Data asynchronous link processing method, device, electronic device and storage medium

Abstract

The present invention relates to artificial intelligence technology and discloses a method for processing asynchronous data links, comprising: constructing an indicator dependency table based on the dependency relationships in the original indicator data, constructing at least two data partitions based on the indicator timeliness in the indicator dependency table, importing the data in the indicator dependency table into the data partitions based on the indicator timeliness, constructing an asynchronous link task, mobilizing the data partitions into which the imported data has been imported according to the asynchronous link task to obtain standard indicator data, performing indicator aggregation on the standard indicator data, and performing indicator ranking based on the aggregation results to obtain indicator ranking results. In addition, the present invention also relates to blockchain technology, and the original indicator data can be obtained from the nodes of the blockchain. The present invention also proposes a data asynchronous link processing device, an electronic device, and a readable storage medium. The present invention can solve the problem of low data processing timeliness.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a data asynchronous link processing method, device, electronic device and readable storage medium. Background Art

[0002] Currently, the data warehouse / data processing architecture in the industry is not designed in a single layer for different topics and business scenarios. Instead, it is often multi-layered and complex. For example, there is a user-granular detail layer, a lightly aggregated middle layer, a service layer that provides interfaces, and even more detailed layers such as the ODS data preparation layer and the DATAMARKET data mart layer. Each layer is responsible for different functions and addresses a variety of demand scenarios. Therefore, the architectural links of data warehouses / data processing are generally designed to be layer-by-layer dependent. Only after the upper-layer tasks are completed can the lower-layer tasks be started. This ensures that the upper-layer data is complete when used downstream. Therefore, the actual output time of the final result of the entire link is the sum of the processing time of each layer of tasks. However, when the business has high timeliness requirements, there is no room for optimization of the upstream data supply time. Even in scenarios where the database environment fluctuates, traditional data links will not be able to meet business needs.

[0003] Existing technologies often attempt to solve the problem through the following methods: 1. Optimizing the code at each level and streamlining the steps. However, the upstream code may have already been optimized, and the optimization space has been exhausted. The more levels there are, the more code needs to be optimized, which is time-consuming and labor-intensive with little effect. 2. Removing the dependencies between tasks at each level and using the latest partition data from each upstream table. However, before executing each layer of code, the partition arrival status of all upstream tasks, i.e., the validity of the data, needs to be checked. This bloated code judgment logic makes it difficult to track the final result's references to upstream partitions. 3. In the case of multiple upstream sources, if the T-1 partition of one upstream source does not meet the timeliness standard, using data from the T-2 partition for all upstream sources will result in low data accuracy and a wide impact downstream. Summary of the Invention

[0004] The present invention provides a data asynchronous link processing method, device, electronic device and readable storage medium, the main purpose of which is to solve the problem of low timeliness of data processing.

[0005] To achieve the above-mentioned object, the present invention provides a data asynchronous link processing method, comprising:

[0006] Obtaining original indicator data, and constructing an indicator dependency table based on dependency relationships in the original indicator data;

[0007] Constructing at least two data partitions according to the indicator timeliness in the indicator dependency table, and importing the data in the indicator dependency table into the data partitions according to the indicator timeliness;

[0008] Constructing an asynchronous link task, and mobilizing data on the data partitions after importing the data according to the asynchronous link task to obtain standard indicator data;

[0009] The standard indicator data is index aggregated, and the indicators are ranked according to the aggregation results to obtain an indicator ranking result.

[0010] Optionally, the acquiring original indicator data and constructing an indicator dependency table according to dependency relationships in the original indicator data includes:

[0011] Extracting indicator themes from the original indicator data, and establishing a dependency relationship between the indicator themes and the original indicator data;

[0012] The original indicator data after the dependency relationship is constructed is aggregated and classified according to a preset aggregation granularity to obtain the indicator dependency table.

[0013] Optionally, importing the data in the indicator dependency table into the data partition according to the indicator timeliness includes:

[0014] Determine whether the aging time of the indicator data in the indicator dependency table meets the preset first aging time;

[0015] Importing the indicator data in the indicator dependency table that meets the first aging time into the first aging partition;

[0016] Import the indicator data in the indicator dependency table that does not meet the first aging time into the second aging partition.

[0017] Optionally, after importing the data in the indicator dependency table into the data partition according to the indicator timeliness, the method further includes:

[0018] Marking the indicator data that meets the first aging time as achieved aging data, and marking the indicator data that does not meet the first aging time as unachieved aging data;

[0019] Build a time monitoring table based on all marked completed data.

[0020] Optionally, mobilizing data partitions after importing data according to the asynchronous link task to obtain standard indicator data includes:

[0021] Starting the asynchronous link task at a preset second aging time;

[0022] The asynchronous link task is used to copy and fill the data of the second time-sensitive partition into the first time-sensitive partition, and the data in the filled first time-sensitive partition is used as the standard indicator data.

[0023] Optionally, the index aggregation of the standard index data and the index ranking according to the aggregation result to obtain the index ranking result include:

[0024] Aggregating the indicators in the standard indicator data according to the aggregation granularity, searching for the hit status and score details of the indicator aggregation in the preset scoring threshold table, summarizing the score details of all indicators, and obtaining an indicator ranking table;

[0025] The indicator ranking table is visualized using a pre-built visualization plug-in, and the visualization result is used as the indicator ranking result.

[0026] Optionally, constructing at least two data partitions according to the indicator timeliness in the indicator dependency table includes: determining a key timeliness node in the indicator dependency table, and constructing a first timeliness partition and a second timeliness partition according to the key timeliness node.

[0027] In order to solve the above problems, the present invention further provides a data asynchronous link processing device, the device comprising:

[0028] An indicator dependency table construction module is used to obtain original indicator data and construct an indicator dependency table based on the dependency relationship in the original indicator data;

[0029] A data partitioning module, configured to construct at least two data partitions according to the indicator timeliness in the indicator dependency table, and import the data in the indicator dependency table into the data partitions according to the indicator timeliness;

[0030] An asynchronous mobilization module is used to construct an asynchronous link task, and to mobilize data partitions after importing data according to the asynchronous link task to obtain standard indicator data;

[0031] The indicator aggregation module is used to perform indicator aggregation on the standard indicator data, and to rank the indicators according to the aggregation results to obtain indicator ranking results.

[0032] In order to solve the above problem, the present invention further provides an electronic device, comprising:

[0033] a memory storing at least one computer program; and

[0034] The processor executes the computer program stored in the memory to implement the above-mentioned data asynchronous link processing method.

[0035] In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned data asynchronous link processing method.

[0036] The present invention constructs an indicator dependency table based on the dependency relationships in the original indicator data without removing the dependencies between the data. During link processing, it is not necessary to detect the validity of the data at each layer, thereby improving the timeliness of data processing. At the same time, the data in the indicator dependency table is imported into the data partition according to the indicator timeliness, and the data partition after the imported data is mobilized according to the asynchronous link task to obtain standard indicator data. Since the asynchronous link task does not affect the processing logic of the original link, and the data in the data partition is adjusted based on the asynchronous link task, the data processing timeliness is improved while also improving the accuracy of data processing. Therefore, the data asynchronous link processing method, device, electronic device, and computer-readable storage medium proposed by the present invention can solve the problem of low data processing timeliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A schematic diagram of a flow chart of a data asynchronous link processing method provided by one embodiment of the present invention;

[0038] Figure 2 for Figure 1 A detailed implementation flow chart of one of the steps in the

[0039] Figure 3 for Figure 1 A detailed implementation flow diagram of another step in FIG;

[0040] Figure 4 for Figure 1 A detailed implementation flow diagram of another step in FIG;

[0041] Figure 5 for Figure 1 A detailed implementation flow diagram of another step in FIG;

[0042] Figure 6 A functional module diagram of a data asynchronous link processing device provided by one embodiment of the present invention;

[0043] Figure 7 A schematic structural diagram of an electronic device for implementing the data asynchronous link processing method provided by one embodiment of the present invention.

[0044] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0045] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0046] An embodiment of the present invention provides a method for processing asynchronous data links. The execution subject of the asynchronous data link processing method includes, but is not limited to, at least one of electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present invention. In other words, the asynchronous data link processing method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0047] Reference Figure 1 FIG. 1 is a flow chart of a data asynchronous link processing method according to an embodiment of the present invention. In this embodiment, the data asynchronous link processing method includes the following steps S1-S5:

[0048] S1. Obtain original indicator data, and construct an indicator dependency table based on the dependency relationship in the original indicator data.

[0049] In an embodiment of the present invention, the original indicator data includes upstream atomic indicator data tables of different departments. For example, in the insurance field, the original indicator data includes tabular data of atomic indicators such as monthly average FYC, monthly average performance, core manpower at the end of the month, supervisor recruitment activity rate, job rate, and per capita invitation rate.

[0050] Among them, the dependency relationship refers to using a preset theme as a dimension to construct a dependency relationship between the theme and the data, thereby refining the original data set and improving the accuracy of data processing. The preset theme can be an indicator theme in the original indicator data, such as monthly average FYC, monthly average performance, core manpower at the end of the month, etc.

[0051] For details, refer to Figure 2 As shown, the acquisition of original indicator data and the construction of an indicator dependency table according to the dependency relationship in the original indicator data include the following steps S10-S11:

[0052] S10, extracting indicator themes from the original indicator data, and establishing a dependency relationship between the indicator themes and the original indicator data;

[0053] S11. Aggregate and classify the original indicator data after the dependency relationship is constructed according to a preset aggregation granularity to obtain the indicator dependency table.

[0054] In the embodiment of the present invention, since the upstream raw indicator data includes dozens or even hundreds of raw indicator tables provided by different departments, data processing of the raw indicator tables based on preset themes and preset aggregation granularity can refine the indicator data and improve the response speed to the data. For example, based on different themes, subject-dependent tables such as Agent Detail Table A, Agent Detail Table B, and Agent Detail Table C can be processed. By aggregating the data at the department-level granularity, indicator-dependent tables such as Department-Level Summary Table A, Department-Level Summary Table B, and Department-Level Summary Table C can be obtained.

[0055] S2. Construct at least two data partitions according to the indicator timeliness in the indicator dependency table, and import the data in the indicator dependency table into the data partitions according to the indicator timeliness.

[0056] In the embodiment of the present invention, since different indicators have different timeliness requirements, for example, the data timeliness of indicators such as the average monthly FYC and the average monthly performance is relatively strong, if data with different timeliness are processed uniformly, it will cause the data processing response to be longer and reduce the data processing speed. Therefore, partitioning according to the timeliness of the indicators can improve the efficiency of data processing.

[0057] Specifically, constructing at least two data partitions according to the indicator timeliness in the indicator dependency table includes: determining a key timeliness node in the indicator dependency table, and constructing a first timeliness partition and a second timeliness partition according to the key timeliness node.

[0058] In an embodiment of the present invention, taking data in a certain insurance field as an example, there are relatively strict requirements on the timeliness of data processing. The insurance business requires that the ranking data of the sales department (such as the rating ranking) can be obtained before 8:30 every day, and the data calculation of the entire scoring and rating system takes about 150 minutes. Working back to the upstream atomic indicator layer, data must be provided before 6 a.m. If some atomic indicator data does not arrive before 6 a.m., the dependent tasks in the lower layer are likely to be delayed. Therefore, 6 a.m. can be used as the key timeliness node, and two timeliness partitions T-1 and T-2 can be constructed to improve the timeliness of data processing.

[0059] In the embodiment of the present invention, referring to Figure 3 As shown, the method of importing the data in the indicator dependency table into the data partition according to the indicator timeliness includes the following steps S20-S22:

[0060] S20, determining whether the aging time of the indicator data in the indicator dependency table meets a preset first aging time;

[0061] S21. Importing the indicator data in the indicator dependency table that meets the first aging time into the first aging partition;

[0062] S22: Import the indicator data in the indicator dependency table that does not meet the first aging time into the second aging partition.

[0063] In an embodiment of the present invention, importing data from an indicator dependency table into a data partition based on indicator timeliness means dividing the indicator data into different partitioned data based on a set time point. At the same time, to facilitate data buffering, the first timeliness time and the key timeliness node can be different. For example, partitioning is performed based on the completion time of the indicator data, where data with a completion time before 7 a.m. is divided into the data of the first timeliness partition (T-1 partition), and data with a completion time after 7 a.m. is divided into the data of the second timeliness partition (T-2 partition).

[0064] In another optional embodiment of the present invention, after importing the data in the indicator dependency table into the data partition according to the indicator timeliness, the method further includes:

[0065] Marking the indicator data that meets the first aging time as achieved aging data, and marking the indicator data that does not meet the first aging time as unachieved aging data;

[0066] Build a time monitoring table based on all marked completed data.

[0067] In an embodiment of the present invention, since all the data in the T-1 partition meet the first aging time, and all the data in the T-1 partition do not meet the first aging time, monitoring can be carried out conveniently. The aging monitoring table includes: indicator name, completion time, aging requirement, whether the aging is achieved, etc. For example, the indicator name is monthly average FYC, the completion time is 2021-12-10 06:12:47, the aging requirement is aging T-1, and whether the aging is achieved is yes; the indicator name is job completion rate, the completion time is 2021-12-10 07:12:07, the aging requirement is aging T-1, and whether the aging is achieved is no.

[0068] Furthermore, by using T-1 or T-2 data to set monitoring tasks, the monitoring efficiency of indicator data and data traceability are improved.

[0069] S3. Construct an asynchronous link task, and mobilize data on the data partitions after importing the data according to the asynchronous link task to obtain standard indicator data.

[0070] In the embodiment of the present invention, in addition to the processing link of the original indicator data, an asynchronous link task is set to mobilize the data in the two time-sensitive partitions. Since it is an asynchronous link, it will not affect the data processing of the original link.

[0071] For details, refer to Figure 4As shown, the data partitions after importing data are mobilized according to the asynchronous link task to obtain standard indicator data, including the following steps S30-S31:

[0072] S30, starting the asynchronous link task at a preset second aging time;

[0073] S31. Use the asynchronous link task to copy the data of the second time-sensitive partition to fill the first time-sensitive partition, and use the filled data in the first time-sensitive partition as the standard indicator data.

[0074] In an optional embodiment of the present invention, an asynchronous link task is started at 1 a.m. (i.e., the second time limit), and the data of the T-2 partition (such as the departmental course summary table) is copied and filled into the T-1 partition. Since the task content is simple, this will be completed within a few minutes, and the task of the T-1 partition will not be affected by the asynchronous link. After the task is completed, the T-1 partition will be overwritten again anyway, which is equivalent to the downstream task being able to use the data of the T-1 partition first, ensuring that the T-1 partition must have data (it may be T-1 data or T-2 data) and will not be an empty value.

[0075] S4. Aggregate the standard indicator data, and rank the indicators based on the aggregation results to obtain an indicator ranking result.

[0076] In the present embodiment, indicator aggregation refers to calculating the hit status of each indicator in the scoring threshold table and the score details according to the preset scoring rules, and aggregating them into a scoring table. Based on the score details, a ranking table is then compiled to show the rating and ranking of each indicator as the indicator ranking result.

[0077] Further, refer to Figure 5 As shown, the index aggregation of the standard index data and the index ranking according to the aggregation result to obtain the index ranking result include the following steps S40-S41:

[0078] S40: Aggregate the indicators in the standard indicator data according to the aggregation granularity, search for hits and score details of the indicator aggregation in a preset scoring threshold table, summarize the score details of all indicators, and obtain an indicator ranking table;

[0079] S41. Visualize the indicator ranking table using a pre-built visualization plug-in, and use the visualization result as the indicator ranking result.

[0080] In an optional embodiment of the present invention, for example, based on the granularity of each course, the scoring rules are used for the course summary tables A, B, and C to calculate the hit status of each indicator of each course in the scoring threshold table and the score details, and aggregate them into a scoring table. Based on the score details and preset rules (weight rules, etc.), an indicator ranking table of the rating and ranking of each course is statistically calculated. The indicator ranking table will eventually be pushed to the data visualization platform and presented to users for query based on visualization plug-ins (such as ECharts).

[0081] The present invention constructs an indicator dependency table based on the dependency relationships in the original indicator data without removing the dependencies between the data. During link processing, it is not necessary to detect the validity of the data at each layer, thereby improving the timeliness of data processing. At the same time, the data in the indicator dependency table is imported into the data partition according to the indicator timeliness, and the data partition after the imported data is mobilized according to the asynchronous link task to obtain standard indicator data. Since the asynchronous link task does not affect the processing logic of the original link, and the data in the data partition is adjusted based on the asynchronous link task, the data processing timeliness is improved while also improving the accuracy of data processing. Therefore, the data asynchronous link processing method proposed by the present invention can solve the problem of low data processing timeliness.

[0082] like Figure 6 , which is a functional module diagram of a data asynchronous link processing device provided by one embodiment of the present invention.

[0083] The data asynchronous link processing device 100 described in the present invention can be installed in an electronic device. Depending on the functionality to be implemented, the data asynchronous link processing device 100 may include an indicator dependency table construction module 101, a data partitioning module 102, an asynchronous mobilization module 103, and an indicator aggregation module 104. A module, also referred to as a unit, is a series of computer program segments that can be executed by an electronic device processor and perform a fixed function, and is stored in the electronic device's memory.

[0084] In this embodiment, the functions of each module / unit are as follows:

[0085] The indicator dependency table construction module 101 is used to obtain original indicator data and construct an indicator dependency table based on the dependency relationship in the original indicator data;

[0086] The data partitioning module 102 is configured to construct at least two data partitions according to the indicator timeliness in the indicator dependency table, and import the data in the indicator dependency table into the data partitions according to the indicator timeliness;

[0087] The asynchronous mobilization module 103 is used to construct an asynchronous link task, and to mobilize the data partitions after the imported data according to the asynchronous link task to obtain standard indicator data;

[0088] The indicator aggregation module 104 is used to perform indicator aggregation on the standard indicator data, and perform indicator ranking according to the aggregation result to obtain an indicator ranking result.

[0089] In detail, the specific implementation of each module of the data asynchronous link processing device 100 is as follows:

[0090] Step 1: Obtain original indicator data and construct an indicator dependency table based on the dependency relationship in the original indicator data.

[0091] In an embodiment of the present invention, the original indicator data includes upstream atomic indicator data tables of different departments. For example, in the insurance field, the original indicator data includes tabular data of atomic indicators such as monthly average FYC, monthly average performance, core manpower at the end of the month, supervisor recruitment activity rate, job rate, and per capita invitation rate.

[0092] Among them, the dependency relationship refers to using a preset theme as a dimension to construct a dependency relationship between the theme and the data, thereby refining the original data set and improving the accuracy of data processing. The preset theme can be an indicator theme in the original indicator data, such as monthly average FYC, monthly average performance, core manpower at the end of the month, etc.

[0093] In detail, the obtaining of original indicator data and constructing an indicator dependency table according to the dependency relationship in the original indicator data include:

[0094] Extracting indicator themes from the original indicator data, and establishing a dependency relationship between the indicator themes and the original indicator data;

[0095] The original indicator data after the dependency relationship is constructed is aggregated and classified according to a preset aggregation granularity to obtain the indicator dependency table.

[0096] In the embodiment of the present invention, since the upstream raw indicator data includes dozens or even hundreds of raw indicator tables provided by different departments, data processing of the raw indicator tables based on preset themes and preset aggregation granularity can refine the indicator data and improve the response speed to the data. For example, based on different themes, subject-dependent tables such as Agent Detail Table A, Agent Detail Table B, and Agent Detail Table C can be processed. By aggregating the data at the department-level granularity, indicator-dependent tables such as Department-Level Summary Table A, Department-Level Summary Table B, and Department-Level Summary Table C can be obtained.

[0097] Step 2: construct at least two data partitions according to the indicator timeliness in the indicator dependency table, and import the data in the indicator dependency table into the data partitions according to the indicator timeliness.

[0098] In the embodiment of the present invention, since different indicators have different timeliness requirements, for example, the data timeliness of indicators such as the average monthly FYC and the average monthly performance is relatively strong, if data with different timeliness are processed uniformly, it will cause the data processing response to be longer and reduce the data processing speed. Therefore, partitioning according to the timeliness of the indicators can improve the efficiency of data processing.

[0099] Specifically, constructing at least two data partitions according to the indicator timeliness in the indicator dependency table includes: determining a key timeliness node in the indicator dependency table, and constructing a first timeliness partition and a second timeliness partition according to the key timeliness node.

[0100] In an embodiment of the present invention, taking data in a certain insurance field as an example, there are relatively strict requirements on the timeliness of data processing. The insurance business requires that the ranking data of the sales department (such as the rating ranking) can be obtained before 8:30 every day, and the data calculation of the entire scoring and rating system takes about 150 minutes. Working back to the upstream atomic indicator layer, data must be provided before 6 a.m. If some atomic indicator data does not arrive before 6 a.m., the dependent tasks in the lower layer are likely to be delayed. Therefore, 6 a.m. can be used as the key timeliness node, and two timeliness partitions T-1 and T-2 can be constructed to improve the timeliness of data processing.

[0101] In the embodiment of the present invention, the step of importing the data in the indicator dependency table into the data partition according to the indicator timeliness includes:

[0102] Determine whether the aging time of the indicator data in the indicator dependency table meets the preset first aging time;

[0103] Importing the indicator data in the indicator dependency table that meets the first aging time into the first aging partition;

[0104] Import the indicator data in the indicator dependency table that does not meet the first aging time into the second aging partition.

[0105] In an embodiment of the present invention, importing data from an indicator dependency table into a data partition based on indicator timeliness means dividing the indicator data into different partitioned data based on a set time point. At the same time, to facilitate data buffering, the first timeliness time and the key timeliness node can be different. For example, partitioning is performed based on the completion time of the indicator data, where data with a completion time before 7 a.m. is divided into the data of the first timeliness partition (T-1 partition), and data with a completion time after 7 a.m. is divided into the data of the second timeliness partition (T-2 partition).

[0106] In another optional embodiment of the present invention, after importing the data in the indicator dependency table into the data partition according to the indicator timeliness, the method further includes:

[0107] Marking the indicator data that meets the first aging time as achieved aging data, and marking the indicator data that does not meet the first aging time as unachieved aging data;

[0108] Build a time monitoring table based on all marked completed data.

[0109] In an embodiment of the present invention, since all the data in the T-1 partition meet the first aging time, and all the data in the T-1 partition do not meet the first aging time, monitoring can be carried out conveniently. The aging monitoring table includes: indicator name, completion time, aging requirement, whether the aging is achieved, etc. For example, the indicator name is monthly average FYC, the completion time is 2021-12-10 06:12:47, the aging requirement is aging T-1, and whether the aging is achieved is yes; the indicator name is job completion rate, the completion time is 2021-12-10 07:12:07, the aging requirement is aging T-1, and whether the aging is achieved is no.

[0110] Furthermore, by using T-1 or T-2 data to set monitoring tasks, the monitoring efficiency of indicator data and data traceability are improved.

[0111] Step 3: Construct an asynchronous link task, and mobilize the data partitions after the data is imported according to the asynchronous link task to obtain standard indicator data.

[0112] In the embodiment of the present invention, in addition to the processing link of the original indicator data, an asynchronous link task is set to mobilize the data in the two time-sensitive partitions. Since it is an asynchronous link, it will not affect the data processing of the original link.

[0113] Specifically, the data partitions after importing data are mobilized according to the asynchronous link task to obtain standard indicator data, including:

[0114] Starting the asynchronous link task at a preset second aging time;

[0115] The asynchronous link task is used to copy and fill the data of the second time-sensitive partition into the first time-sensitive partition, and the data in the filled first time-sensitive partition is used as the standard indicator data.

[0116] In an optional embodiment of the present invention, an asynchronous link task is started at 1 a.m. (i.e., the second time limit), and the data of the T-2 partition (such as the departmental course summary table) is copied and filled into the T-1 partition. Since the task content is simple, this will be completed within a few minutes, and the task of the T-1 partition will not be affected by the asynchronous link. After the task is completed, the T-1 partition will be overwritten again anyway, which is equivalent to the downstream task being able to use the data of the T-1 partition first, ensuring that the T-1 partition must have data (it may be T-1 data or T-2 data) and will not be an empty value.

[0117] Step 4: Aggregate the standard indicator data, and rank the indicators based on the aggregation results to obtain indicator ranking results.

[0118] In the present embodiment, indicator aggregation refers to calculating the hit status of each indicator in the scoring threshold table and the score details according to the preset scoring rules, and aggregating them into a scoring table. Based on the score details, a ranking table is then compiled to show the rating and ranking of each indicator as the indicator ranking result.

[0119] Furthermore, the standard indicator data is aggregated, and the indicators are ranked according to the aggregation results to obtain the indicator ranking results, including:

[0120] Aggregating the indicators in the standard indicator data according to the aggregation granularity, searching for the hit status and score details of the indicator aggregation in the preset scoring threshold table, summarizing the score details of all indicators, and obtaining an indicator ranking table;

[0121] The indicator ranking table is visualized using a pre-built visualization plug-in, and the visualization result is used as the indicator ranking result.

[0122] In an optional embodiment of the present invention, for example, based on the granularity of each course, the scoring rules are used for the course summary tables A, B, and C to calculate the hit status of each indicator of each course in the scoring threshold table and the score details, and aggregate them into a scoring table. Based on the score details and preset rules (weight rules, etc.), an indicator ranking table of the rating and ranking of each course is statistically calculated. The indicator ranking table will eventually be pushed to the data visualization platform and presented to users for query based on visualization plug-ins (such as ECharts).

[0123] The present invention constructs an indicator dependency table based on the dependency relationships in the original indicator data without removing the dependencies between the data. During link processing, it is not necessary to detect the validity of the data at each layer, thereby improving the timeliness of data processing. At the same time, the data in the indicator dependency table is imported into the data partition according to the indicator timeliness, and the data partition after the imported data is mobilized according to the asynchronous link task to obtain standard indicator data. Since the asynchronous link task does not affect the processing logic of the original link, and the data in the data partition is adjusted based on the asynchronous link task, the data processing timeliness is improved while also improving the accuracy of data processing. Therefore, the data asynchronous link processing method proposed by the present invention can solve the problem of low data processing timeliness.

[0124] like Figure 7 FIG. 1 is a schematic diagram of the structure of an electronic device for implementing a data asynchronous link processing method provided by an embodiment of the present invention.

[0125] The electronic device may include a processor 10 , a memory 11 , a communication interface 12 and a bus 13 , and may further include a computer program stored in the memory 11 and executable on the processor 10 , such as a data asynchronous link processing program.

[0126] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 can also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device. Furthermore, the memory 11 can also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of a data asynchronous link processing program, but can also be used to temporarily store data that has been output or is to be output.

[0127] In some embodiments, the processor 10 may be composed of an integrated circuit, such as a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and a combination of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, connecting the various components of the entire electronic device using various interfaces and lines. It executes or runs programs or modules stored in the memory 11 (such as data asynchronous link processing programs) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0128] The communication interface 12 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0129] The bus 13 may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus 13 may be divided into an address bus, a data bus, a control bus, etc. The bus 13 is configured to enable communication between the memory 11 and at least one processor 10.

[0130] Figure 7 Only the electronic device with components is shown, and it can be understood by those skilled in the art that Figure 7 The structure shown does not limit the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0131] For example, although not shown, the electronic device may further include a power source (such as a battery) for powering various components. Preferably, the power source may be logically connected to the at least one processor 10 via a power management device, thereby implementing functions such as charge management, discharge management, and power consumption management through the power management device. The power source may further include any components such as one or more DC or AC power sources, a recharging device, a power failure detection circuit, a power converter or inverter, a power status indicator, etc. The electronic device may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0132] Furthermore, the electronic device may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices.

[0133] Optionally, the electronic device may further include a user interface, which may be a display or an input unit (such as a keyboard). Optionally, the user interface may also be a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0134] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0135] The data asynchronous link processing program stored in the memory 11 of the electronic device is a combination of multiple instructions. When running in the processor 10, it can achieve the following:

[0136] Obtaining original indicator data, and constructing an indicator dependency table based on dependency relationships in the original indicator data;

[0137] Constructing at least two data partitions according to the indicator timeliness in the indicator dependency table, and importing the data in the indicator dependency table into the data partitions according to the indicator timeliness;

[0138] Constructing an asynchronous link task, and mobilizing data on the data partitions after importing the data according to the asynchronous link task to obtain standard indicator data;

[0139] The standard indicator data is index aggregated, and the indicators are ranked according to the aggregation results to obtain an indicator ranking result.

[0140] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0141] Furthermore, if the module / unit integrated into the electronic device is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0142] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program. When the computer program is executed by a processor of an electronic device, the computer program can implement:

[0143] Obtaining original indicator data, and constructing an indicator dependency table based on dependency relationships in the original indicator data;

[0144] Constructing at least two data partitions according to the indicator timeliness in the indicator dependency table, and importing the data in the indicator dependency table into the data partitions according to the indicator timeliness;

[0145] Constructing an asynchronous link task, and mobilizing data on the data partitions after importing the data according to the asynchronous link task to obtain standard indicator data;

[0146] The standard indicator data is index aggregated, and the indicators are ranked according to the aggregation results to obtain an indicator ranking result.

[0147] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the module division is merely a logical function division, and other division methods may be used in actual implementation.

[0148] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules may be selected to achieve the purpose of the solution of this embodiment according to actual needs.

[0149] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional modules.

[0150] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0151] Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the claims are intended to be embraced therein. Any reference to a figure in a claim should not be construed as limiting the claim to which it relates.

[0152] Embodiments of the present invention can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results.

[0153] Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0154] Blockchain, as used in this article, refers to a novel application model for computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Blockchain is essentially a decentralized database, a series of data blocks generated using cryptographic methods. Each block contains information about a batch of online transactions, used to verify the validity of this information (to prevent counterfeiting) and generate the next block. Blockchain can include the underlying blockchain platform, the platform product service layer, and the application service layer.

[0155] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. Second-order terms are used to indicate names and do not imply any particular order.

[0156] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A data asynchronous link processing method, characterized in that: The method comprises: Obtaining original indicator data, extracting indicator themes from the original indicator data, building a dependency relationship between the indicator themes and the original indicator data, and aggregating and classifying the original indicator data with the dependency relationship built according to a preset aggregation granularity to obtain an indicator dependency table; Determine a key timeliness node in the indicator dependency table as an indicator timeliness, construct a first timeliness partition and a second timeliness partition as data partitions according to the indicator timeliness, and classify the data in the indicator dependency table according to whether they meet the indicator timeliness and import them into the data partitions according to the indicator timeliness; Construct an asynchronous link task, and according to the asynchronous link task, mobilize data for the data partitions after importing the data, aggregate the data of all data partitions to the data partition with priority by copying and filling, and obtain standard indicator data; The standard indicator data is index aggregated, and the indicators are ranked according to the aggregation results to obtain an indicator ranking result.

2. The data asynchronous link processing method as claimed in claim 1, characterized in that: The step of classifying the data in the indicator dependency table according to whether they meet the indicator timeliness and importing the data into the data partition includes: Determine whether the aging time of the indicator data in the indicator dependency table meets the preset first aging time; Importing the indicator data in the indicator dependency table that meets the first aging time into the first aging partition; Import the indicator data in the indicator dependency table that does not meet the first aging time into the second aging partition.

3. The data asynchronous link processing method according to claim 2, wherein: After importing the data in the indicator dependency table into the data partition according to the indicator timeliness, the method further includes: Marking the indicator data that meets the first aging time as achieved aging data, and marking the indicator data that does not meet the first aging time as unachieved aging data; Build a time monitoring table based on all marked completed data.

4. The data asynchronous link processing method according to claim 1, wherein: The data partitions after importing data are mobilized according to the asynchronous link task, and the data of all data partitions are aggregated to the data partitions used first by copying and filling, to obtain standard indicator data, including: Starting the asynchronous link task at a preset second aging time; The asynchronous link task is used to copy and fill the data of the second time-sensitive partition into the first time-sensitive partition, and the data in the filled first time-sensitive partition is used as the standard indicator data.

5. The data asynchronous link processing method according to claim 1, wherein: The index ranking result is obtained by aggregating the index of the standard index data and performing index ranking according to the aggregation result, including: Aggregating the indicators in the standard indicator data according to the aggregation granularity, searching for the hit status and score details of the indicator aggregation in the preset scoring threshold table, summarizing the score details of all indicators, and obtaining an indicator ranking table; The indicator ranking table is visualized using a pre-built visualization plug-in, and the visualization result is used as the indicator ranking result.

6. A data asynchronous link processing device, characterized in that: The device comprises: An indicator dependency table construction module is used to obtain original indicator data, extract indicator themes from the original indicator data, construct dependency relationships between the indicator themes and the original indicator data, and aggregate and classify the original indicator data with dependency relationships constructed according to a preset aggregation granularity to obtain an indicator dependency table; A data partitioning module is configured to determine a key timeliness node in the indicator dependency table as an indicator timeliness, construct a first timeliness partition and a second timeliness partition as data partitions based on the indicator timeliness, and classify the data in the indicator dependency table according to whether they meet the indicator timeliness and import them into the data partitions based on the indicator timeliness; An asynchronous mobilization module is used to construct an asynchronous link task, and according to the asynchronous link task, data is mobilized for the data partitions after the data is imported, and the data of all data partitions are aggregated to the data partition with priority by copying and filling, so as to obtain standard indicator data; The indicator aggregation module is used to perform indicator aggregation on the standard indicator data, and to rank the indicators according to the aggregation results to obtain indicator ranking results.

7. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the data asynchronous link processing method according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the data asynchronous link processing method according to any one of claims 1 to 5 is implemented.

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