Intelligent distribution network maintenance automatic permission data model

Abstract

The present application relates to distribution network dispatching automation technology in power system, especially a kind of intelligent distribution network maintenance automatic permission data model.The present application includes data acquisition module, data verification module, automatic permission module, equipment condition verification module before power recovery and data backfilling module.Data acquisition module obtains relevant data from distribution network OMS system and OCS system;Data verification module carries out equipment state consistency check, logic check and correlation check;Automatic permission module automatically permits and generates permission information after check passes;Equipment condition verification module before power recovery checks equipment before power recovery;Data backfilling module backfills permission information to distribution network OMS system.The present application integrates multidimensional data, improves the accuracy, reliability and efficiency of maintenance permission through hierarchical verification, and guarantees maintenance safety and stable operation of power grid.

Description

Technical Field

[0001] This invention relates to distribution network dispatch automation technology in power systems, and in particular to an intelligent automatic authorization data model for distribution network maintenance. Background Technology

[0002] With the rapid growth of my country's economy, the power system has continued to develop and expand, resulting in an increase in the number and scale of distribution network equipment, and an exponential increase in the workload of maintenance. Traditionally, maintenance permits for distribution network equipment are mainly issued by telephone, which is inefficient and has many drawbacks, and can no longer meet the requirements of efficient and reliable operation of modern power systems.

[0003] Limitations of the existing system: The Guangxi Power Grid Safety Production Management System and Distribution Network OMS system were put into operation in 2015. Although the integrated power outage and grid connection management module supports some business of Guangxi distribution network, such as distribution network maintenance plan, maintenance application form, and grid connection start-up scheme, these functional modules only realize the circulation and approval of maintenance business forms. In the daily maintenance process of distribution network, there are still many problems.

[0004] Actual distribution network maintenance faces numerous challenges, such as frequent network tripping and heavy maintenance workloads, while the majority of the process still relies on manual operation. The lack of effective technical means to automate the maintenance process, including execution, analysis, and safety auditing, leads to low grid operation and maintenance efficiency and impacts the reliability of distribution network power supply, failing to meet the current requirements for integrated dispatching and distribution network development.

[0005] Chinese patent CN110349049A discloses an automatic work permit system for distribution network maintenance. It employs a modular design: a data reading module reads distribution network maintenance order data from the power network and transmits it to a distribution network maintenance order list module; a commencement application module submits a commencement application request to a commencement permit module; the commencement permit module automatically permits the commencement application request; a completion application module submits a completion application request to a completion termination module; the completion termination module automatically terminates the completion application request; the distribution network maintenance order list module integrates and displays the data from the data reading module, commencement application module, commencement permit module, completion application module, and completion termination module; a data storage module stores the data from these modules; and a reminder module reminds staff of the automatic permitting or termination of distribution network maintenance orders.

[0006] Current technological needs and limitations have driven the development of automatic authorization technology for distribution network maintenance, leading to the emergence of related data models to improve the efficiency of maintenance work and power supply reliability. Considering the limitations of existing technology, developing an intelligent automatic authorization data model for distribution network maintenance is an inevitable trend and the technical challenge addressed in this development project. Summary of the Invention

[0007] The problem this invention aims to solve is how to provide a more efficient, safer, and more intelligent automatic authorization data model for distribution network maintenance.

[0008] To address the above problems, the present invention proposes the following solution:

[0009] An intelligent automatic authorization data model for distribution network maintenance includes a data acquisition module, a data verification module, an automatic authorization module, a pre-power-back-up equipment condition verification module, and a data backfilling module.

[0010] S-1-1, Data Acquisition Module, used to acquire distribution network maintenance order data, real-time equipment status data, dispatch operation ticket execution data and equipment fault information from the distribution network OMS system and OCS system;

[0011] S-1-2, Data Verification Module, performs multi-source path security verification on the acquired maintenance order data;

[0012] S-1-3, Automatic Permission Module: After the data verification module passes the verification, it automatically approves the maintenance application and generates permission information.

[0013] S-1-4, Equipment Condition Verification Module Before Power Restoration, is used to automatically verify equipment conditions before power restoration. The automatic verification of equipment conditions before power restoration includes four aspects: verification of equipment electrical parameters, verification of equipment protection devices, verification of equipment mechanical components, and verification of equipment wiring connections. After meeting the "five mandatory verifications and one confirmation" requirement, it supports automatic scheduling permission for completion.

[0014] S-1-5, Data Backfill Module, used to automatically backfill permission information to the distribution network OMS system.

[0015] The data verification module includes:

[0016] S-1-2-1, Equipment Status Consistency Verification Unit, is used to automatically obtain the real-time status of OCS equipment and verify the consistency between the equipment status reported when submitting the start-up and completion application on site.

[0017] S-1-2-2, Logic Verification Unit, is used to obtain the execution status of the associated scheduling operation ticket and the status of the pre- and post-orders associated with the maintenance application form for logical verification;

[0018] S-1-2-3, Equipment Fault Information Association Verification Unit, is used to obtain equipment fault information and perform association verification with the relevant equipment in the maintenance request form to determine whether there is a status conflict.

[0019] The specific steps for verifying the electrical parameters of the equipment in the automatic verification of equipment conditions before power restoration include:

[0020] S-3-1. Check the matching of the equipment's rated voltage with the mains voltage;

[0021] S-3-2. Verify the matching between the rated current of the equipment and the actual operating requirements;

[0022] S-3-3. Verify the compatibility of the equipment's power factor with grid requirements and equipment design standards.

[0023] The specific steps for verifying the equipment protection device in the automatic verification of equipment conditions before power restoration include:

[0024] S-4-1. Verify that the overload protection device is working properly. The overload protection device will activate and cut off the circuit when the equipment is overloaded.

[0025] S-4-2. Verify the effectiveness of the short-circuit protection device. The short-circuit protection device should quickly cut off the circuit when a short circuit occurs in the equipment.

[0026] S-4-3. Verify the grounding protection device. The grounding protection device is connected to the ground to introduce leakage current into the ground.

[0027] The specific steps for verifying the mechanical components of the equipment in the automatic verification of equipment conditions before power restoration include:

[0028] S-5-1. Verify the mechanical condition of equipment with moving parts and check for any jamming during equipment operation;

[0029] S-5-2. Verify the wear condition of mechanical parts, observe the scratches and wear marks on the equipment's exterior, and any abnormal noises during operation;

[0030] S-5-3. Verify the tightness of the mechanical connection parts of the equipment.

[0031] Preferably, the specific steps for verifying the equipment lines in the automatic verification of equipment conditions before power restoration include:

[0032] S-6-1. Check the integrity of the connection between the equipment and the line, especially the broken wires and loose connections. Observe the appearance of the connection parts and use a multimeter to measure the resistance.

[0033] S-6-2. Verify the correctness of the line markings to ensure they are clear and accurate;

[0034] S-6-3. Check the insulation performance of the line. Use an insulation resistance tester to measure the insulation resistance of the line. Generally, the insulation resistance should be greater than 0.5MΩ.

[0035] The data acquisition module and data verification module use multi-source data fusion and comprehensive security verification technology to integrate multi-source data from OMS system, OCS / EMS system and DCCS system, and perform security verification from multiple dimensions.

[0036] The multi-source data fusion module includes:

[0037] S-8-1, Multi-interface data acquisition unit, establishes stable communication interfaces with the power grid management platform (OMS), local dispatch energy management systems (OCS / EMS), and distribution network distributed control system (DCCS) to collect data in real time or periodically. The period for collecting maintenance order data with the OMS system is no more than 30 seconds. It maintains a real-time and uninterrupted data connection with the OCS / EMS system to obtain the real-time status of the equipment. When the distribution network DCCS system performs operation ticket-related operations, it immediately obtains operation ticket data.

[0038] S-8-2, Data Preprocessing Unit: Performs format unification, semantic parsing, and association matching on data collected from different systems; parses maintenance order data in the OMS system according to predetermined semantic rules; converts real-time equipment status data from the OCS / EMS system into a format compatible with maintenance order data and associates it with equipment information in the maintenance order based on equipment identifiers; and parses operation steps on operation ticket data from the distribution network DCCS system.

[0039] S-8-3, Data Caching and Update Unit, constructs a high-speed caching mechanism to store the latest pre-processed data and monitor the data update status of each data source in real time; when new data arrives from the data source or the data status changes, the data in the cache is updated immediately, and the data update timestamp is marked to ensure the timeliness and validity of the data, providing the latest data support for security verification and automatic licensing decisions.

[0040] The data caching and updating unit uses a comprehensive eviction strategy based on a potential energy algorithm, considering both timeliness and usage frequency. When cache space is insufficient, for each data x in the cache...

[0041] S-8-3-1. Calculate the timeliness potential energy of data. The difference between the current time and the last update time determines the timeliness potential energy of the data. The smaller the time difference, the higher the potential energy.

[0042] S-8-3-2. Calculate the usage frequency potential energy of data. The more times data is used within a certain period of time, the higher the usage frequency potential energy of the data.

[0043] S-8-3-3. Calculate the comprehensive potential energy of the data, set weighting coefficients to balance the timeliness potential energy and the frequency of use potential energy to determine the comprehensive potential energy of the data;

[0044] S-8-3-4. Based on the selection of weighting coefficients and potential energy thresholds, construct a data benefit model for weighting coefficients and conduct data analysis on the subsequent setting of potential energy thresholds;

[0045] S-8-3-5. Selecting to replace or retain data based on the comprehensive potential energy of the data: Set a potential energy threshold, compare the comprehensive potential energy of each data with the potential energy threshold. If the comprehensive potential energy of the data is lower than the potential energy threshold, replace the data; otherwise, retain the data.

[0046] The comprehensive security verification module includes:

[0047] S-10-1, the multi-dimensional real-time status verification submodule, simultaneously receives the field-reported equipment status information from the OMS system and the real-time equipment status data from the OCS / EMS system after processing by the multi-source data fusion module, and performs precise comparison field by field; during the verification process, a fast comparison technology is used to efficiently compare the key status parameters of the equipment. If any inconsistency is found, a high-priority alarm notification is immediately triggered and the automatic permission process is suspended until the inconsistency is resolved;

[0048] The S-10-2 logical relationship intelligent verification submodule performs in-depth verification of the execution status of associated scheduling operation tickets and the logical relationships between related pre- and post-orders of maintenance application forms based on a pre-set complex logic rule library. During the verification process, a graph theory-based path analysis algorithm is used to simulate the logical flow of maintenance operations. If a logical inconsistency is detected, a detailed alarm report is immediately issued. The report includes the specific logical error point, the maintenance links that may be affected, and prevents the automatic permission process from continuing. At the same time, it provides logical adjustment suggestions to help maintenance personnel quickly correct the problem.

[0049] S-10-3, the Fault Association Deep Analysis Submodule, after obtaining equipment fault information and related equipment information from the maintenance request form, uses knowledge graph-based fault association analysis technology to uncover potential conflict relationships between equipment faults and maintenance plans. By constructing a knowledge graph containing multi-dimensional information such as equipment fault type, fault location, and maintenance equipment scope, it quickly identifies fault factors that may increase the risk of maintenance operations. If a state conflict is found, it promptly issues a precise alarm, which includes fault details, conflict causes, and suggested countermeasures. Automatic permission is disabled to ensure that maintenance work is carried out without fault interference or after the fault has been properly handled.

[0050] The beneficial effects of adopting the above technical solution are as follows:

[0051] The data acquisition module of this invention, through the setting of equipment status data unit, scheduling operation ticket data unit and equipment fault information unit, can comprehensively acquire various data information, providing technical support for subsequent data verification.

[0052] The data acquisition module of this invention includes a multi-interface data acquisition unit, a data preprocessing unit, and a data caching and updating unit. It can connect to multiple data platforms and perform format unification, semantic parsing, and correlation matching processing on the collected data to ensure the synergy of data from various systems.

[0053] The data verification module in this invention verifies data from multiple aspects during the verification process, including equipment status consistency, logical relationships, and conflicts between related equipment statuses. This avoids permission errors caused by insufficient consideration of a single factor, thereby improving the accuracy and reliability of maintenance permissions. Furthermore, because it rigorously verifies information such as equipment status, scheduling operation tickets, and equipment faults, it can promptly detect potential safety hazards. This includes, but is not limited to, issuing alarms when equipment status is inconsistent, logical errors exist, or conflicts occur between related equipment. This allows maintenance personnel to address problems before granting permission, effectively preventing maintenance work from being carried out under unsafe conditions and improving the safety of maintenance work.

[0054] The pre-power-on equipment condition verification module in this invention verifies electrical parameters, equipment protection devices, equipment mechanical components, and equipment wiring connections before power restoration. By collecting various parameters of the equipment, it can effectively reduce the instability and safety of equipment operation after power restoration, improve equipment operating efficiency, and reduce energy consumption.

[0055] The data model in this invention automates data processing and verification. Compared to traditional manual verification methods, it significantly reduces the time and workload of manual operations, increases the speed of maintenance approval, and thus improves the overall efficiency of maintenance work. Fourthly, accurate maintenance approval and safe maintenance work can avoid adverse effects on the power grid caused by improper maintenance. By promptly identifying and addressing equipment problems, the normal operation of power grid equipment is ensured, thereby guaranteeing the stable operation of the power grid.

[0056] The data caching and updating unit in this invention employs a replacement strategy based on a potential energy algorithm that combines timeliness and usage frequency. When the cache is insufficient, it calculates the timeliness potential energy and usage frequency potential energy of the existing data. The weight ratio between the two can be adjusted according to actual needs to determine the overall potential energy of the data. Then, by comparing it with a manually set potential energy threshold, the replacement or retention of the data is finally confirmed. The replacement strategy ensures that the data caching and updating unit has sufficient cache space while retaining key data, providing data support for subsequent security verification and automatic licensing decisions. Attached Figure Description

[0057] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0058] Figure 1 This is a system schematic diagram of the intelligent automatic authorization data model for distribution network maintenance proposed in this invention;

[0059] Figure 2 This is a schematic diagram of the multi-source data fusion module and the comprehensive verification module in the intelligent automatic permission data model for distribution network maintenance proposed in this invention. Detailed Implementation

[0060] To make the features and advantages of the present invention more apparent and understandable, the technical solution will be clearly and completely described below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0061] This invention discloses an intelligent automatic permitting data model for distribution network maintenance.

[0062] Example 1: Self-driving module for power distribution network maintenance order execution

[0063] 1. Intelligent operation ticket generation function

[0064] 1) Data Acquisition and Analysis

[0065] like Figure 1 The data acquisition module in the data model establishes an efficient data acquisition interface with the OMS system through its multi-interface data acquisition unit, periodically (according to a set update cycle, such as ≤30 seconds) acquiring distribution network maintenance order data. The data preprocessing unit uses data parsing algorithms (such as those based on regular expressions or XML parsers) to parse the acquired maintenance order data and extract key information, such as maintenance objects, maintenance content, planned start time, and planned end time.

[0066] For example, if the maintenance order is stored in XML format, an XML parser can be used to extract information such as the equipment name, maintenance items, and schedule.

[0067] During the acquisition phase, when the multi-interface data acquisition unit obtains distribution network maintenance order data from the OMS system, the data caching and update unit can store this data. Since data acquisition is periodic (e.g., ≤30 seconds), it ensures that the latest acquired data is stored in a timely manner, and based on its potential energy-based elimination strategy, it ensures that the maintenance order data in the cache is the optimal combination of timeliness and usage frequency.

[0068] The specific steps of the elimination strategy based on the potential energy algorithm include:

[0069] 1. Calculating the Timeliness Potential Energy of Data: In data processing and management, timeliness is a key factor. The calculation of the timeliness potential energy of data aims to measure the freshness of data and its value at the current moment. Consider the difference between the current time and the last update time of the data. If the difference is small, it indicates strong timeliness of the data, and the corresponding potential energy is high. A timeliness potential energy function can be defined. For example, let the current time be t, and the last update time of data x be t... i The update period for data x is Δt. a When tt i <Δt a At that time, the time-dependent potential energy increases as the time difference decreases, and can be set as follows: When tt i ≥Δt a When the timeliness potential energy is zero, it means that the data has exceeded its expected update cycle, its timeliness has been greatly reduced, and its value in the current decision-making or analysis is close to zero. For example, if the update cycle of a device status data is set to 30 seconds, and the difference between the current time and the last update time reaches or exceeds 30 seconds, the timeliness potential energy of the data is 0. In this case, the system may need to reacquire the latest data to ensure the accuracy of the decision.

[0070] 2. Data usage frequency potential energy calculation: The higher the usage frequency of data, the higher its potential energy should be. Let n be the number of times data x is used within n time units. i The frequency of use is The frequency potential energy can be defined as P. f (n i )=f(n i This means that the more frequently data is used, the higher its usage frequency potential energy. In practical applications, frequently used data often has greater reference value for system operation analysis and decision-making. For example, in the formulation of distribution network maintenance plans, historical fault data of equipment that are frequently queried and referenced have a high usage frequency potential energy, indicating that this data has a high influence on planning maintenance work.

[0071] 3. Comprehensive potential energy calculation of the data: Taking into account both the timeliness potential energy and the frequency of use potential energy, a coefficient vector (α, 1-α) and a potential energy vector (P) are first defined. t (t), P f (x)), where α in the coefficient vector is set manually and can be adjusted according to on-site needs to represent the comprehensive potential energy by calculating the projection length of the potential energy vector onto the coefficient vector.

[0072] Furthermore, the key issue is the selection of α and its optimization. To address this, a data benefit model for different α values ​​was constructed; details are as follows:

[0073] First, let's define a vector. vector The basic characteristics of the two vectors are calculated and analyzed, including: coefficient vector. Length, potential energy vector The length of the vector and the angle between the two vectors; further, it also includes the dot product of the two vectors, according to the definition of the dot product. Where θ is and The included angle. Further consideration is based on vectors. Geometric relationships are used to characterize and analyze the comprehensive potential energy; at this point, the first step is to use vector projection to characterize and analyze the comprehensive potential energy, then: vector In vector Projected length Given the ratio of the inner product of the two to the magnitude of the coefficient vector, we can characterize the combined potential energy P(x) as a vector. In vector Upper projection length and The product of . At this point, we can see that, geometrically speaking, when When α = 0 or α = 1, the combined potential energy P(x) is equal to the vector. In vector The projection length on the surface. When α = 1, it is suitable for the system to clear outdated data and perform system updates, focusing only on the timeliness of the data; when α = 0, it can filter out data with high access frequency, providing a data foundation for subsequent data analysis, such as data filtering based on seasonal power demand and fault detection.

[0074] Meanwhile, considering that the comprehensive potential energy is represented as the product of the projection length of the potential energy vector onto the coefficient vector and the length of the coefficient vector, it can be further seen that the comprehensive potential energy is more sensitive to the potential energy data in the direction of the coefficient vector. We can determine the angle of the corresponding coefficient vector for the angle of the required potential energy vector, which can provide a certain reference for the value of α on site.

[0075] 3. The above presents a data benefit model regarding the value of α. Simultaneously, regarding the threshold setting, a data process is further constructed to mine and decompose the data connotation of the two vectors and their synergistic relationship with the comprehensive potential energy based on trigonometric function relationships. Here, the two variables in the potential energy vector are transformed into trigonometric function forms, represented by the potential energy vector length and angle-related quantities, respectively. This yields an expression for the variables and angle-related quantities in the coefficient vector. Then, differentiating the variable part of the expression, the extreme points and trends of the comprehensive potential energy under different data time-related potential energy and usage frequency potential energy ratios can be obtained, as detailed below:

[0076] First, introduce a new variable. (Right now Then we can express the time-dependent potential energy and the frequency-dependent potential energy using trigonometric functions: in It is a vector The angle with a certain reference axis. Then, the data instance of the comprehensive potential energy is constructed and represented as follows: At this point, we can further analyze the variables in the comprehensive potential energy. First, let us consider letting To find the extreme points of y by taking the derivative with respect to y, i.e. Substituting these values ​​into y yields the extreme values ​​of y, allowing for the analysis of the comprehensive potential energy under different conditions. The changes in the potential energy of different data timeliness and the ratio of potential energy of usage frequency can be analyzed to provide a range reference for the later threshold selection problem.

[0077] 4. Data elimination is based on comprehensive potential energy. A potential energy threshold Ω is set and can be adjusted according to the actual situation.

[0078] For each data x in the cache, if its comprehensive potential energy P(x) ≤ Ω, the data can be replaced; if P(x) > Ω, the data is retained, and the data is sorted and output according to the comprehensive potential energy to facilitate subsequent data analysis.

[0079] 2) Power semantic analysis and operation ticket generation

[0080] Using power semantic analysis algorithms and a knowledge base, semantic analysis is performed on the parsed maintenance order data. Based on the analysis results, structured information such as the maintenance object and maintenance content of the maintenance order is automatically extracted and formed, and associated operation tickets for load transfer, equipment power outage, equipment power restoration, and load recovery are generated. For example, if the maintenance order mentions maintenance of a transformer, the semantic analysis algorithm will identify the transformer as the maintenance object and generate the corresponding operation ticket content according to relevant rules.

[0081] 2. Automatic execution function of dispatch operation tickets

[0082] 1) Interaction with the OCS system

[0083] like Figure 2A bidirectional interactive interface with the OCS system is established through the multi-interface data acquisition unit of the data acquisition module in the data model. Through this interface, on the one hand, real-time data on power outage / restoration and power transfer execution is obtained from the OCS system, and this interactive data can be cached by the data caching and updating unit. Based on its caching strategy, the timeliness and effectiveness of the data are ensured to guide the automatic execution of operation tickets. On the other hand, the execution results of the operation tickets are pushed to the OCS system so that the OCS system can update the relevant equipment status and business records. For example, when the operation ticket reaches the equipment power outage stage, the current equipment status is obtained from the OCS system to ensure the safety and accuracy of the power outage operation, and then the power outage operation result is fed back to the OCS system.

[0084] Simultaneously, the operation ticket data to be executed is sent to the distribution network programmed operation module, automatically generating an operation sequence. Based on the operation sequence and real-time data feedback from the OCS system, the system automatically sends and receives operation commands and confirms conditions in the three process stages of "dispatch order issuance," "dispatch confirmation," and "dispatch order receipt." For example, when a dispatch order is issued, the operation command is sent to the recipient terminal or APP. After the recipient returns the execution result, the system automatically confirms it and sends the result to the setting and tagging module. After verification, the system can automatically execute the next operation.

[0085] 3. Automatic approval function for distribution network maintenance orders

[0086] In this section, the technical team has built a brand-new multi-source data verification mechanism and a pre-power-back equipment condition verification mechanism, detailed below:

[0087] 1) Multi-source data verification mechanism

[0088] Equipment status consistency verification, assuming the equipment status reported on-site is a vector. The current device state obtained in real time from the OCS device is a vector. The core of device status consistency verification lies in comparing these two vector elements to check for consistency. Its vector form is: This indicates that key status information of the equipment should be filled in on-site, such as the switch position of the equipment and the description of its operating conditions. The corresponding real-time device status details are fed back by the OCS system; the verification vector operation is as follows:

[0089]

[0090] When all elements of the result of this operation are close to zero, it indicates that the state is consistent; otherwise, an alarm will be issued.

[0091] Logical verification involves linking the execution status of the dispatch operation ticket with the status of preceding and subsequent maintenance request orders. Let the execution progress vector of the dispatch operation ticket be... The completion status vectors of the maintenance request form and its preceding and subsequent forms are as follows: Specifically: It covers whether each step of the operation ticket has been executed, such as whether the circuit breaker has been closed and whether the power-off operation has been completed. This indicates the approval and completion status information of the maintenance request form and its associated preceding and following orders; the logical verification vector operation is as follows: According to established logical rules, the logic is correct when the product result meets specific conditions; otherwise, an alarm is triggered, indicating that there is a logical conflict in the process.

[0092] Equipment fault information association verification, let the equipment fault information vector be . The vector of relevant equipment information in the maintenance request form is Used to identify potential conflicts between the two: Includes equipment fault codes, fault occurrence time, fault location, and other information; vector For maintenance, data such as equipment model, location, and current operating status are required; correlation verification calculation: judgment When the intersection is an empty set, there is no conflict; if there are intersecting elements, carefully check the conflict points and evaluate whether to issue an alarm according to the rules.

[0093] Before entering the verification process, the above multi-source data undergoes preprocessing by a data caching and update unit to ensure data timeliness and accuracy. Let the data state vector after processing by the caching unit be... It meets the requirements of timeliness and completeness, and avoids verification errors.

[0094] 2) Equipment condition verification mechanism before power restoration

[0095] Verification of electrical parameters begins with voltage matching: assuming the equipment's rated voltage is U. e The measured voltage of the power grid is U g The lower limit of the fluctuation range is U min The upper limit is U max , forming vectors Verification criteria are U min ≤U g ≤U max Ensure the equipment voltage is compatible. Further verify the current matching: the equipment's rated current is I. e The measured operating current is I g Construct vectors When I g ≥I e At that time, a thorough investigation was conducted to identify potential faults and overload hazards. Finally, the power factor was verified: the equipment's design power factor was [value missing]. The power grid requires a power factor of 1. The measured power factor is Vector representation is To ensure the three components are matched, the status of the power factor compensation device is also verified for inductive loads.

[0096] Verify the equipment protection devices, considering multiple aspects, including overload protection verification: Assume the overload protection device's activation threshold is I. th The measured current of the equipment is I. m ,vector When I m ≥I th The device should promptly trip to cut off the circuit; an overload test can be simulated on-site. The short-circuit protection should also be verified: the standard response time for short-circuit protection is t. s The measured response time is t m The accuracy index of the action is A. e The actual accuracy of the action was A. m ,vector The response and operational accuracy were verified using specialized instruments. Grounding protection was also verified: the standard grounding resistance value is R. e The measured grounding resistance is R. m The grounding line connection firmness index is C. e The actual measured strength is C. m ,vector Ensure grounding safety.

[0097] For mechanical components of equipment, rigorous verification is carried out focusing on three key dimensions: mechanical condition, wear condition, and connection tightness. The first step is to verify the mechanical condition: let the smoothness index of the mechanical component's operation be S. e The actual smoothness score is S. m The presence or absence of jamming is indicated by B (0 indicates no jamming, 1 indicates jamming), vector. Manual operation and observation are used to assess the equipment's operating status. Next, wear is verified: the wear limit value for key mechanical components is W. e The measured wear level is W. m ,vector The wear condition is assessed by appearance and sound. The tightness of the connection is then verified: the standard tightening torque is T. e The measured torque is T m Looseness is indicated by L (0 for tight, 1 for loose), vector Regularly inspect bolts, nuts, and other connecting parts.

[0098] Regarding the verification of equipment wiring connections, the approach also focuses on three core aspects: wiring integrity, correct wiring identification, and wiring insulation performance. For the wiring integrity verification step: assuming the standard value of the wiring connection resistance is R... c The measured resistance is R. m Disconnections and loose connections are identified by the symbol V (0 indicates no abnormality, 1 indicates a problem), vector. Judgment is made by combining visual inspection with multimeter measurements. Verification of circuit label correctness: The standard for circuit label completeness is C. e The measured completeness is C. m The accuracy index of the label is A. e The actual accuracy is A. m ,vector Ensure labeling is clear and accurate. Verify circuit insulation performance: the standard insulation resistance value is R. i The measured insulation resistance is R. m ,vector Measured using an insulation resistance tester, R is required to be... m ≥R i .

[0099] The above verification operations can be organized into a determinant as follows:

[0100]

[0101] The "-" indicates that there is no directly related information at the corresponding position. This matrix system presents the entire process of multi-source data verification and equipment condition verification before power restoration. Each vector corresponds to an operation that is verified one by one. Only when all verification results meet the standards can the equipment be automatically authorized to be restored and put into normal operation, thus laying a solid foundation for the stable operation and maintenance of the power system.

[0102] After meeting the "five mandatory verifications and one confirmation" conditions, automatic completion authorization by the dispatcher is supported. Verify whether the equipment is ready for power restoration, including the equipment status, the status of relevant protection devices, and whether there are any outstanding issues.

[0103] 4. Automatic location setting and tagging function for power distribution network

[0104] 1) Integration with distribution network DCCS and OCS systems

[0105] The data acquisition module in the data model establishes an integration interface with the distribution network DCCS system through its multi-interface data acquisition unit, automatically acquiring distribution network operation ticket data from the DCCS. Simultaneously, it establishes integration interfaces with 14 regional distribution network OCS systems, pushing placement instructions to the distribution network OCS systems for execution. For example, during operation ticket execution, a placement instruction is generated based on the operation ticket content. This instruction is then sent to the corresponding OCS system through the integration interface, enabling the setting / placement operation on the OCS diagram.

[0106] After the operation is completed, OCS needs to generate a file to transmit the listing information back to the DCCS system of the local dispatch network. Simultaneously, it provides listing and removal statistics functions. The system automatically records listing and removal information based on the listing and removal actions, including the listing person, listing time, type, listing equipment, and the removal person and removal time. For example, after each listing or removal operation, the system automatically records relevant information in the database for subsequent querying and statistics.

[0107] 5. Intelligent log function for self-driving operation of distribution network maintenance orders

[0108] 1) Log information collection and synchronization

[0109] During the autonomous operation of distribution network maintenance orders, log information such as ticket drafting, approval, network command issuance, programmed control, and error prevention verification is automatically tracked and collected. By establishing an integration interface with the distribution network OMS system, this log information is pushed to the corresponding scheduling log module of the OMS system. In this process, the data acquisition module of the data model provides the data source for log information collection, while the data caching and update unit ensures the integrity and accuracy of log information during storage and transmission. For example, when a ticket drafting operation occurs, the relevant information of the drafter and the drafting time are recorded; when an approval operation occurs, the relevant information of the approver and the approval time are recorded, and so on, synchronizing all relevant log information to the OMS system to provide a basis for traceability and analysis of the maintenance process.

[0110] 6. Automatic archiving function for maintenance procedures.

[0111] 1) Verification of archiving conditions and data synchronization

[0112] The system should have an automatic verification function for archiving, ensuring that maintenance orders are automatically archived only after all power restoration work is completed (or after the issuing party has set this action to be inactive). Upon completion of power restoration and archiving, the system should automatically backfill the power outage / restoration time and work delay information into the OMS maintenance order. For example, after power restoration is completed, the system checks whether the restoration is completely finished. If so, it automatically backfills the power restoration time and work delay information into the OMS maintenance order and updates the order's status to archived. This process involves the data backfilling module in the data model, ensuring closed-loop management of maintenance data. The data verification module also ensures the accuracy and reasonableness of the backfilled data to a certain extent.

[0113] Example 2: Distribution Network Control Autonomous Driving Cockpit Module

[0114] 1. Full-process visualization function for distribution network maintenance orders

[0115] 1) Data association and display

[0116] Based on the self-driving cruise drive engine, such as Figure 1The data acquisition module in the data model is responsible for collecting data from multiple related systems. This data establishes a stable connection with the maintenance business autonomous driving module through a multi-interface data acquisition unit to obtain maintenance-related data. In the multi-source data fusion module, the data preprocessing unit performs format unification, semantic parsing, and correlation matching on the collected data to ensure that the data can accurately display each process of maintenance execution on the cockpit interface, including load transfer, power outage, dispatch permission, permission termination, power restoration, and load recovery. For example, when the mouse hovers over the "Power Outage" step, relevant information about the power outage operation is displayed, such as the equipment affected and the outage time. This relies on the latest data stored in the data caching and update unit, which ensures the timeliness and effectiveness of the data through a caching mechanism. When a maintenance order contains multiple operation items, hovering the mouse over this step should display the execution status of all operation items in this step. This requires the data model to accurately integrate and process data from different data sources to fully present the execution details of the maintenance order.

[0117] 2) Display of abnormal processes

[0118] The prominent display function for abnormal, early termination, cancellation, and postponement stages in the entire maintenance process is based on a comprehensive safety verification mechanism using a data model. During data verification, the equipment status consistency verification module, logic verification module, and equipment fault information association verification unit perform multi-dimensional checks on the maintenance data. When anomalies occur, this information is marked and fed back to the dashboard interface. When the mouse hovers over the stage, the corresponding reason is displayed. For example, if a maintenance order is postponed during execution, it may be due to equipment faults detected during data verification (the equipment fault information association verification unit detects conflicts between relevant equipment fault information and the maintenance plan) or insufficient personnel (the logic verification module may find logical problems related to human resource arrangements). This information is processed and transmitted through the data model and accurately displayed to the user on the dashboard interface.

[0119] 2. Functionality linked to safety error prevention and verification throughout the entire maintenance process

[0120] 1) Real-time monitoring and alarms

[0121] This module is related to the data model, as follows: Figure 2The comprehensive safety verification module is tightly integrated to achieve safety and error prevention verification throughout the entire maintenance process. The multi-dimensional real-time status verification submodule, the logical relationship intelligent verification submodule, and the fault correlation deep analysis submodule continuously perform in-depth analysis of the safety and error prevention verification throughout the entire maintenance process. During this process, the data model acquires data from multiple data sources, such as on-site reported equipment status information from the OMS system, real-time equipment status data from the OCS / EMS system, and equipment fault information. When anomalies are detected in these data during the safety verification process—for example, if the multi-dimensional real-time status verification submodule detects inconsistencies between the real-time equipment status and the reported status, the logical relationship intelligent verification submodule detects logical errors in the maintenance process, or the fault correlation deep analysis submodule uncovers potential conflicts between equipment faults and the maintenance plan—anomaly alarm will be generated. These alarms are displayed on the dashboard interface through the data model's information transmission mechanism. For example, if a safety and error prevention verification fails during maintenance, an alarm window will pop up on the dashboard interface, displaying the reason for the failure and relevant operational suggestions. This is a crucial manifestation of the data model's role in ensuring maintenance safety.

[0122] 3. Task query function

[0123] 1) Conditional query design

[0124] The task query function leverages the data acquisition and processing capabilities of the data model. The data acquisition module collects data from various relevant systems, covering information such as the requesting unit, maintenance order number, maintenance order execution status, and occurrence time. The data caching and updating unit ensures the timeliness and accuracy of this data; data stored in the cache can be quickly retrieved based on user-input query conditions. For example, the maintenance order query function allows for precise searches based on conditions such as the requesting unit, maintenance order number, maintenance order execution status, and occurrence time. If a user wants to query maintenance orders submitted by a specific unit, they only need to enter the requesting unit's name on the query interface, and the system can filter out maintenance orders that meet the criteria. This process relies on the data model's effective management and organization of data.

[0125] 4. Task Overview Function

[0126] 1) Default display and node differentiation

[0127] The task overview function is based on the comprehensive management of maintenance order data through a data model. The data acquisition module in the data model obtains the entire process data of the maintenance order, including the status information of each process node. After the data is processed by the data preprocessing unit, the data is stored and updated through the data caching and updating unit. By default, a list of maintenance orders in progress for the day is displayed, which is based on the data model's filtering and presentation of time information and maintenance order execution status data. When a maintenance order is selected, the operation flowchart of the current maintenance order is displayed, visually representing all process nodes of the maintenance order, including load transfer, power outage, dispatch permission, permission termination, power restoration, load recovery, etc. Different background colors are used to distinguish between executed and unexecuted nodes. For example, executed nodes are represented in green, and unexecuted nodes are represented in red. This visualization is based on the accurate node status information provided by the data model, allowing users to intuitively understand the execution status of the maintenance order.

[0128] 5. Quick Operation Functions

[0129] 1) User interface design

[0130] The implementation of quick operation functions relies on the interaction and data processing between the data model and various related systems. The cockpit interface provides quick operations such as viewing maintenance order details, issuing manual dispatch orders, and suspending autonomous cruise. When the user clicks the "View Maintenance Order Details" button, the system retrieves detailed maintenance order information from relevant data sources through the data acquisition module of the data model, including maintenance content, equipment information, and operation records, and displays it in a pop-up window. If the user wants to issue a manual dispatch order, clicking the corresponding button allows the system to access the manual dispatch interface through the interaction interface with the OCS system and other related systems (the communication interface established by the data acquisition module in the data model) to perform the relevant operations. All of these operations are based on the accurate acquisition of data by the data model and the effective connection between systems.

[0131] 6. One-click viewing function for original form data in core business operations.

[0132] 1) Data Acquisition and Display

[0133] The one-click viewing function for core business original form data leverages the data acquisition and integration capabilities of the data model. The data acquisition module retrieves data from various systems storing original form data through integration interfaces with relevant systems. In the multi-source data fusion module, the data, after preprocessing, can be accurately displayed on the dashboard interface. For example, users can directly open and view the original approved business forms. If a user wants to view the original application form for a specific maintenance order, clicking the corresponding button will directly open the original application form and display all relevant information. This is thanks to the data model's integration of data from different systems and its efficient data acquisition mechanism.

[0134] 7. Business drill-down query function

[0135] 1) Multi-level query design

[0136] The business drill-down query function is implemented based on the deep organization and management of data in the data model. When collecting and processing maintenance order data, the data model records and stores detailed information for each step. When a user uses the business drill-down query function, for example, to understand the specific operational details of the "power outage" step in a maintenance order, the system uses the data structure and retrieval mechanism in the data model to first locate the relevant data for the "power outage" step, and then delves into the next level of the query interface to display detailed information about the power outage operation, such as the power outage equipment, power outage time, and operator, providing users with detailed data query capabilities from the overall to the local and from the macro to the micro.

[0137] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0138] In various embodiments, the hardware implementation of the technology can directly utilize existing smart devices, including but not limited to industrial control computers, PCs, smartphones, handheld devices, and floor-standing devices. Its input device preferably uses an on-screen keyboard, its data storage and computing modules utilize existing memory, calculators, and controllers, its internal communication modules utilize existing communication ports and protocols, and its remote communication utilizes existing GPRS networks, the World Wide Web, etc.

[0139] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments 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. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0140] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and 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 shown or discussed mutual couplings or direct couplings or communication connections may be through some interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms. Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, i.e., 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.

[0141] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. An intelligent automatic authorization data model for distribution network maintenance, characterized in that, The data model includes: S-1-1, Data Acquisition Module, used to acquire distribution network maintenance order data, real-time equipment status data, dispatch operation ticket execution data and equipment fault information from the distribution network OMS system and OCS system; S-1-2, Data Verification Module, performs multi-source path security verification on the acquired maintenance order data; S-1-3, Automatic Permission Module: After the data verification module passes the verification, it automatically approves the maintenance application and generates permission information. S-1-4, Equipment Condition Verification Module Before Power Restoration, is used to automatically verify equipment conditions before power restoration. Specifically, it includes four aspects: verification of equipment electrical parameters, verification of equipment protection devices, verification of equipment mechanical components, and verification of equipment wiring connections. After meeting the "five mandatory verifications and one confirmation" requirement, it supports automatic dispatch permission for completion. S-1-5, Data backfilling module, used to automatically backfill permission information to the distribution network OMS system; The system includes a multi-source data fusion module following the data acquisition module. This module integrates data from the OMS, OCS / EMS, and DCCS systems to perform security verification from multiple dimensions. Specifically, it includes: S-8-1, Multi-interface data acquisition unit, establishes stable communication interfaces with the power grid management platform, local dispatch energy management systems and distribution network distributed control systems to collect data in real time or periodically; S-8-2, Data Preprocessing Unit: Parses maintenance order data in the OMS system according to predetermined semantic rules; converts real-time equipment status data from the OCS / EMS system into a format compatible with maintenance order data, and associates it with equipment information in the maintenance order based on equipment identifiers; parses operation steps in the operation ticket data of the distribution network DCCS system, enabling it to be cross-referenced with maintenance order processes and equipment status data, ensuring the synergy of data from various systems in subsequent processing; S-8-3, Data Caching and Update Unit: This unit constructs a high-speed caching mechanism to store the latest pre-processed data and monitors the data updates from each data source in real time. The data caching and update unit employs an eviction policy based on a potential energy algorithm that considers both timeliness and usage frequency. When cache space is insufficient, for each piece of data in the cache: S-8-3-1. Calculate the timeliness potential energy of the data. The difference between the current time and the last update time determines the timeliness potential energy of the data. The smaller the time difference, the higher the potential energy. S-8-3-2. Calculate the usage frequency potential energy of data by counting the number of times the data is used within a certain time unit; divide this number by the total number of time units to obtain the usage frequency, and thus obtain the usage frequency potential energy of the data. S-8-3-3. Calculate the comprehensive potential energy of the data. Based on the system's emphasis on timeliness and frequency of use, set weighting coefficients to balance the timeliness potential energy and the frequency of use potential energy, and determine the comprehensive potential energy of the data. S-8-3-4. Based on the selection of weighting coefficients and potential energy thresholds, construct a data benefit model for weighting coefficients and conduct data analysis on the subsequent setting of potential energy thresholds; S-8-3-5. Selecting to replace or retain data based on the comprehensive potential energy of the data: Set a potential energy threshold, compare the comprehensive potential energy of each data with the potential energy threshold. If the comprehensive potential energy of the data is lower than the potential energy threshold, replace the data; otherwise, retain the data. The data verification module includes: S-10-1, the multi-dimensional real-time status verification submodule, simultaneously receives the field-reported equipment status information from the OMS system and the real-time equipment status data from the OCS / EMS system after processing by the multi-source data fusion module, and performs precise field-by-field comparison; during the verification process, a fast comparison technology is used to efficiently compare the key status parameters of the equipment. If any inconsistency is found, a high-priority alarm notification is immediately triggered and the automatic permission process is suspended until the inconsistency is resolved; S-10-2, the intelligent logical relationship verification submodule, performs in-depth verification of the logical relationship between the execution status of the associated scheduling operation ticket and the related pre- and post-orders of the maintenance application form based on a pre-set complex logical rule library; during the verification process, a path analysis algorithm based on graph theory is used to simulate the logical flow of the maintenance operation, and if a logical inconsistency is detected, a detailed alarm report is immediately issued. S-10-3, Fault Association Deep Analysis Submodule: After obtaining equipment fault information and related equipment information from maintenance request forms, it uses knowledge graph-based fault association analysis technology to uncover potential conflict relationships between equipment faults and maintenance plans. By constructing a knowledge graph containing multi-dimensional information such as equipment fault type, fault location, and maintenance equipment scope, it quickly identifies fault factors that may increase the risk of maintenance operations. If a state conflict is found, it issues a timely and accurate alarm.

2. The intelligent automatic authorization data model for distribution network maintenance according to claim 1, characterized in that, The verification of the electrical parameters of the equipment includes: S-3-1. Check the matching of the equipment's rated voltage with the mains voltage; S-3-2. Verify the matching between the rated current of the equipment and the actual operating requirements; S-3-3. Verify the compatibility of the equipment's power factor with grid requirements and equipment design standards.

3. The intelligent automatic authorization data model for distribution network maintenance according to claim 1, characterized in that, The verification of the equipment protection device includes: S-4-1. Verify that the overload protection device is working properly. The overload protection device will activate and cut off the circuit when the equipment is overloaded. S-4-2. Verify the effectiveness of the short-circuit protection device. The short-circuit protection device should quickly cut off the circuit when a short circuit occurs in the equipment. S-4-3. Verify the grounding protection device. The grounding protection device is connected to the ground to introduce leakage current into the ground.

4. The intelligent automatic authorization data model for distribution network maintenance according to claim 1, characterized in that, The verification of the mechanical components of the equipment includes: S-5-1. Verify the mechanical condition of equipment with moving parts and check for any jamming during equipment operation; S-5-2. Verify the wear condition of mechanical parts, observe the scratches and wear marks on the equipment's exterior, and any abnormal noises during operation; S-5-3. Verify the tightness of the mechanical connection parts of the equipment.

5. The intelligent automatic authorization data model for distribution network maintenance according to claim 1, characterized in that, The verification of the equipment wiring connections includes: S-6-1. Check the integrity of the connection between the equipment and the line, including broken wires and loose connections, observe the appearance of the connection parts, and use a multimeter to measure the resistance. S-6-2. Verify the correctness of the line markings to ensure they are clear and accurate; S-6-3. Check the insulation performance of the line. Use an insulation resistance tester to measure the insulation resistance of the line. The insulation resistance should be greater than 0.5MΩ.

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