Power grid safety production data mapping method, device, equipment and medium
Through automated data acquisition, classification, feature extraction and feature mapping, combined with the power grid safety knowledge graph, the problems of low efficiency and insufficient accuracy of traditional power grid safety production data mapping methods are solved, and efficient and accurate data mapping and analysis are achieved.
Patent Information
- Application Number
- CN202411973165.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-30
- Publication Date
- 2025-04-25
AI Technical Summary
The traditional grid safety production data mapping method relies on manual analysis, resulting in high costs, low efficiency and insufficient accuracy.
By automatically collecting, classifying, feature extraction and feature mapping of grid safety production data, the pre-trained target feature mapping model is used to correlate and fuse data feature information with the graph features in the grid safety knowledge graph.
It significantly reduces labor costs, improves the efficiency and accuracy of data mapping, realizes intelligent management and application of data, and provides more accurate analysis and decision-making support for the safe production of the power grid.
Smart Images
Figure CN119884714A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of computer technology, and in particular to a power grid safety production data mapping method, device, equipment and medium. Background Art
[0002] With the rapid development of power systems and the improvement of their intelligence levels, the amount of grid safety production data has increased dramatically. These data are crucial to the safe and stable operation of the grid, so a data mapping method is needed to integrate, manage and analyze these data.
[0003] At present, the traditional power grid safety production data mapping method usually analyzes the power grid safety production data one by one manually, and then maps the power grid safety production data one by one. However, the traditional power grid safety production data mapping method has high labor costs and low efficiency and accuracy in power grid safety production data mapping. Summary of the invention
[0004] The present invention provides a power grid safety production data mapping method, device, equipment and medium to realize automatic mapping of power grid safety production data, thereby greatly reducing labor costs and improving the efficiency and accuracy of power grid safety production data mapping.
[0005] In a first aspect, an embodiment of the present invention provides a power grid safety production data mapping method, comprising:
[0006] Collect data from relevant data sources of power grid safety production to obtain power grid safety production data to be mapped;
[0007] Based on the data types in the power grid safety production data, classify the power grid safety production data and extract data features to obtain data feature information corresponding to each type of power grid safety production data;
[0008] Input the data feature information corresponding to each type into the pre-trained target feature mapping model for feature mapping, and determine the feature mapping relationship between the data feature information of each type and the graph features in the power grid security knowledge graph;
[0009] Based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, each type of power grid safety production data is written into the power grid safety knowledge graph to obtain a target knowledge graph after the data is written.
[0010] In a second aspect, an embodiment of the present invention further provides a power grid safety production data mapping device, comprising:
[0011] A data acquisition module is used to collect data from the associated data sources of power grid safety production and obtain the power grid safety production data to be mapped;
[0012] A data feature extraction module, used to classify and extract data features of the power grid safety production data based on the data types in the power grid safety production data, and obtain data feature information corresponding to each type of power grid safety production data;
[0013] A mapping relationship determination module is used to input the data feature information corresponding to each type into a pre-trained target feature mapping model for feature mapping, and determine the feature mapping relationship between the data feature information of each type and the graph features in the power grid security knowledge graph;
[0014] A data mapping module is used to write each type of power grid safety production data into the power grid safety knowledge graph based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, and obtain the target knowledge graph after the data is written.
[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, characterized in that the electronic device comprises: at least one processor; and
[0016] a memory communicatively connected to the at least one processor; wherein,
[0017] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power grid safety production data mapping method provided in any embodiment of the present invention.
[0018] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the power grid production safety data mapping method provided by any embodiment of the present invention when executing.
[0019] The technical solution of the embodiment of the present invention can ensure the accuracy and completeness of subsequent data mapping and analysis by collecting data from the associated data sources of power grid safety production and obtaining the power grid safety production data to be mapped, and provide a solid data foundation for power grid safety production. Based on the data type in the power grid safety production data, the power grid safety production data is classified and data feature extraction is performed to obtain the data feature information corresponding to each type of power grid safety production data, which can simplify the subsequent feature mapping process and improve the efficiency and accuracy of data mapping. The data feature information corresponding to each type is input into the pre-trained target feature mapping model for feature mapping, and the feature mapping relationship between each type of data feature information and the graph features in the power grid safety knowledge graph is determined, so that the power grid safety production data can be associated and integrated with the power grid safety knowledge graph, thereby realizing intelligent management and application of data. Based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, each type of power grid safety production data is written into the power grid safety knowledge graph to obtain the target knowledge graph after the data is written, and the power grid safety production data can be synchronized and updated with the power grid safety knowledge graph in real time, thereby ensuring the consistency and accuracy of the data. Through automated data collection, classification, feature extraction and feature mapping processes, the efficiency and accuracy of data mapping can be significantly improved, and the power grid safety production data can be associated and integrated with the power grid safety knowledge graph, which can realize intelligent management and application of data and provide more accurate analysis and decision-making support for power grid safety production.
[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS
[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0022] Figure 1 is a flowchart of a power grid safety production data mapping method provided according to Embodiment 1 of the present invention;
[0023] Figure 2 is a flow chart of a power grid safety production data mapping method provided according to Embodiment 2 of the present invention;
[0024] Figure 3is a structural schematic diagram of a power grid safety production data mapping device provided according to Embodiment 3 of the present invention;
[0025] Figure 4 It is a structural schematic diagram of an electronic device for implementing the power grid safety production data mapping method according to an embodiment of the present invention. DETAILED DESCRIPTION
[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.
[0027] It should be noted that the terms "target", "current", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0028] Embodiment 1
[0029] Figure 1 A flowchart of a method for mapping power grid safety production data is provided for the first embodiment of the present invention. This embodiment is applicable to the case of mapping power grid safety production data. Figure 1 As shown, the method can be executed by a power grid safety production data mapping device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. Figure 1 As shown, the method specifically comprises the following steps:
[0030] S110 , collecting data from data sources related to power grid safety production to obtain power grid safety production data to be mapped.
[0031] Among them, the associated data source may refer to various data sources related to the safe production of power grids. For example, the associated data source may be power grid operation logs, equipment status monitoring data, and safety event records. Power grid safe production data may refer to data collected and extracted from the associated data source and directly related to the safe production of power grids. For example, power grid safe production data may refer to real-time operation data, equipment status data, etc.
[0032] Specifically, comprehensive and accurate data collection is performed on the relevant data sources of power grid safety production, which may include power grid operation logs, equipment status monitoring data, safety event records, etc., to obtain the power grid safety production data to be mapped, thereby ensuring the accuracy and completeness of subsequent data mapping and analysis, and providing a solid data foundation for power grid safety production.
[0033] Exemplarily, S110 may include: collecting data from data sources related to power grid safety production to obtain original power grid safety production data corresponding to each data source; performing data cleaning and normalization processing on the original power grid safety production data corresponding to each data source to obtain pre-processed original power grid safety production data corresponding to each data source; performing data fusion processing on the pre-processed original power grid safety production data corresponding to each data source to obtain power grid safety production data to be mapped.
[0034] Among them, original power grid production safety data may refer to data directly obtained from sources such as power grid operation, equipment monitoring, and safety incident records. These data have not been processed or converted in any way and retain their original form and status.
[0035] Specifically, it is necessary to clarify various data sources related to power grid safety production, such as power grid operation logs, equipment status monitoring data, safety event records, external environment data, and historical data. Collect data from related data sources separately to obtain the original power grid safety production data corresponding to each data source. By collecting information from multiple data sources, it can be ensured that the obtained power grid safety production data is more comprehensive and accurate, providing a reliable basis for subsequent data analysis and decision-making. Clean the original power grid safety production data corresponding to each data source, including removing redundant data, filling missing values, and processing abnormal values. Normalize the cleaned data, that is, convert the data according to certain rules to make it meet certain standards or ranges. This helps to eliminate data differences between different data sources and improve the comparability between data. Integrate the data from multiple data sources after cleaning and normalization to form a unified data set. Use data fusion technology to associate and match data between different data sources, establish associations between data, obtain power grid safety production data to be mapped, realize comprehensive analysis and utilization of data, and provide more comprehensive information support for power grid safety production.
[0036] S120. Based on the data types in the power grid safety production data, classify the power grid safety production data and extract data features to obtain data feature information corresponding to each type of power grid safety production data.
[0037] Among them, data feature information can refer to information extracted from the data type that can reflect the nature and laws of the data.
[0038] Specifically, the collected power grid safety production data can be classified and processed according to their data types. Then, for each type of data, its key data feature information is extracted. Through classification and feature extraction, the subsequent feature mapping process can be simplified and the efficiency and accuracy of data mapping can be improved. At the same time, feature extraction also helps to explore the potential laws and patterns in the data, providing more in-depth analysis and prediction for power grid safety production.
[0039] Exemplarily, S120 may include: classifying the power grid safety production data based on the data types in the power grid safety production data to obtain target numerical data, target text data and target time data; performing feature extraction on the target numerical data, target text data and target time data respectively to obtain data feature information corresponding to each type of power grid safety production data.
[0040] Among them, target numerical data can refer to information that can be directly quantified and expressed in numerical form. Target text data can refer to information expressed in text form, usually including descriptions of power grids and equipment, status reports, fault records, etc. Target time data can refer to a type of data that records the specific time when an event occurs, which is an indispensable part of power grid safety production data. For example, target time data can include the time of occurrence of a safety event, the operating time of the equipment, the timestamp of the maintenance record, etc.
[0041] Specifically, according to the characteristics of power grid safety production data, it can be divided into target numerical data, target text data and target time data. Target numerical data can include parameters that can be directly quantified, such as voltage, current, and power. Target text data may include content containing text information such as equipment status descriptions and fault reports. Target time data can mainly record the specific time when the event occurred, which is helpful for subsequent data processing and analysis. Then, according to the different processing methods and analysis tools corresponding to different types of data, feature extraction is performed on target numerical data, target text data and target time data respectively, and data feature information corresponding to each type of power grid safety production data is obtained to provide support for the safe operation and decision-making of the power grid.
[0042] Exemplarily, feature extraction is performed on target numerical data, target text data and target time data respectively to obtain data feature information corresponding to each type of power grid safety production data, including: performing numerical feature extraction on target numerical data to obtain target numerical feature information; performing text feature extraction on target text data to obtain target text feature information; performing time feature extraction on target time data to obtain target time feature information.
[0043] Among them, the target numerical feature information may refer to the feature information extracted from the numerical data in the power grid safety production data. These feature information are usually expressed in the form of numerical values and can directly reflect the operating status, performance parameters and abnormal conditions of the power grid and equipment. The target text feature information may refer to the feature information extracted from the text data in the power grid safety production data. These feature information are usually expressed in the form of text and contain key information such as descriptions of the power grid and equipment, status reports, and fault records. The target time feature information may refer to the feature information extracted from the time data in the power grid safety production data. These feature information are usually expressed in the form of time and record the specific time point or time period when the event occurs. For example, time features such as the time of occurrence of a security event, the operating time of the equipment, and the timestamp of the maintenance record can reflect the performance and status of the power grid and equipment in different time periods.
[0044] Specifically, by performing numerical feature extraction on the target numerical data, the basic statistical features of the data, such as mean, variance, maximum, minimum, median, mode, etc., can be calculated. These features can reflect the distribution and discreteness of the data, and the target numerical feature information can be obtained, which can intuitively reflect the operating status of the power grid and equipment, and help to discover abnormal situations in a timely manner; text feature extraction is performed on the target text data, and keywords in the text are extracted. These keywords can reflect the theme and content of the text, and the target text feature information can be obtained. It can reflect the status description, fault report and other information of the power grid and equipment, which helps the staff to quickly understand the operation of the power grid; time feature extraction is performed on the target time data, and the basic statistical features of time data, such as time interval, time frequency, time distribution, etc., are calculated. These features can reflect the distribution and regularity of time data, and the target time feature information can be obtained. It can reflect the performance of the power grid and equipment in different time periods, and help to discover potential rules and patterns.
[0045] S130. Input the data feature information corresponding to each type into the pre-trained target feature mapping model for feature mapping, and determine the feature mapping relationship between the data feature information of each type and the graph features in the power grid security knowledge graph.
[0046] Among them, the target feature mapping model may refer to a model for realizing the mapping relationship between data feature information and graph features in the power grid security knowledge graph. The power grid security knowledge graph may refer to a graphical expression method for representing entities, attributes and relationships between entities in the field of power grid safety production. Graph features may refer to feature information used to represent entities and relationships in the power grid security knowledge graph. These feature information may include the type, attributes, relationships, etc. of the entity, as well as the location and connection method of these entities and relationships in the knowledge graph. The feature mapping relationship may refer to the correspondence between data feature information and graph features in the power grid security knowledge graph.
[0047] Specifically, the data feature information corresponding to each type is input into the pre-trained target feature mapping model. The model can determine the feature mapping relationship between the input data feature information and the graph features in the power grid safety knowledge graph. Through feature mapping, the power grid safety production data can be associated and integrated with the power grid safety knowledge graph, thereby realizing the intelligent management and application of data. At the same time, feature mapping also helps to improve the efficiency and accuracy of data processing, and provide more accurate analysis and decision support for power grid safety production.
[0048] S140. Based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, each type of power grid safety production data is written into the power grid safety knowledge graph to obtain the target knowledge graph after the data is written.
[0049] Among them, the target knowledge graph can refer to a graph specifically used to represent knowledge in the field of power grid safety production. It structures and encodes entities in the power grid (such as equipment, faults, events, etc.), attributes (such as equipment models, status, etc.), and relationships between them (such as the connection relationship between devices, the causal relationship between faults and events, etc.) to form a network structure with semantic information.
[0050] Specifically, each type of power grid safety production data is written into the power grid safety knowledge graph according to the feature mapping relationship. During the writing process, the accuracy and completeness of the data need to be ensured, and the target knowledge graph can be updated to reflect the latest power grid safety production status. Through the target knowledge graph, the power grid safety production data can be synchronized and updated with the power grid safety knowledge graph in real time to ensure the consistency and accuracy of the data. At the same time, the updated knowledge graph can provide more comprehensive and in-depth analysis and prediction capabilities for power grid safety production.
[0051] Exemplarily, S140 may include: determining the position and associated information of power grid safety production data in the power grid safety knowledge graph based on the feature mapping relationship between data feature information and graph features in the power grid safety knowledge graph; adding the power grid safety production data to the power grid safety knowledge graph based on the position and associated information of the power grid safety production data in the power grid safety knowledge graph, and obtaining the target knowledge graph after the data is written.
[0052] Among them, the associated information can refer to additional information related to the location and representation of power grid safety production data in the power grid safety knowledge graph. This information is usually used to establish links and relationships between data, so that the knowledge graph can more comprehensively reflect the actual operation and safety issues of the power grid.
[0053] Specifically, the extracted data feature information is matched with the graph features in the power grid safety knowledge graph. The mapping relationship between data features and graph features can be established through an algorithm. According to the mapping relationship, the specific location of the power grid safety production data in the power grid safety knowledge graph is determined. Other entities, attributes and relationships associated with the data are identified and determined. The power grid safety production data is added to the power grid safety knowledge graph according to the determined location and associated information. It should be noted that if the data contains new entities or attributes, corresponding nodes and relationships need to be created in the knowledge graph. After the data is successfully written, the version information of the power grid safety knowledge graph is updated to ensure that the knowledge graph can reflect the latest power grid safety production data, and the target knowledge graph after the data is written is obtained. The data can be easily queried, analyzed and utilized. The structured representation of the data in the knowledge graph also makes the relationship between the data clearer and easier to understand.
[0054] The technical solution of the embodiment of the present invention can ensure the accuracy and completeness of subsequent data mapping and analysis by collecting data from the associated data sources of power grid safety production and obtaining the power grid safety production data to be mapped, and provide a solid data foundation for power grid safety production. Based on the data type in the power grid safety production data, the power grid safety production data is classified and data feature extraction is performed to obtain the data feature information corresponding to each type of power grid safety production data, which can simplify the subsequent feature mapping process and improve the efficiency and accuracy of data mapping. The data feature information corresponding to each type is input into the pre-trained target feature mapping model for feature mapping, and the feature mapping relationship between each type of data feature information and the graph features in the power grid safety knowledge graph is determined, so that the power grid safety production data can be associated and integrated with the power grid safety knowledge graph, thereby realizing intelligent management and application of data. Based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, each type of power grid safety production data is written into the power grid safety knowledge graph, and the target knowledge graph after the data is written is obtained, so that the power grid safety production data and the power grid safety knowledge graph can be synchronized and updated in real time, thereby ensuring the consistency and accuracy of the data. Through automated data collection, classification, feature extraction and feature mapping processes, the efficiency and accuracy of data mapping can be significantly improved, and the power grid safety production data can be associated and integrated with the power grid safety knowledge graph, which can realize intelligent management and application of data and provide more accurate analysis and decision-making support for power grid safety production.
[0055] Embodiment 2
[0056] Figure 2 A flowchart of a power grid safety production data mapping method provided in the second embodiment of the present invention. In this embodiment, based on the above embodiments, the target feature mapping model includes: a feature extraction sub-model and a mapping relationship determination sub-model, and the step of "inputting the data feature information corresponding to each type into the pre-trained target feature mapping model for feature mapping, and determining the feature mapping relationship between each type of data feature information and the graph features in the power grid safety knowledge graph" is optimized. The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here.
[0057] See also Figure 2 Another power grid safety production data mapping method provided in this embodiment specifically includes the following steps:
[0058] S210: Collect data from data sources related to power grid safety production to obtain power grid safety production data to be mapped.
[0059] S220. Based on the data types in the power grid safety production data, classify the power grid safety production data and extract data features to obtain data feature information corresponding to each type of power grid safety production data.
[0060] S230, inputting the data feature information into the feature extraction sub-model for feature extraction, and determining the key features in the data feature information that are related to the power grid security knowledge graph.
[0061] Among them, the key features may refer to important information extracted from the power grid safety production data that can reflect the safe operation status and laws of the power grid.
[0062] Specifically, in the feature extraction sub-model, machine learning algorithms or deep learning networks are used to select features of data and extract key features related to the power grid safety knowledge graph. These features may include numerical features, time features, spatial features, etc., which can reflect the actual situation and potential risks of power grid safety production. Through feature extraction algorithms, more refined and abstract features are further extracted from data feature information. These features may include statistical features, distribution features, association features, etc., which can more deeply reveal the inherent connections and laws between data. Through data preprocessing and feature selection, redundant and noisy data can be removed, the accuracy and consistency of data can be improved, and a reliable foundation can be provided for subsequent feature mapping and data analysis. Through feature extraction, potential laws and patterns in the data can be excavated, providing more in-depth analysis and prediction capabilities for power grid safety production.
[0063] S240. Input the key features into the mapping relationship determination sub-model for relationship mapping, and determine the feature mapping relationship between each type of data feature information and the graph features in the power grid security knowledge graph.
[0064] Specifically, the key features extracted by the feature extraction sub-model are input into the mapping relationship to determine the sub-model, and matched with the graph features in the power grid security knowledge graph. According to the results of feature matching, the feature mapping relationship between each type of data feature information and the graph features in the power grid security knowledge graph is determined, and the accuracy and effectiveness of the mapping relationship can also be ensured according to the preset mapping rules or algorithms. After the mapping relationship is preliminarily determined, further optimization and adjustment can be performed. This includes adjusting the mapping rules, optimizing the algorithm parameters, etc., to improve the accuracy and robustness of the mapping relationship. By determining the sub-model through the mapping relationship, the power grid safety production data can be associated and integrated with the power grid safety knowledge graph, thereby realizing the intelligent management and application of data, and the potential value and information in the power grid safety production data can be mined, providing more accurate analysis and decision support for power grid safety production.
[0065] Exemplarily, the "mapping relationship determination sub-model" in S240 is specifically used to: determine the similarity between key features and graph features in the power grid security knowledge graph based on a preset similarity algorithm; determine the feature mapping relationship between each type of data feature information and the graph features in the power grid security knowledge graph based on the similarity and a preset similarity threshold.
[0066] The preset similarity threshold may refer to a preset critical value used to determine whether two features are similar enough to establish a mapping relationship.
[0067] Specifically, key features can be first extracted from the power grid safety production data, and these features can be numerical features, text features, or other types of features. At the same time, graph features are extracted from the power grid safety knowledge graph, and these features are usually related to entities, attributes, and relationships in the graph. The similarity between the key features and the graph features is calculated using the selected similarity algorithm. The result of the similarity calculation can be a value between 0 and 1, indicating the degree of similarity between the two features. According to the similarity calculation result and the preset similarity threshold, the feature mapping relationship between each type of data feature information and the graph features in the power grid safety knowledge graph is determined. If the similarity between the key features and the graph features is greater than or equal to the preset similarity threshold, it is considered that there is a feature mapping relationship between them, thereby determining the feature mapping relationship between each type of data feature information and the graph features in the power grid safety knowledge graph, which helps to write the power grid safety production data into the knowledge graph.
[0068] S250. Based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, each type of power grid safety production data is written into the power grid safety knowledge graph to obtain the target knowledge graph after the data is written.
[0069] The technical solution of the embodiment of the present invention, by inputting data feature information into a feature extraction sub-model for feature extraction, determines the key features in the data feature information that are related to the power grid safety knowledge graph, and can mine the potential laws and patterns in the data, providing more in-depth analysis and prediction capabilities for power grid safety production. The key features are input into the mapping relationship determination sub-model for relationship mapping, and the feature mapping relationship between each type of data feature information and the graph features in the power grid safety knowledge graph is determined, so that the power grid safety production data can be associated and integrated with the power grid safety knowledge graph, thereby realizing intelligent management and application of data. Through feature mapping and mapping relationship determination, the potential value and information in the power grid safety production data can be mined, providing more accurate analysis and decision support for power grid safety production.
[0070] Embodiment 3
[0071] Figure 3 This is a schematic diagram of the structure of a power grid safety production data mapping device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes: a data acquisition module 310, a data feature extraction module 320, a different mapping relationship determination module 330 and a data mapping module 340.
[0072] The data acquisition module 310 is used to collect data from the data sources associated with power grid safety production and obtain power grid safety production data to be mapped;
[0073] A data feature extraction module 320 is used to classify and extract data features of the power grid safety production data based on the data types in the power grid safety production data, and obtain data feature information corresponding to each type of power grid safety production data;
[0074] A mapping relationship determination module 330 is used to input the data feature information corresponding to each type into a pre-trained target feature mapping model for feature mapping, and determine the feature mapping relationship between the data feature information of each type and the graph features in the power grid security knowledge graph;
[0075] The data mapping module 340 is used to write each type of power grid safety production data into the power grid safety knowledge graph based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, and obtain the target knowledge graph after the data is written.
[0076] The technical solution of this embodiment, by collecting data from the associated data sources of power grid safety production and obtaining the power grid safety production data to be mapped, can ensure the accuracy and completeness of subsequent data mapping and analysis, and provide a solid data foundation for power grid safety production. Based on the data type in the power grid safety production data, the power grid safety production data is classified and data feature extraction is performed to obtain the data feature information corresponding to each type of power grid safety production data, which can simplify the subsequent feature mapping process and improve the efficiency and accuracy of data mapping. The data feature information corresponding to each type is input into the pre-trained target feature mapping model for feature mapping, and the feature mapping relationship between each type of data feature information and the graph features in the power grid safety knowledge graph is determined, so that the power grid safety production data can be associated and integrated with the power grid safety knowledge graph, thereby realizing intelligent management and application of data. Based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, each type of power grid safety production data is written into the power grid safety knowledge graph to obtain the target knowledge graph after the data is written, and the power grid safety production data can be synchronized and updated with the power grid safety knowledge graph in real time, thereby ensuring the consistency and accuracy of the data. Through automated data collection, classification, feature extraction and feature mapping processes, the efficiency and accuracy of data mapping can be significantly improved, and the power grid safety production data can be associated and integrated with the power grid safety knowledge graph, which can realize intelligent management and application of data and provide more accurate analysis and decision-making support for power grid safety production.
[0077] Optionally, the data acquisition module 310 is specifically used to: collect data from related data sources of power grid safety production to obtain original power grid safety production data corresponding to each data source; perform data cleaning and normalization on the original power grid safety production data corresponding to each data source to obtain pre-processed original power grid safety production data corresponding to each data source; perform data fusion processing on the pre-processed original power grid safety production data corresponding to each data source to obtain the power grid safety production data to be mapped.
[0078] Optionally, the data feature extraction module 320 includes:
[0079] A data classification unit, used for classifying the power grid safety production data based on the data types in the power grid safety production data to obtain target numerical data, target text data and target time data;
[0080] The feature extraction unit is used to extract features from the target numerical data, the target text data and the target time data respectively, so as to obtain data feature information corresponding to each type of power grid safety production data.
[0081] Optionally, the feature extraction unit is specifically used to: perform numerical feature extraction on the target numerical data to obtain target numerical feature information; perform text feature extraction on the target text data to obtain target text feature information; perform time feature extraction on the target time data to obtain target time feature information.
[0082] Optionally, the target feature mapping model includes: a feature extraction submodel and a mapping relationship determination submodel; a different mapping relationship determination module 330, which is specifically used to: input the data feature information into the feature extraction submodel for feature extraction, and determine the key features in the data feature information that are related to the power grid security knowledge graph; input the key features into the mapping relationship determination submodel for relationship mapping, and determine the feature mapping relationship between each type of data feature information and the graph features in the power grid security knowledge graph.
[0083] Optionally, the mapping relationship determination sub-model is specifically used to: determine the similarity between the key features and the graph features in the power grid security knowledge graph based on a preset similarity algorithm; determine the feature mapping relationship between each type of data feature information and the graph features in the power grid security knowledge graph based on the similarity and a preset similarity threshold.
[0084] Optionally, the data mapping module 340 is specifically used to: determine the position and related information of the power grid safety production data in the power grid safety knowledge graph based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph; add the power grid safety production data to the power grid safety knowledge graph based on the position and related information of the power grid safety production data in the power grid safety knowledge graph to obtain the target knowledge graph after the data is written.
[0085] The power grid safety production data mapping device provided in the embodiment of the present invention can execute the power grid safety production data mapping method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.
[0086] Figure 4 A schematic diagram of the structure of an electronic device 12 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as desktop computers, workstations, servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.
[0087] like Figure 4 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).
[0088] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.
[0089] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.
[0090] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4 Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.
[0091] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.
[0092] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0093] The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, implementing a power grid safety production data mapping method provided by the embodiment of the present invention, the method comprising:
[0094] Collect data from relevant data sources of power grid safety production to obtain power grid safety production data to be mapped;
[0095] Based on the data types in the power grid safety production data, classify the power grid safety production data and extract data features to obtain data feature information corresponding to each type of power grid safety production data;
[0096] Input the data feature information corresponding to each type into the pre-trained target feature mapping model for feature mapping, and determine the feature mapping relationship between the data feature information of each type and the graph features in the power grid security knowledge graph;
[0097] Based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, each type of power grid safety production data is written into the power grid safety knowledge graph to obtain a target knowledge graph after the data is written.
[0098] Of course, those skilled in the art will appreciate that the processor may also implement the technical solution of the power grid safety production data mapping method provided in any embodiment of the present invention.
[0099] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the power grid safety production data mapping method provided in any embodiment of the present invention are implemented. The method includes:
[0100] Collect data from relevant data sources of power grid safety production to obtain power grid safety production data to be mapped;
[0101] Based on the data types in the power grid safety production data, classify the power grid safety production data and extract data features to obtain data feature information corresponding to each type of power grid safety production data;
[0102] Input the data feature information corresponding to each type into the pre-trained target feature mapping model for feature mapping, and determine the feature mapping relationship between the data feature information of each type and the graph features in the power grid security knowledge graph;
[0103] Based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, each type of power grid safety production data is written into the power grid safety knowledge graph to obtain a target knowledge graph after the data is written.
[0104] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.
[0105] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0106] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
[0107] Computer program code for performing the operations of the present invention may be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0108] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.
[0109] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims
1. A power grid safety production data mapping method, characterized in that: include: Collect data from relevant data sources of power grid safety production to obtain power grid safety production data to be mapped; Based on the data types in the power grid safety production data, classify the power grid safety production data and extract data features to obtain data feature information corresponding to each type of power grid safety production data; Input the data feature information corresponding to each type into the pre-trained target feature mapping model for feature mapping, and determine the feature mapping relationship between the data feature information of each type and the graph features in the power grid security knowledge graph; Based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, each type of power grid safety production data is written into the power grid safety knowledge graph to obtain a target knowledge graph after the data is written.
2. The method according to claim 1, characterized in that The data collection of the data sources associated with power grid safety production to obtain the power grid safety production data to be mapped includes: Collect data from relevant data sources of power grid safety production, and obtain original power grid safety production data corresponding to each data source; Performing data cleaning and normalization processing on the original power grid safety production data corresponding to each data source to obtain the pre-processed original power grid safety production data corresponding to each data source; Data fusion processing is performed on the pre-processed original power grid safety production data corresponding to each data source to obtain the power grid safety production data to be mapped.
3. The method according to claim 1, characterized in that The method of classifying and extracting data features of the power grid safety production data based on the data types in the power grid safety production data to obtain data feature information corresponding to each type of power grid safety production data includes: Based on the data types in the power grid safety production data, the power grid safety production data is classified to obtain target numerical data, target text data and target time data; Feature extraction is performed on the target numerical data, the target text data and the target time data respectively to obtain data feature information corresponding to each type of power grid safety production data.
4. The method according to claim 3, characterized in that The feature extraction is performed on the target numerical data, the target text data and the target time data respectively to obtain data feature information corresponding to each type of power grid safety production data, including: Extracting numerical features of the target numerical data to obtain target numerical feature information; Performing text feature extraction on the target text data to obtain target text feature information; The time feature extraction is performed on the target time type data to obtain the target time feature information.
5. The method according to claim 1, characterized in that The target feature mapping model includes: a feature extraction sub-model and a mapping relationship determination sub-model; The data feature information corresponding to each type is input into the pre-trained target feature mapping model for feature mapping, and the feature mapping relationship between the data feature information of each type and the graph features in the power grid security knowledge graph is determined, including: Inputting the data feature information into the feature extraction sub-model to perform feature extraction, and determining key features in the data feature information related to the power grid security knowledge graph; The key features are input into the mapping relationship determination sub-model for relationship mapping to determine the feature mapping relationship between each type of data feature information and the graph features in the power grid security knowledge graph.
6. The method according to claim 5, characterized in that The mapping relationship determines the sub-model, which is specifically used for: Based on a preset similarity algorithm, determining the similarity between the key feature and the graph feature in the power grid security knowledge graph; Based on the similarity and a preset similarity threshold, a feature mapping relationship between each type of data feature information and the graph features in the power grid security knowledge graph is determined.
7. The method according to claim 1, characterized in that The step of writing each type of power grid safety production data into the power grid safety knowledge graph based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph to obtain a target knowledge graph after the data is written includes: Based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, determine the position and associated information of the power grid safety production data in the power grid safety knowledge graph; Based on the position and associated information of the power grid safety production data in the power grid safety knowledge graph, the power grid safety production data is added to the power grid safety knowledge graph to obtain a target knowledge graph after the data is written.
8. A power grid safety production data mapping device, characterized in that: include: A data acquisition module is used to collect data from the associated data sources of power grid safety production and obtain the power grid safety production data to be mapped; A data feature extraction module, used to classify and extract data features of the power grid safety production data based on the data types in the power grid safety production data, and obtain data feature information corresponding to each type of power grid safety production data; A mapping relationship determination module is used to input the data feature information corresponding to each type into a pre-trained target feature mapping model for feature mapping, and determine the feature mapping relationship between the data feature information of each type and the graph features in the power grid security knowledge graph; A data mapping module is used to write each type of power grid safety production data into the power grid safety knowledge graph based on the feature mapping relationship between the data feature information and the graph features in the power grid safety knowledge graph, and obtain the target knowledge graph after the data is written.
9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power grid safety production data mapping method described in any one of claims 1-7.
10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power grid safety production data mapping method according to any one of claims 1 to 7 when executed.