenance task are constructed based on this data.

[0057] Next, the server uses a coupling rule base to approve job tasks. This rule base contains association rules between the business chain and the data chain. At this point, the server verifies the job task's initiation process based on these rules, specifically whether the initial state of the business chain and data chain is suitable for entering the actual production stage. If approval is granted, the server enters a continuous monitoring state, continuously monitoring changes in business events within the business chain and changes in data status within the data chain. This allows for timely updates to the data chain and dynamic adjustments to the business chain in response to changes in business events or data status, achieving deep integration and real-time interaction between business and data.

[0058] For example, when a "work order submission" (business event) is triggered, the server not only verifies whether the personnel are registered (data status), but also checks in real time whether the participants have been assigned other tasks (data status) to avoid overloading them; at the same time, it checks whether there is a typhoon warning during the planned work period (data status). If any condition is not met, the server sends an "approval rejection" control command to the business chain and explains the reason.

[0059] Specifically, a business event change refers to a change in the state, generated business data, or result of any business link during the execution of the business chain. Examples include starting the execution of the next business node or adjusting the work plan. A data state change refers to a change in the value of any data item in the data chain. Examples include changes in the operating environment temperature or changes in the power of tools and equipment.

[0060] Furthermore, monitoring changes in business events and data states can be accomplished through a built-in real-time monitoring module. This module continuously monitors business events in the business chain and data states in the data chain, ensuring that appropriate response mechanisms are triggered immediately when any changes occur.

[0061] S103: When a change in a business event is detected in the business chain, update the data in the data chain according to the change in the business event.

[0062] In this step, when a change in a business event within the business chain is detected, an update operation on the data chain is triggered. Specifically, the specific changes in the business event can first be identified, such as a change in the status of a certain step from "pending approval" to "approved," or a change in the work plan from "initial state" to "delayed execution." Based on these changes, the specific data items that need to be updated can be determined. For example, if the business event change is "personnel qualification review passed," the qualification status data related to that person in the data chain is updated from "unreviewed" to "passed." This dynamic update mechanism not only ensures that the data in the data chain remains consistent with the actual state of the business chain but also reflects the latest progress of the work tasks in real time. For example, in cross-regional power grid maintenance tasks, if the work plan for a certain area is postponed due to weather conditions, the equipment operation data and personnel scheduling data related to that area can be updated promptly, ensuring that all relevant personnel and systems have access to the latest information, thereby better coordinating the execution of the work tasks.

[0063] S104: When a change in the data state in the data chain is detected, query the coupling rule in the coupling rule base that matches the change in data state, and intervene in the business chain according to the coupling rule.

[0064] Among them, coupling rules refer to the logical rules used in power operation tasks to describe the relationship between data link state changes and business chain intervention measures.

[0065] In this step, when a change in the data state of the data chain is detected, this change can be matched against rules in the coupling rule base to query the coupling rules that match the data state change. Then, based on the content of these coupling rules, business interventions are performed on the business chain to dynamically regulate the production process. For example, a coupling rule might stipulate that when equipment operating parameters are abnormal, on-site operations must be suspended and maintenance personnel notified.

[0066] For example, suppose a temperature sensor on a power device displays an excessively high temperature. Upon detecting this data change, the server immediately queries the coupling rule base to find the coupling rule related to the temperature anomaly. Based on this coupling rule, the server may automatically pause ongoing equipment maintenance work and send an alert to maintenance personnel, requesting them to check the equipment's cooling system. This automated intervention mechanism not only responds quickly to data changes but also ensures that tasks can continue to be performed safely.

[0067] In the above embodiments, by acquiring the corresponding coupling rule base when a task is triggered and determining the corresponding business chain and data chain, automated approval is performed using the coupling rule base. After approval, continuous monitoring of changes in business events and data states enables real-time linkage between the business chain and the data chain. When a business event changes, the data chain is automatically updated to ensure data real-time performance and accuracy. Simultaneously, when the data state changes, the coupling rule base is queried to find the coupling rule matching the data state change, and business intervention is performed on the business chain based on the coupling rule, ensuring dynamic adjustment and optimization of the business process. This solution not only reduces labor costs but also achieves deep integration and real-time interaction between business and data, thereby improving the management efficiency and accuracy of power grid safety production and enhancing the intelligence level of power grid production management processes.

[0068] In one embodiment, the process of constructing the coupling rule base includes:

[0069] S1: Identify each executable node in the business chain.

[0070] S2: For each executable node, analyze the node tasks of that executable node, and set multiple risk conditions and corresponding rules for each risk condition based on the analysis results.

[0071] S3: Generate a coupled rule base based on the risk conditions and rules of each executable node.

[0072] The business chain comprises multiple executable nodes, each representing a specific task or operational step that can be executed independently, such as personnel qualification verification or on-site work execution. The analysis results refer to the conclusions drawn from a detailed analysis of the tasks at each executable node, including execution conditions, potential risks, and dependencies. The data obtained from these analyses provides a basis for setting risk conditions and rules.

[0073] In this embodiment, each executable node in the business chain is first identified. For each executable node, the corresponding node task is analyzed to determine its task execution conditions, potential risk points, and other information. Based on this information, risk conditions and rules for responding to the triggering of these risk conditions are constructed. Finally, the risk conditions and rules for each executable node are compiled and summarized to generate a coupled rule base. Furthermore, risk conditions refer to specific situations or states that may lead to security risks, operational errors, or business interruptions.

[0074] Specifically, when analyzing the node tasks corresponding to executable nodes, a pre-trained deep learning model can be used. This model can be trained iteratively using relevant information about the node tasks as training data and risk conditions and their rules as label data. Upon completion of training, the resulting deep learning model can be applied to the node task analysis. Furthermore, during the process of compiling and summarizing the risk conditions and rules for each executable node, this data can be preprocessed and stored in a structured manner to obtain a coupled database.

[0075] For example, in the "Issue Work Ticket" node, potential risks might include "allowing an unqualified team to perform the job". In this case, risk conditions can be set from aspects such as "personnel qualifications are not up to standard" and "key tools and equipment are not ready". For example, whether the personnel qualification certificates are valid and whether the key tools and equipment are available.

[0076] In one example, when setting rules for risk conditions, the specific rules can be determined based on the risk level after the risk condition is triggered. For example, if the risk level is low, the rule can be set to issue an early warning and alert relevant personnel; if the risk level is medium, the rule can be set to forcibly suspend the business process until the problem is resolved; if the risk level is high, the rule can be set to terminate the business flow and notify security management personnel to intervene.

[0077] It is understandable that by systematically identifying executable nodes and analyzing their task content, potential risk points at each node can be accurately identified, allowing for the setting of reasonable rules for each risk point. This systematic analysis and rule setting not only improves the intelligence level of production management but also enhances risk warning and intervention capabilities. Furthermore, real-time monitoring and response to various risk conditions during task execution ensures that tasks are carried out smoothly under safe and standardized conditions.

[0078] In one embodiment, after generating the coupling rule base, the bidirectional coupling method further includes:

[0079] S1: Perform simulation verification on the coupled rule base and obtain the simulation verification results.

[0080] S2: When the coupling rule base fails the simulation verification, adjust the coupling rule base according to the simulation verification results, and use the adjusted coupling rule base as the final coupling rule base for application.

[0081] Among them, the simulation verification results refer to the verification data and evaluation conclusions obtained after testing the coupled rule base through simulation, including but not limited to whether the expected goals are met, whether there are logical errors or vulnerabilities, and the ability to respond to potential risks.

[0082] In this embodiment, rules in the coupled rule base can be run according to predefined test scenarios and input conditions to simulate the execution process of actual tasks. During the simulation, the system records the triggering of rules, execution results, and any abnormal behaviors, ultimately generating simulation verification results. Subsequently, if the simulation verification results show that the coupled rule base fails verification—that is, there are problems such as rule conflicts, logical vulnerabilities, or inability to effectively respond to certain risk conditions—the coupled rule base will be adjusted based on these verification results. Adjustments may include modifying rule conditions, adding or deleting rules, etc.

[0083] Understandably, simulation can comprehensively test the performance of the coupled rule base in a virtual environment, thereby discovering potential logical errors, rule conflicts or vulnerabilities, optimizing the coupled rule base in advance, and avoiding risks in actual operation.

[0084] In one embodiment, the approval of job tasks is performed using a coupled rule base, including:

[0085] S1: Determine the start conditions for job tasks based on the coupling rule base and business chain.

[0086] S2: Verify each data item in the data chain based on the start conditions, and identify data items that fail the verification as risky data items during the verification process.

[0087] S3: When the verification is completed, if there are risky data items in the data chain, a rejection report indicating that the approval has not been passed will be generated based on each risky data item; otherwise, the job task is considered to have been approved.

[0088] Among them, the initiation condition refers to the prerequisite conditions that must be met before the task can begin execution. Risk data items refer to data items in the data chain that have failed validation, representing potential risk points.

[0089] In this embodiment, by combining the coupling rule base and the business chain, the conditions that must be met when a job task is started are clearly defined. Specifically, the coupling rule corresponding to the first node in the business chain in the coupling rule base is used as the start condition. Then, each data item in the data chain is verified based on this start condition. The verification process involves checking the value or status of each data item to ensure it conforms to the preset rules and conditions; otherwise, it is identified as a risky data item. Finally, a rejection report representing the failure to pass approval is generated based on these risky data items. When all data items pass verification, the job task is considered approved. This data verification-based approval mechanism not only improves the security and reliability of job task startup but also enhances the intelligence level of production management, reduces the need for manual intervention, and lowers the risk caused by human error.

[0090] In one example, when the "Submit Work Ticket" business event is triggered, the server immediately retrieves all rules related to that event from the coupled rule base. Then, it extracts the latest status of all relevant data related to the work ticket from the real-time data view of that business event. This data may include:

[0091] Personnel Status: Whether the qualifications of the work team members are valid, and whether they currently have other tasks.

[0092] Equipment status: Whether the planned insulating gloves, safety belts, etc. are within the validity period of the inspection, and whether they are currently occupied by other work orders.

[0093] Environmental conditions: During the planned work period, are there any warnings of severe weather such as thunderstorms, strong winds, or heavy rain at the work site?

[0094] Plan conflict: Are there any conflicting high-risk work plans in the same area and at the same time?

[0095] Match the latest data status with predefined rule conditions. For example, the activation rule might be "IF Personnel qualification status != 'Valid' OR Tool status = 'Occupied' OR Lightning warning level > 'Yellow' THEN Approval result = 'Rejected'".

[0096] Ultimately, the engine outputs control commands such as "allow passage," "reject," or "pass with conditions," along with the specific decision-making basis.

[0097] In one embodiment, updating the data chain based on changes in business events includes:

[0098] S1: Analyze changes in business events to determine the data items affected by those changes.

[0099] S2: Determine the update rules that match the data items affected by the business event changes in the preset update rule set, and determine the data items in the data chain that correspond to the data items affected by the business event changes as the target data items.

[0100] S3: Update each target data item in the data chain according to the determined update rules.

[0101] Among them, update rules refer to the specific logic and operation instructions used to guide the updating of relevant data items in the data chain when business events change.

[0102] In this embodiment, the data items affected by the business event change are first identified. Then, an update rule matching the business event change and its impact on the data items is searched from a preset set of update rules. These update rules define which data items need to be updated and the specific update operations when a specific business event change occurs. For example, if a work plan is postponed, the update rule might specify that the equipment's "pending maintenance" status needs to be changed to "postponed." Once a matching update rule is determined, the data items in the data chain corresponding to the data items affected by the business event change are identified as target data items, and these target data items are updated according to the update rule. This ensures that the data in the data chain reflects changes in the business chain in real time, and changes in the data chain can also adjust the business chain in reverse. This achieves deep integration and real-time interaction between business and data.

[0103] Furthermore, data items corresponding to different business events can be pre-associated. When determining the data items affected by changes in business events, the relevant data items can be determined based on the pre-associated information.

[0104] In one embodiment, the update rule set can also be stored in the coupled rule base, and the corresponding update rule is determined by accessing the coupled rule base when the data is updated.

[0105] In one embodiment, querying the coupling rule base for coupling rules that match the data state change includes:

[0106] S1: Extract semantic information about changes in data state.

[0107] S2: Query the target risk conditions that match the semantic information in the coupled rule base.

[0108] S3: Obtain the rules corresponding to the target risk conditions from the coupling rule base, and determine the obtained rules as coupling rules.

[0109] Semantic information refers to extracting key content from changes in data state. Semantic information typically includes a specific description of the data changes, the context of the changes, and possible related factors.

[0110] In this embodiment, semantic information about data state changes is extracted so that, during matching, this semantic information is matched against risk conditions in the coupled rule base. During matching, the semantic similarity between the two conditions is calculated, and the target risk condition with the highest matching degree is determined in the coupled rule base. Then, the rule corresponding to the target risk condition is obtained and identified as the coupled rule to be applied. Searching for matching risk conditions in the coupled rule base allows for the rapid identification of rules related to the risk, ensuring that appropriate business intervention measures can be taken.

[0111] In one embodiment, business intervention is performed on the business chain according to coupling rules, including:

[0112] S1: Extract the intervention instructions corresponding to the coupling rules.

[0113] S2: Issue intervention instructions to the business chain to intervene in the business chain.

[0114] Intervention commands include allowing continued execution, issuing warnings, forcibly suspending, and terminating business processes.

[0115] In this embodiment, by extracting specific intervention instructions from the coupling rules, it is possible to clarify the operations that need to be performed in specific situations, thereby providing clear guidance for subsequent business interventions.

[0116] In one example, when a worker scans the work order QR code to "start work" (business event), the server initiates full monitoring of the work order. If the server detects that the qualification of a key personnel in the work team expires during the work period (data status change), it immediately sends a "high-risk warning" to the on-site mobile terminal and the monitoring center, and automatically sets the work order status to "paused," requiring a qualified replacement before continuing (intervention command). Furthermore, during the work process, UWB positioning data (data link) shows that a person has entered a high-voltage danger zone (data status change). After matching the rules, the server immediately sends an audible and visual alarm to the person's smart safety helmet (business terminal) and notifies the supervisor (intervention command).

[0117] In one embodiment, such as Figure 2 As shown, Figure 2 This is an example diagram illustrating the bidirectional coupling of the data chain and the business chain provided in an embodiment of this application. From... Figure 2 As can be seen, changes in business events can trigger data update commands, while changes in data state can in turn trigger control commands (i.e., intervention commands) on the business chain. Figure 2 In this context, the coupling engine refers to the server in the above embodiments.

[0118] 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 of other steps.

[0119] The bidirectional coupling device provided in the embodiments of this application is described below. The bidirectional coupling device described below can be referred to in correspondence with the bidirectional coupling method described above.

[0120] like Figure 3 As shown, this application provides a bidirectional coupling device 200, the device comprising:

[0121] The rule acquisition module 201 is used to acquire the coupled rule library corresponding to the current job task when the job task is triggered.

[0122] The task approval module 202 is used to determine the business chain and data chain corresponding to the job task, and to approve the job task using the coupling rule base. If the approval is successful, it continuously monitors the changes in business events in the business chain and the changes in data status in the data chain.

[0123] The data update module 203 is used to update the data chain according to the changes in business events when changes in business events are detected in the business chain.

[0124] The business adjustment module 204 is used to query the coupling rule base that matches the data state change when a change in the data state is detected in the data chain, and to intervene in the business chain according to the coupling rule.

[0125] In the above embodiments, by acquiring the corresponding coupling rule base when a task is triggered and determining the corresponding business chain and data chain, automated approval is performed using the coupling rule base. After approval, continuous monitoring of changes in business events and data states enables real-time linkage between the business chain and the data chain. When a business event changes, the data chain is automatically updated to ensure data real-time performance and accuracy. Simultaneously, when the data state changes, the coupling rule base is queried to find the coupling rule matching the data state change, and business intervention is performed on the business chain based on the coupling rule, ensuring dynamic adjustment and optimization of the business process. This solution not only reduces labor costs but also achieves deep integration and real-time interaction between business and data, thereby improving the management efficiency and accuracy of power grid safety production and enhancing the intelligence level of power grid production management processes.

[0126] In one embodiment, the rule acquisition module includes:

[0127] The node identification submodule is used to identify each executable node in the business chain;

[0128] The Condition Setting Submodule is used to analyze the node tasks of each executable node and set multiple risk conditions and corresponding rules for each risk condition based on the analysis results.

[0129] The rule generation submodule is used to generate a coupled rule base based on the risk conditions and rules of each executable node.

[0130] In one embodiment, the bidirectional coupling device further includes:

[0131] The simulation verification module is used to perform simulation verification on the coupled rule base and obtain the simulation verification results.

[0132] The rule adjustment module is used to adjust the coupled rule base according to the simulation verification results when the coupled rule base fails the simulation verification, and to use the adjusted coupled rule base as the final coupled rule base for application.

[0133] In one embodiment, the task approval module includes:

[0134] The condition determination submodule is used to determine the start conditions of job tasks based on the coupled rule base and business chain;

[0135] The data verification submodule is used to verify each data item in the data chain based on the startup conditions, and to identify data items that fail the verification as risky data items during the verification process.

[0136] The approval submodule is used to generate a rejection report indicating that the approval has failed if there are risky data items in the data chain when the verification is completed; otherwise, the task is considered to have been approved.

[0137] In one embodiment, the data update module includes:

[0138] The change analysis submodule is used to analyze changes in business events in order to determine the data items affected by these changes.

[0139] The data item determination submodule is used to determine the update rules that match the data items of the business event changes and their impact from the preset update rule set, and to determine the data items in the data chain that correspond to the data items affected by the business event changes as the target data items;

[0140] The data update submodule is used to update each target data item in the data chain according to the determined update rules.

[0141] In one embodiment, the service adjustment module includes:

[0142] The information extraction submodule is used to extract semantic information about changes in data state.

[0143] The conditional query submodule is used to query target risk conditions that match semantic information in the coupled rule base;

[0144] The rule determination submodule is used to obtain the rules corresponding to the target risk conditions in the coupling rule base and determine the obtained rules as coupling rules.

[0145] In one embodiment, the service adjustment module includes:

[0146] The instruction extraction submodule is used to extract the intervention instructions corresponding to the coupling rules. The intervention instructions include allowing continued execution, issuing warnings, forcibly pausing and terminating the business flow.

[0147] The business intervention submodule is used to issue intervention commands to the business chain in order to intervene in the business chain.

[0148] The division of modules in the above-described bidirectional coupling device is merely illustrative. In other embodiments, the bidirectional coupling device can be divided into different modules as needed to complete all or part of its functions. Each module in the above-described bidirectional coupling device 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 in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0149] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the bidirectional coupling method as described in any of the above embodiments.

[0150] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the bidirectional coupling method as described in any of the above embodiments.

[0151] Indicatively, such as Figure 4 As shown, Figure 4 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 4 The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions, such as application programs, that can be executed by the processing component 302. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the bidirectional coupling method of any of the above embodiments.

[0152] The computer device 300 may also include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.

[0153] Those skilled in the art will understand that Figure 4 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.

[0154] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising a…" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, the singular forms "a," "an," and "the" may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having” specify the presence of the stated features, wholes, steps, operations, components, parts or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0155] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.

[0156] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A bidirectional coupling method, characterized by, The method comprises: When a job task is triggered, a current coupling rule base corresponding to the job task is obtained; A business chain and a data chain corresponding to the job task are determined, and the job task is approved by using the coupling rule base. If the approval is passed, the business event change in the business chain and the data state change in the data chain are continuously monitored; When the business event change in the business chain is monitored, the data chain is updated according to the business event change; When the data state change in the data chain is monitored, a coupling rule matched with the data state change is queried in the coupling rule base, and the business chain is intervened according to the coupling rule.

2. The bidirectional coupling method of claim 1, wherein, The construction process of the coupling rule base comprises: Each executable node in the business chain is identified; For each executable node, the node task of the executable node is analyzed, and a plurality of risk conditions and rules corresponding to each risk condition are set according to the analysis result; According to the risk conditions and rules of each executable node, a coupling rule base is generated.

3. The bidirectional coupling method of claim 2, wherein, After the coupling rule base is generated, the method further comprises: The coupling rule base is simulated and verified to obtain a simulation verification result; When the coupling rule base fails to pass the simulation verification, the coupling rule base is adjusted according to the simulation verification result, and the adjusted coupling rule base is used as the final applied coupling rule base.

4. The bidirectional coupling method of claim 1, wherein, The approval of the job task by using the coupling rule base comprises: The start condition of the job task is determined according to the coupling rule base and the business chain; Each data item in the data chain is verified based on the start condition, and a data item that fails to pass the verification is determined as a risk data item in the verification process; When the verification is completed, if there is a risk data item in the data chain, a rejection report indicating that the approval fails is generated based on each risk data item, otherwise, it is considered that the job task passes the approval.

5. The bidirectional coupling method of claim 1, wherein, The data update of the data chain according to the business event change comprises: The business event change is analyzed to determine the data item affected by the business event change; In a preset update rule set, an update rule matched with the business event change and the data item affected thereby is determined, and a data item in the data chain corresponding to the data item affected by the business event change is determined as a target data item; Each target data item in the data chain is updated according to the determined update rule.

6. The bidirectional coupling method of claim 1, wherein, The coupling rule matched with the data state change is queried in the coupling rule base, which comprises: Semantic information of the data state change is extracted; A target risk condition matched with the semantic information is queried in the coupling rule base; The rule corresponding to the target risk condition in the coupling rule base is obtained, and the obtained rule is determined as a coupling rule.

7. The bidirectional coupling method of claim 1, wherein, The business intervention of the business chain according to the coupling rule comprises: An intervention instruction corresponding to the coupling rule is extracted, the intervention instruction comprising allowing to continue execution, issuing a warning, forcibly pausing, and terminating a business flow; Intervention instructions are sent to the service chain to intervene in the service chain.

8. A bidirectional coupling device, characterized by The device comprises: A rule acquisition module is configured to acquire a coupling rule base corresponding to a current job task when the job task is triggered. A task approval module is configured to determine a service chain and a data chain corresponding to the job task, and to use the coupling rule base to approve the job task, and if the approval is passed, to continuously monitor changes in business events in the service chain and changes in data states in the data chain. A data update module is configured to update the data chain according to the changes in business events when the changes in business events in the service chain are monitored. A business adjustment module is configured to query a coupling rule matched with the changes in data states in the coupling rule base when the changes in data states in the data chain are monitored, and to intervene in the service chain according to the coupling rule.

9. A storage medium characterized by: The storage medium has computer readable instructions stored therein, and the computer readable instructions, when executed by one or more processors, cause the one or more processors to perform the steps of the bidirectional coupling method according to any one of claims 1 to 7.

10. A computer device, comprising: Comprise: One or more processors, and a memory; The memory has computer readable instructions stored therein, and the computer readable instructions, when executed by the one or more processors, perform the steps of the bidirectional coupling method according to any one of claims 1 to 7.

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