A machine language conversion method and system for power grid dispatching instruction information

Abstract

This invention discloses a machine language conversion method and system for power grid dispatching instruction information, belonging to the field of smart grid dispatching automation technology. The method includes constructing an instruction function library, abstracting power grid operating procedures into computable and reversibly convertible functions and their inverse functions; converting text instructions into structured machine language entities during the drafting stage; calling the inverse functions to restore the text and comparing it with the original instructions during the review stage to achieve consistency verification between human and machine understanding; directly reading the structured entities for precise analysis during the safety verification stage; and converting the structured entities into protocol instructions for issuance during the intelligent command issuance stage. This invention realizes the digitization of dispatching instructions from the source to full-process semantic integration, constructs a closed-loop reliability verification mechanism, provides accurate input for safety verification and programmed control, and improves the automation level and safety of power grid dispatching operations.

Description

Technical Field

[0001] This invention relates to a machine language conversion method and system for power grid dispatching instruction information, belonging to the field of smart grid dispatching automation technology. Background Technology

[0002] The text format of power grid dispatch instructions originated from the manual recording and transmission of early power system dispatch operations. Before the 1990s, dispatch instructions were mainly completed by handwriting or telephone fax, which resulted in low efficiency, high error rates, and difficulty in traceability. With the popularization of computer technology, text format gradually became the mainstream, for example, by using tools such as Word and Excel to create operation tickets. However, this method still relies on manual input of each item, which is cumbersome and lacks standardization.

[0003] After 2000, with the expansion of the power grid and the increasing demand for automation, text formats gradually became standardized. For example, the classification of dispatch instructions (such as integrated instructions, individual instructions, and item-by-item instructions) and standardized terminology were clarified, promoting the unification of text instructions. Simultaneously, intelligent ticketing technology based on SVG graphics or topology analysis began to be applied, automatically generating instructions through equipment status logic, reducing manual intervention. However, the limitations of text formats gradually became apparent, especially in areas such as dialect differences, terminology standardization, and intelligent support, which urgently require breakthroughs.

[0004] In power grid dispatching operations, safety verification is a core step before the execution of dispatching instructions. It requires instruction data to be complete, accurate, logically consistent, tamper-proof, and real-time. However, the colloquial and unstructured nature of text-based power grid operation instructions makes it difficult to accurately extract key parameters (equipment, status, scenario) needed for safety verification. This leads to semantic ambiguity, confusion between similar instructions, and difficulties in cross-unit adaptation, severely impacting the accuracy and efficiency of safety verification. These problems essentially stem from the contradiction between unstructured text and the requirements of structured safety verification, and must be resolved through structured processing, standardization, and intelligent technologies.

[0005] In the field of power grid operation and control, text-based instructions, due to their unstructured nature and ambiguous semantic expression, cause intelligent control systems to be unable to accurately understand the intent of the instructions, resulting in poor cross-system compatibility, imperfect verification mechanisms, and insufficient scalability. These limitations have become bottlenecks in the evolution of intelligent control towards "cognitive intelligence," and urgently need to be addressed through technologies such as structured processing, standardization, and intelligentization to achieve automation and intelligence in intelligent control.

[0006] However, whether it's traditional text instructions or semi-structured instructions based on fixed templates, their essence remains natural language or its variations designed for human reading. Existing technological solutions (such as terminology standardization and graphical invoicing) mainly solve the problems of input standardization and generation efficiency, but fail to address the issue of deep machine understandability of instructions. This leads to the need for complex parsing procedures or manual intervention in subsequent advanced application stages that require precise and unambiguous semantic input, such as security verification and procedural control, forming a bottleneck for intelligent development. Therefore, there is an urgent need for a fundamental solution that can endow instructions with clear machine semantics from the source and maintain the consistency of this semantics throughout the entire business process. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and provide a machine language conversion method and system for power grid dispatching instruction information. It solves the core problems of difficult machine semantic parsing of text instructions, poor cross-system interaction compatibility, and low degree of automation of security verification. It realizes the connection between manual compilation of dispatching instructions and machine understanding and intelligent execution, and provides a reliable data foundation for programmed operation and advanced intelligent applications of the power grid.

[0008] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0009] In a first aspect, the present invention provides a machine language conversion method for power grid dispatching instruction information, comprising:

[0010] During the function library construction phase, atomic operations are defined as computable and reversibly convertible functions according to the power grid operation procedures, and the functions and their inverse functions are stored in the instruction function library.

[0011] In the drafting stage, the received text instructions are sent to the instruction function library for function matching; if the matching is successful, the matched function is called to generate a structured machine language entity corresponding to the text instructions.

[0012] During the review process, the structured machine language entity is sent to the instruction function library, the inverse function is called to restore it to text, and the restored text is automatically compared with the text instruction. If the comparison is consistent, the structured machine language entity is passed to the security verification process.

[0013] In the security verification process, the structured machine language entity is directly read, and security analysis is performed based on the device identifier and state transition information in the structured machine language entity.

[0014] In the intelligent command issuance stage, the structured machine language entities that have passed the security analysis are converted into protocol instructions required by the automation system or equipment controller and then issued.

[0015] Furthermore, during the function library construction phase, a unique function is defined for each atomic operation according to the power grid operation procedures. The function takes the operation object and action described in natural language as input parameters and outputs a standardized and semantically rich structured machine language entity. At the same time, a corresponding inverse function is defined for each function. The inverse function is used to reconstruct the standardized text description from the structured machine language entity.

[0016] Furthermore, in the drafting stage, when the received text instructions are sent to the instruction function library for structured conversion, the key parameters in the text instructions are automatically extracted as query keys for function matching. If the match is successful, the matched function is called to generate the structured machine language entity and saved with the operation ticket. If the match fails, a semantic non-standard prompt is given and a correction is requested.

[0017] Furthermore, during the review process, if the automatic comparison results are inconsistent, the process is triggered to backtrack to the drafting stage.

[0018] Furthermore, in the safety verification stage, the structured machine language entity is directly read, and the operational consequences simulation and power flow calculation are performed in the power grid model based on the device identifier and state transition information explicitly contained in the structured machine language entity, thereby forming a safety analysis result.

[0019] Furthermore, in the intelligent command issuance stage, the structured machine language entity that has passed the security analysis is sent to the instruction function library for final format confirmation, then converted into the protocol instructions required by the downstream automation system or equipment controller, and issued for execution.

[0020] Furthermore, the structured machine language entity generated during the drafting process includes operation type, target device identifier, device name, device type, state transition information, timestamp, and tamper-proof hash value.

[0021] Furthermore, the structured machine language entity is a structured data object in JSON format, and the protocol instruction is an IEC 61850 MMS message.

[0022] Secondly, the present invention provides a machine language conversion system for power grid dispatching instruction information, used to implement the machine language conversion method for power grid dispatching instruction information as described in any one of the preceding claims, comprising:

[0023] The function library construction module is used to define atomic operations as computable and reversibly convertible functions according to the power grid operation procedures, and store the functions and their inverse functions into the instruction function library;

[0024] The drafting module is used to send the received text instructions into the instruction function library for function matching; if the matching is successful, the matched function is called to generate a structured machine language entity corresponding to the text instruction;

[0025] The audit module is used to send the structured machine language entity into the instruction function library, call the inverse function to restore it to text, and automatically compare the restored text with the text instruction. If the comparison is consistent, the structured machine language entity is passed to the security verification module.

[0026] The security verification module is used to directly read the structured machine language entity and perform security analysis based on the device identifier and state transition information in the structured machine language entity.

[0027] The intelligent command module is used to convert the structured machine language entities after security analysis into protocol instructions required by the automation system or equipment controller and issue them.

[0028] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0029] Fourthly, the present invention provides an electronic device, comprising:

[0030] Memory, used to store computer programs / instructions;

[0031] A processor for executing the computer program / instructions to implement the steps of any of the methods described above.

[0032] Fifthly, the present invention provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of any of the methods described above.

[0033] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0034] 1. This invention provides a machine language conversion method and system for power grid dispatching instruction information. Through a function library construction phase, power grid operating procedures are abstracted into computable and reversibly convertible functions. During the drafting stage, the text instructions input by the dispatcher are converted into structured machine language entities in real time, achieving precise machine semantics injected into the instructions from their inception. This structured entity, in a standardized data format, permeates all business processes including drafting, review, security verification, and intelligent command issuance. Each process directly reads or calls the inverse function to process this entity, completely eliminating semantic ambiguity and inter-system semantic barriers caused by repeated natural language parsing in traditional solutions. This provides a unified data foundation for the full-process automation of power grid dispatching operations.

[0035] 2. This invention innovatively introduces a backtracking verification mechanism based on the "text → structured → text" inverse function. During the review process, the system restores the generated structured machine language entities to standard text using an inverse function and automatically compares it with the original text input by the dispatcher. If they match, the process proceeds to the next stage; otherwise, it triggers a backtracking process to the drafting stage for correction. This closed-loop verification mechanism automatically verifies the consistency between "human understanding" and "machine understanding" at key review nodes, constructing an unprecedented error-prevention closed loop and greatly improving the reliability and security of power grid dispatching operation instructions.

[0036] 3. The structured machine language entity provided by this invention includes precise parameters such as device identifier, state transition information, operation type, timestamp, and tamper-proof hash value. In the security verification stage, the verification engine directly reads the device identifier and state transition information from this entity and performs operation consequence simulation and power flow calculation in the power grid model. This eliminates the need for complex parsing of natural language text, avoiding misjudgments caused by semantic ambiguity in traditional text verification, and significantly improving the accuracy and efficiency of verification. In the intelligent command issuance stage, the system directly converts the structured entity into protocol instructions required by downstream automation systems or device controllers and issues them for execution, realizing end-to-end automation from scheduling instructions to field operations.

[0037] 4. This invention extracts parameters and performs function matching on the text instructions input by the dispatcher during the drafting stage. If the matching fails, it prompts that the semantics are not standardized and requests correction, thus achieving real-time error prevention at the source of instruction generation. This design forces dispatch instructions to conform to predefined standardized terminology and operating specifications, ensuring the standardization and integrity of instruction data from the source, and reducing subsequent process repetitions and security risks caused by human input errors. Attached Figure Description

[0038] Figure 1 This is a flowchart of a machine language conversion method for power grid dispatching instruction information provided in an embodiment of the present invention. Detailed Implementation

[0039] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0040] Example 1: This example introduces a machine language conversion method for power grid dispatching instruction information, including:

[0041] During the function library construction phase, atomic operations are defined as computable and reversibly convertible functions according to the power grid operation procedures, and the functions and their inverse functions are stored in the instruction function library.

[0042] In the drafting stage, the received text instructions are sent to the instruction function library for function matching; if the matching is successful, the matched function is called to generate a structured machine language entity corresponding to the text instructions.

[0043] During the review process, the structured machine language entity is sent to the instruction function library, the inverse function is called to restore it to text, and the restored text is automatically compared with the text instruction. If the comparison is consistent, the structured machine language entity is passed to the security verification process.

[0044] In the security verification process, the structured machine language entity is directly read, and security analysis is performed based on the device identifier and state transition information in the structured machine language entity.

[0045] In the intelligent command issuance stage, the structured machine language entities that have passed the security analysis are converted into protocol instructions required by the automation system or equipment controller and then issued.

[0046] This embodiment provides a method for converting power grid dispatching command information into machine language. This method converts dispatching commands into machine language and verifies the command format, as well as providing structured input for security error prevention and programmed control, at stages such as drafting, review, security verification, and intelligent command issuance. For the specific operation flow of this method, please refer to [link to relevant documentation]. Figure 1 This includes the following steps:

[0047] S1. Scheduling function definition—Building a machine-understandable instruction knowledge base;

[0048] This step is the foundation of the present invention, which aims to formalize knowledge (equipment, operation, rules) in the field of power grid dispatching into a machine-computable and callable function model.

[0049] 1. Function Abstraction: All atomic operation instructions in the "Power Grid Dispatch Operation Procedures" are analyzed and abstracted. For example, the operation of "changing the 'Dongli Line 161 Switch' of the 220kV Dongshan Substation from the 'operating' state to the 'maintenance' state" is abstracted into a function and named "Switch to Maintenance Function".

[0050] 2. Function Rule Definition: Define the rules for each abstract function. The rules clarify the mapping logic from input (text parameters) to output (structured entity). As described in claim 2, the function takes the operation object and action described in natural language as input parameters and outputs a standardized structured machine language entity.

[0051] Taking the "switch-to-maintenance function" as an example:

[0052] Input parameters include the substation name (e.g., "220kV Dongshan Substation") and equipment name (e.g., "Dongli Line 161 Switch"), which are derived from the natural language text entered by the dispatcher when drafting the ticket.

[0053] After the function is executed, a standardized structured data object is generated. This object precisely describes the semantics of the operation, including the operation type, target device identifier, device name, device type, state transition information, timestamp, and tamper-proof hash value.

[0054] 3. Inverse Function Definition: Define the inverse function for each function. The inverse function can receive the above structured entities as input and accurately reproduce standardized text instructions that are readable by humans, such as: "Change the 161 switch of the Dongli line of the 220kV Dongshan substation from operation to maintenance".

[0055] 4. Function Library Storage: All defined function rules and their corresponding inverse functions are stored in the central "Instruction Function Library." This library is an extensible knowledge base that supports the dynamic addition of functions based on new devices and new operation types.

[0056] S2, Ticket Drafting Stage—Real-time Digitalization of Instructions and Source Error Correction;

[0057] This stage is where scheduling instructions are generated; in this embodiment, natural language is converted into machine language.

[0058] 1. In the operation ticket system, the dispatcher enters a text command according to traditional practice, such as: "Close the 102 switch of the Haicheng line of the 110kV Haibin substation".

[0059] 2. The system triggers the structured transformation process:

[0060] First, the received text instructions are parsed to extract key parameters, including the operating unit "110kV Haibin Substation", the equipment name "Haicheng Line 102 Switch", and the operation action "Close".

[0061] Then, the extracted operation action and device type are used as query keys to perform function matching in the instruction function library to find a function that can handle the operation.

[0062] 3. Matching and Transformation:

[0063] If a match is successful, the system calls the matched function, passing the extracted plant name and equipment name as input parameters. After execution, the function immediately generates a structured machine language entity corresponding to the text instruction. This entity contains standardized equipment identifiers, a target state of "closed," and other information, and is saved with the operation ticket. Essentially, this process demonstrates the machine's real-time understanding of the instruction's semantics.

[0064] If a match fails, it indicates that the input text does not conform to the predefined specifications or terminology database. The system immediately displays "Term not recognized" or "Operation not defined," requiring the scheduler to correct it. This process achieves real-time error prevention at the source.

[0065] S3, Review Process—Closed-Loop Verification of Consistency Between Human and Machine Understanding;

[0066] This step is a critical verification point to ensure the accuracy of the instructions, and this embodiment introduces automated verification.

[0067] 1. When an operation ticket enters the review process, the system retrieves the stored structured machine language entity corresponding to the instructions on the ticket.

[0068] 2. The system triggers a structured conversion process, sends the structured entity into the instruction function library, and calls the inverse function corresponding to the entity.

[0069] 3. The inverse function accurately restores the structured entity to a standardized text instruction, such as: "Close the Haicheng line 102 switch of the 110kV Haibin substation from the open state."

[0070] 4. Automatic comparison and processing:

[0071] The system automatically compares the text restored by the inverse function with the original text entered by the dispatcher on the ticket.

[0072] If the two are completely semantically identical, the verification passes, and the structured machine language entity is passed to the security verification stage.

[0073] If there is a discrepancy, the system will issue an alarm, indicating that "the semantics of the instruction may have been distorted during the conversion," and the review will fail. The process will then be reverted to the drafting stage, requiring reconfirmation or modification.

[0074] This closed loop of "forward conversion-reverse verification" is the core innovation that ensures consistency in human-machine understanding.

[0075] S4, Security Verification Process—Automated Analysis Based on Precise Semantics;

[0076] This step requires analyzing the impact of instruction execution on the power grid. This embodiment provides unambiguous input for the safety verification engine.

[0077] 1. After the operation ticket is approved, the security verification process begins.

[0078] 2. The security verification module does not need to parse the natural language again, but directly reads the structured machine language entity corresponding to each instruction in the operation ticket.

[0079] 3. The system call instruction function library provides necessary auxiliary information (such as device models and topology connections associated with functions), but the core input is structured entity data.

[0080] 4. The safety verification algorithm utilizes explicit equipment identifiers and state transition information (such as from operating state to maintenance state) in entities to quickly and accurately simulate operational consequences in the power grid model, performing power flow calculations and risk assessments. This eliminates misjudgments caused by semantic ambiguity in traditional text verification, greatly improving the accuracy and efficiency of the verification.

[0081] S5, Intelligent Command Issuance Stage—The final instruction that drives automated execution;

[0082] This step is the final execution stage of the instruction. In this embodiment, a command that the machine can directly execute is generated.

[0083] 1. After the safety verification is passed, the operation ticket enters the execution (order issuance) stage.

[0084] 2. The intelligent command module (or programmed operating system) directly receives the structured machine language entity of each instruction as input.

[0085] 3. After the system calls the instruction function library to confirm the final format, it converts the structured entity into the protocol instructions required by the downstream automation system or equipment controller.

[0086] 4. Structured entities can use JSON formatted data objects, and protocol commands can use IEC 61850 MMS messages. For example, based on the device identifier "SB-110-HAIBIN::SWITCH-102" and the target status "closed" in the entity, a "close" remote control command can be generated and issued. This achieves end-to-end automation from dispatch instructions to field operations, which is the ultimate value embodiment of the "dispatch instruction machine language".

[0087] Compared with the prior art, this embodiment has the following significant advantages:

[0088] 1. End-to-end semantic integration: Through functional modeling, precise machine semantics are injected at the very beginning of instruction creation. This semantics, carried by "structured entities", flows seamlessly through all subsequent stages, completely eliminating semantic barriers between systems.

[0089] 2. Closed-loop reliability assurance: The original "text->structured->text" inverse function backtracking verification mechanism automatically verifies the consistency of human and machine understanding in key stages such as review, building an unprecedented error prevention closed loop and greatly improving operational reliability.

[0090] 3. Empowering advanced intelligence: The provided "structured entities" are machine-friendly, standardized data, providing ideal input for advanced artificial intelligence applications such as knowledge-based intelligent ticketing, real-time risk simulation, and operation plan optimization. It is a key infrastructure for the scheduling system to move from "process automation" to "decision intelligence".

[0091] Example 2: This example provides a machine language conversion system for power grid dispatching command information, including:

[0092] The function library construction module is used to define atomic operations as computable and reversibly convertible functions according to the power grid operation procedures, and store the functions and their inverse functions into the instruction function library;

[0093] The drafting module is used to send the received text instructions into the instruction function library for function matching; if the matching is successful, the matched function is called to generate a structured machine language entity corresponding to the text instruction;

[0094] The audit module is used to send the structured machine language entity into the instruction function library, call the inverse function to restore it to text, and automatically compare the restored text with the text instruction. If the comparison is consistent, the structured machine language entity is passed to the security verification module.

[0095] The security verification module is used to directly read the structured machine language entity and perform security analysis based on the device identifier and state transition information in the structured machine language entity.

[0096] The intelligent command module is used to convert the structured machine language entities after security analysis into protocol instructions required by the automation system or equipment controller and issue them.

[0097] The specific functions of each module described above are explained in the relevant content of the method in Embodiment 1, and will not be repeated here.

[0098] Example 3: This example provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described in Example 1.

[0099] Example 4: This example provides an electronic device, including:

[0100] Memory, used to store computer programs / instructions;

[0101] A processor for executing the computer program / instructions to implement the steps of any of the methods described in Embodiment 1.

[0102] Example 5: This example provides a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the method described in any one of Examples 1.

[0103] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0104] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0106] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.

Claims

1. A method for converting power grid dispatching command information into machine language, characterized in that, include: During the function library construction phase, atomic operations are defined as computable and reversibly convertible functions according to the power grid operation procedures, and the functions and their inverse functions are stored in the instruction function library. In the drafting stage, the received text instructions are sent to the instruction function library for function matching; if the matching is successful, the matched function is called to generate a structured machine language entity corresponding to the text instructions. During the review process, the structured machine language entity is sent to the instruction function library, the inverse function is called to restore it to text, and the restored text is automatically compared with the text instruction. If the comparison is consistent, the structured machine language entity is passed to the security verification process. In the security verification process, the structured machine language entity is directly read, and security analysis is performed based on the device identifier and state transition information in the structured machine language entity. In the intelligent command issuance stage, the structured machine language entities that have passed the security analysis are converted into protocol instructions required by the automation system or equipment controller and then issued.

2. The machine language conversion method for power grid dispatching instruction information according to claim 1, characterized in that, During the function library construction phase, a unique function is defined for each atomic operation according to the power grid operation procedures. The function takes the operation object and action described in natural language as input parameters and outputs a standardized and semantically rich structured machine language entity. At the same time, a corresponding inverse function is defined for each function. The inverse function is used to restore the standardized text description from the structured machine language entity.

3. The machine language conversion method for power grid dispatching instruction information according to claim 1, characterized in that, During the drafting process, when the received text instructions are sent to the instruction function library for structured conversion, the key parameters in the text instructions are automatically extracted as query keys for function matching. If the match is successful, the matched function is called to generate the structured machine language entity and saved with the operation ticket. If the match fails, a message indicating semantic non-standardity is displayed and a correction is requested.

4. The machine language conversion method for power grid dispatching instruction information according to claim 1, characterized in that, If the automatic comparison results are inconsistent during the review process, the process will be triggered to backtrack to the drafting stage.

5. The machine language conversion method for power grid dispatching instruction information according to claim 1, characterized in that, In the safety verification stage, the structured machine language entity is directly read, and the operational consequences simulation and power flow calculation are performed in the power grid model based on the device identifier and state transition information explicitly contained in the structured machine language entity, thus forming a safety analysis result.

6. The machine language conversion method for power grid dispatching instruction information according to claim 1, characterized in that, In the intelligent command generation stage, the structured machine language entity, after passing the security analysis, is sent to the instruction function library for final format confirmation, then converted into the protocol instructions required by the downstream automation system or equipment controller, and issued for execution.

7. The machine language conversion method for power grid dispatching instruction information according to claim 1, characterized in that, The structured machine language entity generated during the drafting process includes operation type, target device identifier, device name, device type, state transition information, timestamp, and tamper-proof hash value.

8. The machine language conversion method for power grid dispatching instruction information according to claim 1, characterized in that, The structured machine language entity is a structured data object in JSON format, and the protocol instruction is an IEC 61850 MMS message.

9. A machine language conversion system for power grid dispatching instruction information, used to implement the machine language conversion method for power grid dispatching instruction information as described in any one of claims 1-8, characterized in that, include: The function library construction module is used to define atomic operations as computable and reversibly convertible functions according to the power grid operation procedures, and store the functions and their inverse functions into the instruction function library; The drafting module is used to send the received text instructions into the instruction function library for function matching; if the matching is successful, the matched function is called to generate a structured machine language entity corresponding to the text instruction; The audit module is used to send the structured machine language entity into the instruction function library, call the inverse function to restore it to text, and automatically compare the restored text with the text instruction. If the comparison is consistent, the structured machine language entity is passed to the security verification module. The security verification module is used to directly read the structured machine language entity and perform security analysis based on the device identifier and state transition information in the structured machine language entity. The intelligent command module is used to convert the structured machine language entities after security analysis into protocol instructions required by the automation system or equipment controller and issue them.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-8.

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