Ledger deduction method, electronic device, storage medium and program product

Abstract

Embodiments of the present application provide a kind of ledger deduction method, electronic equipment, storage medium and program product.The present application relates to the field of power system ledger deduction, and the method comprises: obtaining target ledger data;The target ledger data is checked, and the target ledger data is input into power grid operation deduction system, and the deduction result of the target ledger data is obtained.The method is used to improve the technical effect of the accuracy of ledger deduction.

Description

Technical Field

[0001] This application relates to the field of ledger simulation, and in particular to a ledger simulation method, electronic device, storage medium and program product. Background Technology

[0002] In the field of power system operation and management, the scientific formulation of operation modes is crucial for ensuring the safe and stable operation of the power grid. Currently, operation mode specialists in power dispatch and control centers face numerous challenges in this work. With the continuous expansion of the power grid and the increasing complexity of operation modes, a large number of maintenance orders from various disciplines, including substations, transmission lines, and infrastructure, need to be compiled by the operation mode specialists. This massive volume of maintenance work logs requires manual verification, which is not only time-consuming and labor-intensive but also prone to errors due to the subjectivity and limitations of manual operation, affecting the quality and efficiency of subsequent work. Furthermore, operation mode formulation heavily relies on the personal experience of operation mode specialists. Different personnel may have different judgments and approaches to the same problem, and the lack of unified and effective verification methods makes it difficult to guarantee the accuracy and reliability of the mode arrangements. Errors could lead to power grid failures or even safety accidents, posing a significant threat to the stable operation of the power system.

[0003] To improve the efficiency and accuracy of operational mode development, relevant technical personnel have undertaken a series of tasks and achieved certain results. In terms of ledger management, current technology attempts to store and organize various ledger data through information technology, making data retrieval and retrieval relatively convenient. Simultaneously, certain algorithms and rules are used to perform preliminary analysis and processing of some data, providing some data support for operational mode development. Regarding operational mode simulation, some auxiliary tools have been developed that can perform simple deductions and analyses of specific operational scenarios based on existing operational data and parameters, providing reference for dispatchers. Furthermore, in terms of verification, some automated verification modules have been introduced to perform preliminary verification of some key data and logic, reducing the workload and error probability of manual verification.

[0004] However, existing technologies still have significant shortcomings. First, while ledger management has achieved information storage, effective methods for verifying the accuracy and consistency of ledger data are still lacking. Manual verification remains unavoidable, and its efficiency and accuracy are difficult to guarantee when dealing with massive amounts of data. Second, existing simulation tools have relatively limited functionality, only able to analyze specific scenarios and simple operating modes. They cannot comprehensively and accurately simulate complex actual operating conditions, especially regarding their limited ability to predict future operating modes, making it difficult to meet the forward-looking and flexible requirements of scheduling decisions. Finally, the integration between various tools is low, and data flow is inefficient, making it difficult to form a complete and efficient system for developing and managing operating modes, thus affecting overall work efficiency and decision-making quality. Therefore, existing technologies suffer from low accuracy in ledger simulations. Summary of the Invention

[0005] This application provides a ledger simulation method, electronic device, storage medium, and program product to achieve the technical effect of improving the accuracy of ledger simulation.

[0006] In a first aspect, embodiments of this application provide a ledger simulation method, including:

[0007] Obtain the target ledger data;

[0008] Verify the target ledger data, input the target ledger data into the power grid operation simulation system, and obtain the simulation results of the target ledger data; the simulation results shall include at least a visual chart, an alarm summary table, and decision recommendations.

[0009] The power grid operation simulation system is obtained by training the initial simulation system based on historical ledger data and a pre-established multi-dimensional rule base.

[0010] In one possible implementation, the training process of the power grid operation simulation system includes:

[0011] Obtain the structured features corresponding to each historical ledger data entry;

[0012] Input the structured features into the initial inference system;

[0013] The initial simulation system is trained based on a pre-established multi-dimensional rule base to obtain a power grid operation simulation system.

[0014] In one possible implementation, the structured features corresponding to the historical ledger data are obtained, including:

[0015] Extract equipment status parameters, power grid operation constraints, time scenario information, and load and power supply data from each historical ledger data entry;

[0016] Based on natural language processing algorithms, the device status parameters, power grid operation constraints, time scenario information, and load and power data are analyzed to obtain the corresponding text features;

[0017] Constructing a power grid knowledge graph based on text features;

[0018] Verify the rationality of parameters based on the power grid stability rule base;

[0019] A basic parameter set is established based on device status parameters. The basic parameter set includes a device parameter library and a scenario rule library.

[0020] Among them, the power grid knowledge graph and the basic parameter set are structured features.

[0021] In one possible implementation, the power grid knowledge graph includes a device relationship graph and power grid topology associations.

[0022] In one possible implementation, the multidimensional rule base includes overload determination rules, undervoltage determination rules, protection coordination rules, operational constraint rules, and data traversal rules.

[0023] In one possible implementation, the target ledger data is input into the power grid operation simulation system to obtain the simulation results of the target ledger data, including:

[0024] By using the power grid operation simulation system and multi-dimensional rule base, the equipment information of each device involved in the target ledger data is simulated and verified to obtain the verification results for each device; the verification results include alarm reports and the devices with alarm reports.

[0025] Based on the power grid knowledge graph in the power grid operation simulation system, the equipment with alarm reports is calibrated.

[0026] The text content involved in the target ledger data is verified based on a multi-dimensional rule base to obtain the text verification results;

[0027] The text content is simulated chronologically using a power grid operation simulation system to generate a simulation report;

[0028] Based on the audit results, text verification results, and simulation report, simulation results are generated.

[0029] Secondly, embodiments of this application provide a ledger simulation device, comprising:

[0030] The acquisition module is used to acquire target ledger data;

[0031] The processing module verifies the target ledger data and inputs it into the power grid operation simulation system to obtain the simulation results of the target ledger data. The simulation results include at least visualization charts, alarm summary tables, and decision recommendations. The power grid operation simulation system is trained on the initial simulation system based on historical ledger data and a pre-established multi-dimensional rule base.

[0032] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0033] The memory stores computer-executed instructions;

[0034] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0035] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0036] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0037] The ledger simulation method, electronic device, storage medium, and program product provided in this application acquire target ledger data, verify the target ledger data, and input the target ledger data into a power grid operation simulation system to obtain simulation results for the target ledger data. These simulation results include at least visual charts, alarm summary tables, and decision suggestions. The power grid operation simulation system is trained on an initial simulation system based on historical ledger data and a pre-established multi-dimensional rule base. This method, through a pre-trained power grid operation simulation system, achieves accurate multi-dimensional simulation of target ledger data, thereby effectively improving the technical effect of ledger simulation accuracy. Attached Figure Description

[0038] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0039] Figure 1 Flowchart of the ledger deduction method provided in this application Figure 1 ;

[0040] Figure 2 Flowchart of the ledger deduction method provided in this application Figure 2 ;

[0041] Figure 3 Flowchart of the ledger deduction method provided in this application Figure 3 ;

[0042] Figure 4 A schematic diagram of the ledger simulation device provided in this application;

[0043] Figure 5 A hardware schematic diagram of the ledger simulation device provided in this application.

[0044] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0045] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and approaches consistent with some aspects of this application as detailed in the appended claims.

[0046] In the field of power system operation and management, the scientific formulation of operation modes plays a crucial role in ensuring the safe and stable operation of the power grid. However, current mode specialists in power dispatch and control centers face severe challenges in this work. On the one hand, as the power grid continues to expand and operation modes become increasingly complex, a large number of maintenance orders from multiple disciplines such as substations, transmission lines, and infrastructure are collected by the mode specialists. The massive number of maintenance work logs can only be checked manually one by one. This process not only consumes a lot of time and energy, but is also prone to errors due to the subjectivity and limitations of manual operation, which in turn affects the quality and efficiency of subsequent work. On the other hand, the formulation of operation modes is highly dependent on the personal experience of mode specialists. Different personnel have different judgments and handling methods for the same problem, and there is a lack of unified and effective verification methods, making it difficult to guarantee the accuracy and reliability of mode arrangements. Once an error occurs, it is very likely to cause power grid failures or even safety accidents, posing a great threat to the stable operation of the power system. To improve the efficiency and accuracy of operational mode compilation, relevant technical personnel have carried out a series of work and achieved certain results. For example, in terms of ledger management, information technology is used to store and organize various ledger data, making data retrieval and retrieval relatively convenient. At the same time, certain algorithms and rules are used to perform preliminary analysis and processing of some data, providing some data support for operational mode compilation. In terms of operational mode simulation, some auxiliary tools have been developed, which can perform simple deductions and analyses of specific operational scenarios based on existing operational data and parameters, providing reference for dispatchers. Furthermore, some automated verification modules have been introduced to perform preliminary verification of some key data and logic, reducing the workload and error probability of manual verification. However, existing technologies still have significant shortcomings, particularly in ledger management. While information storage has been achieved in the management aspect, there is a lack of effective methods to verify the accuracy and consistency of the ledger data. Manual verification is still unavoidable, and the efficiency and accuracy of manual verification are difficult to guarantee when faced with massive amounts of data. Simulation and simulation tools have limited functions, only able to analyze specific scenarios and simple operating modes, and cannot comprehensively and accurately simulate complex actual operating conditions. Their ability to predict possible future operating modes is limited, making it difficult to meet the forward-looking and flexible requirements of scheduling decisions. The integration between various tools is not high, and data flow is not smooth, making it difficult to form a complete and efficient operating mode compilation and management system, thus affecting overall work efficiency and decision-making quality. Therefore, existing technologies have the technical problem of low accuracy when performing ledger simulations.

[0047] The ledger simulation method, electronic device, storage medium, and program product provided in this application acquire target ledger data, verify the target ledger data, and input the target ledger data into a power grid operation simulation system to obtain simulation results for the target ledger data. These simulation results include at least visual charts, alarm summary tables, and decision suggestions. The power grid operation simulation system is trained on an initial simulation system based on historical ledger data and a pre-established multi-dimensional rule base. This method, through a pre-trained power grid operation simulation system, achieves accurate multi-dimensional simulation of target ledger data, thereby effectively improving the technical effect of ledger simulation accuracy.

[0048] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0049] Figure 1 Flowchart of the ledger deduction method provided in this application Figure 1 ,like Figure 1 As shown, the method includes:

[0050] S101. Obtain the target ledger data.

[0051] In this embodiment, the target ledger data includes, but is not limited to: equipment parameters, real-time operating status, historical maintenance records, fault logs, and planned maintenance tasks. Equipment parameters include, but are not limited to, transformer capacity and line impedance; real-time operating status includes, but is not limited to, voltage, current, and power; data acquisition methods may involve database queries, API calls, or file imports, and data cleaning and format conversion are required to ensure data integrity and consistency.

[0052] S102. Verify the target ledger data, input the target ledger data into the power grid operation simulation system, and obtain the simulation results of the target ledger data.

[0053] In this embodiment, target ledger data is input into the power grid operation simulation system to obtain simulation results, thereby enabling the verification of the target ledger. The simulation results include at least visual charts, alarm summary tables, and decision recommendations. The power grid operation simulation system is trained on an initial simulation system based on historical ledger data and a pre-established multi-dimensional rule base. Visual charts intuitively display the power grid's operating status; alarm summary tables highlight potential risks; and decision recommendations provide actionable countermeasures. This step achieves a closed-loop process from data input to decision support, enabling power grid dispatchers to make scientific decisions based on dynamic simulation results. The introduction of the multi-dimensional rule base enhances the accuracy of the simulation system. Visual charts and alarm summary tables improve information transmission efficiency, while decision recommendations directly serve the safe and economical operation of the power grid, significantly reducing the workload of manual analysis and minimizing the possibility of human error.

[0054] The ledger simulation method, electronic device, storage medium, and program product provided in this application acquire target ledger data, verify the target ledger data, and input the target ledger data into a power grid operation simulation system to obtain simulation results of the target ledger data. These simulation results include at least visual charts, alarm summary tables, and decision suggestions. The power grid operation simulation system is trained on an initial simulation system based on historical ledger data and a pre-established multi-dimensional rule base. This method, through a pre-trained power grid operation simulation system, achieves accurate multi-dimensional simulation of the target ledger data, thereby completing accurate verification of the target ledger. It avoids the technical problems of low efficiency and poor accuracy inherent in manual verification, effectively improving the accuracy of ledger simulation.

[0055] Figure 2 Flowchart of the ledger deduction method provided in this application Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 1 Based on the examples, this paper provides a detailed explanation of how to train a power grid operation simulation system in the ledger simulation method. The method includes:

[0056] S201. Extract equipment status parameters, power grid operation constraints, time scenario information, and load and power supply data from each historical ledger data.

[0057] In this embodiment, equipment status parameters include, but are not limited to, equipment operation indicators such as power generation, transmission, and transformation, such as generator power, transformer oil temperature, and line insulation resistance, which are key data reflecting equipment operating conditions; power grid operation constraints include, but are not limited to, power balance, voltage and frequency range, and line power flow restrictions, as well as various technical restrictions and regulations to ensure the safe and stable operation of the power grid; time scenario information includes, but is not limited to, seasonal and time-period differences and the impact of special events, such as the characteristics of power grid operation at different times, such as high load in summer and surge in electricity consumption during holidays; in load and power supply data, load refers to the electricity demand of various users, and power supply covers the power generation capacity and characteristics of thermal power, hydropower, etc., reflecting the data situation on both the supply and demand sides.

[0058] S202. Based on natural language processing algorithms, analyze equipment status parameters, power grid operation constraints, time scenario information, and load and power supply data to obtain corresponding text features.

[0059] In this embodiment, optionally, NLP technology can be used to perform semantic parsing on structured or unstructured data to extract key features. For equipment status parameters, abnormal states can be identified through keyword matching, such as "oil temperature too high" and "power fluctuation". For grid operation constraints, rule text can be parsed, such as "voltage needs to be maintained at 0.95~1.05 pu", to extract numerical thresholds and logical relationships. For time-related information, time-related descriptions, such as "during peak summer season", can be identified and mapped to standardized scene labels. For load and power data, load type and power characteristics can be extracted from descriptive text. NLP can automatically extract structured features from complex text, solving the problem of low efficiency in manual annotation. Text features can enhance the model's ability to understand non-numerical data and improve the predictability and interpretability of grid operation.

[0060] S203. Construct a power grid knowledge graph based on text features; verify the rationality of parameters based on the power grid stability rule base; establish a basic parameter set based on equipment status parameters, including an equipment parameter base and a scenario rule base; the power grid knowledge graph and the basic parameter set are determined as the structured features corresponding to each historical ledger data.

[0061] In this embodiment, equipment status parameters, power grid operation constraints, time scenario information, and load and power supply data and their relationships are represented by a graph structure to form a knowledge graph. The power grid knowledge graph includes equipment relationship graphs and power grid topology associations. The knowledge graph enables data association storage and reasoning, supports complex queries and cross-domain analysis, and can also capture implicit knowledge to improve the intelligence level of power grid operation. On the other hand, based on the power grid stability rule base, the "Guidelines for the Safety and Stability of Power Systems" can be selected to check whether the parameters meet the standards; specifically, this may include, but is not limited to, numerical verification and logical verification, thereby ensuring data quality and avoiding misjudgments due to incorrect parameters. Verification results can mark abnormal data for manual review, improving the reliability of power grid operation. Furthermore, core features are extracted from equipment status parameters to construct an equipment parameter library and a scenario rule base. The equipment parameter library stores static attributes such as equipment model, rated capacity, and historical failure rate; the scenario rule base defines operating strategies under different time scenarios. The basic parameter set provides standardized data support for power grid analysis, such as dynamically adjusting the load forecasting model through the scenario rule base to improve forecast accuracy.

[0062] S204. Input the structured features into the initial simulation system, and train the initial simulation system based on the pre-established multi-dimensional rule base to obtain the power grid operation simulation system.

[0063] In this embodiment, the multidimensional rule base includes overload judgment rules, undervoltage judgment rules, protection coordination rules, operation constraint rules, and data traversal rules.

[0064] Optionally, the overload judgment rule sets thresholds based on parameters such as power grid equipment current and load. Combined with equipment capacity and line parameter ledgers, it defines the dynamic threshold and logical relationship for overload judgment. For 220kV main transformers / lines, the active power value transmitted down or back reaches 70%-100% of the rated capacity. For 110kV and below main transformers / lines, the heavy load range is 80%-100%. If the assessed current and load exceed the threshold, a local simulation report is generated and the overloaded equipment is marked in red.

[0065] The undervoltage determination rule uses voltage monitoring data and grid topology analysis, including basic information such as the undervoltage occurrence time, power restoration time, and cause of the undervoltage. The undervoltage status needs to be verified using technical means such as bus voltage curve screenshots, alarm window records, and historical alarm query results. If an undervoltage is determined, a local simulation report is generated, and the undervoltage area is highlighted.

[0066] The protection coordination rules identify the operating mode of the currently executed equipment power outage through the power grid topology, check whether it is correct according to the setting value ledger, and if it is incorrect, generate an alarm and a local simulation report, and note the setting value number that should be executed.

[0067] Operational constraint rules include: avoiding simultaneous shutdowns of multiple critical devices in the same power supply area; identifying the operational mode of currently executed equipment power outages; generating alarms if power outages occur within the same loop, chain, or external communication / support lines; generating alarms if power outages involving hydropower equipment occur during the flood season; highlighting items where the same project, equipment, or related equipment appear repeatedly in the logbook. Based on the on-site work content and schedule logbook, verifying whether the work content matches the normal work schedule; generating alarms if they exceed this.

[0068] Data traversal rules enable structured access: Ledger data is read through programming interfaces (such as SQL queries), and traversed row by row and column by column.

[0069] In one possible implementation, the initial simulation system is obtained by mirroring the integrated power grid operation intelligent system. Specifically, it adopts a three-in-one architecture of "real-time simulation + power grid operation + dispatch control," dividing the initial simulation system into: a real-time digital simulation layer, an electromechanical transient layer, and a quasi-steady-state layer. The real-time digital simulation layer uses an FPGA-based electromagnetic transient simulation module to handle dynamics at the renewable energy power plant level; the electromechanical transient layer uses GPU parallel computing technology to achieve rapid simulation of the regional power grid; and the quasi-steady-state layer supports a lightweight model for annual plan simulation. A hierarchical parameter management mechanism is established. For example, L0-level parameters are dynamically updated hierarchically; L1-level parameters are updated hourly through the parameter management platform; and L2-level parameters are updated daily through the parameter management platform.

[0070] In terms of safety constraints, the analysis integrates N-1 verification and transient stability joint analysis, while in terms of economics, it embeds an LMP node price prediction model. An intelligent switching strategy is adopted, employing a decision tree for model selection constructed based on deep Q-learning.

[0071] A dual-active data synchronization architecture is applied. It employs a "layered control + blockchain notarization" technology: on one hand, it achieves μs-level data synchronization between the main rule base and the shadow database; on the other hand, change operations are verified across multiple nodes via smart contracts. A knowledge distillation update mechanism is established. A BERT semantic parsing model for policy texts is constructed to automatically extract rule features. Through comparative learning, the differences between the old and new rule bases are quantified to establish a rule impact assessment matrix, prioritizing the updating of highly sensitive rules.

[0072] Figure 3 Flowchart of the ledger deduction method provided in this application Figure 3 ,like Figure 1 As shown, in this embodiment... Figure 1 Based on the examples, this paper provides a detailed explanation of how to obtain the deduction results of the target ledger data in the ledger deduction method. The method includes:

[0073] S301. Through the power grid operation simulation system and multi-dimensional rule base, the equipment information of each device involved in the target ledger data is simulated and audited to obtain the audit result of each device.

[0074] In this embodiment, the audit results include alarm reports and the devices with alarm reports. The system uses a power grid operation simulation system and a multi-dimensional rule base to simulate and audit the information of each device in the target ledger data. This process combines historical operating data and preset rule logic to simulate and analyze the current state of the devices, identify potential risks, and generate alarm reports. The alarm reports not only include descriptions of abnormal states but also clearly identify the problematic devices, providing precise location for subsequent handling. The significance of this step lies in early detection of potential equipment problems, avoiding power grid accidents caused by equipment failures, and providing clear maintenance guidance for operation and maintenance personnel.

[0075] S302. Based on the power grid knowledge graph in the power grid operation simulation system, calibrate the equipment with alarm reports.

[0076] In this embodiment, devices with alarm reports are calibrated based on a power grid knowledge graph. The power grid knowledge graph integrates topological relationships, operational constraints, and historical correlation information between devices. Graph analysis can identify the impact range of alarm devices, such as the cascading risk to upstream and downstream devices. The calibration process makes alarm information more intuitive and helps dispatchers quickly understand the fault propagation path, thereby developing more effective response measures. This step significantly improves the efficiency of fault location and enhances the controllability of power grid operation.

[0077] S303. Based on the multi-dimensional rule base, the text content involved in the target ledger data is verified to obtain the text verification results.

[0078] In this embodiment, a multi-dimensional rule base is used to verify the text content in the target ledger data, thereby determining the text verification result. Since the ledger data may contain unstructured information such as manually recorded operation logs and maintenance reports, the rule base ensures data integrity and consistency through semantic analysis and logical matching. For example, it checks whether operation records conform to scheduling procedures or verifies whether equipment parameters are within a reasonable range. This step effectively filters out erroneous or omitted information, improves data quality, and thus ensures the reliability of subsequent inference and analysis.

[0079] S304. The text content is simulated in chronological order through the power grid operation simulation system to generate a simulation report; the simulation results are generated based on the review results, text verification results and simulation report.

[0080] The text content is dynamically simulated in a time series using a power grid operation simulation system, generating a simulation report, and the final simulation conclusion is formed by combining the review results. The time-series simulation models the evolution of the power grid under different operating conditions, such as load fluctuations and fault propagation scenarios, thereby predicting potential risks and assessing the effectiveness of control measures. The text verification results ensure the integrity and consistency of the target ledger data in the power grid; the simulation report not only provides future trend predictions but also allows for retrospective analysis of historical events to verify the rationality of decisions. By combining the review results, text verification results, and simulation report, the simulation results of the target ledger are determined. This step achieves a closed-loop analysis from data verification to dynamic simulation, providing a scientific basis for the safe and economical operation of the power grid.

[0081] Figure 4 A schematic diagram of the ledger simulation device provided in this application is shown below. Figure 4 As shown, the ledger simulation device 40 provided in this embodiment includes:

[0082] Module 401 is used to acquire target ledger data;

[0083] The processing module 402 is used to verify the target ledger data, input the target ledger data into the power grid operation simulation system, and obtain the simulation results of the target ledger data. The simulation results include at least a visualization chart, an alarm summary table, and decision suggestions. The power grid operation simulation system is obtained by training the initial simulation system based on historical ledger data and a pre-established multi-dimensional rule base.

[0084] In one possible implementation, the processing module 402 is further configured to:

[0085] Obtain the structured features corresponding to each historical ledger data entry;

[0086] Input the structured features into the initial inference system;

[0087] The initial simulation system is trained based on a pre-established multi-dimensional rule base to obtain a power grid operation simulation system.

[0088] In one possible implementation, the acquisition module 401 is further configured to:

[0089] Extract equipment status parameters, power grid operation constraints, time scenario information, and load and power supply data from each historical ledger data entry;

[0090] Based on natural language processing algorithms, the device status parameters, power grid operation constraints, time scenario information, and load and power data are analyzed to obtain the corresponding text features;

[0091] Constructing a power grid knowledge graph based on text features;

[0092] Verify the rationality of parameters based on the power grid stability rule base;

[0093] A basic parameter set is established based on device status parameters. The basic parameter set includes a device parameter library and a scenario rule library.

[0094] Among them, the power grid knowledge graph and the basic parameter set are structured features.

[0095] In one possible implementation, the power grid knowledge graph includes a device relationship graph and power grid topology associations.

[0096] In one possible implementation, the multidimensional rule base includes overload determination rules, undervoltage determination rules, protection coordination rules, operational constraint rules, and data traversal rules.

[0097] In one possible implementation, the processing module 402 is further configured to:

[0098] By using the power grid operation simulation system and multi-dimensional rule base, the equipment information of each device involved in the target ledger data is simulated and verified to obtain the verification results for each device; the verification results include alarm reports and the devices with alarm reports.

[0099] Based on the power grid knowledge graph in the power grid operation simulation system, the equipment with alarm reports is calibrated.

[0100] The text content in the target ledger data is verified based on a multi-dimensional rule base;

[0101] The text content is simulated chronologically using a power grid operation simulation system to generate a simulation report;

[0102] Based on the audit results, text verification results, and simulation report, simulation results are generated.

[0103] The ledger simulation device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0104] Figure 5 This is a hardware schematic diagram of the ledger simulation device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, memory 502, and communication component 503 are connected via a bus 504.

[0105] In a specific implementation, at least one processor 501 executes computer execution instructions stored in memory 502, causing at least one processor 501 to perform the above-described method.

[0106] The specific implementation process of processor 501 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0107] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0108] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0109] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0110] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0111] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0112] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0113] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0114] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, methods, or units, and may be electrical, mechanical, or other forms.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0117] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0119] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for ledger deduction, characterized in that, The method includes: Obtain the target ledger data; The target ledger data is verified, and then input into the power grid operation simulation system to obtain the simulation results of the target ledger data; the simulation results include at least a visualization chart, an alarm summary table, and decision recommendations. The power grid operation simulation system is obtained by training the initial simulation system based on historical ledger data and a pre-established multi-dimensional rule base.

2. The method according to claim 1, characterized in that, The training process of the power grid operation simulation system includes: Obtain the structured features corresponding to each of the aforementioned historical ledger data entries; The structured features are input into the initial inference system; The initial simulation system is trained based on a pre-established multi-dimensional rule base to obtain the power grid operation simulation system.

3. The method according to claim 2, characterized in that, The step of obtaining the structured features corresponding to the historical ledger data includes: Extract the equipment status parameters, power grid operation constraints, time scenario information, and load and power data from each historical ledger data entry; Based on natural language processing algorithms, the device status parameters, the power grid operation constraints, the time scenario information, and the load and power data are parsed to obtain corresponding text features; Construct a power grid knowledge graph based on the aforementioned text features; Verify the rationality of parameters based on the power grid stability rule base; A basic parameter set is established based on the device status parameters. The basic parameter set includes a device parameter library and a scenario rule library. The power grid knowledge graph and the basic parameter set are the structured features.

4. The method according to claim 3, characterized in that, The power grid knowledge graph includes a device relationship graph and power grid topology associations.

5. The method according to claim 2, characterized in that, The multidimensional rule base includes overload judgment rules, undervoltage judgment rules, protection coordination rules, operation constraint rules, and data traversal rules.

6. The method according to claim 1, characterized in that, The step of inputting the target ledger data into the power grid operation simulation system to obtain the simulation results of the target ledger data includes: The power grid operation simulation system and the multi-dimensional rule base are used to simulate and verify the equipment information of each device involved in the target ledger data, and the verification result of each device is obtained; the verification result includes alarm reports and the devices that have alarm reports. Based on the power grid knowledge graph in the power grid operation simulation system, the devices that have the alarm reports are calibrated. The text content involved in the target ledger data is verified based on the multidimensional rule base to obtain the text verification result; The power grid operation simulation system is used to simulate the text content in chronological order and generate a simulation report. The deduction result is generated based on the audit result, the text verification result, and the deduction report.

7. A ledger simulation device, characterized in that, include: The acquisition module is used to acquire target ledger data; The processing module is used to input the target ledger data into the power grid operation simulation system to obtain the simulation results of the target ledger data; the simulation results include at least visualization charts, alarm summary tables and decision suggestions; wherein, the power grid operation simulation system is obtained by training the initial simulation system based on historical ledger data and a pre-established multi-dimensional rule base.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method described in any one of claims 1-6.

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