Association relation determination method and system of electric power meteorological data

Abstract

The invention provides a method and a system for determining an association relationship of electric power meteorological data. The method comprises the following steps: acquiring multiple pieces of meteorological data in a set time period; screening out a plurality of target meteorological data from the plurality of meteorological data based on the plurality of meteorological data in combination with a pre-constructed power and meteorological relationship database; analyzing each piece of target meteorological data to obtain a plurality of entities corresponding to each piece of target meteorological data; and based on a plurality of entities corresponding to each piece of target meteorological data, in combination with a pre-constructed power database, obtaining an association relationship between the power data and the meteorological data. The association relationship between the power data and the meteorological data is determined based on the plurality of entities and the power database corresponding to each piece of target meteorological data, so that the determined association relationship of the meteorological data is ensured to have accuracy, and the association relationship between the determined association relationship of the meteorological data and the power is ensured to exist; the purpose of accurately determining the association relationship of the meteorological data from the electric power angle is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method and system for determining the association relationship of electric power meteorological data. Background Art

[0002] In recent years, meteorological data has been widely used in various industries. For example, the Civil Aviation Administration of China has built the China Civil Aviation Meteorological Big Data Sharing and Service Cloud Platform, which has unified data from more than 50,000 ground meteorological observation stations, nearly 200 weather radars, multiple meteorological satellites and hundreds of lightning observation stations. The amount of meteorological data provided daily exceeds 500G. Through the integrated calculation of meteorological data and business data such as flight plans and flight routes, it can reduce operating costs and improve the safety and efficiency of aviation operations.

[0003] However, in response to the increasing amount of massive meteorological data, the current correlation relationships of meteorological data are mostly correlation analyses conducted on the meteorological data themselves. It is not appropriate to directly apply meteorological data and the correlation relationships of meteorological data to the power industry. The above data are not accurately applicable to the power industry, and no correlation relationships have been found for power-related meteorological data. Summary of the invention

[0004] In order to overcome the above-mentioned deficiencies of the prior art, in a first aspect, the present invention proposes a method for determining the correlation relationship of power meteorological data, comprising:

[0005] Get multiple meteorological data within a set time period;

[0006] Based on the plurality of meteorological data and a pre-built power and meteorological relationship database, a plurality of target meteorological data are screened out from the plurality of meteorological data;

[0007] Parsing each of the target meteorological data to obtain multiple entities corresponding to each of the target meteorological data;

[0008] Based on the multiple entities corresponding to each of the target meteorological data and in combination with a pre-constructed power database, an association relationship between the power data and the meteorological data is obtained.

[0009] Preferably, the obtaining of the association relationship between the power data and the meteorological data based on the multiple entities corresponding to each of the target meteorological data in combination with a pre-built power database includes:

[0010] Based on each of the target meteorological data, searching in a pre-built power database for multiple target power data that are completely or partially identical to multiple entities in the target meteorological data;

[0011] Based on each of the target meteorological data, calculating the similarity between the target meteorological data and each of the target power data;

[0012] Based on the similarity corresponding to each of the target meteorological data, the correlation relationship between the power data and the meteorological data is determined.

[0013] Preferably, the pre-construction process of the power and meteorological relationship database includes:

[0014] Obtain multiple historical power data and multiple historical meteorological data within a historical time period;

[0015] Parse each of the historical meteorological data to obtain multiple entities corresponding to each of the historical meteorological data; parse each of the historical power data to obtain multiple entities corresponding to each of the historical power data;

[0016] Establishing an association relationship between historical meteorological data and historical power data with the same or partially identical entities;

[0017] Based on the historical meteorological data and historical electricity data with established correlation, a database on the relationship between electricity and meteorology is constructed.

[0018] Preferably, determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data includes:

[0019] Based on the similarity corresponding to each of the target meteorological data, a plurality of target meteorological data and a plurality of target power data having a similarity greater than or equal to a first predetermined similarity are screened out;

[0020] The plurality of target meteorological data and the plurality of target power data whose similarities are greater than or equal to the first predetermined similarity are analyzed according to entity relationships to obtain a first data relationship.

[0021] Preferably, determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data includes:

[0022] Based on the similarity corresponding to each of the target meteorological data, multiple target meteorological data and multiple target power data with similarity less than the first predetermined similarity and greater than the second predetermined similarity and the same entity are selected; the relationship between the multiple target meteorological data and the multiple target power data with the same entity is used as the second data relationship;

[0023] And screen out multiple target meteorological data and multiple target power data whose similarity is less than the first predetermined similarity and greater than the second predetermined similarity and whose entities are different; and use the relationship between the multiple target meteorological data and multiple target power data with different entities as the third data relationship.

[0024] Preferably, determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data includes:

[0025] Based on the similarity corresponding to each of the target meteorological data, a plurality of target meteorological data and a plurality of target power data having a similarity less than or equal to a second predetermined similarity are screened out;

[0026] The plurality of target meteorological data and the plurality of target power data having a similarity less than or equal to the second predetermined similarity are analyzed according to entity relationships to obtain a fourth data relationship.

[0027] Preferably, after determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data, the method further includes:

[0028] The first data relationship, the second data relationship, the third data relationship and the fourth data relationship are stored.

[0029] Preferably, after acquiring the association relationship between the power data and the meteorological data based on the multiple entities corresponding to each of the target meteorological data in combination with the pre-built power database, the method further includes:

[0030] The association relationship between each of the target meteorological data, the power data and the meteorological data is saved in a meteorological database;

[0031] The meteorological database is used to carry out meteorological monitoring, meteorological forecasting and meteorological early warning.

[0032] In a second aspect, the present invention also proposes a system for determining the association relationship of power meteorological data, comprising:

[0033] An acquisition module is used to acquire multiple meteorological data within a set time period;

[0034] A screening module, configured to screen out a plurality of target meteorological data from the plurality of meteorological data based on the plurality of meteorological data in combination with a pre-built power and meteorological relationship database;

[0035] A parsing module, used for parsing each of the target meteorological data to obtain multiple entities corresponding to each of the target meteorological data;

[0036] The determination module is used to obtain the association relationship between the power data and the meteorological data based on multiple entities corresponding to each of the target meteorological data in combination with a pre-built power database.

[0037] In a third aspect, the present invention application further proposes an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0038] The memory is used to store one or more programs;

[0039] When the one or more programs are executed by the at least one processor, the method for determining the association relationship of electric power meteorological data is implemented.

[0040] In a fourth aspect, the present invention application further proposes a readable storage medium having an execution program stored thereon, and when the execution program is executed, the method for determining the correlation relationship of electric power meteorological data is implemented.

[0041] Compared with the closest prior art, the present invention has the following beneficial effects:

[0042] The present invention provides a method and system for determining the association relationship of electric power meteorological data, which obtains multiple meteorological data within a set time period; based on the multiple meteorological data combined with a pre-constructed electric power and meteorological relationship database, multiple target meteorological data are screened out from the multiple meteorological data; each of the target meteorological data is parsed to obtain multiple entities corresponding to each of the target meteorological data; based on the multiple entities corresponding to each of the target meteorological data combined with a pre-constructed electric power database, the association relationship between the electric power data and the meteorological data is obtained. Multiple target meteorological data are screened out, the interference of irrelevant meteorological data is eliminated, and the amount of meteorological data is reduced. The target meteorological data is screened from the electric power perspective, which lays the foundation for accurately determining the association relationship of meteorological data from the electric power perspective. Based on the multiple entities corresponding to each target meteorological data and the electric power database, the association relationship between the electric power data and the meteorological data is determined, which not only ensures the accuracy of the association relationship of the determined meteorological data, but also ensures that the association relationship of the determined meteorological data is related to electric power, so as to achieve the purpose of accurately determining the association relationship of meteorological data from the electric power perspective. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of a method for determining the correlation relationship of electric power meteorological data provided by the present invention;

[0044] Figure 2 An example diagram of the logical association between target meteorological data and power data provided by the present invention;

[0045] Figure 3 A schematic diagram of the integrated design of the meteorological data logic model provided in the present invention;

[0046] Figure 4 A schematic diagram of meteorological monitoring provided for the present invention application;

[0047] Figure 5 A schematic diagram of weather forecast provided by the present invention;

[0048] Figure 6 A schematic diagram of weather warning provided by the present invention;

[0049] Figure 7 An architectural diagram of a system for determining the association relationship of electric power meteorological data provided by the present invention;

[0050] Figure 8 A schematic diagram of the operation of an electronic device provided in the present invention application. DETAILED DESCRIPTION

[0051] The specific implementation methods of the present invention are further described in detail below with reference to the accompanying drawings.

[0052] Embodiment 1:

[0053] like Figure 1 As shown, the present invention proposes a method for determining the association relationship of power meteorological data, which may include:

[0054] Step 1: Obtain multiple meteorological data within a set time period;

[0055] Step 2: Based on the plurality of meteorological data and a pre-built power and meteorological relationship database, a plurality of target meteorological data are screened out from the plurality of meteorological data;

[0056] Step 3: parse each target meteorological data to obtain multiple entities corresponding to each target meteorological data;

[0057] Step 4: Based on the multiple entities corresponding to each of the target meteorological data and the pre-built power database, the correlation relationship between the power data and the meteorological data is obtained.

[0058] The scheduled time period in the above step 1 is determined according to actual needs. For example, when the user has a demand for the meteorological data in 2024, the scheduled time period is 2024. It should be noted that the multiple meteorological data include not only weather data, such as lightning, wind and rain, but also other data related to the weather data, such as meteorological sites and environmental information. The weather data in the meteorological data can be obtained from multiple channels. For example, it can be obtained from the meteorological big data cloud platform of the Meteorological Bureau, and other data in the meteorological data can be obtained from relevant websites.

[0059] In the above step 2, each meteorological data can be searched in the electricity and meteorological relationship database to obtain multiple target meteorological data. The reason for screening out multiple target meteorological data from multiple meteorological data is that not all meteorological data will have an impact on the electricity data. In order to determine the correlation between meteorological data based on the electricity data, the meteorological data that is not related to the electricity data can be eliminated first. The electricity and meteorological relationship database is a pre-constructed database that reflects meteorological data related to electricity. Through the electricity and meteorological relationship database, multiple target meteorological data are screened out from multiple meteorological data. The screened target meteorological data have an impact on electricity, and the interference of irrelevant meteorological data is eliminated, reducing the amount of meteorological data. Data is screened from the perspective of electricity, laying the foundation for accurately determining the correlation between meteorological data from the perspective of electricity.

[0060] As mentioned above, the power and meteorological relationship database is a pre-built database that can reflect the relationship between power and meteorological data. The meteorological data stored in the power and meteorological relationship database has an impact on the power data. Since the relationship of meteorological data needs to be determined from the perspective of power, the meteorological data to be processed subsequently must have a relationship with power. Finding the same target meteorological data as the meteorological data in the power and meteorological data relationship database indicates that the target meteorological data has a relationship with power, eliminating the interference of irrelevant meteorological data and reducing the amount of meteorological data.

[0061] The pre-construction process of the power and meteorology relationship database may include:

[0062] Step a, obtaining a plurality of historical power data and a plurality of historical meteorological data within a historical time period;

[0063] Step b, parsing each of the historical meteorological data to obtain multiple entities corresponding to each of the historical meteorological data; parsing each of the historical power data to obtain multiple entities corresponding to each of the historical power data;

[0064] Step c, establishing an association relationship between historical meteorological data and historical power data that are completely or partially identical in entity;

[0065] Step d: constructing a power and meteorological relationship database based on the historical meteorological data and historical power data with established associations.

[0066] As mentioned above, data collection is required in the early stage of building the power and meteorological database. The data collection process is as follows: Based on the survey and collection of meteorological data collected by various parties in the early stage, with the support of power-related meteorological business and comprehensive demonstration application needs as input, combined with the current status of the meteorological data dictionary collected, taking into account the different meteorological business management models caused by regional differences, identify the core business objects, form data objects, and form the "List of Core Business Data Objects of Meteorological Data" for the main data objects, clarify the definition, attributes, relationships, and coding rules of the core business data objects, and complete the management of the core business data objects. Responsibilities, including the business group, responsible business department, data source and other information, provide information input for the subsequent design and integration of the power and meteorological database. Combined with the current status of the construction of power meteorological data service capabilities, sort out the core business, focus on the cross-professional business collaboration and comprehensive application demonstration needs of meteorological data related to the power business, clarify the core business scope, and form the "List of Meteorological Core Business Scope". Based on the "List of Meteorological Data Core Business Scope" and the meteorological business data collected in the early stage of the survey, carry out the collection and sorting of data dictionaries. The data dictionary sorting work is mainly based on the data provided by the meteorological business and the data of the main business systems that interact with it. The business system construction manufacturers involved cooperate to complete the export of data dictionary information from the operating business system, and combine the existing meteorological business data, and complete the supplementary and improvement of business description information such as tables, fields, associations and related coding tables to form the "Meteorological Core Business Data Dictionary". Based on the "Meteorological Core Business Data Dictionary", the core business data objects and cross-domain data objects are identified, the uniqueness of the data table is determined, the meaning, name and related coding rules of the data attributes are unified, the data source, the business department to which it belongs, and whether it is a master data object, etc., and finally a detailed data object, attribute and association relationship is formed, forming the "Meteorological Core Business Data Object List" and the "Master Data Object Recommendation List".

[0067] The power and meteorological database mainly includes two parts: conceptual model and logical model. In combination with the current status of power meteorological data service capacity building, based on the "List of Meteorological Core Business Data Objects", three subject domain models are designed: meteorological monitoring domain, meteorological forecast domain, and disaster warning domain; the logical model design work is completed first, and on the basis of the logical model design results, the conceptual model verification is completed, and finally a complete set of power and meteorological database design results is formed.

[0068] In the above step 3, each target meteorological data is parsed to obtain multiple entities corresponding to the target meteorological data, wherein an entity refers to a collection of a certain type of things in the target meteorological data, and each entity contains a set of attributes, which are used to describe the characteristics of the entity. It should be noted that the target meteorological data can be parsed through a machine learning model. By parsing the target meteorological data into multiple entities, the information contained in the target meteorological data can be accurately reflected, thereby achieving the purpose of refining the target meteorological data.

[0069] In the above step 4, the power database is a pre-built database, which may have been put into use. At the same time, the power database is continuously optimized during use, so the data relationship of the power data stored in the power database is accurate. The power data includes not only power data, but also other meteorological data related to the power data. Since the meteorological data includes not only weather data, but also other data related to the weather data, there is data overlap between the target meteorological data and the power data. The multiple entities corresponding to the target meteorological data can reflect the meteorological data represented by the target meteorological data, and the data relationship of the power data in the power database is accurate. Therefore, based on the multiple entities corresponding to each target meteorological data and the power database, the association relationship of each target meteorological data is determined, which not only ensures the accuracy of the association relationship of the determined meteorological data, but also ensures that the association relationship of the determined meteorological data is related to electricity, so as to achieve the purpose of accurately determining the association relationship of the meteorological data from the perspective of electricity.

[0070] In the above step 4, the acquisition of the association relationship between the power data and the meteorological data based on the multiple entities corresponding to each of the target meteorological data and the pre-built power database may include:

[0071] Step 4.1, based on each of the target meteorological data, searching a pre-built power database for multiple target power data that are completely or partially identical to multiple entities in the target meteorological data;

[0072] Step 4.2, based on each of the target meteorological data, calculating the similarity between the target meteorological data and each of the target power data;

[0073] Step 4.3: Determine the correlation between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data.

[0074] In the above steps 4.1 to 4.3, each power data in the power database corresponds to multiple entities. For each target meteorological data, power data that are completely or partially identical to multiple entities in the target meteorological data are searched in the power database as multiple target power data. If the target meteorological data and the target power data have entities that are completely or partially identical, it means that the target meteorological data and the target power data may be related. Calculate the similarity between the target meteorological data and each target power data to determine the similarity between the target meteorological data and the target power data. Based on the similarity, the similarity can reflect the degree of association between the target meteorological data and the target power data, and then determine the association between the power data and the meteorological data.

[0075] In order to determine the relationship between power data and meteorological data, it is necessary to first design a method for logically associating the target meteorological data and power data. When designing the logical association, for example Figure 2As shown, the following principles are followed: ① According to the business needs of the power industry, sort out and analyze the data relationships in the existing results of the power database, such as entities, attributes, and associations; for example, the entities of the power data can be: the device code, upper terminal, and device type in the remote terminal device determined by whether the substation is a hub station, the operation and maintenance team, and whether it is centralized monitoring; for example, the attributes of the power data can be the administrative division (regional level, Chinese abbreviation of the administrative district, Chinese name of the administrative district, administrative district code) determined by whether the substation is a hub station, the operation and maintenance team, and whether it is centralized monitoring , parent area code); for example, the attributes of meteorological data can be the administrative divisions (regional level, Chinese abbreviations of administrative regions, Chinese names of administrative regions, administrative region codes, and parent area codes) determined according to the geographical phenomena, astronomical phenomena, environmental information, and environmental location types in the environmental phenomena, and the entities of meteorological data can be earthquake information monitoring; it can be seen that the relationship between power data and meteorological data lies in the regional level, Chinese abbreviations of administrative regions, Chinese names of administrative regions, administrative region codes, and parent area codes in the administrative divisions; if the data relationship existing in the power database can meet the actual business needs, this data relationship is directly used. ② Referring to the existing achievements of the power database, if a certain data relationship can partially meet the data requirements of the data object, according to the actual business situation, select this data relationship and create a new data relationship for the rest of the data. The created new data relationship inherits the selected data relationship and adds entities, attributes and association relationships to the newly created data relationship according to business needs; ③ If the existing achievements of the power database do not have a data relationship that fully meets the data requirements of the data object, create a new data relationship. The newly created data relationship must inherit other data relationships or directly inherit the base data relationship (some data relationships are themselves base data relationships), and then add entities and new attributes to the newly created data relationship according to the actual business, and associate it with other data relationships according to business needs.

[0076] The logical associations can be obtained through the meteorological data logic model constructed in advance. When designing the meteorological data logic model, the design method is as follows: Design a meteorological data logic model based on the meteorological database standard. When designing the meteorological data logic model, the three subject domains of meteorological monitoring domain, meteorological forecast domain, and disaster warning domain must follow the following basic principles: Use the existing results of the existing power database to the greatest extent possible without destroying the meteorological database standard. Based on the design results of each business group, combined with the cross-disciplinary data objects in the list of meteorological core business objects, analyze and discuss, standardize the data, unify the meaning, deduplicate, merge, integrate, and split it to form a unified set of meteorological logic model design results. Figure 3 As shown, Figure 3The schematic diagram of the integrated design of the meteorological data logical model is shown in the figure. The integrated design of the meteorological data logical model follows the following principles: ① Establish the mapping relationship between electricity and meteorology. For cross-professional business data objects that do not have cross-management, combined with the association relationship of cross-professional business data objects, cross-discussion between related domains is carried out to determine the association relationship between cross-domain entities. ② Split and merge data table objects. For the same business data objects managed by different professions, the first design method is to analyze the existing power data model according to the predetermined requirements, split the power data object into different entities according to whether the entity meets the modeling requirements, and manage it. For the entities that do not meet the modeling requirements, new data relationships are established according to similar data relationships, and then new attributes and additional relationships are added until the predetermined requirements are met. After meeting the predetermined requirements, a logical association model is formed; the second design method is to attribute the management rights of the business data object to a certain subject domain, and the attributes of other domains are merged. According to the integrated logical design results, based on the concept, combined with the logical adjustment content, the conceptual model is further verified in reverse, the conceptual entities and the relationships between entities are adjusted, and the logical association of meteorological data is formed.

[0077] In the above step 4.3, determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data may include:

[0078] Based on the similarity corresponding to each of the target meteorological data, a plurality of target meteorological data and a plurality of target power data having a similarity greater than or equal to a first predetermined similarity are screened out;

[0079] The plurality of target meteorological data and the plurality of target power data whose similarities are greater than or equal to the first predetermined similarity are analyzed according to entity relationships to obtain a first data relationship.

[0080] For each similarity, when the similarity is greater than or equal to the first predetermined similarity, the first predetermined similarity is approximately 100%, for example, the first predetermined similarity is 99%, indicating that the target power data and the target meteorological data can be regarded as the same data. Therefore, the data relationship determined for the target power data can be determined as the data relationship for the target meteorological data, without creating the relationship for the target meteorological data. The data relationship includes the attributes corresponding to the entity, the association relationship corresponding to the entity, the other entities corresponding to the entity, and the derivative relationship corresponding to the other entities, etc. For example, the derivative relationship can be the attributes corresponding to the other entities, the association relationship corresponding to the other entities, the other entities corresponding to the other entities, and the derivative relationship corresponding to the other entity. Since the first data relationship of the target power data in the power database that has been put into actual use has been optimized many times, the target meteorological data and the first data relationship of the target power data in the power database are stored in association, and the association relationship between each target meteorological data, each target power data, and the first data relationship can be determined efficiently and accurately.

[0081] In the above step 4.3, determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data may also include:

[0082] Based on the similarity corresponding to each of the target meteorological data, multiple target meteorological data and multiple target power data with similarity less than the first predetermined similarity and greater than the second predetermined similarity and the same entity are selected; the relationship between the multiple target meteorological data and the multiple target power data with the same entity is used as the second data relationship;

[0083] And screen out multiple target meteorological data and multiple target power data whose similarity is less than the first predetermined similarity and greater than the second predetermined similarity and whose entities are different; and use the relationship between the multiple target meteorological data and multiple target power data with different entities as the third data relationship.

[0084] In the above, for each similarity, it is determined that the similarity is less than the first predetermined similarity and greater than the second predetermined similarity, based on the same entity of the target meteorological data and the target power data, the second data relationship corresponding to the same entity is screened out in the power database, and based on the target meteorological data, a third data relationship corresponding to the different entity of the target power data in the target meteorological data is created. For each similarity, when the similarity is less than the first predetermined similarity and greater than the second predetermined similarity, wherein the first predetermined similarity is approximately 100% and the second predetermined similarity is approximately 0%, for example, the first predetermined similarity is 99% and the second predetermined similarity is 1%, indicating that there is some identical data between the target power data and the target meteorological data, the data relationship of the partially identical data in the target power data can be determined as the data relationship of the partially identical data in the target meteorological data, and the second data relationship is obtained, without creating all the data relationships of the target data. Wherein, the data relationship includes the attributes corresponding to the entity, the association relationship corresponding to the entity, the other entities corresponding to the entity, and the derivative relationship corresponding to the other entities, etc. For example, the derivative relationship can be the attributes corresponding to the other entities, the association relationship corresponding to the other entities, the other entity corresponding to the other entity, and the derivative relationship corresponding to the other entity. Based on the target meteorological data, data analysis is performed on the data in the target meteorological data that is different from the target power data to obtain the attributes corresponding to the entities of the target meteorological data and the association relationships corresponding to the entities. In the case where there is a predetermined derivative relationship between the data in the target meteorological data that is different from the target power data, the establishment of the predetermined derivative relationship of the different data is completed. By performing data analysis on the above-mentioned different data and establishing a predetermined derivative relationship, a third data relationship corresponding to different entities of the target power data in the target meteorological data is created. The target meteorological data, the second data relationship, and the third data relationship are stored in association. Since the second data relationship of the target power data in the power database that has been put into actual use has been optimized many times, the third data relationship is determined by performing multiple processing on the data in the target meteorological data that is different from the target power data. Therefore, the association relationship between multiple target meteorological data, the second data relationship, and the third data can be determined efficiently and accurately.

[0085] In the above step 4.3, determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data may also include:

[0086] Based on the similarity corresponding to each of the target meteorological data, a plurality of target meteorological data and a plurality of target power data having a similarity less than or equal to a second predetermined similarity are screened out;

[0087] The plurality of target meteorological data and the plurality of target power data having a similarity less than or equal to the second predetermined similarity are analyzed according to entity relationships to obtain a fourth data relationship.

[0088] In the above, for each similarity, it is determined that the similarity is less than or equal to the second predetermined similarity, and based on the target meteorological data, a fourth data relationship corresponding to the target meteorological data is created. In the case where the similarity is less than or equal to the second predetermined similarity, wherein the second predetermined similarity is approximately 0%, for example, the second predetermined similarity is 1%, indicating that the target power data and the target meteorological data can be regarded as two completely different data, therefore, there is no data relationship corresponding to the target power data in the power database, and it is necessary to create a fourth data relationship corresponding to the target meteorological data. Based on the target meteorological data, the target meteorological data is analyzed to obtain the attributes corresponding to the entity of the target meteorological data and the association relationship corresponding to the entity. In the case where the target meteorological data has a predetermined derivative relationship, the establishment of the predetermined derivative relationship of the target meteorological data is completed. By performing data analysis on the target meteorological data and establishing a predetermined derivative relationship, the fourth data relationship corresponding to the target meteorological data is created, wherein the fourth data relationship includes the target meteorological data. The fourth data relationship is stored, and the fourth data relationship is determined by performing multiple processing on the target meteorological data, therefore, the association relationship between each target meteorological data, each target power data, and the fourth data relationship can be accurately determined.

[0089] The above-mentioned, predetermined derivative relationships can be determined in advance through the following process: Based on the work manual and phased results template generated by the overall planning of the power industry and meteorological business, each relevant business group takes the lead in carrying out the sub-domain design work. The meteorological business data collected in the early stage is analyzed and summarized to support the implementation of meteorological data-related scenarios. In the process of sub-domain design, more than 47 data tables and more than 476 fields are organized and sorted, and the integrity of meteorological data is ensured based on cross-domain discussions and cross-domain correlation design implementation. The design team of each business field completes the first draft of the design of each domain, and conducts reports and cross-business group discussions. The first draft is modified and improved based on the conclusions of the reports and discussions, and finally forms the design results of each domain containing the content of the domain and cross-domain correlation. Through the above process, the correlation between the determined meteorological data and the power data can be improved, and the improved data includes the predetermined derivative relationship.

[0090] In the above, after determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data, the method further includes:

[0091] The first data relationship, the second data relationship, the third data relationship and the fourth data relationship are stored.

[0092] As mentioned above, during storage, the multiple target meteorological data, multiple target power data, and the first data relationship corresponding to the similarity greater than or equal to the first predetermined similarity are associated and stored in the power database; the multiple target meteorological data, multiple target power data, the second data relationship, and the third data relationship corresponding to the similarity less than the first predetermined similarity and greater than the second predetermined similarity are associated and stored in the power database; the multiple target meteorological data, multiple target power data, and the fourth data relationship corresponding to the similarity less than or equal to the second predetermined similarity are associated and stored in the power database; the first data relationship, the second data relationship, the third data relationship, and the fourth data relationship can be converted, and the associated relationships after data conversion can be centrally stored in the power database to optimize the power database and facilitate the use of the associated relationships.

[0093] After acquiring the association relationship between the power data and the meteorological data based on the plurality of entities corresponding to each of the target meteorological data and the pre-built power database, the method further includes:

[0094] The association relationship between each of the target meteorological data, the power data and the meteorological data is saved in a meteorological database;

[0095] The meteorological database is used to carry out meteorological monitoring, meteorological forecasting and meteorological early warning.

[0096] When forming the meteorological database, all target meteorological data and the association relationship corresponding to each target meteorological data can be centrally stored to obtain the meteorological database. Exemplarily, the entities in the association relationship are converted into data tables, the attributes of the entities in the association relationship are converted into fields, and the association relationship of the entities in the association relationship is converted into the primary and foreign key relationship between tables. The association relationship is uniformly managed to obtain the meteorological database, which is convenient for the use of the association relationship.

[0097] The specific process of determining the meteorological database can be as follows: on the basis of meeting the standardization requirements, complete the mapping relationship between entities, attributes, tables, and fields. According to the characteristics of the database product, complete the physical data model design. According to the characteristics of the selected database product, the physical table can be designed and divided into five forms such as copy table, random distribution table, hash distribution table, temporary table and database (nocopies) table, and the data table storage optimization design based on the technical characteristics of the selected database can be completed. According to the characteristics of the selected database, the data table field type is converted into the data type supported by the selected database. Based on the results of the physical table classification planning, fill in the list of physical data model type tables, and export the standard database construction script from the physical data model. Based on the script conversion program, complete the generation of the selected database script. It is necessary to unify the data in the meteorological database according to the unified data model. Among them, the unified data model aims to: unify the original data of various professions and business fields from the perspective of enterprise and industry, realize the unified structure, unified semantics, and unified type of multi-source heterogeneous data, establish a unified data standard, and support the integration and sharing of data applications. Based on the unified data model design method, in order to guide various units to carry out meteorological data management, support meteorological data access, serve the efficient application of meteorological data in various scenarios, establish and improve meteorological data standards, promote a unified and converged meteorological database, combine the meteorological database design needs and current situation, take into account the differences in business management models of provincial (municipal) companies, take meteorological business needs as input, and use a method of using object classes and attributes and the relationships between them to represent meteorological data relationships. With reference to the common information model (Smart Grid-Common Information Model, SG-CIM) standard and the international standard system (International Electrotechnical Commission, IEC), etc., innovatively construct a unified data model design method and specification suitable for existing meteorological data application fields, adopt the design method of key data aggregation technologies based on interactive data models in typical business scenarios, and build a meteorological database that supports meteorological data sharing and meteorological business collaboration.

[0098] After the meteorological database is established, it has practical applications in the power industry. As mentioned above, the meteorological database can be used for meteorological monitoring, meteorological forecasting and meteorological warning. Meteorological monitoring can monitor meteorological data such as temperature, humidity, wind direction in real time, and calculate the probability of ice dancing on the line in combination with the on-site situation, etc., to provide a basis for early assessment of line risks and ensure the safe and stable operation of the power grid. Meteorological forecasts can warn of extreme weather in advance, help the power industry take measures to protect power grid facilities, reduce failures, and ensure stable power supply. Meteorological warnings can notify the power industry of possible extreme weather in advance, so that it can take measures to protect power grid facilities and reduce the risk of failures and power outages. According to meteorological warnings, the power industry can adjust power production and dispatching plans to ensure supply and demand balance and avoid waste of resources. Meteorological warnings also help the power industry formulate emergency plans, improve the efficiency and accuracy of responding to natural disasters, and reduce losses.

[0099] Meteorological monitoring requires the use of multiple data in the target meteorological data, and the multiple data are related to each other, such as Figure 4 As shown, Figure 4 This is a schematic diagram of meteorological monitoring applied for by the present invention. The above-mentioned multiple data include environmental monitoring stations and site basic information. The site basic information has a transmission information relationship with the environmental monitoring station. In addition, the above-mentioned multiple data also include lightning monitoring data and lightning strikes. The lightning monitoring data has a monitoring relationship with lightning strikes. Figure 4 There are also multiple data relationships in it, which will not be described one by one here. Weather forecasting requires multiple data in the target weather data, and the above multiple data have correlation relationships, such as Figure 5 As shown, Figure 5 This is a schematic diagram of the weather forecast applied for by the present invention. The above multiple data include weather forecasts and city daily weather forecasts. The weather forecasts and city daily weather forecasts have an inclusion relationship. In addition, the above multiple data also include units and management organizations. The units and management organizations have an inclusion relationship. Figure 5 There are also multiple data relationships in it, which will not be described one by one here. Meteorological warning requires multiple data in the target meteorological data, and the above multiple data have correlation relationships, such as Figure 6 As shown, Figure 6 This is a schematic diagram of the meteorological warning applied for by the present invention. The above-mentioned multiple data include environmental warning and tower typhoon warning, and the environmental warning and tower typhoon warning have an inclusion relationship. In addition, the above-mentioned multiple data also include typhoon basic information and typhoon path real-time status, and the typhoon basic information and typhoon path real-time status have an inclusion relationship. Figure 6 There are also multiple data relationships, which will not be described one by one here.

[0100] When designing a meteorological database, we can create a design method and specification for power and meteorological data models around meteorological monitoring data, meteorological forecast data, and disaster warning data, carry out the design of power and meteorological databases, form a meteorological database with unified standards, realize the unified semantics, unified structure, and unified form of multi-source meteorological data, and effectively support the fusion and sharing application of unified and converged meteorological data. Referring to the design concept of the enterprise-level unified information model, we make overall planning based on the early collection and data combing of meteorological data, and formulate the design specifications of meteorological databases, the data dictionary for combing existing data, and the overall design ideas for meteorological business association processes. Unify ideas, build a meteorological database framework, and design an overall meteorological database that integrates various meteorological business domains. Considering the readability, manageability, and division of work of the model library, combined with the construction of power and meteorological data service capabilities, divide it into three meteorological subject domains, clarify the organizational structure and work responsibilities of the design work, formulate a work manual to guide further work and the templates of the results of each stage, and organize centralized training to make full preparations for entering the domain design stage. Take the domain design results as one of the inputs, and the cross-business scenarios sorted out based on cross-business needs as the second input, and carry out overall coordinated design. The overall design aims to realize the enterprise-level model. The design generates a "core clustering diagram" composed of core cross-business association classes as the core sub-diagram of the meteorological data model, and conducts concentrated discussions and designs around this diagram, which eventually forms the design results of the meteorological database. Based on the previous investigation and collection of various types of meteorological data, with the coordination of meteorological business and power business and the demand for comprehensive demonstration applications as input, the common information model (SG-CIM) standard is referred to, and international standard systems such as IEC are used for reference. Taking into account the differences in meteorological business management models in different regions of provincial (municipal) companies, the meteorological database design is carried out. According to the design ideas of the model library, the meteorological database design can be divided into three steps: demand analysis, meteorological data model design, and physical data model design.

[0101] The practical application of the data relationship of the determined meteorological data in the power industry will effectively support the power grid analysis and various professional application scenario design involving meteorological data, and promote the construction of power meteorological data service capabilities. Through the optimization and improvement of the design, management and application of the power database, the business scope covered by the meteorological database design has been continuously expanded and improved, and the quality of the design results has been further improved. The meteorological database has been initially applied in the basic data base, enterprise business middle platform, source business system, and cross-domain business scenarios, and good results have been achieved. In order to support the construction of power meteorological data service capabilities, it is urgent to focus on further improving the effectiveness of design management work in power and meteorological databases, refine and improve the existing power database, establish a standard model for centralized procurement and access to meteorological data, and promote the sharing and application of centralized procurement and access data. By improving the description of the meteorological database and establishing the relationship between data types, it supports the analysis of meteorological impact on the power grid and the construction of various professional application scenarios. Among them, the meteorological data model can include the relationship between the target meteorological data.

[0102] In summary, the method of the present invention obtains multiple meteorological data within a predetermined historical time period. Based on a pre-constructed database of the relationship between electricity and meteorology, multiple target meteorological data are screened out from the multiple meteorological data, and the data is screened from the perspective of electricity, which lays a foundation for accurately determining the correlation relationship of the meteorological data from the perspective of electricity. For each target meteorological data, the target meteorological data is parsed to obtain multiple entities corresponding to the target meteorological data, so as to achieve the purpose of refining the target meteorological data. Based on the multiple entities corresponding to each target meteorological data and the pre-constructed electricity database, the correlation relationship of each target meteorological data is determined, which not only ensures the accuracy of the correlation relationship of the determined meteorological data, but also ensures that the correlation relationship of the determined meteorological data is related to electricity, so as to achieve the purpose of accurately determining the correlation relationship of the meteorological data from the perspective of electricity.

[0103] As described in the background technology section, the China Meteorological Administration began building a national and provincial meteorological big data cloud platform (TianQing) in 2017 to achieve unified management and services for the entire meteorological data set, provide cloud processing, mining, analysis and computing services for various types of meteorological data, open platforms, meteorological data, and meteorological analysis algorithm resources, and provide integrated meteorological services for internal business and the entire society. There are 611 types of data, 221.49 billion records, and 296 million new records are added every day.

[0104] The increasing amount of massive meteorological data has important uses in all walks of life. For example, the Civil Aviation Administration of China has built the China Civil Aviation Meteorological Big Data Sharing and Service Cloud Platform, which has unified access to data from more than 50,000 ground meteorological observation stations, nearly 200 weather radars, multiple meteorological satellites and hundreds of lightning observation stations. The amount of meteorological data provided daily exceeds 500G. Through the integrated calculation of meteorological data and business data such as flight plans and flight routes, it can reduce operating costs and improve the safety and efficiency of aviation operations.

[0105] The method for determining the correlation relationship of the electric power meteorological data based on the present invention can also mainly include: 1) Formulate enterprise-level meteorological database standards, data transmission standards and data sharing service standards with electric power characteristics to realize unified storage services for electric power meteorological data, which will help integrate meteorological data applications and improve data quality. At the same time, the application of artificial intelligence technology can build an extreme weather warning model library, optimize the establishment of power equipment emergency repairs, disaster weather classification warnings, and drone patrol decision-making business scenarios, support electric power meteorological value-added services, effectively improve the power grid's ability to defend against meteorological disasters, and further improve the power grid planning and design, operation management, and emergency response mechanisms, which play an important supporting role in enterprise production and power dispatching. 2) With meteorological data as the core, around the needs of exchanging, sharing, mining and analyzing some electric power data, adapt to the future establishment of a meteorological data resource sharing mechanism around electric power scenarios, and provide strong support for smart energy use and other aspects.

[0106] The advantages of the meteorological database constructed by the method of the present invention mainly include: 1) It will help to integrate meteorological data and power data applications and improve data quality. At the same time, it can optimize the construction of business scenarios such as power equipment emergency repair, disaster weather classification warning, and drone line patrol decision-making, support power meteorological value-added services, and effectively enhance the power grid's ability to defend against meteorological disasters. 2) Construct enterprise-level meteorological database design specifications to achieve unified storage and services for power meteorological data, effectively enhance the power grid's ability to defend against meteorological disasters, further improve power grid planning and design, operation management, and emergency response mechanisms, and play an important supporting role in enterprise production and power dispatching. 3) It lays a solid foundation for the subsequent continuous development of external data model design and provides effective and feasible external data model design ideas and practical conditions.

[0107] Embodiment 2:

[0108] like Figure 7 As shown, the present invention also provides a system for determining the association relationship of power meteorological data, comprising:

[0109] An acquisition module is used to acquire multiple meteorological data within a set time period;

[0110] A screening module, configured to screen out a plurality of target meteorological data from the plurality of meteorological data based on the plurality of meteorological data in combination with a pre-built power and meteorological relationship database;

[0111] A parsing module, used for parsing each of the target meteorological data to obtain multiple entities corresponding to each of the target meteorological data;

[0112] The determination module is used to obtain the association relationship between the power data and the meteorological data based on multiple entities corresponding to each of the target meteorological data in combination with a pre-built power database.

[0113] Among them, entity refers to a collection of a certain type of things in the target meteorological data, and each entity contains a set of attributes that are used to describe the characteristics of the entity.

[0114] As mentioned above, the early stage of building the power and meteorological database requires data collection. The data collection process is as follows: Based on the survey and collection of meteorological data collected by all parties in the early stage, with the support of power-related meteorological business and comprehensive demonstration application needs as input, combined with the current status of the meteorological data dictionary collected, taking into account the different meteorological business management models caused by regional differences, identify the core business objects, form data objects, and form the "List of Core Business Data Objects of Meteorological Data" for the main data objects, clarify the definition, attributes, relationships, and coding rules of the core business data objects, complete the management of the core business data objects, including the business group, responsible business department, data source and other information, and provide information input for the subsequent design and integration of the power and meteorological database. Combined with the current status of the construction of power meteorological data service capabilities, sort out the core business, focus on the cross-professional business collaboration and comprehensive application demonstration needs of meteorological data related to the power business, clarify the core business scope, and form the "List of Meteorological Core Business Scope". Based on the "List of Meteorological Data Core Business Scope" and the meteorological business data collected in the early stage of the survey, carry out the collection and sorting of data dictionaries. The data dictionary sorting work is mainly based on the data provided by the meteorological business and the data of the main business systems that interact with it. The business system construction manufacturers involved cooperate to complete the export of data dictionary information from the operating business system, and combine the existing meteorological business data, and complete the supplementary and improvement of business description information such as tables, fields, associations and related coding tables to form the "Meteorological Core Business Data Dictionary". Based on the "Meteorological Core Business Data Dictionary", the core business data objects and cross-domain data objects are identified, the uniqueness of the data table is determined, the meaning, name and related coding rules of the data attributes are unified, the data source, the business department to which it belongs, and whether it is a master data object, etc., and finally a detailed data object, attribute and association relationship is formed, forming the "Meteorological Core Business Data Object List" and the "Master Data Object Recommendation List".

[0115] The power and meteorological database mainly includes two parts: conceptual model and logical model. In combination with the current status of power meteorological data service capacity building, based on the "List of Meteorological Core Business Data Objects", three subject domain models are designed: meteorological monitoring domain, meteorological forecast domain, and disaster warning domain; the logical model design work is completed first, and on the basis of the logical model design results, a complete set of power and meteorological database design results is finally formed.

[0116] The acquisition module, screening module, parsing module and analysis module mentioned above realize the acquisition of multiple meteorological data within a predetermined time period. Based on the pre-constructed electricity and meteorological relationship database, multiple target meteorological data are screened out from the multiple meteorological data, and the data is screened from the electricity perspective, which lays the foundation for accurately determining the correlation between meteorological data from the electricity perspective. For each target meteorological data, the target meteorological data is parsed to obtain multiple entities corresponding to the target meteorological data, so as to achieve the purpose of refining the target meteorological data. Based on the multiple entities corresponding to each target meteorological data and the pre-constructed electricity database, the correlation between the electricity data and the meteorological data is determined, which not only ensures the accuracy of the correlation between the determined meteorological data, but also ensures that the correlation between the determined meteorological data is related to electricity, so as to achieve the purpose of accurately determining the correlation between the electricity data and the meteorological data from the electricity perspective.

[0117] As mentioned above, each power data in the power database corresponds to multiple entities. For each target meteorological data, power data that is completely or partially identical to multiple entities in the target meteorological data is searched in the power database as multiple target power data. The similarity between the target meteorological data and each target power data is calculated to determine the similarity between the target meteorological data and the target power data. Based on the similarity, the degree of association between the target meteorological data and the target power data can be reflected through the similarity, thereby determining the association relationship between the power data and the meteorological data.

[0118] Furthermore, the determination module includes:

[0119] A target power data determination submodule is used to search, based on each of the target meteorological data, in a pre-built power database for a plurality of target power data that are completely or partially identical to a plurality of entities in the target meteorological data;

[0120] A similarity calculation submodule, used for calculating the similarity between the target meteorological data and each target power data based on each target meteorological data;

[0121] The association relationship determination submodule is used to determine the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data.

[0122] In order to determine the association between power data and meteorological data, it is necessary to first design a method for logically associating the target meteorological data and power data. When designing the logical association, the following principles are followed: ① According to the business needs of the power industry, sort out and analyze the data relationships in the existing results of the power database, such as entities, attributes, and association relationships; for example, the entities of power data can be: the device code, upper terminal, and device type in the remote terminal device determined by whether the substation is a hub station, the operation and maintenance team, and whether it is centralized monitoring; for example, the attributes of power data can be the administrative divisions (regional level, industry level, etc.) determined by whether the substation is a hub station, the operation and maintenance team, and whether it is centralized monitoring. Chinese abbreviation of administrative region, Chinese name of administrative region, administrative region code, parent region code); for example, the attribute of meteorological data can be the administrative division determined according to the geographical phenomenon, astronomical phenomenon, environmental information and environmental location type in the environmental phenomenon (regional level, Chinese abbreviation of administrative region, Chinese name of administrative region, administrative region code, parent region code), and the entity of meteorological data can be earthquake information monitoring; it can be seen that the relationship between power data and meteorological data lies in the regional level, Chinese abbreviation of administrative region, Chinese name of administrative region, administrative region code and parent region code in the administrative division; if the data relationship existing in the power database can meet the actual business needs, this data relationship is used directly. ② Referring to the existing achievements of the power database, if a certain data relationship can partially meet the data requirements of the data object, according to the actual business situation, select this data relationship and create a new data relationship for the rest of the data. The created new data relationship inherits the selected data relationship and adds entities, attributes and association relationships to the newly created data relationship according to business needs; ③ If the existing achievements of the power database do not have a data relationship that fully meets the data requirements of the data object, create a new data relationship. The newly created data relationship must inherit other data relationships or directly inherit the base data relationship (some data relationships are themselves base data relationships), and then add entities and new attributes to the newly created data relationship according to the actual business, and associate it with other data relationships according to business needs.

[0123] Among them, the logical association can be obtained through the meteorological data logic model built in advance. When designing the meteorological data logic model, the design method is as follows: Based on the meteorological data model standard, design the meteorological data logic model. When designing the meteorological data logic model, the three subject domains of meteorological monitoring domain, meteorological forecast domain, and disaster warning domain must follow the following basic principles: Use the existing results of the existing power database to the greatest extent possible without destroying the meteorological database standard. Based on the design results of each business group, combined with the cross-professional data objects in the list of meteorological core business objects, analyze and discuss, standardize the data, unify the meaning, remove duplication, merge, integrate, and split, and form a set of unified meteorological logic model design results.

[0124] Furthermore, the system also includes: a power and meteorological relationship database construction module, which is used to:

[0125] Obtain multiple historical power data and multiple historical meteorological data within a historical time period;

[0126] Parse each of the historical meteorological data to obtain multiple entities corresponding to each of the historical meteorological data; parse each of the historical power data to obtain multiple entities corresponding to each of the historical power data;

[0127] Establishing an association relationship between historical meteorological data and historical power data with the same or partially identical entities;

[0128] Based on the historical meteorological data and historical electricity data with established correlation, a database on the relationship between electricity and meteorology is constructed.

[0129] Furthermore, the association relationship determination submodule includes: a first data relationship determination unit, which is used to:

[0130] Based on the similarity corresponding to each of the target meteorological data, a plurality of target meteorological data and a plurality of target power data having a similarity greater than or equal to a first predetermined similarity are screened out;

[0131] The plurality of target meteorological data and the plurality of target power data whose similarities are greater than or equal to the first predetermined similarity are analyzed according to entity relationships to obtain a first data relationship.

[0132] For each similarity, when the similarity is greater than or equal to the first predetermined similarity, where the first predetermined similarity is approximately 100%, the data relationship determined for the target power data can be determined as the data relationship for the target meteorological data, without creating the relationship for the target meteorological data. The data relationship includes attributes corresponding to the entity, association relationships corresponding to the entity, other entities corresponding to the entity, and derivative relationships corresponding to other entities.

[0133] Furthermore, the association relationship determination submodule further includes: second and third data relationship determination units, which are used to:

[0134] Based on the similarity corresponding to each of the target meteorological data, multiple target meteorological data and multiple target power data with similarity less than the first predetermined similarity and greater than the second predetermined similarity and the same entity are selected; the relationship between the multiple target meteorological data and the multiple target power data with the same entity is used as the second data relationship;

[0135] And screen out multiple target meteorological data and multiple target power data whose similarity is less than the first predetermined similarity and greater than the second predetermined similarity and whose entities are different; and use the relationship between the multiple target meteorological data and multiple target power data with different entities as the third data relationship.

[0136] In the above, for each similarity, it is determined that the similarity is less than the first predetermined similarity and greater than the second predetermined similarity, based on the same entities of the target meteorological data and the target power data, the second data relationship corresponding to the same entity is screened out in the power database, and based on the target meteorological data, a third data relationship corresponding to different entities in the target meteorological data and the target power data is created. For each similarity, when the similarity is less than the first predetermined similarity and greater than the second predetermined similarity, wherein the first predetermined similarity is approximately 100% and the second predetermined similarity is approximately 0%, the data relationship of part of the same data in the target power data can be determined as the data relationship of part of the same data in the target meteorological data, and the second data relationship is obtained. Among them, the data relationship includes the attributes corresponding to the entity, the association relationship corresponding to the entity, other entities corresponding to the entity, and the derivative relationship corresponding to other entities, etc.

[0137] In the above, for each similarity, it is determined that the similarity is less than or equal to the second predetermined similarity, and based on the target meteorological data, a fourth data relationship corresponding to the target meteorological data is created. In the case where the similarity is less than or equal to the second predetermined similarity, the second predetermined similarity is approximately 0%. Based on the target meteorological data, data analysis is performed on the target meteorological data to obtain the attributes corresponding to the entities of the target meteorological data and the association relationships corresponding to the entities. In the case where the target meteorological data has a predetermined derivative relationship, the establishment of the predetermined derivative relationship of the target meteorological data is completed. By performing data analysis on the target meteorological data and establishing a predetermined derivative relationship, the fourth data relationship corresponding to the target meteorological data is created.

[0138] The above-mentioned, predetermined derivative relationships can be determined in advance through the following process: Based on the work manuals and phased results templates generated by the overall planning of the power industry and meteorological business, the relevant business groups take the lead in carrying out the domain design work. Carry out analysis and summary of the meteorological business data collected in the early stage to support the implementation of meteorological data-related scenarios. The design teams in each business field complete the draft design of each domain, and conduct reports and cross-business group discussions. Based on the conclusions of the reports and discussions, the drafts are modified and improved, and finally the design results of each domain containing the content of the domain and cross-domain associations are formed. Through the above process, the correlation between the determined meteorological data and the power data can be improved, and the improved data includes the predetermined derivative relationship.

[0139] Furthermore, the association relationship determination submodule further includes: a fourth data relationship determination unit, configured to:

[0140] Based on the similarity corresponding to each of the target meteorological data, a plurality of target meteorological data and a plurality of target power data having a similarity less than or equal to a second predetermined similarity are screened out;

[0141] The plurality of target meteorological data and the plurality of target power data having a similarity less than or equal to the second predetermined similarity are analyzed according to entity relationships to obtain a fourth data relationship.

[0142] Furthermore, the system also includes a storage module, which is used to:

[0143] The first data relationship, the second data relationship, the third data relationship and the fourth data relationship are stored.

[0144] As mentioned above, during storage, the multiple target meteorological data, multiple target power data, and the first data relationship corresponding to the similarity being greater than or equal to the first predetermined similarity are associated and stored in the power database; the multiple target meteorological data, multiple target power data, the second data relationship, and the third data relationship corresponding to the similarity being less than the first predetermined similarity and greater than the second predetermined similarity are associated and stored in the power database; the multiple target meteorological data, multiple target power data, and the fourth data relationship corresponding to the similarity being less than or equal to the second predetermined similarity are associated and stored in the power database.

[0145] Furthermore, the system also includes: a meteorological application module, which is used to:

[0146] The association relationship between each of the target meteorological data, the power data and the meteorological data is saved in a meteorological database; and the meteorological database is used for meteorological monitoring, meteorological forecasting and meteorological warning.

[0147] The system of the present invention realizes the unified structure, unified semantics, and unified type of multi-source heterogeneous data such as power data and meteorological data, establishes a unified data standard, and supports the integration and sharing of data applications. The system for determining the association relationship based on power meteorological data provided by the present invention application can obtain multiple meteorological data within a predetermined historical time period. Based on the pre-constructed power and meteorological relationship database, multiple target meteorological data are screened out from multiple meteorological data, and the data are screened from the power perspective, laying the foundation for accurately determining the association relationship of meteorological data from the power perspective. For each target meteorological data, the target meteorological data is parsed to obtain multiple entities corresponding to the target meteorological data, so as to achieve the purpose of refining the target meteorological data. Based on the multiple entities corresponding to each target meteorological data and the pre-constructed power database, the association relationship of each target meteorological data is determined, which not only ensures the accuracy of the association relationship of the determined meteorological data, but also ensures that the association relationship of the determined meteorological data is related to electricity, so as to achieve the purpose of accurately determining the association relationship of meteorological data from the power perspective.

[0148] In summary, the system of the present invention has the following characteristics:

[0149] 1. Based on multiple entities corresponding to meteorological data and the pre-built power database, screening and parsing the target meteorological data and its corresponding entities, determining the correlation between each target meteorological data, and formulating meteorological database standards with power characteristics will help improve data quality, support the application of value-added services for power-meteorological fusion data, and effectively enhance the power grid's ability to defend against meteorological disasters.

[0150] 2. With meteorological data as the core, we will carry out the exchange, sharing, mining and analysis of some power data, adapt to the establishment of a meteorological data resource sharing mechanism around power scenarios in the future, and provide strong support for smart energy use and other aspects.

[0151] Embodiment 3:

[0152] like Figure 8 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0153] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of a method for determining the correlation relationship of electric power meteorological data in the above-mentioned embodiment.

[0154] Embodiment 4:

[0155] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It can be understood that the storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a method for determining the association relationship of power meteorological data in the above embodiment.

[0156] Those skilled in the art will appreciate that the embodiments of the present invention application can be provided as methods, systems, or computer program products. Therefore, the present invention application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present invention application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0157] The present invention application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present invention application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0158] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention application rather than to limit its protection scope. Although the present invention application is described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that after reading the present invention application, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims to be approved.

Claims

1. A method for determining the correlation relationship of power meteorological data, characterized in that: include: Get multiple meteorological data within a set time period; Based on the plurality of meteorological data and a pre-built power and meteorological relationship database, a plurality of target meteorological data are screened out from the plurality of meteorological data; Parsing each of the target meteorological data to obtain multiple entities corresponding to each of the target meteorological data; Based on the multiple entities corresponding to each of the target meteorological data and in combination with a pre-constructed power database, an association relationship between the power data and the meteorological data is obtained.

2. The method according to claim 1, characterized in that The obtaining of the association relationship between the power data and the meteorological data based on the multiple entities corresponding to each of the target meteorological data and the pre-built power database includes: Based on each of the target meteorological data, searching in a pre-built power database for multiple target power data that are completely or partially identical to multiple entities in the target meteorological data; Based on each of the target meteorological data, calculating the similarity between the target meteorological data and each of the target power data; Based on the similarity corresponding to each of the target meteorological data, the correlation relationship between the power data and the meteorological data is determined.

3. The method according to claim 1, characterized in that The pre-construction process of the power and meteorological relationship database includes: Obtain multiple historical power data and multiple historical meteorological data within a historical time period; Parse each of the historical meteorological data to obtain multiple entities corresponding to each of the historical meteorological data; parse each of the historical power data to obtain multiple entities corresponding to each of the historical power data; Establishing an association relationship between historical meteorological data and historical power data with the same or partially identical entities; Based on the historical meteorological data and historical electricity data with established correlation, a database on the relationship between electricity and meteorology is constructed.

4. The method according to claim 2, characterized in that: The determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data includes: Based on the similarity corresponding to each of the target meteorological data, a plurality of target meteorological data and a plurality of target power data having a similarity greater than or equal to a first predetermined similarity are screened out; The plurality of target meteorological data and the plurality of target power data whose similarities are greater than or equal to the first predetermined similarity are analyzed according to entity relationships to obtain a first data relationship.

5. The method according to claim 4, characterized in that The determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data includes: Based on the similarity corresponding to each of the target meteorological data, multiple target meteorological data and multiple target power data with similarity less than the first predetermined similarity and greater than the second predetermined similarity and the same entity are selected; the relationship between the multiple target meteorological data and the multiple target power data with the same entity is used as the second data relationship; And screen out multiple target meteorological data and multiple target power data whose similarity is less than the first predetermined similarity and greater than the second predetermined similarity and whose entities are different; and use the relationship between the multiple target meteorological data and multiple target power data with different entities as the third data relationship.

6. The method according to claim 5, characterized in that The determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data includes: Based on the similarity corresponding to each of the target meteorological data, a plurality of target meteorological data and a plurality of target power data having a similarity less than or equal to a second predetermined similarity are screened out; The plurality of target meteorological data and the plurality of target power data having a similarity less than or equal to the second predetermined similarity are analyzed according to entity relationships to obtain a fourth data relationship.

7. The method according to claim 6, characterized in that After determining the association relationship between the power data and the meteorological data based on the similarity corresponding to each of the target meteorological data, the method further includes: The first data relationship, the second data relationship, the third data relationship and the fourth data relationship are stored.

8. The method according to claim 1, characterized in that After acquiring the association relationship between the power data and the meteorological data based on the multiple entities corresponding to each of the target meteorological data and the pre-built power database, the method further includes: The association relationship between each of the target meteorological data, the power data and the meteorological data is saved in a meteorological database; The meteorological database is used to carry out meteorological monitoring, meteorological forecasting and meteorological early warning.

9. A system for determining the association relationship of power meteorological data, characterized in that: include: An acquisition module is used to acquire multiple meteorological data within a set time period; A screening module, configured to screen out a plurality of target meteorological data from the plurality of meteorological data based on the plurality of meteorological data in combination with a pre-built power and meteorological relationship database; A parsing module, used for parsing each of the target meteorological data to obtain multiple entities corresponding to each of the target meteorological data; The determination module is used to obtain the association relationship between the power data and the meteorological data based on multiple entities corresponding to each of the target meteorological data in combination with a pre-built power database.

10. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, a method for determining an association relationship of electric power meteorological data as described in any one of claims 1 to 8 is implemented.

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