Corpus construction and updating system and method for direct current transmission operation and maintenance

Abstract

The invention discloses a corpus construction and updating system and method for direct current transmission operation and maintenance, and relates to the technical field of corpus construction data processing. The system comprises a DC power transmission operation and maintenance data acquisition and processing module, a data quality judgment module and a DC power transmission operation and maintenance data optimization module. According to the method, direct-current transmission operation and maintenance original data is acquired and processed to obtain direct-current transmission operation and maintenance data, data quality validity and data timeliness influence values are obtained through quantification based on the direct-current transmission operation and maintenance data, whether direct-current transmission operation and maintenance data optimization is carried out or not is judged, and if direct-current transmission operation and maintenance data optimization is carried out, direct-current transmission operation and maintenance data optimization is carried out. If yes, corpus updating is carried out based on the optimized direct-current transmission operation and maintenance data, otherwise, corpus updating is directly carried out, the updating reliability of the corpus used for direct-current transmission operation and maintenance is improved, and the problem that in the prior art, due to the difference between data quality effectiveness and data timeliness, the updating reliability of the corpus is low is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of corpus construction data processing, and in particular to a corpus construction and updating system and method for direct current transmission operation and maintenance. Background Art

[0002] With global economic development and population growth, energy demand continues to increase. At the same time, to address climate change and environmental pollution, countries are actively promoting energy transitions and developing clean energy sources such as wind and solar power. DC transmission, as an efficient long-distance power transmission method, can deliver clean energy from remote areas to load centers. With the rapid development of artificial intelligence and big data technologies, these technologies are increasingly being applied in power systems. In the field of DC transmission operation and maintenance, building a corpus can leverage artificial intelligence and big data technologies to mine and analyze massive amounts of operation and maintenance data, enabling functions such as fault warning, status assessment, and intelligent decision-making, thereby improving the intelligence level of operation and maintenance. To standardize the management and technical requirements of DC transmission operation and maintenance, relevant industry standards and specifications are continuously being issued and improved. These standards and specifications provide guidance and a basis for the construction of the DC transmission operation and maintenance corpus, helping to improve its quality and practicality.

[0003] Existing systems use data storage technology to store structured data; use natural language processing for semantic analysis and sentiment analysis of text; and use cloud computing and big data technologies to provide scalable computing and storage resources and process large-scale data sets.

[0004] For example, the patent application with publication number CN112434129A discloses a method and system for generating a professional corpus in the field of power grid dispatching, which includes: extracting control knowledge, fusing the extracted control knowledge to generate a dispatching professional entity corpus; generating a dispatching professional event corpus based on the dispatching professional entity corpus and the overall business operation intention. The present invention constructs a corpus of "general corpus + dispatching professional corpus", which contains the proper noun expressions in the control field and can effectively support the realization of speech recognition and control voice interaction; the accuracy rate of extracting dispatching professional ontology knowledge entities in the control text is above 95%, which can well support the construction of a professional corpus and is much better than existing word segmentation tools. The dispatching professional knowledge entities in the structured and unstructured data in the control field are extracted to form a professional corpus.

[0005] For example, the patent application with publication number CN119669421A discloses a method for constructing a large language model corpus for the power supply industry, including: step 1, deploying a RAG system in the working environment of the power supply industry; step 2, using the RAG system to perform knowledge retrieval to obtain a question text and multiple corresponding answer texts, namely, question and answer texts; step 3, vectorizing the question text to obtain a question vector; step 4, clustering all question vectors and encoding each cluster to obtain a cluster code; step 5, encoding all question and answer texts according to the cluster category, and dividing them into question segments in time sequence; step 6, extracting the questions of the corpus in the question segments, generating answers, and calculating the confidence of the answers; step 7, cataloging the questions, corresponding answers and confidences into a corpus record, and saving all the corpus records to obtain a large language model corpus for the power supply industry.

[0006] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] The same fault corresponds to multiple unprioritized solution strategies, and operation and maintenance personnel may choose non-optimal solutions. Although the corpus stores the "equipment-fault-solution" triples, it does not record verification data such as the fault recurrence rate and equipment status changes after parameter adjustment, making it impossible to analyze the relationship between the adjustment effect and long-term operation. In addition, the granularity of DC transmission operation and maintenance data collection is insufficient. At the same time, the fixed-cycle update of the corpus does not match the long verification cycle of parameter adjustment, resulting in the premature solidification of unverified temporary solutions. There is a problem of incomplete DC transmission operation and maintenance data. Real-time monitoring tasks and corpus update tasks compete for resources due to fixed priority allocation, resulting in task interruption or data loss. There is a problem of low reliability of corpus updates due to differences in data quality validity and data timeliness. Summary of the Invention

[0008] The embodiments of the present application provide a corpus construction and update system and method for DC transmission operation and maintenance, thereby solving the problem of low corpus update reliability due to differences in data quality validity and data timeliness in the prior art, and improving the update reliability of the corpus used for DC transmission operation and maintenance.

[0009] The embodiment of the present application provides a corpus construction and updating system for DC transmission operation and maintenance, including: a DC transmission operation and maintenance data acquisition and processing module, a data quality determination module and a DC transmission operation and maintenance data optimization module: wherein the DC transmission operation and maintenance data acquisition and processing module is used to monitor the operation and maintenance of the DC transmission system, obtain the original DC transmission operation and maintenance data and process it to obtain DC transmission operation and maintenance data, and the DC transmission operation and maintenance data is used to initialize and update the constructed corpus; the data quality determination module is used to quantify the quality and timeliness of the DC transmission operation and maintenance data respectively through the DC transmission operation and maintenance data to obtain quantitative results, and the quantitative results include the data quality effective value and the data timeliness impact value; the DC transmission operation and maintenance data optimization module is used to quantify the quality and timeliness of the DC transmission operation and maintenance data respectively through the DC transmission operation and maintenance data to obtain quantitative results, and the quantitative results include the data quality effective value and the data timeliness impact value; The module is used to determine whether to optimize the DC transmission operation and maintenance data based on the obtained quantitative results. If the DC transmission operation and maintenance data is optimized, the corpus is updated based on the optimized DC transmission operation and maintenance data. Otherwise, the corpus is updated directly based on the obtained DC transmission operation and maintenance data. The DC transmission operation and maintenance data optimization means processing the DC transmission operation and maintenance data through data quality optimization methods and data timeliness optimization methods to improve the timeliness of the corpus update. The data quality optimization method means improving the quality of the DC transmission operation and maintenance data by processing the DC transmission operation and maintenance data. The data timeliness optimization method means improving the timeliness of the DC transmission operation and maintenance data processing by optimizing the efficiency of the DC transmission operation and maintenance data processing.

[0010] The embodiment of the present application provides a corpus construction and updating method for DC transmission operation and maintenance, comprising: performing operation and maintenance monitoring on a DC transmission system, obtaining and processing raw DC transmission operation and maintenance data to obtain DC transmission operation and maintenance data, wherein the DC transmission operation and maintenance data is used to initialize and update the constructed corpus; quantifying the quality and timeliness of the DC transmission operation and maintenance data using the DC transmission operation and maintenance data to obtain quantitative results, wherein the quantitative results include a data quality effective value and a data timeliness impact value; judging whether to perform DC transmission operation and maintenance data optimization based on the obtained quantitative results, and if the DC transmission operation and maintenance data is optimized, According to the optimization, the corpus is updated based on the optimized DC transmission operation and maintenance data, otherwise the corpus is updated directly based on the acquired DC transmission operation and maintenance data. The DC transmission operation and maintenance data optimization means processing the DC transmission operation and maintenance data through data quality optimization methods and data timeliness optimization methods to improve the timeliness of the corpus update. The data quality optimization method means processing the DC transmission operation and maintenance data to improve the quality of the DC transmission operation and maintenance data. The data timeliness optimization method means optimizing the efficiency of DC transmission operation and maintenance data processing to improve the timeliness of DC transmission operation and maintenance data processing.

[0011] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0012] 1. By acquiring and processing the original DC transmission operation and maintenance data, the DC transmission operation and maintenance data is obtained. Based on the DC transmission operation and maintenance data, the data quality validity and data timeliness impact values ​​are quantified respectively. Then, it is determined whether to optimize the DC transmission operation and maintenance data. If the DC transmission operation and maintenance data is optimized, the corpus is updated based on the optimized DC transmission operation and maintenance data. Otherwise, the corpus is directly updated, thereby improving the update reliability of the corpus used for DC transmission operation and maintenance.

[0013] 2. By quantifying the data quality validity and data timeliness impact values ​​based on the DC transmission operation and maintenance data, it is determined whether to optimize the DC transmission operation and maintenance data. By quantifying the data quality validity and data timeliness impact values, errors, anomalies or inconsistencies in the data can be identified, and corresponding optimization measures can be taken to ensure that the data in the corpus is accurate and reliable, thereby improving the real-time performance of the data and ensuring that the corpus can promptly reflect the latest status of the DC transmission system.

[0014] 3. By optimizing the DC transmission operation and maintenance data, the corpus is updated based on the optimized DC transmission operation and maintenance data. Otherwise, the corpus is updated directly to more accurately reflect the actual operating status of the DC transmission system. Using the optimized DC transmission operation and maintenance data to update the corpus can ensure that the information in the corpus is accurate and reliable, reducing errors and deviations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of the structure of a corpus construction and update system for DC transmission operation and maintenance provided in an embodiment of the present application;

[0016] Figure 2 This is a diagram of the homepage interface of the corpus construction and update system for DC transmission operation and maintenance provided in an embodiment of the present application;

[0017] Figure 3 A flow chart of a data quality optimization method for a corpus construction and update system for DC transmission operation and maintenance provided in an embodiment of the present application;

[0018] Figure 4 A flow chart of a method for optimizing data timeliness in a corpus construction and update system for DC transmission operation and maintenance provided in an embodiment of the present application;

[0019] Figure 5 This is a diagram of the corpus verification result interface of the corpus construction and update system for DC transmission operation and maintenance provided in an embodiment of the present application;

[0020] Figure 6 A diagram of a corpus update interface of a corpus construction and update system for DC transmission operation and maintenance provided in an embodiment of the present application;

[0021] Figure 7 A flowchart of a corpus construction and updating method for DC transmission operation and maintenance is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiments of the present application provide a corpus construction and update system and method for DC transmission operation and maintenance, solving the problem of low corpus update reliability in the prior art due to differences in data quality validity and data timeliness. By acquiring and processing raw DC transmission operation and maintenance data to obtain DC transmission operation and maintenance data, the system quantifies the data quality validity and data timeliness impact values ​​based on the DC transmission operation and maintenance data, and then determines whether to optimize the DC transmission operation and maintenance data. If optimization is required, the system updates the corpus based on the optimized DC transmission operation and maintenance data; otherwise, the system directly updates the corpus, thereby improving the update reliability of the corpus for DC transmission operation and maintenance.

[0023] The technical solution in the embodiments of the present application is to solve the problem of low reliability of corpus updates due to differences in data quality validity and data timeliness. The overall idea is as follows:

[0024] By acquiring and processing HVDC operation and maintenance data, the data is used to initialize and update the corpus. Based on the HVDC operation and maintenance data, the quality validity and timeliness of the HVDC operation and maintenance data are quantified respectively to obtain the data quality effective value and data timeliness impact value. The data quality effective value and data timeliness impact value are compared and analyzed with the thresholds in the database to trigger the optimization of HVDC operation and maintenance data. The optimized HVDC operation and maintenance data is used to update the corpus to ensure its timeliness and accuracy. Deviations in data quality and timeliness are also addressed by dynamically adjusting resource allocation and priority labeling. When necessary, alarm or circuit breaker mechanisms are triggered. By processing the HVDC operation and maintenance data, the timeliness of corpus updates is improved.

[0025] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0026] like Figure 1As shown, it is a structural diagram of a corpus construction and updating system for DC transmission operation and maintenance provided by an embodiment of the present application. The corpus construction and updating system for DC transmission operation and maintenance provided by an embodiment of the present application includes: a DC transmission operation and maintenance data acquisition and processing module, a data quality determination module and a DC transmission operation and maintenance data optimization module: wherein the DC transmission operation and maintenance data acquisition and processing module is used to monitor the operation and maintenance of the DC transmission system, obtain the original DC transmission operation and maintenance data and process it to obtain DC transmission operation and maintenance data, and the DC transmission operation and maintenance data is used to initialize and update the constructed corpus; the data quality determination module is used to quantify the quality and timeliness of the DC transmission operation and maintenance data respectively through the DC transmission operation and maintenance data to obtain quantitative results, and the quantitative results include the data quality effective value and Data timeliness impact value; the DC transmission operation and maintenance data optimization module is used to determine whether to optimize the DC transmission operation and maintenance data based on the obtained quantitative results. If the DC transmission operation and maintenance data is optimized, the corpus is updated based on the optimized DC transmission operation and maintenance data. Otherwise, the corpus is updated directly based on the obtained DC transmission operation and maintenance data. DC transmission operation and maintenance data optimization means processing the DC transmission operation and maintenance data through data quality optimization methods and data timeliness optimization methods to improve the timeliness of corpus updates. The data quality optimization method means improving the quality of the DC transmission operation and maintenance data by processing the DC transmission operation and maintenance data. The data timeliness optimization method means improving the timeliness of the DC transmission operation and maintenance data processing by optimizing the efficiency of the DC transmission operation and maintenance data processing.

[0027] In this embodiment, if Figure 2As shown in the figure, it is a homepage interface diagram of the corpus construction and updating system for DC transmission operation and maintenance provided by the embodiment of the present application. The DC transmission operation and maintenance data acquisition and processing module monitors the DC transmission system and processes the DC transmission operation and maintenance raw data to obtain DC transmission operation and maintenance data, thereby improving the efficiency of obtaining and processing DC transmission operation and maintenance data; and the data quality determination module quantifies the data quality validity and timeliness, and the data optimization module optimizes the data according to the quantification results, thereby improving the timeliness of corpus update and the data quality validity, realizing the optimization of DC transmission operation and maintenance data, and solving the problem of data quality validity and data The difference in timeliness leads to low reliability in corpus updates. Specifically, the DC transmission operation and maintenance data is annotated, including semantic annotation of the DC transmission operation and maintenance data and event annotation of the time series DC transmission operation and maintenance data. The annotated DC transmission operation and maintenance data is stored in the DC transmission system. The annotated DC transmission operation and maintenance data is extracted from the DC transmission system and converted according to the data format and structure predetermined in the DC transmission operation and maintenance database to conform to the storage of the corpus. The converted DC transmission operation and maintenance data is loaded into the corpus storage system, and an index is constructed based on the characteristics of the DC transmission operation and maintenance data. For example, indexes can be established for fields such as device name, timestamp, and event type to facilitate subsequent data retrieval and analysis. The initialized corpus is validated using the validation rules preset in the DC transmission operation and maintenance database to ensure that it meets the predetermined DC transmission operation and maintenance data quality.

[0028] Furthermore, the DC transmission operation and maintenance data includes data quality data and data timeliness data; the data quality data includes the data adjustment verification record rate of the DC transmission operation and maintenance data within the monitoring period, the DC transmission operation and maintenance abnormal data identification index, the operation and maintenance fault warning priority marking rate and the data sampling frequency; the data timeliness data includes the data update-verification cycle synchronization rate, data sampling frequency, corpus update frequency and DC transmission operation and maintenance task delay of the DC transmission operation and maintenance data within the monitoring period; the quality and timeliness of the DC transmission operation and maintenance data are quantified respectively through the DC transmission operation and maintenance data, which also previously included: obtaining data quality thresholds, data quality compensation amounts, and data timeliness thresholds from the constructed DC transmission operation and maintenance database and data timeliness compensation; data quality thresholds include data adjustment and verification record rate thresholds, operation and maintenance fault warning priority labeling rate thresholds and data sampling frequency thresholds; data quality compensation includes data adjustment and verification record rate compensation, DC transmission operation and maintenance abnormal data identification index compensation, operation and maintenance fault warning priority labeling rate compensation and data sampling frequency compensation; data timeliness thresholds include data update-verification cycle synchronization rate thresholds, data sampling frequency thresholds, corpus update frequency thresholds and DC transmission operation and maintenance task delay thresholds; data timeliness compensation includes data update-verification cycle synchronization rate compensation, data sampling frequency compensation, corpus update frequency compensation and DC transmission operation and maintenance task delay compensation.

[0029] Among them, the data adjustment and verification record rate, operation and maintenance fault warning priority marking rate and data sampling frequency are extracted from the DC transmission system logs and configuration files; the DC transmission operation and maintenance abnormal data identification index is obtained by analyzing historical DC transmission operation and maintenance data; the data update-verification cycle synchronization rate, corpus update frequency and DC transmission operation and maintenance task delay are obtained from the DC transmission system logs.

[0030] Furthermore, the specific analysis steps of the data quality effective value are as follows: compensating the analysis result of the proportion of the data adjustment verification record rate and the data adjustment verification record rate threshold by the data adjustment verification record rate compensation amount to obtain a first data quality effective value component; compensating the DC transmission operation and maintenance abnormal data identification index by the DC transmission operation and maintenance abnormal data identification index compensation amount to obtain a second data quality effective value component; compensating the analysis result of the proportion of the operation and maintenance fault warning priority labeling rate and the operation and maintenance fault warning priority labeling rate threshold by the operation and maintenance fault warning priority labeling rate compensation amount to obtain a third data quality effective value component; compensating the analysis result of the proportion of the data sampling frequency and the data sampling frequency threshold by the data sampling frequency compensation amount to obtain a fourth data quality effective value component; deriving the data quality effective value by coupling each data quality effective value component; the data quality effective value represents quantitative data of the degree to which the DC transmission operation and maintenance system is affected by the effectiveness of the DC transmission operation and maintenance data quality, and the above-mentioned proportion analysis represents a division operation.

[0031] In this embodiment, the specific expression of the effective value of the data quality of the DC transmission operation and maintenance data within the monitoring period is:

[0032]

[0033] In the formula, DQI is the effective value of the data quality of the DC transmission operation and maintenance data during the monitoring period; AVR is the data adjustment verification record rate of the DC transmission operation and maintenance data during the monitoring period, which measures the proportion of verification of the adjustment effect and recording of relevant data after the DC transmission operation and maintenance data is adjusted; AVR0 is the preset data adjustment verification record rate threshold obtained in the DC transmission operation and maintenance database; SI is the DC transmission operation and maintenance abnormal data identification index of the DC transmission operation and maintenance data during the monitoring period, which is used to evaluate the problems in the quality collection process of the DC transmission operation and maintenance data; SPR is the operation and maintenance fault warning priority labeling rate of the DC transmission operation and maintenance data during the monitoring period, which measures the proportion of fault warnings with clear priority or recommendation order among all solutions recorded in the corpus; SPR0 is the DC transmission operation and maintenance database. The preset operation and maintenance fault warning priority labeling rate threshold obtained; SF is the data sampling frequency of the DC transmission operation and maintenance data within the monitoring period; SF0 is the preset data sampling frequency threshold obtained in the DC transmission operation and maintenance database; ODP is the number of DC transmission operation and maintenance data points whose sampling interval of the DC transmission operation and maintenance data within the monitoring period exceeds the sampling interval threshold; TDP is the total number of DC transmission operation and maintenance data points within the monitoring period; ρ1 is the preset data adjustment verification record rate compensation obtained in the DC transmission operation and maintenance database; ρ2 is the preset DC transmission operation and maintenance abnormal data identification index compensation obtained in the DC transmission operation and maintenance database; ρ3 is the preset operation and maintenance fault warning priority labeling rate compensation obtained in the DC transmission operation and maintenance database; ρ4 is the preset data sampling frequency compensation obtained in the DC transmission operation and maintenance database.

[0034] A mapping table of compensation amounts is obtained from a database. For example, a table is constructed based on historical data adjustment verification record rates, the DC transmission operation and maintenance abnormal data identification index, the operation and maintenance fault warning priority labeling rate, and the corresponding compensation amounts. This table includes compensation amounts for the data adjustment verification record rate, the DC transmission operation and maintenance abnormal data identification index, the operation and maintenance fault warning priority labeling rate, and the data sampling frequency. This mapping table defines a clear set of association rules that converts the specific values ​​of the data adjustment verification record rate, the DC transmission operation and maintenance abnormal data identification index, the operation and maintenance fault warning priority labeling rate, and the data sampling frequency into their corresponding compensation amounts. This mechanism effectively achieves dynamic acquisition of compensation amounts, whether achieving a one-to-one exact match or aggregating multiple parameters into a many-to-one relationship.

[0035] A higher DC transmission operation and maintenance abnormal data identification index can provide richer data, help to more comprehensively verify the adjustment effect, and thus improve the data adjustment verification record rate; a higher data sampling frequency means that more data points are collected, and the DC transmission operation and maintenance abnormal data identification index is higher; a higher operation and maintenance fault warning priority labeling rate means that the solutions recorded in the corpus have a clear priority or recommended order, which helps to quickly select and verify the optimal solution after parameter adjustment, thereby improving the data adjustment verification record rate.

[0036] There is a positive correlation between the data adjustment verification record rate and the effective value of data quality. A higher data adjustment verification record rate means that more adjustment events have been verified and recorded, and the effective value of data quality is greater. There is a positive correlation between the DC transmission operation and maintenance abnormal data identification index and the effective value of data quality. A higher DC transmission operation and maintenance abnormal data identification index means that the data sampling interval is more reasonable, the data points are more sufficient, and the effective value of data quality is greater. There is a positive correlation between the operation and maintenance fault warning priority marking rate and the effective value of data quality. A higher operation and maintenance fault warning priority marking rate means that the selection and execution of solutions are more based on evidence, the quality of operation and maintenance decisions is higher, and the effective value of data quality is greater. There is a positive correlation between the data sampling frequency and the effective value of data quality. A higher data sampling frequency means more timely data collection and a greater effective value of data quality.

[0037] Furthermore, the specific analysis steps of the data timeliness impact value are as follows: compensating the data update-verification cycle synchronization rate threshold and the data update-verification cycle synchronization rate ratio analysis result by the data update-verification cycle synchronization rate compensation amount to obtain a first data timeliness impact value component; compensating the data sampling frequency threshold and the data sampling frequency ratio analysis result by the data sampling frequency compensation amount to obtain a second data timeliness impact value component; compensating the corpus update frequency threshold and the corpus update frequency ratio analysis result by the corpus update frequency compensation amount to obtain a third data timeliness impact value component; compensating the DC transmission operation and maintenance task delay and the DC transmission operation and maintenance task delay threshold ratio analysis result by the DC transmission operation and maintenance task delay compensation amount to obtain a fourth data timeliness impact value component; deriving the data timeliness impact value by coupling each data timeliness impact value component; the data timeliness impact value represents quantitative data of the degree to which the DC transmission operation and maintenance system is affected by the DC transmission operation and maintenance data timeliness, and the above-mentioned ratio analysis represents a division operation.

[0038] In this embodiment, the specific expression of the data timeliness impact value of the DC transmission operation and maintenance data within the monitoring period is:

[0039]

[0040] σ1+σ2+σ3+σ4=1;

[0041] DTL is the data timeliness impact value of the DC transmission operation and maintenance data within the monitoring period; UVS0 is the preset data update-verification cycle synchronization rate threshold obtained from the DC transmission operation and maintenance database; UVS is the data update-verification cycle synchronization rate of the DC transmission operation and maintenance data within the monitoring period, expressed as the ratio of the number of successfully synchronized data update-verification operations to the total number of data update-verification operations; DSF0 is the preset data sampling frequency threshold obtained from the DC transmission operation and maintenance database; DSF is the data sampling frequency of the DC transmission operation and maintenance data within the monitoring period; CUF0 is the preset corpus update frequency obtained from the DC transmission operation and maintenance database threshold; CUF is the corpus update frequency of the DC transmission operation and maintenance data within the monitoring period; TD is the DC transmission operation and maintenance task delay of the DC transmission operation and maintenance data within the monitoring period; TD0 is the preset DC transmission operation and maintenance task delay threshold obtained from the DC transmission operation and maintenance database; σ1 is the preset data update-verification cycle synchronization rate compensation obtained from the DC transmission operation and maintenance database; σ2 is the preset data sampling frequency compensation obtained from the DC transmission operation and maintenance database; σ3 is the preset corpus update frequency compensation obtained from the DC transmission operation and maintenance database; σ4 is the preset DC transmission operation and maintenance task delay compensation obtained from the DC transmission operation and maintenance database.

[0042] A mapping table of compensation amounts is obtained from a database. For example, a table is constructed based on historical data update-verification cycle synchronization rates, data sampling frequencies, corpus update frequencies, and DC transmission operation and maintenance task delays, and corresponding compensation amounts. This table defines a clear set of association rules that convert the specific values ​​of the data update-verification cycle synchronization rate, data sampling frequencies, corpus update frequencies, and DC transmission operation and maintenance task delays into their corresponding compensation amounts. This mechanism effectively achieves dynamic acquisition of compensation amounts, whether achieving a one-to-one exact match or aggregating multiple parameters into a many-to-one relationship.

[0043] A higher data sampling frequency means that data is collected in a timely manner, which can provide a more accurate and real-time data basis for data updating and verification, and help improve the synchronization rate of the data update-verification cycle; a higher data sampling frequency means that new operation and maintenance data can be obtained more frequently, thereby providing more and more timely data sources for corpus updates, and the higher the corpus update frequency; a higher corpus update frequency means that the operation and maintenance knowledge base can reflect the latest operation and maintenance experience and knowledge more promptly, which helps to reduce the delay of operation and maintenance tasks, and the lower the delay of DC transmission operation and maintenance tasks.

[0044] There is a negative correlation between the data update-verification cycle synchronization rate and the data timeliness impact value. A higher data update-verification cycle synchronization rate means that the data update and verification cycles are consistent, and the data timeliness impact value is lower. There is a negative correlation between the data sampling frequency and the data timeliness impact value. A higher data sampling frequency means more frequent data collection, which can reflect the system status more promptly, and the data timeliness impact value is lower. There is a negative correlation between the corpus update frequency and the data timeliness impact value. A higher corpus update frequency means that the information in the operation and maintenance knowledge base is updated more promptly, which can reflect the latest operation and maintenance experience and knowledge, and the data timeliness impact value is lower. There is a positive correlation between the delay of DC transmission operation and maintenance tasks and the data timeliness impact value. A higher delay of DC transmission operation and maintenance tasks means that the operation and maintenance tasks are not completed on time, and the data timeliness impact value is greater.

[0045] Furthermore, the specific determination process for determining whether to optimize the DC transmission operation and maintenance data based on the obtained quantitative results is as follows: comparing the effective value of the data quality of the DC transmission operation and maintenance data within the safety monitoring period with the data quality threshold: if the effective value of the data quality of the DC transmission operation and maintenance data within the safety monitoring period is greater than or equal to the data quality threshold, the data quality optimization method is not triggered; if the effective value of the data quality of the DC transmission operation and maintenance data within the safety monitoring period is less than the data quality threshold, the data quality optimization method is triggered.

[0046] Specifically, the specific steps of triggering the data quality optimization method are: comparing the obtained data quality threshold with the data quality effective value to obtain the data quality effectiveness deviation value, which is used to reflect the difference in the degree of influence of the DC transmission operation and maintenance data quality on the DC transmission operation and maintenance system; if the obtained data quality effectiveness deviation value is less than or equal to the safety data quality effectiveness deviation threshold in the DC transmission operation and maintenance database, it will not be processed temporarily, and continuous monitoring is required without active intervention; if the data quality effectiveness deviation value is within the data quality safety range, dynamic resource allocation adjustment and semi-automatic priority labeling adjustment are triggered. Dynamic resource allocation adjustment means allocating bandwidth to the DC transmission operation and maintenance data collection task by mapping the bandwidth configuration obtained by mapping the data quality effectiveness deviation value to the preset DC transmission operation and maintenance database. The specific process of semi-automatic priority labeling adjustment is: if the data quality effectiveness deviation value is lower than the safety threshold, the retrieval weight is adjusted according to the obtained retrieval weight adjustment amount. Adjustment is performed and it is determined whether the re-acquired data quality validity deviation value is within the data quality safety interval. If so, no additional processing is performed. Otherwise, an alarm is automatically triggered and the operation and maintenance personnel are notified to upgrade the priority to the preset highest priority for processing. The retrieval weight adjustment amount represents the result of mapping the acquired data quality deviation and the data quality validity deviation value into the preset DC transmission operation and maintenance database. The data quality safety interval represents the range corresponding to the data quality validity deviation value being greater than the safety data quality validity deviation value threshold and less than the preset data quality validity deviation value threshold. If the data quality validity deviation value is greater than or equal to the preset data quality validity deviation value threshold, backfilling of missing data is triggered. Backfilling of missing data means filling in the missing DC transmission operation and maintenance data with data automatically generated by analyzing historical logs, and determining whether the data quality validity deviation value re-acquired within the preset time interval is still greater than or equal to the preset data quality validity deviation value threshold. If so, the system-level fuse mechanism is triggered and an alarm is issued to the operation and maintenance team. If not, the occupied resources are released.

[0047] In this embodiment, if Figure 3As shown, it is a flow chart of the data quality optimization method for the corpus construction and updating system for DC transmission operation and maintenance provided by the embodiment of the present application. The initial judgment is made based on the comparison between the data quality effective value and the threshold, and then the deviation value analysis stage is entered. According to the degree of deviation of the deviation value from the safety threshold and the preset threshold (divided into three levels: the deviation value is less than or equal to the safety threshold, the deviation value is between the safety threshold and the preset threshold, and the deviation value is greater than the preset threshold), the safety threshold is the safety data quality effectiveness deviation value threshold, and the preset threshold is the preset data quality effectiveness deviation threshold, which triggers "temporarily not processing, continuous monitoring", "dynamic resource allocation adjustment" and "system level" respectively. Operations such as "fuse and data repair" are carried out; dynamic resource allocation includes bandwidth allocation and semi-automatic priority labeling adjustment. Semi-automatic priority labeling adjustment determines whether the deviation value is lower than the safety threshold to adjust the retrieval weight. If not, an alarm is triggered. If so, the deviation value is re-obtained to determine whether it is within the safety range. If so, no additional processing is performed to ensure the stability of the high-priority DC transmission operation and maintenance data channel; by determining whether the deviation value within the preset interval is greater than or equal to the preset threshold, if so, the missing data is backfilled to trigger the system fuse mechanism. If not, resources are released to improve the response efficiency of data quality issues and system reliability.

[0048] It should be explained that there is a negative correlation between the data quality validity deviation value and the data quality effective value. The larger the data quality validity deviation value, the greater the gap between the data quality effective value and the data quality threshold, which means that the worse the quality of DC transmission operation and maintenance data, the smaller the data quality effective value. The data quality validity deviation value represents the difference between the data quality threshold and the data quality effective value. The data quality validity deviation value is within the data quality validity safety interval, which does not include the endpoints. The preset data quality validity deviation value threshold represents the maximum upper limit of the data quality validity deviation value. The safety data quality validity deviation value threshold represents the benchmark value for measuring the degree of data quality validity deviation. The safety data quality validity deviation value threshold is less than the preset data quality validity deviation value threshold. If the data quality validity deviation value is within the data quality safety interval, data quality validity sub-priority optimization is triggered. Through dynamic resource allocation adjustment and semi-automatic priority labeling adjustment, the specific steps of dynamic resource allocation adjustment are as follows: based on the current data quality validity deviation value, the bandwidth corresponding to the preset data quality validity deviation value in the database is obtained as the bandwidth allocated to the DC transmission operation and maintenance data collection task; an adjustment value is matched based on the data quality validity deviation value. The adjustment value is determined based on the amount of deviation from the data quality effective value; and this adjustment value is added to the original bandwidth size of the DC transmission operation and maintenance data collection task to increase the bandwidth of the DC transmission operation and maintenance data collection task. The matching process is to establish a mapping relationship based on the data quality validity deviation value and the adjustment value. For example, the calculated data quality validity deviation value is used as input, and the corresponding adjustment value is found according to the mapping relationship to ensure that the sampling frequency of the converter of key equipment reaches the preset standard sampling rate, avoiding incomplete data collection or reduced frequency due to resource competition. During non-fault periods, the bandwidth priority of the corpus update task is reduced by one level (from QoS level 3 to level 4), freeing up bandwidth resources for the DC transmission operation and maintenance monitoring task, ensuring that the monitoring data can be transmitted in a timely and complete manner.

[0049] The Drools rule engine is used to generate priority recommendations based on preset rules. Priority labeling rules are defined in the Drools rule engine. The input solution data is matched and inferred according to the defined rules to generate priority recommendations. At the same time, the retrieval weight of unlabeled DC transmission operation and maintenance solutions in the corpus is temporarily reduced to improve the utilization rate of labeled solutions. In the corpus retrieval system, each solution is assigned a retrieval weight in the initial stage to control the ranking of the solution in the retrieval results. The higher the weight, the higher the ranking of the solution in the retrieval results. Solutions without priority labels are identified and their retrieval weight is reduced to the preset retrieval weight threshold. When operation and maintenance personnel perform solution retrieval, the ranking of unlabeled solutions will be reduced, and the ranking of labeled solutions will be relatively improved. The retrieval weight of the labeled solutions is dynamically adjusted according to the data quality validity deviation value. If the data quality validity deviation value of the DC transmission operation and maintenance solution is less than the safety data quality validity deviation value threshold, it means that the data quality validity of the solution is high, and its retrieval weight can be increased to the preset upper limit retrieval weight.

[0050] If the data quality validity deviation value of a scheme is greater than the safety data quality validity deviation value threshold and the data quality validity deviation value is less than the preset data quality validity deviation value threshold, even though the scheme has been marked with a priority, its weight needs to be reduced to the preset lower limit search weight to reflect the potential problem of its data quality validity. The search weight is a numerical standard used to measure and rank the importance of different DC transmission operation and maintenance schemes, which determines the order and priority of the DC transmission operation and maintenance schemes in the search results. Based on the current data quality validity deviation value, the search weight corresponding to the preset data quality validity deviation value in the database is obtained as the search weight adjustment value. An adjustment value is matched based on the data quality validity deviation value. The adjustment value is determined based on the amount of data quality validity value deviation. This adjustment value is added to the original search weight to increase or decrease the search weight. The matching process is based on the mapping relationship established between the data quality validity deviation value and the adjustment value. For example, the calculated data quality validity deviation value is used as input and the corresponding adjustment value is found according to the mapping relationship.

[0051] If the data quality validity deviation value is greater than or equal to the preset data quality validity deviation value threshold, the missing data will be backfilled. For missing DC transmission operation and maintenance data, supplementary data will be automatically generated by analyzing historical logs. The data quality validity deviation value will be refreshed once every preset refresh time threshold, and the monitoring results will be displayed in real time on the Grafana large screen. If the data quality validity deviation value is not less than the preset data quality validity deviation value threshold within the preset time, the system-level circuit breaker mechanism will be triggered and an alarm notification will be sent to the operation and maintenance team.

[0052] Furthermore, judging whether to perform DC transmission operation and maintenance data optimization based on the obtained quantitative results also includes: comparing the data timeliness impact value of the DC transmission operation and maintenance data within the safety monitoring period with the data timeliness threshold; if the data timeliness impact value of the DC transmission operation and maintenance data within the safety monitoring period is lower than or equal to the data timeliness threshold, then the data timeliness optimization method is not triggered; if the data timeliness impact value of the DC transmission operation and maintenance data within the safety monitoring period is greater than the data timeliness threshold, then the data timeliness optimization method is triggered.

[0053] Specifically, the specific steps of triggering the data timeliness optimization method are as follows: comparing the obtained data timeliness impact value with the data timeliness threshold to obtain the data timeliness deviation value, which is used to reflect the degree of difference in the timeliness of the DC transmission operation and maintenance data; if the obtained data timeliness deviation value is less than or equal to the safety data timeliness deviation threshold and the DC transmission operation and maintenance task delay is less than or equal to the safety DC transmission operation and maintenance task delay threshold, it will not be processed temporarily; if the obtained data timeliness deviation value is within the data validity safety interval and the DC transmission operation and maintenance task delay is within the DC transmission operation and maintenance task delay safety interval, dynamic adjustment of bandwidth allocation is triggered. Dynamic adjustment of bandwidth allocation means that the bandwidth allocation for monitoring the DC transmission operation and maintenance task will be increased to the maximum bandwidth upper limit threshold during the fault period, and the bandwidth allocation will be reduced to the minimum bandwidth upper limit threshold during the non-fault period. The data validity safety interval indicates The data timeliness deviation value is greater than the safe data timeliness deviation threshold and less than the range corresponding to the preset data timeliness deviation threshold. The DC transmission operation and maintenance task delay safety interval indicates that the DC transmission operation and maintenance task delay is greater than the safe DC transmission operation and maintenance task delay threshold and less than the range corresponding to the preset DC transmission operation and maintenance task delay threshold. If the obtained data timeliness deviation value is greater than the preset data timeliness deviation threshold and the DC transmission operation and maintenance task delay is greater than the preset DC transmission operation and maintenance task delay threshold, then the preset highest priority optimization of data timeliness is triggered. The preset highest priority optimization of data timeliness means that after the DC transmission operation and maintenance data timeliness and DC transmission operation and maintenance task delay adjustments are verified, a corpus update is triggered to determine whether the DC transmission operation and maintenance system failure rate obtained after the update is greater than the preset failure rate threshold. If so, the system automatically rolls back to the previous version and restores the data. Otherwise, no additional processing is performed.

[0054] In this embodiment, if Figure 4As shown, it is a flow chart of the data timeliness optimization method for the corpus construction and update system for DC transmission operation and maintenance provided by the embodiment of the present application, calculating the data timeliness deviation value, judging whether the data timeliness deviation value is less than or equal to the safety threshold (the safety threshold is the safety data timeliness deviation threshold), if so, judging whether the task delay threshold is less than or equal to the safety threshold (the safety threshold is the safety DC transmission operation and maintenance task delay threshold), if so, then temporarily not processing; if the data timeliness deviation value is greater than the safety threshold and the task delay is greater than the safety threshold, then further judging whether the data timeliness deviation value is within the validity safety interval, and then judging whether the task delay is within the delay safety interval, ... within the delay safety interval, then further judging whether the data timeliness deviation value is less than or equal to the safety threshold, and then judging whether the task delay is within the delay safety interval, if the data timeliness deviation value is less than or equal to the safety threshold, then judging whether the task timeliness deviation value is less than or equal to the safety threshold, If the data timeliness deviation value is within the validity safety range and the task delay is within the delay safety range, the bandwidth allocation is dynamically adjusted, increasing the bandwidth to the upper limit during the fault period and reducing the bandwidth to the lower limit during the non-fault period. If the data timeliness deviation value is not within the validity safety range and the task delay is not within the delay safety range, it is determined whether the deviation value is greater than the preset threshold (the preset threshold is the preset data timeliness deviation threshold). If so, the highest priority optimization is triggered and the adjustment of data timeliness and DC transmission operation and maintenance task delay is verified. Based on whether the failure rate after the update is greater than the threshold, it is decided whether to automatically roll back to the previous version or maintain the current version to improve data timeliness and DC transmission system stability.

[0055] It should be explained that there is a positive correlation between the data timeliness deviation value and the data timeliness impact value. The larger the data timeliness deviation value, the lower the real-time nature or update speed of the DC transmission operation and maintenance data, the worse the degree of synchronization with actual conditions, and the greater the data timeliness impact value. The data validity safety interval represents the range within which the data timeliness deviation value is greater than the safety data timeliness deviation threshold and less than the preset data timeliness deviation threshold. The safety data timeliness deviation threshold represents a baseline value based on the degree of deviation of the data timeliness deviation value, and the preset data timeliness deviation threshold represents the maximum upper limit of the degree of deviation of the data timeliness deviation value. The DC transmission operation and maintenance task delay safety interval represents the range within which the DC transmission operation and maintenance task delay is greater than the safety DC transmission operation and maintenance task delay threshold and less than the preset DC transmission operation and maintenance task delay threshold. The safety DC transmission operation and maintenance task delay threshold represents a baseline value based on the DC transmission operation and maintenance task delay, and the preset DC transmission operation and maintenance task delay threshold represents the maximum acceptable DC transmission operation and maintenance task delay value. If the obtained data timeliness deviation value is within the data validity safety interval and the DC transmission operation and maintenance task delay is within the DC transmission operation and maintenance task delay safety interval, the dynamic adjustment of bandwidth allocation is triggered. The specific steps for triggering dynamic adjustment of bandwidth allocation are: Since the bandwidth requirements during the fault period and the non-fault period are different, it is necessary to dynamically adjust the bandwidth allocation according to actual needs. The monitoring task during the fault period needs to occupy more bandwidth, while the bandwidth allocation can be reduced during the non-fault period. During the fault period, the bandwidth allocation for monitoring the DC transmission operation and maintenance task is increased to the maximum upper limit of the DC transmission operation and maintenance task bandwidth threshold obtained in the database to ensure that key data can be transmitted in time, reduce the data timeliness deviation value and Task delay; During non-fault periods, the bandwidth allocation for monitoring DC transmission operation and maintenance tasks is reduced to the minimum lower limit DC transmission operation and maintenance task bandwidth threshold obtained in the database, releasing excess bandwidth for use by other tasks, further optimizing resource allocation, and reducing DC transmission operation and maintenance task delays. Verification is performed based on data quality validity adjustment, data timeliness deviation value, and DC transmission operation and maintenance task delay adjustment; if the obtained data timeliness deviation value is greater than the preset data timeliness deviation threshold and the DC transmission operation and maintenance task delay is greater than the preset DC transmission operation and maintenance task delay threshold, then the data timeliness preset highest priority optimization is triggered. The specific steps for triggering the data timeliness preset highest priority optimization are as follows: Figure 5As shown, this is a corpus verification result interface diagram of the corpus construction and update system for DC transmission operation and maintenance provided by an embodiment of the present application. When the data timeliness deviation value and the DC transmission operation and maintenance task delay adjustment are verified, the corpus update is immediately triggered, and the corpus is fully scanned within the preset scanning time. The current corpus is compared with the results of the previous scan to identify newly added, modified, or deleted triples. For example, if the amount of new data added per hour accounts for a percentage of the total corpus, then this percentage data is the change data. The identified change data is marked for subsequent incremental update operations. The part that needs to be updated is extracted from the marked change data, and the extracted change data is transmitted to the DC transmission system, such as Figure 6 As shown, this is a corpus update interface diagram of a corpus construction and update system for DC transmission operation and maintenance provided by an embodiment of the present application. When applying updates in the DC transmission system, the changed data is merged into the existing corpus, and only the changed data is transmitted and only the changed triples are updated instead of a full update. If the system failure rate is greater than the preset failure rate threshold within the preset time threshold after the update, it will automatically roll back to the previous version, restore the parameters and corpus data of the previous version from the backup, and load the rolled-back corpus to ensure that the system runs stably before the rollback process ends.

[0056] Furthermore, the specific analysis steps for updating the corpus are as follows: comparing the DC transmission operation and maintenance data in the corpus with the DC transmission operation and maintenance data after the DC transmission operation and maintenance data is optimized to obtain DC transmission operation and maintenance deviation data, where the DC transmission operation and maintenance deviation data indicates data indicating deviations between the DC transmission operation and maintenance data in the corpus and the DC transmission operation and maintenance data after the DC transmission operation and maintenance data is optimized; updating the corpus based on the DC transmission operation and maintenance deviation data, and adding the DC transmission operation and maintenance deviation data to the corpus; after completing the corpus update, verifying the updated corpus using the DC transmission operation and maintenance deviation data to determine whether the updated corpus passes verification, that is, whether the DC transmission operation and maintenance data in the corpus is consistent with the DC transmission operation and maintenance data after the DC transmission operation and maintenance data is optimized; if they are consistent, it indicates that the updated corpus passes verification and is not updated; otherwise, the corpus is iteratively updated based on the DC transmission operation and maintenance deviation data in the updated corpus.

[0057] like Figure 7As shown, a flowchart of a corpus construction and updating method for DC transmission operation and maintenance is provided in an embodiment of the present application. The corpus construction and updating method for DC transmission operation and maintenance provided in an embodiment of the present application includes: performing operation and maintenance monitoring on a DC transmission system, obtaining and processing DC transmission operation and maintenance raw data to obtain DC transmission operation and maintenance data, and using the DC transmission operation and maintenance data to initialize and update the constructed corpus; quantifying the quality and timeliness of the DC transmission operation and maintenance data using the DC transmission operation and maintenance data to obtain quantified results, the quantified results including a data quality effective value and a data timeliness impact value; and judging whether to perform DC transmission based on the obtained quantified results. Optimization of DC transmission operation and maintenance data. If DC transmission operation and maintenance data optimization is performed, the corpus is updated based on the optimized DC transmission operation and maintenance data. Otherwise, the corpus is updated directly based on the acquired DC transmission operation and maintenance data. DC transmission operation and maintenance data optimization means processing the DC transmission operation and maintenance data through data quality optimization methods and data timeliness optimization methods to improve the timeliness of corpus updates. The data quality optimization method means processing the DC transmission operation and maintenance data to improve the quality of the DC transmission operation and maintenance data. The data timeliness optimization method means optimizing the efficiency of DC transmission operation and maintenance data processing to improve the timeliness of DC transmission operation and maintenance data processing.

[0058] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0062] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0063] Obviously, those skilled in the art may make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations fall within the scope of the claims and their equivalents, the present invention is intended to include such modifications and variations.

Claims

1. A corpus construction and updating system for DC transmission operation and maintenance, characterized by: It includes a DC transmission operation and maintenance data acquisition and processing module, a data quality determination module, and a DC transmission operation and maintenance data optimization module: The DC transmission operation and maintenance data acquisition and processing module is used to monitor the operation and maintenance of the DC transmission system, obtain the original DC transmission operation and maintenance data, and process it to obtain DC transmission operation and maintenance data. The DC transmission operation and maintenance data is used to initialize and update the constructed corpus. The data quality determination module is used to quantify the quality and timeliness of the DC transmission operation and maintenance data respectively through the DC transmission operation and maintenance data to obtain a quantified result, wherein the quantified result includes a data quality effective value and a data timeliness impact value; The DC transmission operation and maintenance data optimization module is used to determine whether to perform DC transmission operation and maintenance data optimization based on the obtained quantification results. If the DC transmission operation and maintenance data optimization is performed, the corpus is updated based on the optimized DC transmission operation and maintenance data; otherwise, the corpus is updated directly based on the obtained DC transmission operation and maintenance data. The DC transmission operation and maintenance data optimization means processing the DC transmission operation and maintenance data through a data quality optimization method and a data timeliness optimization method to improve the timeliness of the corpus update. The data quality optimization method means improving the quality of the DC transmission operation and maintenance data by processing the DC transmission operation and maintenance data. The data timeliness optimization method means improving the timeliness of the DC transmission operation and maintenance data processing by optimizing the efficiency of the DC transmission operation and maintenance data processing.

2. The corpus construction and updating system for DC transmission operation and maintenance according to claim 1, characterized in that: The DC transmission operation and maintenance data includes data quality data and data timeliness data; The data quality data includes the data adjustment verification record rate of DC transmission operation and maintenance data within the monitoring period, the DC transmission operation and maintenance abnormal data identification index, and the operation and maintenance fault Warning priority marking rate and data sampling frequency; The data timeliness data includes the data update-verification cycle synchronization rate, data sampling frequency, corpus update frequency and DC transmission operation and maintenance task delay of the DC transmission operation and maintenance data within the monitoring period; The quality and timeliness of DC transmission operation and maintenance data are quantified by using DC transmission operation and maintenance data, and previously also include: Obtain data quality thresholds, data quality compensation amounts, data timeliness thresholds, and data timeliness compensation amounts from the constructed HVDC operation and maintenance database; The data quality thresholds include a data adjustment verification record rate threshold, an operation and maintenance fault warning priority marking rate threshold, and a data sampling frequency threshold; The data quality compensation includes compensation for data adjustment verification record rate, compensation for DC transmission operation and maintenance abnormal data identification index, compensation for operation and maintenance fault warning priority marking rate, and compensation for data sampling frequency; The data timeliness thresholds include a data update-verification cycle synchronization rate threshold, a data sampling frequency threshold, a corpus update frequency threshold, and a DC transmission operation and maintenance task delay threshold; The data timeliness compensation includes data update-verification cycle synchronization rate compensation, data sampling frequency compensation, corpus update frequency compensation and DC transmission operation and maintenance task delay compensation.

3. The corpus construction and updating system for DC transmission operation and maintenance according to claim 1, characterized in that: The specific analysis steps of the data quality effective value are: Compensating the analysis result of the ratio of the data adjustment verification record rate to the data adjustment verification record rate threshold by the data adjustment verification record rate compensation amount to obtain a first data quality effective value component; Compensating the DC transmission operation and maintenance abnormal data identification index by using the DC transmission operation and maintenance abnormal data identification index compensation amount to obtain a second data quality effective value component; The third data quality effective value component is obtained by compensating the analysis result of the ratio of the operation and maintenance fault warning priority labeling rate to the operation and maintenance fault warning priority labeling rate threshold through the operation and maintenance fault warning priority labeling rate compensation amount; Compensating the analysis result of the ratio of the data sampling frequency to the data sampling frequency threshold by the data sampling frequency compensation amount to obtain a fourth data quality effective value component; The data quality effective value is obtained by coupling each data quality effective value component; The data quality effective value represents quantitative data of the degree to which the DC transmission operation and maintenance system is affected by the effectiveness of the DC transmission operation and maintenance data quality.

4. The corpus construction and updating system for DC transmission operation and maintenance according to claim 1, characterized in that: The specific analysis steps of the data timeliness impact value are as follows: Compensating the data update-verification cycle synchronization rate threshold and the data update-verification cycle synchronization rate ratio analysis result by using the data update-verification cycle synchronization rate compensation amount to obtain a first data timeliness impact value component; Compensating the data sampling frequency threshold and the data sampling frequency ratio analysis result by the data sampling frequency compensation amount to obtain a second data timeliness impact value component; Compensating the corpus update frequency threshold and the analysis result of the corpus update frequency ratio by the corpus update frequency compensation amount to obtain a third data timeliness impact value component; Compensating the analysis result of the ratio of the DC transmission operation and maintenance task delay to the DC transmission operation and maintenance task delay threshold by the DC transmission operation and maintenance task delay compensation amount to obtain a fourth data timeliness impact value component; The data timeliness impact value is obtained by coupling the data timeliness impact value components; The data timeliness impact value represents quantitative data of the degree to which the DC transmission operation and maintenance system is affected by the timeliness of the DC transmission operation and maintenance data.

5. The corpus construction and updating system for DC transmission operation and maintenance according to claim 1, characterized in that: The specific determination process of whether to perform DC transmission operation and maintenance data optimization based on the obtained quantitative results is as follows: Compare the effective data quality value of DC transmission operation and maintenance data within the safety monitoring period with the data quality threshold: If the effective data quality value of the DC transmission operation and maintenance data within the safety monitoring period is greater than or equal to the data quality threshold, the data quality optimization method will not be triggered; If the effective value of the data quality of the DC transmission operation and maintenance data within the safety monitoring period is less than the data quality threshold, the data quality optimization method is triggered.

6. The corpus construction and updating system for DC transmission operation and maintenance according to claim 5, characterized in that: The specific steps of the trigger data quality optimization method are: Comparing the obtained data quality threshold with the data quality effective value to obtain a data quality effectiveness deviation value, wherein the data quality effectiveness deviation value is used to reflect the difference in the degree of impact of the HVDC operation and maintenance data quality on the HVDC operation and maintenance system; If the obtained data quality validity deviation value is less than or equal to the safety data quality validity deviation threshold in the DC transmission operation and maintenance database, no processing will be done temporarily; If the data quality validity deviation value is within the data quality safety range, dynamic resource allocation adjustment and semi-automatic priority labeling adjustment are triggered. The dynamic resource allocation adjustment means allocating bandwidth to the DC transmission operation and maintenance data collection task based on the bandwidth configuration obtained by mapping the data quality validity deviation value. The bandwidth configuration means inputting the acquired data quality deviation value into the preset DC transmission operation and maintenance database for mapping. The specific process of semi-automatic priority labeling adjustment is as follows: If the data quality validity deviation value is lower than the safety threshold, the retrieval weight is adjusted according to the obtained retrieval weight adjustment amount, and it is determined whether the re-acquired data quality validity deviation value is within the data quality safety range. If so, no additional processing is performed; otherwise, an alarm is automatically triggered and the operation and maintenance personnel are notified to upgrade the priority to the preset highest priority for processing. The retrieval weight adjustment amount represents the result of mapping the acquired data quality deviation value into the preset DC transmission operation and maintenance database. The data quality safety range represents the range within which the data quality validity deviation value is greater than the safety data quality validity deviation value threshold and less than the preset data quality validity deviation threshold. If the data quality validity deviation value is greater than or equal to the preset data quality validity deviation value threshold, backfilling of missing data is triggered. Backfilling of missing data means filling in the missing DC transmission operation and maintenance data with data automatically generated by analyzing historical logs, and judging whether the data quality validity deviation value re-acquired within the preset time interval is still greater than or equal to the preset data quality validity deviation value threshold. If so, the system-level fuse mechanism is triggered and an alarm is issued to the operation and maintenance team. If not, the occupied resources are released.

7. The corpus construction and updating system for DC transmission operation and maintenance according to claim 1, characterized in that: The determining whether to perform DC transmission operation and maintenance data optimization based on the obtained quantified results further includes: Compare the data timeliness impact value of HVDC operation and maintenance data within the safety monitoring period with the data timeliness threshold; If the data timeliness impact value of the HVDC operation and maintenance data within the safety monitoring period is lower than or equal to the data timeliness threshold, the data timeliness optimization method will not be triggered; If the data timeliness impact value of the HVDC operation and maintenance data within the safety monitoring period is greater than the data timeliness threshold, the data timeliness optimization method is triggered.

8. The corpus construction and updating system for DC transmission operation and maintenance according to claim 7, characterized in that: The specific steps of the trigger data timeliness optimization method are: Comparing the obtained data timeliness impact value with the data timeliness threshold to obtain a data timeliness deviation value, wherein the data timeliness deviation value is used to reflect the degree of difference in timeliness of the HVDC operation and maintenance data; If the obtained data timeliness deviation value is less than or equal to the safety data timeliness deviation threshold and the DC transmission operation and maintenance task delay is less than or equal to the safety DC transmission operation and maintenance task delay threshold, no processing will be done temporarily; If the obtained data timeliness deviation value is within the data validity safety interval and the DC transmission operation and maintenance task delay is within the DC transmission operation and maintenance task delay safety interval, dynamic adjustment of bandwidth allocation is triggered. The dynamic adjustment of bandwidth allocation means that the bandwidth allocation for monitoring the DC transmission operation and maintenance task is increased to the maximum bandwidth upper limit threshold during the fault period, and the bandwidth allocation is reduced to the minimum bandwidth upper limit threshold during the non-fault period. The data validity safety interval indicates the range within which the data timeliness deviation value is greater than the safe data timeliness deviation threshold and less than the preset data timeliness deviation threshold. The DC transmission operation and maintenance task delay safety interval indicates the range within which the DC transmission operation and maintenance task delay is greater than the safe DC transmission operation and maintenance task delay threshold and less than the preset DC transmission operation and maintenance task delay threshold. If the obtained data timeliness deviation value is greater than the preset data timeliness deviation threshold and the DC transmission operation and maintenance task delay is greater than the preset DC transmission operation and maintenance task delay threshold, the preset highest priority optimization of data timeliness is triggered. The preset highest priority optimization of data timeliness means that after the DC transmission operation and maintenance data timeliness and the DC transmission operation and maintenance task delay adjustment are verified, a corpus update is triggered. It is determined whether the DC transmission operation and maintenance system failure rate obtained after the update is greater than the preset failure rate threshold. If so, the system automatically rolls back to the previous version and restores the data. Otherwise, no additional processing is performed.

9. The corpus construction and updating system for DC transmission operation and maintenance according to claim 1, characterized in that: The specific analysis steps for updating the corpus are: Comparing the DC transmission operation and maintenance data in the corpus with the DC transmission operation and maintenance data after the DC transmission operation and maintenance data is optimized to obtain DC transmission operation and maintenance deviation data, where the DC transmission operation and maintenance deviation data indicates data that deviates between the DC transmission operation and maintenance data in the corpus and the DC transmission operation and maintenance data after the DC transmission operation and maintenance data is optimized; The corpus is updated based on the DC transmission operation and maintenance deviation data, and the DC transmission operation and maintenance deviation data is added to the corpus; After the corpus update is completed, the updated corpus is verified using the DC transmission operation and maintenance deviation data to determine whether the updated corpus passes the verification, that is, whether the DC transmission operation and maintenance data in the corpus is consistent with the DC transmission operation and maintenance data after the DC transmission operation and maintenance data is optimized. If they are consistent, it means that the updated corpus passes the verification and is not updated. Otherwise, the corpus is iteratively updated based on the DC transmission operation and maintenance deviation data in the updated corpus.

10. A corpus construction and updating method for DC transmission operation and maintenance, characterized in that: include: Performing operation and maintenance monitoring on the DC transmission system, obtaining and processing raw DC transmission operation and maintenance data to obtain DC transmission operation and maintenance data, wherein the DC transmission operation and maintenance data is used to initialize and update the constructed corpus; quantifying the quality and timeliness of the DC transmission operation and maintenance data respectively through the DC transmission operation and maintenance data to obtain quantitative results, wherein the quantitative results include a data quality effective value and a data timeliness impact value; Based on the obtained quantitative results, it is determined whether to perform DC transmission operation and maintenance data optimization. If the DC transmission operation and maintenance data optimization is performed, the corpus is updated based on the optimized DC transmission operation and maintenance data. Otherwise, the corpus is directly updated based on the obtained DC transmission operation and maintenance data. The DC transmission operation and maintenance data optimization means processing the DC transmission operation and maintenance data through a data quality optimization method and a data timeliness optimization method to improve the timeliness of the corpus update. The data quality optimization method means improving the quality of the DC transmission operation and maintenance data by processing the DC transmission operation and maintenance data. The data timeliness optimization method means improving the timeliness of the DC transmission operation and maintenance data processing by optimizing the efficiency of the DC transmission operation and maintenance data processing.

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