Power grid operation data statistical method, device, equipment and medium

Abstract

The invention discloses a power grid operation data statistical method, device and equipment and a medium, and the method comprises the steps: carrying out the extraction of power grid operation data of a plurality of data sources related to a power system, and obtaining the original operation data corresponding to a plurality of power grid operations in the power system; based on a preset data statistics condition, screening the original job data, and determining target job data to be counted; and performing data statistics based on the target operation data and a data statistics template corresponding to the preset data screening condition, and determining a data statistics result corresponding to the power system. Through the technical scheme of the embodiment of the invention, automatic statistical analysis of the power grid operation data is realized, so that the labor cost is reduced, the efficiency of data statistics of a power system is improved, and the safety and reliability of power grid operation are effectively guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, equipment and medium for statistically analyzing power grid operation data. Background Art

[0002] With the development of the power industry, the power system, as an important pillar of national energy security, has a great significance for the national economy and social life. However, the current power system operation involves many challenges in the statistics of power grid operation data.

[0003] At present, the statistical analysis of power grid operation data is usually carried out through manual statistics. However, the traditional statistical method of power grid operation data has high labor costs, low efficiency in statistical analysis of power grid operation data of power systems, and cannot guarantee the timeliness and accuracy of subsequent data processing, thus failing to effectively ensure the safety and reliability of power grid operation. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for statistical analysis of power grid operation data, so as to realize automatic statistical analysis of power grid operation data, thereby reducing labor costs, improving the efficiency of data statistics on the power system, and effectively ensuring the safety and reliability of power grid operation.

[0005] In a first aspect, an embodiment of the present invention provides a method for collecting statistics of power grid operation data, including:

[0006] Extracting power grid operation data from multiple data sources associated with the power system to obtain original operation data corresponding to multiple power grid operations in the power system;

[0007] Based on the preset data statistics conditions, the original operation data is screened to determine the target operation data to be counted;

[0008] Data statistics are performed based on the target operation data and the data statistics template corresponding to the preset data screening conditions to determine the data statistics results corresponding to the power system.

[0009] In a second aspect, an embodiment of the present invention further provides a power grid operation data statistics device, including:

[0010] A raw data acquisition module, used to extract power grid operation data from multiple data sources associated with the power system, and obtain raw operation data corresponding to multiple power grid operations in the power system;

[0011] A target data acquisition module is used to screen the original operation data based on preset data statistical conditions to determine the target operation data to be counted;

[0012] The data statistics module is used to perform data statistics based on the target operation data and the data statistics template corresponding to the preset data screening conditions, and determine the data statistics results corresponding to the power system.

[0013] 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

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] 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 operation data statistics method provided by any embodiment of the present invention.

[0016] 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 operation data statistics method provided by any embodiment of the present invention when executing.

[0017] The technical solution of the embodiment of the present invention extracts power grid operation data from multiple data sources associated with the power system to obtain the original operation data corresponding to multiple power grid operations in the power system, ensures the comprehensiveness and accuracy of the data, and provides a reliable basis for subsequent statistics. Based on the preset data statistical conditions, the original operation data is screened to determine the target operation data to be counted, remove irrelevant and redundant data, and improve the efficiency and accuracy of data statistics. Data statistics are performed based on the data statistical template corresponding to the target operation data and the preset data screening conditions to determine the data statistical results corresponding to the power system, which can improve the efficiency and accuracy of data statistics. By extracting data from multiple data sources associated with the power system, the comprehensiveness and accuracy of the data are guaranteed, and the original operation data is further screened to remove irrelevant and redundant data, and the efficiency and accuracy of data analysis are improved. The use of data statistical templates can conveniently adjust and optimize statistical indicators to adapt to different power grid operation requirements. While realizing automatic statistical analysis of power grid operation data, it reduces labor costs and improves the efficiency of data statistics of the power system, thereby effectively ensuring the safety and reliability of power grid operation.

[0018] 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

[0019] 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.

[0020] Figure 1 is a flow chart of a power grid operation data statistics method provided according to the first embodiment of the present invention;

[0021] Figure 2 is a flow chart of a power grid operation data statistics method provided according to the second embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of a power grid operation data statistics device provided according to Embodiment 3 of the present invention;

[0023] Figure 4 It is a structural schematic diagram of an electronic device for implementing the power grid operation data statistics method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] 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.

[0025] 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.

[0026] Embodiment 1

[0027] Figure 1 A flowchart of a method for collecting statistics of power grid operation data is provided for the first embodiment of the present invention. This embodiment is applicable to the case of collecting statistics of power grid operation data. Figure 1 As shown, the method can be executed by a power grid operation data statistics 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:

[0028] S110, extracting power grid operation data from multiple data sources associated with the power system to obtain original operation data corresponding to multiple power grid operations in the power system.

[0029] Among them, the power system may refer to a system for realizing the generation, transmission, distribution and use of electric energy. The data source may refer to various types of information systems in the power system. The power grid operation data may refer to the data generated when various operations (such as inspection, maintenance, dispatching, etc.) are performed in the power system. The power grid operation may refer to various operation activities related to the operation, maintenance, overhaul, testing, etc. of the power grid carried out in the power system. The raw power grid operation data may refer to the unprocessed power grid operation data extracted from the data source.

[0030] Specifically, identify multiple data sources associated with the power system, such as monitoring and data acquisition systems, energy management systems, distribution management systems, and fault recording systems. Establish connections with multiple data sources through appropriate interfaces or protocols. Data related to power grid operations can be extracted from each data source based on preset data extraction rules or query statements. This may include operation time, location, type, equipment involved, personnel, operation results, fault information, etc. Obtain the original operation data corresponding to multiple power grid operations in the power system. By connecting multiple data sources, it can be ensured that the extracted power grid operation data fully covers all aspects of the power system to avoid information omissions.

[0031] Exemplarily, S110 may include: extracting power grid operation data from multiple data sources associated with the power system to obtain power grid operation data corresponding to each data source associated with the power system; performing correlation analysis on the power grid operation data corresponding to each data source to generate correlation data analysis results; based on the correlation data analysis results, merging the power grid operation data corresponding to each data source to generate original operation data corresponding to multiple power grid operations in the power system.

[0032] The associated data analysis result may refer to the association relationship between different data sources obtained by performing a multi-dimensional in-depth analysis on the power operation data corresponding to each data source.

[0033] Specifically, all data sources associated with the power system are determined, which may include but are not limited to monitoring and data acquisition systems, energy management systems, distribution management systems, smart meters, sensors, etc. Then, by accessing these data sources and setting corresponding data extraction programs according to the data format and structure of each data source, the power grid operation data is extracted from the data source. At the same time, the data can also be subjected to necessary format conversion and standardization processing to ensure the consistency and comparability of the data. The results of the analysis of the associated data between different data sources are mined using the preset association algorithm. According to the results of the analysis of the associated data, the strategies and rules for data merging can be determined. These rules and strategies can take into account factors such as the source, type, and timestamp of the data to ensure the consistency and accuracy of the merged data. According to the formulated merging strategies and rules, the power grid operation data corresponding to each data source is merged to generate the original operation data corresponding to multiple power grid operations in the power system, so as to achieve data integration and unification, and provide a more comprehensive and accurate data basis for subsequent statistics and processing.

[0034] Exemplarily, correlation analysis is performed on the power grid operation data corresponding to each data source to generate correlation data analysis results, including: based on the data generation time, data generation location and data generation device in the power grid operation data corresponding to each data source, correlation analysis is performed on the power grid operation data corresponding to each data source in terms of time dimension, space dimension and equipment dimension to generate correlation data analysis results.

[0035] The data generation time may refer to the specific time point or time period when the power grid operation data is recorded or generated. The data generation location may refer to the geographical location or spatial location when the power grid operation data is recorded or generated. The data generation device may refer to the specific device or system used to record or generate the power grid operation data.

[0036] Specifically, extract the timestamp information of the power grid operation data in each data source, such as the data generation time, operation start time, operation end time, etc., sort and group the data according to time, and analyze the distribution of the power grid operation data in different time periods; extract the geographical location information of the power grid operation data in each data source, such as the substation location, line direction, equipment distribution, etc., and analyze the distribution of the power grid operation data in different geographical areas; extract the equipment information of the power grid operation data in each data source, such as equipment type, equipment ID, equipment status, etc., and classify and group the power grid operation data according to the equipment information to analyze the power grid operation data under different equipment types or equipment status; synthesize the analysis results of the time dimension, space dimension and equipment dimension to generate the analysis results of the associated data between multiple data sources. Through association analysis, data sharing and integration between different data sources can be promoted, which helps to improve data utilization.

[0037] S120: Based on preset data statistics conditions, the original operation data is screened to determine the target operation data to be counted.

[0038] The preset data statistics conditions may refer to the conditions and standards for screening, filtering or classifying data set in advance according to the statistical purpose and needs before the data statistics are performed. The target operation data may refer to the specific data that is concerned and analyzed when the data statistics are performed.

[0039] Specifically, the original operation data can be screened according to preset data statistical conditions using database query, data filtering and other technologies to obtain target operation data that meets statistical requirements. Furthermore, the screened data can be verified to ensure the accuracy and completeness of the data and avoid the impact of erroneous data on the statistical results. By setting specific screening conditions, the power grid operation data that needs to be counted can be accurately located to improve the pertinence and accuracy of the statistics.

[0040] S130. Perform data statistics based on the data statistics template corresponding to the target operation data and the preset data screening conditions to determine the data statistics results corresponding to the power system.

[0041] The data statistics template may refer to a preset data statistics framework or tool for guiding the data collection, organization, analysis and display process. The data statistics result may refer to the conclusion obtained after analyzing the collected data by applying the data statistics template.

[0042] Specifically, according to statistical requirements, a data statistics template containing statistical indicators, calculation formulas, data display methods, etc. is designed. The template should correspond to the preset data screening conditions to ensure the accuracy and consistency of the statistical results. Fill the screened target operation data into the data statistics template, perform data statistics according to the calculation formulas and indicators in the template, and determine the data statistics results corresponding to the power system, which can realize the automation and standardization of the statistical process and improve statistical efficiency.

[0043] Exemplarily, S130 may include: performing regression analysis on target operation data to determine a prediction result of a power grid operation risk level; performing data statistics on a data statistical template based on the target operation data and the prediction result of a power grid operation risk level to determine a data statistical result corresponding to the power system.

[0044] Among them, the prediction result of the grid operation risk level refers to the conclusion drawn after evaluating and predicting the risks that may be faced in future grid operations.

[0045] Specifically, the trained regression model can be used to predict the target operation data to obtain the prediction results of the power grid operation risk level. According to the data statistical template corresponding to the data statistical requirements of the power system, the power grid operation risk level prediction results and the target operation data are sorted, analyzed and displayed according to the requirements of the data statistical template to generate the data statistical results corresponding to the power system and improve statistical efficiency.

[0046] Exemplarily, based on the target operation data and the grid operation risk level prediction results, data statistics are performed on the data statistical template to determine the data statistical results corresponding to the power system, including: matching the target operation data and the grid operation risk level prediction results to determine the risk level matching results; based on the risk level matching results, entering the target operation data and the grid operation risk level prediction results into the data statistical template to determine the data statistical results corresponding to the power system.

[0047] Specifically, the target operation data is matched with the prediction results of the risk level of the power grid operation. This is usually achieved by comparing key fields in the data (such as operation time, location, equipment, etc.). According to the matching rules, the prediction results of the power grid operation risk level corresponding to each target operation data are determined. The matched target operation data and the prediction results of the power grid operation risk level are entered into the data statistics template. The entered data is counted and analyzed using the statistical tools and analysis functions in the data statistics template. Based on the results of the statistics and analysis, the corresponding data statistical results of the power system are determined. These results may be presented in the form of charts, reports, etc., to show the risk status, trends and potential problems of power grid operations, and provide strong support for the safe management and decision-making of the power system.

[0048] The technical solution of the embodiment of the present invention obtains the original operation data corresponding to multiple power grid operations in the power system by extracting power grid operation data from multiple data sources associated with the power system, ensuring the comprehensiveness and accuracy of the data, and providing a reliable basis for subsequent statistics. Based on the preset data statistical conditions, the original operation data is screened, the target operation data to be counted is determined, irrelevant and redundant data are removed, and the efficiency and accuracy of data statistics are improved. Data statistics are performed based on the data statistical template corresponding to the target operation data and the preset data screening conditions, and the data statistical results corresponding to the power system are determined, which can improve the efficiency and accuracy of data statistics. By extracting data from multiple data sources associated with the power system, the comprehensiveness and accuracy of the data are guaranteed, and the original operation data is further screened, irrelevant and redundant data are removed, and the efficiency and accuracy of data analysis are improved. The use of data statistical templates can conveniently adjust and optimize statistical indicators to adapt to different power grid operation requirements, while realizing automatic statistical analysis of power grid operation data, reducing labor costs, and improving the efficiency of data statistics of the power system, thereby effectively ensuring the safety and reliability of power grid operation.

[0049] Exemplarily, the target operation data is imported into the data statistics template corresponding to the preset data screening conditions. Specifically, the original operation data can be written into the "Smart Safety Supervision System Export Data" table in the data statistics template, and the "Management Department", "Operation Team", "Calculation of Whether There Is a Team", "Calculation of Risk Formula Output Value", "Actual Number of Workers in the Unit (including Work Leaders)" columns are added. Data statistics are performed according to the calculation formulas and indicators in the template, and the corresponding data is supplemented for subsequent data statistics. The specific supplementary logic can be as follows:

[0050] Management Department: Get the corresponding management department data according to the "Work Unit Name" column;

[0051] Operation team: According to the "Work leader" and "First person in charge on site", search for the corresponding operation team in the basic data of team members;

[0052] Calculate whether there is a team: Check whether the "work leader" and "first person in charge on site" columns are in the team basic personnel data;

[0053] Actual number of people working in this unit: When the “Work Unit” column is “This Unit”, it is set to the number of people working in this unit.

[0054] Combined with the team data in the data statistics template, the number of work items and risk levels (risk value column) that each team is responsible for are counted to generate the "Daily Plan Quantity of Team" table;

[0055] After writing the number of work items the team is responsible for and the risk level into the "Daily Plan Quantity for the Team" table, you can use a bar chart to express the data more intuitively.

[0056] The "Secondary Plan Statistics" table in the data statistics template will automatically calculate the number of work items and risk attributes that each major secondary department is responsible for based on the written data and the built-in formula.

[0057] In the "Planned Work Quantity Trend" table in the data statistics template, enter the daily work plan quantity (including today) data for this quarter, and use an area chart to display it.

[0058] Enter the risk level and job type data in the "Risk Level Distribution" table in the data statistics template, and use pie charts and bar charts to display them.

[0059] Calculate the saturation of each team in the "Saturation Calculation" table of the data statistics template as follows:

[0060] Number of work tasks (number of required work leaders): the number of "this unit" in the "work subject" column of the work items that the team is responsible for;

[0061] Number of operators required: the total number of data in the "Actual number of workers in this unit" column in the work item that the team is responsible for;

[0062] Daily work saturation of the team (work leader): the data of "Number of work tasks (number of work leaders required)" corresponding to the team divided by the "Number of team members" column corresponding to the team in the "Team Statistics" table;

[0063] Daily operation saturation (operators): The "Required number of operators" data corresponding to the team is divided by the "Number of people in the team" column corresponding to the team in the "Team Statistics" table.

[0064] The data is written into the "Overall Summary" table in the statistical template and can be converted into a picture after the PDF is generated.

[0065] Embodiment 2

[0066] Figure 2 This is a flow chart of a power grid operation data statistics method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment optimizes the step of "screening the original operation data based on preset data statistics conditions to determine the target operation data to be counted". The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here.

[0067] See also Figure 2 Another power grid operation data statistics method provided in this embodiment specifically includes the following steps:

[0068] S210: Extract power grid operation data from multiple data sources associated with the power system to obtain original operation data corresponding to multiple power grid operations in the power system.

[0069] S220: Based on the original operation data and the preset risk level, determine the number of power grid operations in the original operation data whose risk level is the preset risk level.

[0070] Among them, the preset risk level may refer to a series of level standards set in advance according to the risk management requirements of the power system for assessing the level of risk of power grid operations.

[0071] Specifically, data processing tools can be used to screen the risk levels corresponding to the original operation data, and the number of power grid operations with a risk level of a preset risk level can be counted. By determining the number of power grid operations with a risk level of a preset risk level, the risk distribution of power grid operations can be preliminarily understood.

[0072] S230: Based on the number of power grid operations with a preset risk level in the original operation data and preset data statistical conditions, the original operation data is screened to determine target operation data to be counted.

[0073] Specifically, according to the number of power grid operations with a preset risk level in the original operation data, the power grid operation data that meets the preset data statistical conditions in the original operation data are screened to obtain the target operation data to be counted, so that the target operation data to be counted can be accurately determined.

[0074] Exemplarily, S230 may include: detecting whether the number of power grid operations with medium and high risk levels in the original operation data is greater than the preset number of operations in the preset data statistical conditions; if so, determining the external power grid operation with the same operation content as the internal power grid operation, and deleting the operation data of the external power grid operation in the original operation data to obtain the target operation data to be counted; wherein, the internal power grid operation is the power grid operation that the internal equipment in the power system is responsible for, and the external power grid operation is the power grid operation that the external equipment in the power system is responsible for; if the number of power grid operations with medium and high risk levels in the original operation data is less than or equal to the preset number of operations, the original operation data is determined as the target operation data to be counted.

[0075] Specifically, the number of grid operations with medium and high risk levels in the original operation data is counted and compared with the preset number of operations. When the number of medium and high risk grid operations is greater than the preset number of operations, the grid operations (internal grid operations) responsible for the internal equipment in the power system and the grid operations (external grid operations) responsible for the external equipment are identified in the original operation data. This can usually be achieved by comparing information such as the operation location and equipment number. For the identified internal grid operations, check whether there are operations with the same content as the external grid operations. If there are internal grid operations with the same content as the external grid operations, the operation data of these internal grid operations are deleted from the original operation data, thereby avoiding repeated statistics and ensuring the uniqueness of the data, and obtaining the original operation data after deleting the repeated data, that is, the target operation data to be counted. If the number of grid operations with medium and high risk levels in the original operation data is less than or equal to the preset number of operations, no additional data processing and analysis is required. The original operation data is directly determined as the target operation data to be counted, thereby improving work efficiency.

[0076] S240: Perform data statistics based on the target operation data and the data statistics template corresponding to the preset data screening conditions to determine the data statistics results corresponding to the power system.

[0077] The technical solution of the embodiment of the present invention can clearly understand the risk distribution of power grid operations by determining the number of power grid operations with a preset risk level in the original operation data based on the original operation data and the preset risk level, and provide a basis for subsequent data screening and statistics. Based on the number of power grid operations with a preset risk level in the original operation data and the preset data statistical conditions, the original operation data is screened to determine the target operation data to be counted, which can more accurately focus on key data and improve the efficiency and accuracy of data statistics. Screening the original operation data through preset data statistical conditions helps to clarify the risk distribution of power grid operations. At the same time, by focusing on key data, the efficiency and accuracy of data statistics can be improved, providing a strong guarantee for the safe and stable operation of the power system.

[0078] Embodiment 3

[0079] Figure 3 This is a schematic diagram of the structure of a power grid operation data statistics device provided by the third embodiment of the present invention. Figure 3 As shown, the device includes: an original data acquisition module 310, a target data acquisition module 320 and a data statistics module 330.

[0080] The original data acquisition module 310 is used to extract power grid operation data from multiple data sources associated with the power system to obtain original operation data corresponding to multiple power grid operations in the power system;

[0081] The target data acquisition module 320 is used to screen the original operation data based on preset data statistical conditions to determine the target operation data to be counted;

[0082] The data statistics module 330 is used to perform data statistics based on the target operation data and the data statistics template corresponding to the preset data screening condition, and determine the data statistics results corresponding to the power system.

[0083] The technical solution of this embodiment is to extract the power grid operation data from multiple data sources associated with the power system, obtain the original operation data corresponding to multiple power grid operations in the power system, ensure the comprehensiveness and accuracy of the data, and provide a reliable basis for subsequent statistics. Based on the preset data statistical conditions, the original operation data is screened, the target operation data to be counted is determined, irrelevant and redundant data are removed, and the efficiency and accuracy of data statistics are improved. Data statistics are performed based on the data statistical template corresponding to the target operation data and the preset data screening conditions, and the data statistical results corresponding to the power system are determined, which can improve the efficiency and accuracy of data statistics. By extracting data from multiple data sources associated with the power system, the comprehensiveness and accuracy of the data are guaranteed, and the original operation data is further screened, irrelevant and redundant data are removed, and the efficiency and accuracy of data analysis are improved. The use of data statistical templates can conveniently adjust and optimize statistical indicators to adapt to different power grid operation requirements. While realizing automatic statistical analysis of power grid operation data, it reduces labor costs and improves the efficiency of data statistics of the power system, thereby effectively ensuring the safety and reliability of power grid operation.

[0084] Optionally, the original data acquisition module 310 includes:

[0085] An operation data acquisition unit, used to extract power grid operation data from multiple data sources associated with the power system, and acquire power grid operation data corresponding to each data source associated with the power system;

[0086] A data analysis unit, used to perform correlation analysis on the power grid operation data corresponding to each data source and generate correlation data analysis results;

[0087] The data merging unit is used to merge the power grid operation data corresponding to each data source based on the associated data analysis result to generate original operation data corresponding to multiple power grid operations in the power system.

[0088] Optionally, the data analysis unit is specifically used to: based on the data generation time, data generation location and data generation device in the power grid operation data corresponding to each data source, perform correlation analysis on the time dimension, space dimension and equipment dimension of the power grid operation data corresponding to each data source, and generate correlation data analysis results.

[0089] Optionally, the target data acquisition module 320 includes:

[0090] A risk operation quantity determination unit, configured to determine the quantity of power grid operations having a risk level of a preset risk level in the original operation data based on the original operation data and the preset risk level;

[0091] The target data determination unit is used to screen the original operation data based on the number of power grid operations with a preset risk level in the original operation data and a preset data statistical condition, and determine the target operation data to be counted.

[0092] Optionally, the target data determination unit is specifically used to: detect whether the number of power grid operations with medium and high risk levels in the original operation data is greater than the preset number of operations in the preset data statistical conditions; if so, determine the external power grid operation with the same operation content as the internal power grid operation, and delete the operation data of the external power grid operation in the original operation data to obtain the target operation data to be counted; wherein, the internal power grid operation is the power grid operation for which the internal equipment in the power system is responsible, and the external power grid operation is the power grid operation for which the external equipment in the power system is responsible; if the number of power grid operations with medium and high risk levels in the original operation data is less than or equal to the preset number of operations, the original operation data is determined as the target operation data to be counted.

[0093] Optionally, the data statistics module 330 includes:

[0094] A prediction result determination unit, used to perform regression analysis on the target operation data to determine a prediction result of a power grid operation risk level;

[0095] A data statistics unit is used to perform data statistics on a data statistics template based on the target operation data and the power grid operation risk level prediction result to determine the data statistics result corresponding to the power system.

[0096] Optionally, the data statistics unit is specifically used to: match the target operation data with the power grid operation risk level prediction result to determine the risk level matching result; based on the risk level matching result, enter the target operation data and the power grid operation risk level prediction result into the data statistics template to determine the data statistics result corresponding to the power system.

[0097] The power grid operation data statistics device provided in the embodiment of the present invention can execute the power grid operation data statistics method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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 4Not 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.

[0103] 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.

[0104] 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.

[0105] 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 operation data statistics method provided by the embodiment of the present invention, the method comprising:

[0106] Extracting power grid operation data from multiple data sources associated with the power system to obtain original operation data corresponding to multiple power grid operations in the power system;

[0107] Based on the preset data statistics conditions, the original operation data is screened to determine the target operation data to be counted;

[0108] Data statistics are performed based on the target operation data and the data statistics template corresponding to the preset data screening conditions to determine the data statistics results corresponding to the power system.

[0109] Of course, those skilled in the art can understand that the processor can also implement the technical solution of the power grid operation data statistics method provided by any embodiment of the present invention.

[0110] 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 operation data statistics method provided in any embodiment of the present invention are implemented. The method includes:

[0111] Extracting power grid operation data from multiple data sources associated with the power system to obtain original operation data corresponding to multiple power grid operations in the power system;

[0112] Based on the preset data statistics conditions, the original operation data is screened to determine the target operation data to be counted;

[0113] Data statistics are performed based on the target operation data and the data statistics template corresponding to the preset data screening conditions to determine the data statistics results corresponding to the power system.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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 statistical method for power grid operation data, characterized in that: include: Extracting power grid operation data from multiple data sources associated with the power system to obtain original operation data corresponding to multiple power grid operations in the power system; Based on the preset data statistics conditions, the original operation data is screened to determine the target operation data to be counted; Data statistics are performed based on the target operation data and the data statistics template corresponding to the preset data screening conditions to determine the data statistics results corresponding to the power system.

2. The method according to claim 1, characterized in that The extracting of power grid operation data from multiple data sources associated with the power system to obtain original operation data corresponding to multiple power grid operations in the power system includes: Extracting power grid operation data from multiple data sources associated with the power system, and obtaining power grid operation data corresponding to each data source associated with the power system; Perform correlation analysis on the power grid operation data corresponding to each data source to generate correlation data analysis results; Based on the analysis result of the associated data, the power grid operation data corresponding to each data source is merged to generate original operation data corresponding to multiple power grid operations in the power system.

3. The method according to claim 2, characterized in that The performing correlation analysis on the power grid operation data corresponding to each data source to generate correlation data analysis results includes: Based on the data generation time, data generation location and data generation device in the power grid operation data corresponding to each data source, the power grid operation data corresponding to each data source is subjected to correlation analysis in terms of time dimension, space dimension and device dimension to generate correlation data analysis results.

4. The method according to claim 1, characterized in that: The method of screening the original operation data based on the preset data statistics condition to determine the target operation data to be counted includes: Based on the original operation data and the preset risk level, determining the number of power grid operations in the original operation data having a risk level of the preset risk level; Based on the number of power grid operations with a risk level of a preset risk level in the original operation data and a preset data statistical condition, the original operation data is screened to determine the target operation data to be counted.

5. The method according to claim 4, characterized in that The method of screening the original operation data based on the number of power grid operations with a preset risk level in the original operation data and a preset data statistical condition to determine the target operation data to be counted includes: Detecting whether the number of power grid operations with medium or high risk levels in the original operation data is greater than the preset number of operations in the preset data statistical condition; If so, determine the external power grid operation with the same operation content as the internal power grid operation, and delete the operation data of the external power grid operation in the original operation data to obtain the target operation data to be counted; wherein the internal power grid operation is the power grid operation that the internal equipment in the power system is responsible for, and the external power grid operation is the power grid operation that the external equipment in the power system is responsible for; If the number of power grid operations with medium or high risk levels in the original operation data is less than or equal to the preset number of operations, the original operation data is determined as the target operation data to be counted.

6. The method according to claim 1, characterized in that The performing data statistics based on the data statistics template corresponding to the target operation data and the preset data screening condition to determine the data statistics result corresponding to the power system includes: Performing regression analysis on the target operation data to determine a prediction result of the power grid operation risk level; Based on the target operation data and the power grid operation risk level prediction result, data statistics are performed on the data statistical template to determine the data statistical results corresponding to the power system.

7. The method according to claim 6, characterized in that The step of performing data statistics on a data statistical template based on the target operation data and the prediction result of the risk level of the power grid operation to determine the data statistical result corresponding to the power system includes: Matching the target operation data with the power grid operation risk level prediction result to determine a risk level matching result; Based on the risk level matching result, the target operation data and the power grid operation risk level prediction result are entered into the data statistical template to determine the data statistical result corresponding to the power system.

8. A power grid operation data statistics device, characterized in that: include: A raw data acquisition module, used to extract power grid operation data from multiple data sources associated with the power system, and obtain raw operation data corresponding to multiple power grid operations in the power system; A target data acquisition module is used to screen the original operation data based on preset data statistical conditions to determine the target operation data to be counted; The data statistics module is used to perform data statistics based on the target operation data and the data statistics template corresponding to the preset data screening conditions, and determine the data statistics results corresponding to the power system.

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 operation data statistics method according to any one of claims 1 to 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 operation data statistics method according to any one of claims 1 to 7 when executed.

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