Business data processing method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a business data processing method and device, equipment and a storage medium. The method comprises the steps that task description information and data topology information are acquired; determining a business data analysis task, at least one business data query task and a region node server corresponding to each business data query task according to the task description information and the data topology information; sending each business data query task to a corresponding regional node server, so that the regional node server performs node library table information query on a set node database based on the structured query statement to obtain business data, and feeds back the business data to the central node server; and under the condition that the business data matched with the business data analysis task is received, performing statistical analysis processing on the business data based on the business data analysis task to obtain a data analysis result. According to the scheme, business data query is efficiently completed, different data analysis requirements are met, the data analysis efficiency is improved, and the flexibility is high.

Description

Technical Field

[0001] The embodiments of the present application relate to the technical field of business data processing, and in particular, to a business data processing method, apparatus, device and storage medium. Background Art

[0002] With the rapid development of network information technology, different enterprises or institutions will generate various business data in the process of production and operation. In order to better guide business decisions, optimize operational efficiency and improve customer experience, the analysis and processing of business data is particularly important. Effective business data analysis can help enterprises or institutions optimize business management to cope with the increasingly fierce market competition environment.

[0003] In related technologies, in order to meet the needs of different users and business scenarios, professional data analysts or developers are required to retrieve the centrally stored data, and design and write corresponding algorithms and programs according to the established business analysis needs to conduct targeted analysis of various specific tasks. However, different business analysis needs require data analysts or developers to frequently make manual adjustments or recode, which makes it difficult to quickly adapt to new analysis needs or changes in data types, affecting data analysis efficiency and having poor flexibility. Summary of the invention

[0004] The embodiments of the present application provide a business data processing method, apparatus, device and storage medium to solve the problem that related technologies are difficult to quickly adapt to new analysis requirements or changes in data types, affecting data analysis efficiency, poor flexibility and high labor costs. It realizes the reasonable division of business data analysis tasks corresponding to the central node server and business data query tasks corresponding to each regional node server, adapts to new analysis requirements or changes in data types, can efficiently complete business data queries, and meet different data analysis requirements for business data processing, thereby improving data analysis efficiency and having high flexibility.

[0005] In a first aspect, an embodiment of the present application provides a method for processing business data, the method comprising:

[0006] Acquire task description information and data topology information, wherein the data topology information records multiple regional node servers associated with the central node server and node library table information managed by each of the regional node servers;

[0007] Determine, according to the task description information and the data topology information, a business data analysis task, at least one business data query task, and a regional node server corresponding to each of the business data query tasks;

[0008] Send each of the business data query tasks to the corresponding regional node server, so that the regional node server inputs the business data query task into the trained node semantic big model to obtain a structured query statement, performs a node library table information query on the set node database based on the structured query statement to obtain business data, and feeds back the business data to the central node server;

[0009] When business data matching the business data analysis task is received, statistical analysis processing is performed on the business data based on the business data analysis task to obtain a data analysis result.

[0010] Optionally, determining the business data analysis task, at least one business data query task, and a regional node server corresponding to each business data query task according to the task description information and the data topology information includes:

[0011] Inputting the task description information and the data topology information into the trained central semantic big model to obtain task planning information;

[0012] A business data analysis task, at least one business data query task, and a regional node server corresponding to each of the business data query tasks are extracted from the task planning information.

[0013] Optionally, determining the business data analysis task, at least one business data query task, and a regional node server corresponding to each business data query task according to the task description information and the data topology information includes:

[0014] Extracting data analysis description information, at least one regional location, and data query description information corresponding to each regional location from the task description information;

[0015] According to the at least one regional location and the data query description information corresponding to each of the regional locations, determining from the data topology information at least one regional node server and the library table query parameters corresponding to each of the regional node servers that satisfy the highest semantic similarity, each of the regional node servers being provided with a corresponding node server address;

[0016] Determine the business data query task corresponding to each of the regional node servers according to at least one regional node server, data query description information corresponding to each of the regional node servers, and library table query parameters;

[0017] Determine the business data analysis task according to the data analysis description information and the library table query parameters. Optionally, the step of performing statistical analysis on the business data based on the business data analysis task to obtain the data analysis result includes:

[0018] Determine a target statistical analysis algorithm from a set of statistical analysis algorithms based on the business data analysis task;

[0019] Performing data integration processing on the business data to obtain input parameter information of the target statistical analysis algorithm,

[0020] The input parameter information is subjected to corresponding statistical analysis processing according to the target statistical analysis algorithm to obtain a data analysis result.

[0021] Optionally, the data topology information further includes access password information associated with each of the regional node servers, and before sending each of the business data query tasks to the corresponding regional node server, it further includes:

[0022] Add access password information associated with the corresponding regional node server to each of the business data query tasks.

[0023] Optionally, before obtaining the task description information and the data topology information, the method further includes:

[0024] Receive node metadata information fed back by node servers in each region;

[0025] Data topology information is generated based on the node metadata information and basic attribute information corresponding to each regional node server.

[0026] Optionally, before obtaining the task description information and the data topology information, the method further includes:

[0027] Receive task voice information sent by the client;

[0028] Perform voice recognition on the task voice information to obtain task description information.

[0029] In a second aspect, an embodiment of the present application further provides a business data processing device, including:

[0030] An acquisition unit configured to acquire task description information and data topology information, wherein the data topology information records multiple regional node servers associated with the central node server and node library table information managed by each of the regional node servers;

[0031] A task determination unit, configured to determine a business data analysis task, at least one business data query task, and a regional node server corresponding to each of the business data query tasks according to the task description information and the data topology information;

[0032] The task distribution unit is configured to send each of the business data query tasks to the corresponding regional node server, so that the regional node server inputs the business data query task into the trained node semantic big model to obtain a structured query statement, performs a node library table information query on the set node database based on the structured query statement to obtain business data, and feeds back the business data to the central node server;

[0033] The data processing unit is configured to, upon receiving business data matching the business data analysis task, perform statistical analysis on the business data based on the business data analysis task to obtain a data analysis result.

[0034] The task determination unit includes:

[0035] A task information determination module is configured to input task description information and data topology information into the trained central semantic big model to obtain task planning information;

[0036] The task extraction module is configured to extract the business data analysis task, at least one business data query task and the regional node server corresponding to each business data query task from the task planning information.

[0037] The task determination unit includes:

[0038] A description information extraction module, configured to extract data analysis description information, at least one regional location and data query description information corresponding to each regional location from the task description information;

[0039] A query information determination module is configured to determine, from the data topology information, at least one regional node server and the library table query parameters corresponding to each regional node server that meet the highest semantic similarity, according to at least one regional location and the data query description information corresponding to each regional location, wherein each regional node server is provided with a corresponding node server address;

[0040] A query task determination module is configured to determine the business data query task corresponding to each regional node server according to at least one regional node server, data query description information corresponding to each regional node server, and library table query parameters;

[0041] The analysis task determination module is configured to determine the business data analysis task based on the data analysis description information and the library table query parameters.

[0042] The data processing unit includes:

[0043] An analysis algorithm determination module, configured to determine a target statistical analysis algorithm from a set of statistical analysis algorithms based on a business data analysis task;

[0044] The input parameter determination module is configured to perform data integration processing on the business data to obtain the input parameter information of the target statistical analysis algorithm.

[0045] The data analysis module is configured to perform corresponding statistical analysis processing on the input parameter information according to the target statistical analysis algorithm to obtain the data analysis results.

[0046] The data topology information also includes access password information associated with each regional node server, and the device also includes:

[0047] The access password adding unit is configured to add the access password information associated with the corresponding regional node server to each business data query task.

[0048] The device further comprises:

[0049] The topology information determination unit is configured as follows:

[0050] Receive node metadata information fed back by node servers in each region; generate data topology information based on the node metadata information and basic attribute information corresponding to each regional node server.

[0051] The device further comprises:

[0052] The description information determination unit is configured as follows:

[0053] Receive the task voice information sent by the client; perform voice recognition on the task voice information to obtain task description information.

[0054] In a third aspect, an embodiment of the present application further provides an electronic device, the device comprising:

[0055] one or more processors;

[0056] a storage device configured to store one or more programs,

[0057] When the one or more programs are executed by the one or more processors, the one or more processors implement the business data processing method described in the embodiment of the present application.

[0058] In a fourth aspect, an embodiment of the present application further provides a non-volatile storage medium storing computer executable instructions, wherein the computer executable instructions are configured to execute the business data processing method described in the embodiment of the present application when executed by a computer processor.

[0059] In an embodiment of the present application, by obtaining task description information and data topology information, the data topology information records multiple regional node servers associated with the central node server and node library table information managed by each regional node server; according to the task description information and the data topology information, determine the business data analysis task, at least one business data query task and the regional node server corresponding to each business data query task; send each business data query task to the corresponding regional node server, so that the regional node server inputs the business data query task into the trained node semantic big model to obtain a structured query statement, and based on the structured query statement, query the node library table information of the set node database to obtain business data, and feedback the business data to the central node server; when receiving business data matching the business data analysis task, perform statistical analysis on the business data based on the business data analysis task to obtain data analysis results. In the above scheme, by determining the business data analysis task, at least one business data query task and the regional node server corresponding to each business data query task according to the task description information and the data topology information, it is possible to effectively generate tasks in accordance with different business analysis requirements based on the business analysis requirements provided by the task description information and the data distributed storage conditions provided by the data topology information, and reasonably divide the business data analysis tasks corresponding to the central node server and the business data query tasks corresponding to each regional node server to adapt to new analysis requirements or changes in data types; by sending the business data query tasks to the corresponding regional node servers respectively, and performing statistical analysis and processing on the feedback business data based on the business data analysis tasks to obtain data analysis results, it is possible to efficiently complete business data queries, and meet different data analysis requirements for business data processing, thereby improving data analysis efficiency and having high flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A flowchart of a business data processing method provided in an embodiment of the present application;

[0061] Figure 2 A flowchart of a specific implementation process of generating data topology information provided in an embodiment of the present application;

[0062] Figure 3 A data topology diagram corresponding to data topology information provided in an embodiment of the present application;

[0063] Figure 4 A flowchart of a specific implementation process of generating task description information provided in an embodiment of the present application;

[0064] Figure 5 A flowchart of a specific implementation process of determining a business data analysis task and a business data query task provided in an embodiment of the present application;

[0065] Figure 6 Another flowchart of a specific implementation process of determining a business data analysis task and a business data query task provided in an embodiment of the present application;

[0066] Figure 7 A flowchart of a specific implementation process of performing statistical analysis on business data based on a business data analysis task to obtain data analysis results provided in an embodiment of the present application;

[0067] Figure 8 A structural block diagram of a business data processing device provided in an embodiment of the present application;

[0068] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0069] The embodiments of the present application are further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the embodiments of the present application, rather than to limit the embodiments of the present application. It should also be noted that, for ease of description, only parts related to the embodiments of the present application are shown in the accompanying drawings, rather than all structures.

[0070] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0071] The business data processing method provided in the embodiment of the present application can be applied to the central festival server to query and process relevant business data based on task description information that meets user needs. The relevant application scenarios may include: e-commerce, e-health, smart cities, etc., suitable for different analysis tasks, such as market trend prediction, customer behavior analysis, operation effect evaluation, etc. The several application scenarios listed above are only exemplary and explanatory. In actual applications, the business data processing method can also be used in analysis tasks in other scenarios, and the embodiment of the present application does not limit this.

[0072] Figure 1This is a flowchart of a business data processing method provided in an embodiment of the present application. The business data processing method can be implemented with a central node server as the execution subject. Figure 1 As shown, the business data processing method specifically includes the following steps:

[0073] Step S110: Obtain task description information and data topology information, where the data topology information records multiple regional node servers associated with the central node server and node library table information managed by each regional node server.

[0074] It should be noted that the task description information may be text information describing the target library table data and analysis targets involved in the data analysis task expected by the user. For example, the specific content of the task description information is: compare the average number of target products sold by the target brand in North China in year y1 with the average number of target products sold in South China in each month, and determine which region has the most target products sold on average each month in year y1. For another example, the specific content of the task description information is: compare the total number of target products sold by the target brand in all regions in year y1 with the total number of target products sold by the target brand in all regions in year y2, and determine the proportional change in the total number of target products sold by the target brand in year y1 relative to year y2. The data topology information may be metadata information that records the hierarchical relationship of the pre-constructed distributed data network and nodes in different regions, for example, multiple regional node servers associated with the central node server in the embodiment of the present application and node library table information managed by each regional node server.

[0075] In a specific implementation, a process for generating data topology information is described. Figure 2 , which is a flowchart of a specific implementation process of generating data topology information provided by an embodiment of the present application. Before obtaining task description information and data topology information, such as Figure 2 As shown, the specific implementation steps for generating data topology information are as follows:

[0076] Step S101: Receive node metadata information fed back by node servers in each region.

[0077] Step S102: Generate data topology information based on the node metadata information and basic attribute information corresponding to each regional node server.

[0078] It is worth noting that the node metadata information can be a hierarchical description of the data managed by the node server in the region. For example, the node metadata information can include the hierarchical structure, table structure, field name and field description of the data. Specifically, by receiving the node metadata information fed back by the node servers in each region, and combining the basic attribute information corresponding to each regional node server, such as the node server address, data topology information can be generated accordingly to provide key reference information for the subsequent generation of business data analysis tasks and business data query tasks. For example, Figure 3 A data topology diagram corresponding to a data topology information provided in an embodiment of the present application, such as Figure 3 As shown, the central node server 110 is associated with multiple regional node servers 120, for example, the North China node server, the South China node server, the Northeast node server, etc. Each regional node server 120 is provided with corresponding node metadata information 130. For example, the node metadata information corresponding to the North China node includes node access password information and node library table information. The node library table information may include a TV table, a computer table, a washing machine table, etc. The TV table may include multiple fields, for example, field 1 is the name of the TV, field 2 is the sales volume of the TV, and field 3 is the price of the TV. The distributed data network storage architecture adopted in the embodiment of the present application can make the business data dispersedly stored in the databases managed by the corresponding regional node servers, and each database stores the business data corresponding to the region, thereby alleviating the storage pressure of large-scale data and improving the efficiency and reliability of data storage. In addition, the regional node server and the central node server can realize business data sharing and synchronization through the network, making data access more convenient. In addition, by receiving the node metadata information corresponding to each regional node server, basic reference information can be provided for subsequent data query and analysis, making the query and analysis process more efficient and accurate.

[0079] In another specific implementation, a process for generating task description information is described. Figure 4 , which is a flowchart of a specific implementation process of generating task description information provided by an embodiment of the present application. Before obtaining the task description information and data topology information, such as Figure 4 As shown, the specific implementation steps for generating task description information are as follows:

[0080] Step S103: receiving the task voice information sent by the client.

[0081] Step S104: Perform voice recognition on the task voice information to obtain task description information.

[0082] It should be noted that the user can describe the specific data analysis tasks that need to be performed by the central node server through voice input. Correspondingly, the client can send the collected task voice information to the central node server. After the central node server receives the task voice information, it can perform voice recognition on the task voice information to obtain the task description information in text form.

[0083] Step S120: Determine a business data analysis task, at least one business data query task, and a regional node server corresponding to each business data query task according to the task description information and the data topology information.

[0084] Among them, the business data analysis task can be a data processing operation that needs to be performed by the central node server, which can include data comparison, data anomaly detection, data statistical calculation, etc. The business data query task can be a data query operation that needs to be performed by the regional node server to query the required business data from the database. Optionally, different types of data processing information can be extracted from the task description information, and matched with the data topology information to determine the node server address, library table query parameters and other task parameters, and then generate the corresponding business data query tasks and business data analysis tasks. The task description information and data topology information can also be used as input information of the central semantic big model to obtain task planning information, and based on the task planning information, business data analysis tasks and business data query tasks can be extracted respectively.

[0085] In a specific embodiment, a process for determining a business data analysis task and a business data query task is described. Figure 5 , which is a flowchart of a specific implementation process of determining a business data analysis task and a business data query task provided by an embodiment of the present application, such as Figure 5 As shown, the specific implementation steps of determining the business data analysis task, at least one business data query task, and the regional node server corresponding to each business data query task according to the task description information and the data topology information are as follows:

[0086] Step S121: Input the task description information and data topology information into the trained central semantic big model to obtain task planning information.

[0087] Step S122: extracting a business data analysis task, at least one business data query task, and a regional node server corresponding to each business data query task from the task planning information.

[0088] Specifically, the central semantic big model can be obtained through supervised training based on the preset sample task description information, sample data topology information and target task planning information, and is used to analyze the specific data processing requirements of the task description information and convert it into specific task planning information. For example, the specific content of the task description information can be "compare the total number of target products sold by the target brand in all regions in year y1 with the total number of target products sold by the target brand in all regions in year y2, and determine the proportional change in the total number of target products sold by the target brand in year y1 relative to year y2". The regional node servers included in the data topology information include the North China node server, the South China node server and the Northeast China node server. Each regional node server stores the library table information corresponding to the target product, and the library table information records the specific data of the field "target product sales volume". After the task description information and data topology information are input into the trained central semantic big model, the task planning information can be obtained. The specific content of the task planning information can be "Step 1: The central node server sends a request to the North China node server, the South China node server and the Northeast node server respectively, "Query the target product sales volume corresponding to the target brand in year y1 and year y2"; Step 2: Receive the target product sales volume corresponding to the target brand in year y1 and year y2 respectively fed back by the North China node server, the South China node server and the Northeast node server; Step 3: The central node server sums up the target product sales volume corresponding to the target brand in year y1 fed back by the North China node server, the South China node server and the Northeast node server respectively to obtain the total product sales volume in year y1, and sums up the target product sales volume corresponding to the target brand in year y2 to obtain the total product sales volume in year y2, and then calculates the growth rate or decrease rate of the total product sales volume in year y1 and the total product sales volume in year y2." After the generation of the task planning information is completed, the business data analysis task, at least one business data query task and the regional node server corresponding to each business data query task can be extracted accordingly. For example, from the sample content of the task planning information provided above, we can extract the business data analysis task as "summing up the target product sales corresponding to the target brand in year y1 fed back by the North China node server, South China node server and Northeast China node server to get the total product sales in year y1, and summing up the target product sales corresponding to the target brand in year y2 to get the total product sales in year y2, and then calculating the growth rate or decline rate of the total product sales in year y1 and the total product sales in year y2." The business data query tasks corresponding to the North China node server, South China node server and Northeast China node server are "querying the target product sales corresponding to the target brand in years y1 and y2". Therefore, by using the central semantic big model to reasonably divide the data processing tasks corresponding to the central node server and the regional node server, we can accurately complete the task planning and meet the different business data processing needs.

[0089] In a specific embodiment, another process of determining a business data analysis task and a business data query task is described. Figure 6 , which is another flowchart of a specific implementation process of determining a business data analysis task and a business data query task provided by an embodiment of the present application, such as Figure 6 As shown, the specific implementation steps of determining the business data analysis task, at least one business data query task, and the regional node server corresponding to each business data query task according to the task description information and the data topology information are as follows:

[0090] Step S123: extracting data analysis description information, at least one regional location, and data query description information corresponding to each regional location from the task description information.

[0091] Among them, extracting data analysis description information, at least one regional location and data query description information corresponding to each regional location may be to pre-set corresponding text matching templates or reference keywords according to the grammatical habits and content distribution of the user's specific task description, and split the task description information into different types of description information based on the text matching templates or reference keywords. For example, the specific content of the task description information may be "compare the average number of target products sold by the target brand in North China in y1 year on average per month with the average number of target products sold in South China in y1 year on average per month, and determine which region has the most target products sold on average per month in y1 year", and accordingly, the regional locations include "North China" and "South China", and the data query description information corresponding to the "North China" is "the average number of target products sold by the target brand in y1 year on average per month", and the data query description information corresponding to the "South China" is "the average number of target products sold by the target brand in y1 year on average per month", and the data analysis description information is "compare and determine whether the target brand in North China or South China has the most target products sold on average per month in y1 year".

[0092] Step S124: According to at least one regional location and the data query description information corresponding to each regional location, determine from the data topology information at least one regional node server and the library table query parameters corresponding to each regional node server that meet the highest semantic similarity, and each regional node server is provided with a corresponding node server address.

[0093] It should be noted that after extracting the data analysis description information, at least one regional location and the data query description information corresponding to each regional location, the corresponding regional node server and the library table query parameters corresponding to each regional node server can be determined by performing semantic similarity calculation from the data topology information according to the regional location and the data query description information corresponding to each regional location. The semantic similarity calculation can be to convert the relevant information of the regional location, the corresponding data query description information and the data topology information into vectors, and perform vector similarity calculation to determine the semantic similarity. Specifically, the vector similarity calculation can be performed based on methods such as cosine similarity and Euclidean distance, which are not limited in this application. For example, the regional location is "North China", and the data query description information corresponding to the "North China" is "the average number of target products sold by the target brand per month in year y1". Through semantic similarity calculation, it can be determined that the "North China node server" is the best match for the "North China" in the data topology information, and the library table query parameter that best matches the "average number of target products sold by the target brand per month in year y1" is "y1 year, target brand and target product sales". Therefore, the field content "target product sales volume" that best matches the "quantity of target products sold" can be determined from the data topology information, and the same field content as the node library table information stored in the regional node server is provided for generating business data query tasks and business data analysis tasks, so that subsequent regional node servers and central node servers can understand and execute corresponding business data query tasks and business data analysis tasks respectively.

[0094] Step S125: Determine the business data query task corresponding to each regional node server according to at least one regional node server, the data query description information corresponding to each regional node server, and the library table query parameters.

[0095] Specifically, taking the regional location as "North China", the data query description information corresponding to the "North China" as "the average number of target products sold each month by the target brand in year y1", and the database table query parameters as "year y1, target brand and target product sales", some of the text content in the data query description information can be replaced with database table query parameters and paraphrased as a business data query task that meets the expected task description requirements. Then the "North China" corresponds to the "North China node server", and its business data query task is specifically "query the average monthly target product sales of the target brand in year y1".

[0096] Step S126: Determine the business data analysis task according to the data analysis description information and the library table query parameters.

[0097] Specifically, taking the data analysis description information as "Compare and determine whether the target brand in year y1 sells the most target products in North China or South China on average every month" and the database table query parameters as "y1 year, target brand and target product sales", some text content in the data analysis description information can be replaced with database table query parameters and paraphrased into a business data analysis task that meets the expected task description requirements, and its business data analysis task is specifically "Compare and determine the larger value of the target product sales of the target brand in North China and South China on average every month in year y1". Therefore, business data analysis tasks and business data query tasks that meet the preset description specifications can be divided according to the task description information and data topology information, which is conducive to the central node server and the regional node server to accurately understand the task content and perform corresponding data processing operations, thereby improving data processing efficiency.

[0098] Step S130, each business data query task is sent to the corresponding regional node server, so that the regional node server inputs the business data query task into the trained node semantic model to obtain a structured query statement, performs a node library table information query on the set node database based on the structured query statement to obtain business data, and feeds back the business data to the central node server.

[0099] It is worth noting that each regional node server can be provided with a trained node semantic big model, which can be obtained through supervised training based on pre-set sample business data query tasks and target structured query statements, and can be used to generate corresponding structured query statements based on business data query tasks. For example, "query the average monthly target product sales of the target brand in year y1" can be converted into a corresponding SQL query statement, which is used to query the average monthly target product sales of the target brand in year y1 from the node database. Therefore, the regional node server can accurately convert the business data query task into the structured query statement corresponding to the database by using the node semantic big model, and feed back the queried business data to the central node server to meet the query requirements of the business data, so that the central node server can efficiently aggregate the business data fed back by the node servers in different regions.

[0100] Optionally, the data topology information also includes access password information associated with each regional node server. Before sending each business data query task to the corresponding regional node server, it also includes: adding the access password information associated with the corresponding regional node server to each business data query task.

[0101] Among them, the access password information includes IP information, user name and password, which can be used to provide valid identity credentials to the regional node server, ensure the access security between the central node server and the regional node server, and prevent abnormal data leakage.

[0102] Step S140: When business data matching the business data analysis task is received, statistical analysis is performed on the business data based on the business data analysis task to obtain data analysis results.

[0103] In a specific embodiment, the business data analysis task may include simple data analysis objectives, such as sum calculation, ratio calculation, size comparison, etc. The central node server can directly process the business data using the corresponding statistical calculation method to obtain the data analysis result. In another specific embodiment, the business data analysis task may include complex data analysis objectives, which need to be processed in combination with a pre-set statistical analysis algorithm set. Please refer to Figure 7 , which is a flowchart of a specific implementation process of performing statistical analysis on business data based on a business data analysis task to obtain data analysis results provided by an embodiment of the present application, such as Figure 7 As shown, the specific implementation steps for performing statistical analysis on business data based on the business data analysis task to obtain data analysis results are as follows:

[0104] Step S141 : determining a target statistical analysis algorithm from a set of statistical analysis algorithms based on the business data analysis task.

[0105] It is worth noting that since the business data analysis task may include complex data analysis objectives, such as mutation detection of business data, seasonal change detection of business data, etc., a set of statistical analysis algorithms with better data processing effects can be pre-set, and the appropriate target statistical analysis algorithm can be selected according to the specific data analysis objectives of the business data analysis task, so as to efficiently complete the business data analysis and processing. For example, the specific content of the business data analysis task is "detect whether there is seasonal change anomaly in the sales volume of the target product of the target brand in South China in year y1", then the Seasonal_AD (Seasonal Anomaly Detection) algorithm can be selected from the statistical analysis algorithm set. For another example, the specific content of the business data analysis task is "detect whether there is mutation anomaly in the sales volume of the target product of the target brand in South China in year y1", then the Persist_AD (PersistAnomaly Detection) algorithm can be selected from the statistical analysis algorithm set.

[0106] Step S142: Perform data integration processing on the business data to obtain input parameter information of the target statistical analysis algorithm.

[0107] Among them, since different target statistical analysis algorithms have different requirements for the corresponding input parameter information, it is necessary to integrate the business data. For example, for the aforementioned time series anomaly detection algorithms such as the Seasonal_AD algorithm and the Persist_AD algorithm, it is necessary to convert the discrete business data into time series data, and convert the time series data into dimensionless numerical values ​​for standardization, etc. Thus, the business data can be converted into input parameter information that meets the input requirements of the target statistical analysis algorithm.

[0108] Step S143: Perform corresponding statistical analysis processing on the input parameter information according to the target statistical analysis algorithm to obtain data analysis results.

[0109] Therefore, the target statistical analysis algorithm can be used to accurately analyze business data, output data analysis results, and efficiently complete data analysis goals, which is suitable for complex data analysis and detection application scenarios.

[0110] In the above, by obtaining task description information and data topology information, the data topology information records multiple regional node servers associated with the central node server and the node library table information managed by each regional node server; according to the task description information and data topology information, determine the business data analysis task, at least one business data query task and the regional node server corresponding to each business data query task; send each business data query task to the corresponding regional node server, so that the regional node server inputs the business data query task into the trained node semantic big model to obtain a structured query statement, and based on the structured query statement, query the node library table information of the set node database to obtain business data, and feedback the business data to the central node server; when receiving business data matching the business data analysis task, perform statistical analysis on the business data based on the business data analysis task to obtain data analysis results. In the above scheme, by determining the business data analysis task, at least one business data query task and the regional node server corresponding to each business data query task according to the task description information and the data topology information, it is possible to effectively generate tasks based on the business analysis requirements provided by the task description information and the data distributed storage conditions provided by the data topology information to adapt to different business analysis requirements, and reasonably divide the business data analysis tasks corresponding to the central node server and the business data query tasks corresponding to each regional node server to adapt to new analysis requirements or changes in data types; by sending the business data query tasks to the corresponding regional node servers respectively, and performing statistical analysis and processing on the feedback business data based on the business data analysis tasks to obtain data analysis results, it is possible to efficiently complete business data queries, and meet different data analysis requirements for business data processing, thereby improving data analysis efficiency and having high flexibility.

[0111] Figure 8 This is a structural block diagram of a business data processing device provided in an embodiment of the present application. The device is configured to execute the business data processing method provided in the above embodiment and has the corresponding functional modules and beneficial effects of the execution method. Figure 8 As shown, the device specifically includes:

[0112] The acquisition unit 210 is configured to acquire task description information and data topology information, wherein the data topology information records multiple regional node servers associated with the central node server and node library table information managed by each regional node server;

[0113] The task determination unit 220 is configured to determine the business data analysis task, at least one business data query task and the regional node server corresponding to each business data query task according to the task description information and the data topology information;

[0114] The task distribution unit 230 is configured to send each business data query task to the corresponding regional node server, so that the regional node server inputs the business data query task into the trained node semantic big model to obtain a structured query statement, performs a node database table information query on the set node database based on the structured query statement to obtain business data, and feeds back the business data to the central node server;

[0115] The data processing unit 240 is configured to, upon receiving business data matching the business data analysis task, perform statistical analysis on the business data based on the business data analysis task to obtain a data analysis result.

[0116] As described above, by determining the business data analysis task, at least one business data query task and the regional node server corresponding to each business data query task according to the task description information and the data topology information, it is possible to effectively generate tasks based on the business analysis requirements provided by the task description information and the data distributed storage conditions provided by the data topology information, adapt to different business analysis requirements, and reasonably divide the business data analysis tasks corresponding to the central node server and the business data query tasks corresponding to each regional node server to adapt to new analysis requirements or changes in data types; by sending the business data query tasks to the corresponding regional node servers respectively, and performing statistical analysis and processing on the feedback business data based on the business data analysis tasks to obtain data analysis results, it is possible to efficiently complete business data queries, and meet different data analysis requirements for business data processing, thereby improving data analysis efficiency and having high flexibility.

[0117] In a possible embodiment, the task determination unit 220 includes:

[0118] A task information determination module is configured to input task description information and data topology information into the trained central semantic big model to obtain task planning information;

[0119] The task extraction module is configured to extract the business data analysis task, at least one business data query task and the regional node server corresponding to each business data query task from the task planning information.

[0120] In a possible embodiment, the task determination unit 220 includes:

[0121] A description information extraction module, configured to extract data analysis description information, at least one regional location and data query description information corresponding to each regional location from the task description information;

[0122] A query information determination module is configured to determine, from the data topology information, at least one regional node server and the library table query parameters corresponding to each regional node server that meet the highest semantic similarity, according to at least one regional location and the data query description information corresponding to each regional location, wherein each regional node server is provided with a corresponding node server address;

[0123] A query task determination module is configured to determine the business data query task corresponding to each regional node server according to at least one regional node server, data query description information corresponding to each regional node server, and library table query parameters;

[0124] The analysis task determination module is configured to determine the business data analysis task based on the data analysis description information and the library table query parameters.

[0125] In a possible embodiment, the data processing unit 240 includes:

[0126] An analysis algorithm determination module, configured to determine a target statistical analysis algorithm from a set of statistical analysis algorithms based on a business data analysis task;

[0127] The input parameter determination module is configured to perform data integration processing on the business data to obtain the input parameter information of the target statistical analysis algorithm.

[0128] The data analysis module is configured to perform corresponding statistical analysis processing on the input parameter information according to the target statistical analysis algorithm to obtain the data analysis results.

[0129] In a possible embodiment, the data topology information further includes access password information associated with each regional node server, and the device further includes:

[0130] The access password adding unit is configured to add the access password information associated with the corresponding regional node server to each business data query task.

[0131] In a possible embodiment, the device further includes:

[0132] The topology information determination unit is configured as follows:

[0133] Receive node metadata information fed back by node servers in each region; generate data topology information based on the node metadata information and basic attribute information corresponding to each regional node server.

[0134] In a possible embodiment, the device further includes:

[0135] The description information determination unit is configured as follows:

[0136] Receive the task voice information sent by the client; perform voice recognition on the task voice information to obtain task description information.

[0137] Fig. 9 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Fig. 9 As shown, the device includes a processor 310, a memory 320, an input device 330 and an output device 340; the number of processors 310 in the device can be one or more. Fig. 9 A processor 310 is taken as an example; the processor 310, memory 320, input device 330 and output device 340 in the device can be connected via a bus or other means. Fig. 9 The example of connecting through a bus is taken. The memory 320, as a computer-readable storage medium, can be configured to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the business data processing method in the embodiment of the present application. The processor 310 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 320, that is, implements the above-mentioned business data processing method. The input device 330 can be configured to receive input digital or character information, and generate key signal input related to user settings and function control of the device. The output device 340 may include display devices such as display screens.

[0138] The electronic device provided above can be used to execute the business data processing method provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0139] An embodiment of the present application also provides a non-volatile storage medium containing computer executable instructions, which are configured to execute a business data processing method described in the above embodiment when executed by a computer processor, including: obtaining task description information and data topology information, the data topology information recording multiple regional node servers associated with the central node server and node library table information managed by each regional node server; determining a business data analysis task, at least one business data query task and the regional node server corresponding to each business data query task according to the task description information and the data topology information; sending each business data query task to the corresponding regional node server, so that the regional node server inputs the business data query task into the trained node semantic big model to obtain a structured query statement, querying the node library table information of the set node database based on the structured query statement to obtain business data, and feeding back the business data to the central node server; when receiving business data matching the business data analysis task, performing statistical analysis on the business data based on the business data analysis task to obtain a data analysis result.

[0140] Storage medium - any of various types of memory devices or storage devices. The term "storage medium" is intended to include: installation media, such as CD-ROM, floppy disk or tape device; computer system memory or random access memory, such as DRAM, DDR RAM, SRAM, EDO RAM, Rambus RAM, etc.; non-volatile memory, such as flash memory, magnetic media or optical storage; registers or other similar types of memory elements, etc. Storage media may also include other types of memory or combinations thereof. In addition, the storage medium may be located in the first computer system in which the program is executed, or may be located in a different second computer system, which is connected to the first computer system via a network (such as the Internet). The second computer system can provide program instructions to the first computer for execution. The term "storage medium" may include two or more storage media residing in different locations (e.g., in different computer systems connected via a network). The storage medium may store program instructions (e.g., embodied as a computer program) that can be executed by one or more processors.

[0141] Of course, the storage medium containing computer executable instructions provided in the embodiment of the present application, whose computer executable instructions are not limited to the above business data processing method, can also execute related operations in the business data processing method provided in any embodiment of the present application.

[0142] It is worth noting that in the embodiments of the above-mentioned electronic device, the various units and modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not configured to limit the protection scope of the embodiments of the present application.

[0143] It should be noted that the numbering of each step in this scheme is only used to describe the overall design framework of this scheme, and does not represent the necessary order relationship between the steps. On the basis that the overall implementation process conforms to the overall design framework of this scheme, it belongs to the protection scope of this scheme, and the order of precedence in the form of text during description is not an exclusive limitation on the specific implementation process of this scheme. It should be understood by those skilled in the art that the embodiments of the present application can be provided as methods, systems, or computer program products. In a typical configuration, a computing device includes one or more processors (CPU), an input / output interface, a network interface, and a memory. The memory may include non-permanent memory in a computer-readable medium, a random access memory (RAM) and / or a non-volatile memory in the form of a read-only memory (ROM) or a flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0144] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0145] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A business data processing method, applied to a central node server, characterized in that: include: Acquire task description information and data topology information, wherein the data topology information records multiple regional node servers associated with the central node server and node library table information managed by each of the regional node servers; Determine, according to the task description information and the data topology information, a business data analysis task, at least one business data query task, and a regional node server corresponding to each of the business data query tasks; Send each of the business data query tasks to the corresponding regional node server, so that the regional node server inputs the business data query task into the trained node semantic big model to obtain a structured query statement, performs a node library table information query on the set node database based on the structured query statement to obtain business data, and feeds back the business data to the central node server; When business data matching the business data analysis task is received, statistical analysis processing is performed on the business data based on the business data analysis task to obtain a data analysis result.

2. The business data processing method according to claim 1, characterized in that: The determining, according to the task description information and the data topology information, a business data analysis task, at least one business data query task, and a regional node server corresponding to each of the business data query tasks comprises: Inputting the task description information and the data topology information into the trained central semantic big model to obtain task planning information; A business data analysis task, at least one business data query task, and a regional node server corresponding to each of the business data query tasks are extracted from the task planning information.

3. The business data processing method according to claim 1, characterized in that: The determining, according to the task description information and the data topology information, a business data analysis task, at least one business data query task, and a regional node server corresponding to each of the business data query tasks comprises: Extracting data analysis description information, at least one regional location, and data query description information corresponding to each regional location from the task description information; According to the at least one regional location and the data query description information corresponding to each of the regional locations, determining from the data topology information at least one regional node server and the library table query parameters corresponding to each of the regional node servers that satisfy the highest semantic similarity, each of the regional node servers being provided with a corresponding node server address; Determine the business data query task corresponding to each of the regional node servers according to at least one regional node server, data query description information corresponding to each of the regional node servers, and library table query parameters; Determine the business data analysis task according to the data analysis description information and the library table query parameters.

4. The business data processing method according to claim 1, characterized in that: The performing statistical analysis on the business data based on the business data analysis task to obtain a data analysis result includes: Determine a target statistical analysis algorithm from a set of statistical analysis algorithms based on the business data analysis task; Performing data integration processing on the business data to obtain input parameter information of the target statistical analysis algorithm, The input parameter information is subjected to corresponding statistical analysis processing according to the target statistical analysis algorithm to obtain a data analysis result.

5. The business data processing method according to claim 1, characterized in that: The data topology information also includes access password information associated with each of the regional node servers, and before sending each of the business data query tasks to the corresponding regional node server, it also includes: Add access password information associated with the corresponding regional node server to each of the business data query tasks.

6. The business data processing method according to any one of claims 1 to 5, characterized in that: Before obtaining the task description information and the data topology information, the method further includes: Receive node metadata information fed back by node servers in each region; Data topology information is generated based on the node metadata information and basic attribute information corresponding to each regional node server.

7. The business data processing method according to any one of claims 1 to 5, characterized in that: Before obtaining the task description information and the data topology information, the method further includes: Receive task voice information sent by the client; Perform voice recognition on the task voice information to obtain task description information.

8. A business data processing device, characterized in that: include: An acquisition unit configured to acquire task description information and data topology information, wherein the data topology information records multiple regional node servers associated with the central node server and node library table information managed by each of the regional node servers; A task determination unit, configured to determine a business data analysis task, at least one business data query task, and a regional node server corresponding to each of the business data query tasks according to the task description information and the data topology information; The task distribution unit is configured to send each of the business data query tasks to the corresponding regional node server, so that the regional node server inputs the business data query task into the trained node semantic big model to obtain a structured query statement, performs a node library table information query on the set node database based on the structured query statement to obtain business data, and feeds back the business data to the central node server; The data processing unit is configured to, upon receiving business data matching the business data analysis task, perform statistical analysis on the business data based on the business data analysis task to obtain a data analysis result.

9. An electronic device, comprising: one or more processors; A storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, enables the one or more processors to implement the business data processing method described in any one of claims 1-7.

10. A non-volatile storage medium storing computer executable instructions, wherein the computer executable instructions are configured to execute the business data processing method according to any one of claims 1 to 7 when executed by a computer processor.

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