Government affair data processing method and device based on time sequence

Abstract

The invention relates to a government affair data processing method and device based on a time sequence. The method comprises the following steps: regularly obtaining original government affair data, performing preprocessing based on the original government affair data to obtain basic government affair data, and storing the basic government affair data in a convergence library; basic dimension data are obtained based on the basic government affair data in the convergence library, the basic dimension data comprise dates, item types, handling modes, handling results and intermediate values, basic indexes of the intermediate values are obtained based on the basic dimension data, child comprehensive indexes are obtained based on the basic indexes, and parent comprehensive indexes are obtained based on the child comprehensive indexes; and constructing an index system based on the basic index, the child comprehensive index and the parent comprehensive index. According to the method, the index system can be automatically and accurately constructed based on the government affair data, so that more timely, accurate and reliable data support can be provided for government decision makers.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data processing, and more particularly, to a time series-based government affair data processing method and device. BACKGROUND

[0002] With the continuous advancement of government informatization construction, the amount of government affair data is growing. The existing government affair data index system construction method mostly relies on manual operation, which is not only inefficient but also prone to errors in the big data scenario. Therefore, how to automatically and accurately construct an index system based on government affair data is very important, which can provide more timely, accurate and reliable data support for government decision makers, and help to improve the scientificity and effectiveness of policy making. SUMMARY

[0003] In a first aspect of the embodiments of the present disclosure, a time series-based government affair data processing method is provided. The method includes periodically acquiring original government affair data, pre-processing the original government affair data based on the original government affair data to obtain basic government affair data, and storing the basic government affair data in a convergence library; acquiring basic dimension data based on the basic government affair data in the convergence library, the basic dimension data including date, matter type, handling method, handling result and intermediate value, and acquiring a basic index of the intermediate value based on the basic dimension data, acquiring a sub-comprehensive index based on the basic index, acquiring a parent comprehensive index based on the sub-comprehensive index; and constructing an index system based on the basic index, the sub-comprehensive index and the parent comprehensive index.

[0004] In a second aspect of the embodiments of the present disclosure, a time series-based government affair data processing device is provided. The device includes a basic government affair data acquisition module configured to periodically acquire original government affair data, pre-process the original government affair data based on the original government affair data to obtain basic government affair data, and store the basic government affair data in a convergence library; an index acquisition module configured to acquire basic dimension data based on the basic government affair data in the convergence library, the basic dimension data including date, matter type, handling method, handling result and intermediate value, and acquire a basic index of the intermediate value based on the basic dimension data, acquire a sub-comprehensive index based on the basic index, and acquire a parent comprehensive index based on the sub-comprehensive index; and an index system construction module configured to construct an index system based on the basic index, the sub-comprehensive index and the parent comprehensive index.

[0005] In a third aspect of the embodiments of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the method provided in the first aspect.

[0006] In a fourth aspect of the embodiments of the present disclosure, an electronic device is provided, comprising one or more processors, and a memory associated with the one or more processors, the memory configured to store program instructions that, when read and executed by the one or more processors, perform the method according to the first aspect.

[0007] It should be understood that the description in the summary is not intended to identify key or essential features of embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become more fully understood from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0008] The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent upon reading of the following detailed description, taken in conjunction with the accompanying drawings, in which like references refer to like elements, and in which: Figure 1 A flow chart of a time series-based government affair data processing method according to some embodiments of the present disclosure is shown; Figure 2 A block diagram of a time series-based government affair data processing apparatus according to some embodiments of the present disclosure is shown; Figure 3 A block diagram of an electronic device according to some embodiments of the present disclosure is shown; Figure 4 A schematic diagram of a way of obtaining original government affair data according to some embodiments of the present disclosure is shown; Figure 5 A historical index value and a predicted index value of a monthly case handling success rate of a certain department according to some embodiments of the present disclosure are shown. DETAILED DESCRIPTION

[0009] Embodiments of the present disclosure will be described in more detail with reference to the drawings. Although certain embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be embodied in various forms and should not be construed as being limited to the embodiments set forth herein, but rather, the embodiments are provided so that the present disclosure can be more thoroughly and completely understood. It should be understood that the drawings and embodiments of the present disclosure are merely for illustrative purposes and should not be construed as limiting the scope of protection of the present disclosure.

[0010] In the description of the embodiments of the present disclosure, the term “comprising” and its conjugations should be understood to encompass the meanings of “consisting of” and “consisting essentially of”. The term “based on” should be understood as “based at least in part on”. The term “one embodiment” or “the embodiment” should be understood as “at least one embodiment”. The terms “first”, “second”, etc. can refer to different or identical objects. Other explicit and implicit definitions can also be included below.

[0011] Figure 1 A flowchart of a time-series-based e-government data processing method 100 according to some embodiments of the present disclosure is shown. The e-government data processing method of this embodiment is executed by an e-government data processing system. The method 100 includes: 102. Regularly acquire raw government data, preprocess the raw government data to obtain basic government data, and store the basic government data in the aggregation repository.

[0012] like Figure 4 As shown, the original government data includes, but is not limited to, publicly available government information, administrative approval data, and social public service data. These are obtained from various departmental channels, and the source methods are unrestricted: they can originate from departmental government systems, collecting data at business frequency via interface acquisition or through push notifications; they can also originate from big data platforms (such as Dataworks), acquiring data through offline collection or push notifications; they can also originate from the front-end database of business systems, acquiring data through offline or real-time collection; and they can also originate from message middleware, acquiring data through message parsing. Furthermore, the original government data in this embodiment is time-series data. Time-series data refers to data collected at different times, exhibiting obvious time fluctuation characteristics. This type of data reflects the state or degree of change of a certain thing or phenomenon over time.

[0013] After obtaining the raw government data, it is necessary to preprocess it: the collected data needs to be cleaned and transformed. Due to the differences and backwardness of business system construction between regions and even departments, the data generated by government often has certain defects, such as null values ​​and incorrect data formats. Therefore, it is necessary to remove abnormal and invalid data in advance and convert the data into a unified format and standard to obtain standardized basic government data.

[0014] The method in this embodiment acquires raw government data periodically, transforms it into basic government data, and finally stores the basic government data in a data aggregation repository. In other words, the basic government data in the aggregation repository is updated regularly. In summary, the method in this embodiment can automatically, comprehensively, and promptly acquire basic government data belonging to "time-series data."

[0015] Figure 1 A flowchart of a time-series-based government data processing method 100 according to some embodiments of the present disclosure is shown. The method 100 further includes: 104. Obtain the basic dimension data based on the basic government data in the aggregation library, the basic dimension data including date, matter type, handling mode, handling result and intermediate value, and obtain the basic index of the intermediate value based on the basic dimension data, obtain the sub-comprehensive index based on the basic index, and obtain the parent comprehensive index based on the sub-comprehensive index.

[0016] Wherein, the date is XX year X month X day, for example, 2022 year 2 month 2 day, and again for example, 2023 year 5 month 8 day. Since the method of the embodiment is based on "time series data", the basic dimension data must include date.

[0017] The matter type can be: diploma handling, ID card handling, non-motor vehicle registration, household registration, marriage registration, etc.

[0018] The basic government data in the aggregation library is stored for a region (for example, XX city), for example, the basic government data of A city is stored in A aggregation library, and the basic government data of B city is stored in B aggregation library. When the government data processing system is applied in A city, the government data processing system regularly obtains the basic government data of A city and stores it in A aggregation library; when the government data processing system is applied in B city, the government data processing system regularly obtains the basic government data of B city and stores it in B aggregation library.

[0019] Usually, one matter type in the same region is responsible for a department. For example, diploma handling is responsible for "diploma handling" department, ID card handling is responsible for "ID card handling" department, and non-motor vehicle registration is responsible for "non-motor vehicle registration" department. Of course, a department can have multiple offices in the region, for example, "ID card handling" department has "ID card handling" office 1, "ID card handling" office 2 and "ID card handling" office in A city. The embodiment attributes all "ID card handling" offices in the region to the "ID card handling" department of the local government.

[0020] The handling mode can be: nearby handling, online handling, etc.

[0021] The handling result can be: success and failure.

[0022] The intermediate value can be: the number of handled cases, the average duration of handled cases, etc.

[0023] So, when the intermediate value is "the number of handled cases", the basic dimension data can be: 2023 March 1st-educational certificate handling-close at hand-success-11; 2023 March 1st-educational certificate handling-close at hand-failure-2; 2023 March 1st-educational certificate handling-online-success-25; 2023 March 1st-educational certificate handling-online-failure-4; ……; 2023 March 2nd-household registration-close at hand-success-8; 2023 March 2nd-household registration-close at hand-failure-0; 2023 March 2nd-household registration-online-success-16; 2023 March 2nd-household registration-online-failure-1.

[0024] When the intermediate value is "the average time length of handled cases", the basic dimension data can be: 2023 March 1st-educational certificate handling-close at hand-success-20 minutes; 2023 March 1st-educational certificate handling-close at hand-failure-7 minutes; 2023 March 1st-educational certificate handling-online-success-18 minutes; 2023 March 1st-educational certificate handling-online-failure-5 minutes; ……; 2023 March 2nd-household registration-close at hand-success-16 minutes; 2023 March 2nd-household registration-close at hand-failure-3 minutes; 2023 March 2nd-household registration-online-success-21 minutes; 2023 March 2nd-household registration-online-failure-15 minutes.

[0025] The method of the embodiment needs to obtain the basic dimension data before obtaining the basic index, the main purpose is to facilitate the obtaining of the basic index, and to make the obtained basic index more accurate. The basic dimension data is the statistical data of single day, single type of matter, single handling method, single handling result and single "intermediate value", that is, the statistical data of the smallest unit. When the statistical data of the smallest unit is determined, the subsequent required basic index (which can be multi-day or multi-type of matter or multi-handling method, which is set according to actual needs) can be easily obtained and is not prone to errors.

[0026] In the embodiment, the intermediate value based on the basic dimension data is obtained. The basic index is specifically: The first basic index of the same intermediate value under the required dimension is obtained based on the basic dimension data statistics; The second basic index of the same intermediate value under the required dimension is calculated based on the first basic index.

[0027] Suppose that the government data processing system is applied to the A convergence library in A city and has the following basic dimension data, taking the intermediate value as "the number of handled cases" for example: Data 1: 2024 January 1st-educational certificate handling-close at hand-success-8; Data 2: January 1, 2024 - Academic Certificate Application - Local Processing - Failed - 2 applications; Data 3: January 1, 2024 - Academic Certificate Processing - Online Processing - Successful - 2 items; Data 4: January 1, 2024 - Academic Certificate Processing - Online Processing - Failure - 0 cases; Data 5: January 1, 2024 - ID card processing - nearby processing - successful - 26 cases; Data 6: January 1, 2024 - ID card application - application at the nearest service center - failed - 2 applications; Data 7: January 1, 2024 - ID card application - online application - successful - 12 cases; Data 8: January 1, 2024 - ID card application - online application - failed - 1 case; Data 9: January 1, 2024 - Non-motorized vehicle registration - Local processing - Successful - 37 cases; Data 10: January 1, 2024 - Non-motorized vehicle registration - Local processing - Failed - 3 cases; Data 11: January 1, 2024 - Non-motorized vehicle registration - Online processing - Successful - 22 cases; Data 12: January 1, 2024 - Non-motorized vehicle registration - Online application - Failed - 5 cases; Data 13: January 1, 2024 - Household Registration - Local Processing - Successful - 12 cases; Data 14: January 1, 2024 - Household Registration - Local Processing - Failed - 0 cases; Data 15: January 1, 2024 - Household Registration - Online Processing - Successful - 14 cases; Data 16: January 1, 2024 - Household Registration - Online Application - Failed - 1 case; Data 17: January 2, 2024 - Academic Certificate Processing - Processed at the nearest location - Successful - 1 item; Data 18: January 2, 2024 - Academic Certificate Processing - Local Processing - Failed - 0 cases; Data 19: January 2, 2024 - Academic Certificate Processing - Online Processing - Successful - 6 items; Data 20: January 2, 2024 - Academic Certificate Application - Online Application - Failed - 1 item; Data 21: January 2, 2024 - ID card processing - processed at the nearest service center - successful - 30 cases; Data 22: January 2, 2024 - ID card processing - nearby processing - failed - 0 cases; Data 23: January 2, 2024 - ID card processing - online processing - successful - 11 cases; Data 24: January 2, 2024 - ID card application - online application - failed - 2 cases; Data 25: January 2, 2024 - Non-motorized vehicle registration - Local processing - Successful - 43 cases; Data 26: January 2, 2024 - Non-motorized vehicle registration - Local processing - Failed - 1 case; Data 27: January 2, 2024 - Non-motorized vehicle registration - Online processing - Successful - 16 cases; Data 28: January 2, 2024 - Non-motorized vehicle registration - Online application - Failed - 2 cases; Data 29: January 2, 2024 - Household Registration - Local Processing - Successful - 22 cases; Data 30: January 2, 2024 - Household Registration - Local Processing - Failed - 1 case; Data 31: January 2, 2024 - Household Registration - Online Processing - Successful - 3 Items; Data 32: January 2, 2024 - Household Registration - Online Application - Failed - 0 cases.

[0028] Therefore, the basic indicator with the median value of "case volume" can include: The number of cases processed on January 1, 2024 was 137. The required dimension is "January 1, 2024". This required dimension includes all types of matters, all processing methods and all processing results on that day, with the same median value being "number of cases processed".

[0029] The number of cases processed on January 2, 2024 was 139. The required dimension is "January 2, 2024". This required dimension includes all types of matters, all processing methods and all processing results on a single day, with the same median value being "number of cases processed".

[0030] The number of academic certificates processed on January 1, 2024 and January 2, 2024 is 20. The required dimension is "academic certificate processing on January 1, 2024 and January 2, 2024". This required dimension includes all processing methods and all processing results for both days, with the same median value being "number of applications".

[0031] The number of ID card applications processed on January 1, 2024 and January 2, 2024 was 84. The required dimension is "ID card applications processed on January 1, 2024 and January 2, 2024". This required dimension includes all processing methods and all processing results for both days, with the same median value being "number of applications".

[0032] On January 1, 2024 and January 2, 2024, 188 cases were processed through the local service. The required dimension is "processed through the local service on January 1, 2024 and January 2, 2024". This required dimension includes all types of matters and all processing results for both days, with the same median value being "case volume".

[0033] The number of online applications processed on January 1, 2024 and January 2, 2024 was 98. The required dimension is "online applications processed on January 1, 2024 and January 2, 2024". This required dimension includes all types of matters and all processing results for both days, with the same median value being "number of applications".

[0034] The number of successful applications on January 1, 2024 and January 2, 2024 was 266. The required dimension is "successfully processed on January 1, 2024 and January 2, 2024". This required dimension includes all types of matters and all processing methods on both days, with the same median value being "number of applications".

[0035] There were 21 cases that failed to be processed on January 1, 2024 and January 2, 2024. The required dimension is "failed to be processed on January 1, 2024 and January 2, 2024". This required dimension includes all types of matters and all processing methods on both days, with the same median value being "case volume".

[0036] On January 1, 2024, 10 academic certificates were successfully processed. The required dimension is "Academic Certificates Successfully Processed on January 1, 2024". This required dimension includes all processing methods on a single day, with the same median value being "Processing Quantity".

[0037] There was 1 failed application for an academic certificate on January 2, 2024. The required dimension is "failed application for academic certificate on January 2, 2024". This required dimension includes all processing methods on a single day, with the same median value being "application volume".

[0038] There were 3 failed ID card applications on January 1, 2024. The required dimension is "failed ID card applications on January 1, 2024". This required dimension includes all application methods on a single day, with the same median value being "application volume".

[0039] In short, the specific dimensions and median values ​​of the basic indicators that need to be statistically analyzed can be set according to actual usage needs.

[0040] Furthermore, secondary basic indicators are calculated based on primary basic indicators. For example, the "application success rate" is calculated based on the "application volume." In this case, the "application success rate" is the median value of the secondary basic indicators. Secondary basic indicators can specifically include: The success rate of applications processed on January 1, 2024 was 0.905, with the required dimension being "January 1, 2024" and the same median value being "application success rate". This secondary basic indicator is calculated from the primary basic indicator "131 applications successfully processed on January 1, 2024" and the primary basic indicator "137 applications processed on January 1, 2024".

[0041] The success rate for academic certificate processing on January 1st and 2nd, 2024 was 0.850. The required dimension is "Academic Certificate Processing on January 1st and 2nd, 2024," with the same median value being "Processing Success Rate." This secondary basic indicator is calculated from the primary basic indicators "17 successful academic certificate processing applications on January 1st and 2nd, 2024" and "20 academic certificate processing applications on January 1st and 2nd, 2024."

[0042] In summary, the specific secondary basic indicators that need to be statistically analyzed, including which dimension and median value, can be set according to actual usage requirements.

[0043] Assume that the government data processing system is applied to the B aggregation database in City B, which has the following basic data dimensions, taking "case volume" and "average case processing time" as the median values: Data 1: January 1, 2024 - Academic Certificate Processing - Local Processing - Successful - 8 cases; Data 2: January 1, 2024 - Academic Certificate Application - Local Processing - Failed - 2 applications; Data 3: January 1, 2024 - Academic Certificate Processing - Online Processing - Successful - 2 items; Data 4: January 1, 2024 - Academic Certificate Processing - Online Processing - Failure - 0 cases; Data 5: January 1, 2024 - Academic Certificate Application - Processed at the nearest location - Successful - 15 minutes; Data 6: January 1, 2024 - Academic Certificate Application - Local Application - Failed - 5 minutes; Data 7: January 1, 2024 - Academic Certificate Application - Online Application - Success - 18 minutes; Data 8: January 1, 2024 - Academic Certificate Application - Online Application - Failed - 0 minutes.

[0044] Therefore, the basic indicator with the median value of "case volume" can include: Statistics on the number of cases processed on the same date: 12 cases were processed on January 1, 2024.

[0045] Statistics on the number of applications processed under the same type of matter: 12 applications were processed for academic certificates.

[0046] Statistics on the number of cases processed using the same method: 10 cases processed at the nearest service location; 2 cases processed online.

[0047] Statistics on the number of cases processed under the same outcome: 10 cases were successfully processed; 2 cases were unsuccessful.

[0048] The primary basic indicator with the median value of "average processing time" can include (wherein, the primary basic indicator of "average processing time" is obtained based on statistics from all basic dimension data, that is, it can be based not only on basic dimension data with the median value of "average processing time" but also on basic dimension data with the median value of "processing volume"): The average processing time for cases on the same date is 14 minutes.

[0049] Average processing time for the same type of item: The average processing time for academic certificate processing is 14 minutes.

[0050] The average processing time for cases processed using the same method is as follows: 15 minutes for cases processed at the nearest service location; and 18 minutes for cases processed online.

[0051] The average processing time for cases with the same processing result is as follows: the average processing time for successful cases is 18 minutes; the average processing time for unsuccessful cases is 2 minutes.

[0052] If there is a secondary basic indicator for the "intermediate value" in the future, then the secondary basic indicator for the "intermediate value" will be calculated based on the primary basic indicator; if there is no secondary basic indicator, then there is no need to calculate the secondary basic indicator.

[0053] In this embodiment, the intermediate value "processing success rate" has a secondary basic indicator, so it is necessary to calculate the secondary basic indicator of "processing success rate" based on the primary basic indicator of "processing volume".

[0054] Typically, the basic metrics that can be obtained based on basic dimension data include at least the following: the number of cases processed (a primary basic metric), the success rate of cases processed (a secondary basic metric), and the average processing time for cases processed (a primary basic metric).

[0055] Furthermore, in this embodiment, the sub-comprehensive indicators are obtained based on the basic indicators as follows: Determine the sub-comprehensive index that needs to be calculated, and determine the basic index required for the calculation of the corresponding sub-comprehensive index; Input each basic indicator into its corresponding indicator score to obtain the model and get the corresponding indicator score; The value of the corresponding sub-comprehensive index is calculated based on the scores of all indicators.

[0056] Sub-indicators could be the efficiency of a department in a given month, or the public satisfaction of a particular group in a given month, etc.

[0057] Assume the sub-comprehensive indicator to be calculated is the efficiency of the "household registration" department in May, and further assume the basic indicators required for calculating this sub-comprehensive indicator include: Basic Indicator A: The number of "household registration" applications processed from May 1st to May 31st of XX year was 1888.

[0058] Basic Indicator B: The success rate of "household registration" applications from May 1st to May 31st of XX year was 0.95%.

[0059] Basic indicator C: The average processing time for "household registration" from May 1st to May 31st of XX year was 10 minutes.

[0060] Then, input the basic indicator A into the "case volume" indicator score acquisition model to obtain the "case volume" indicator score (assuming it is 88 points). At the same time, input the basic indicator B into the "case success rate" indicator score acquisition model to obtain the "case success rate" indicator score (assuming it is 99 points). Then, input the basic indicator C into the "case average processing time" indicator score acquisition model to obtain the "case average processing time" indicator score (assuming it is 85 points).

[0061] Taking the "case volume" indicator score acquisition model as an example, the model can store multiple score tables, each corresponding to a department. When it is necessary to obtain the "case volume" indicator score for the "household registration" department, it is only necessary to call the score table corresponding to the "household registration" department. The score table records: what the indicator score is when the daily case volume falls within a certain range (for example, when the daily case volume falls within the F1 range, the indicator score is f1; when the daily case volume falls within the F2 range, the indicator score is f3; ...; when the daily case volume falls within the Fn range, the indicator score is fn). In this embodiment, the model for obtaining the "case volume" indicator score only needs to first determine how many days from May 1st to May 31st are rest days (when "household registration" is not conducted), then subtract the number of rest days from 31 days to get the number of working days, then divide 1888 cases by the number of working days to get the daily "case volume", and finally look up the score table based on the daily "case volume" to obtain the "case volume" indicator score.

[0062] Finally, the efficiency of the "Household Registration" department in May is calculated as follows: Value 1 (the score of the "Number of Cases Processed" multiplied by the weight of "Number of Cases Processed"), Value 2 (the score of the "Success Rate of Cases Processed" multiplied by the weight of "Success Rate of Cases Processed"), and Value 3 (the score of the "Average Processing Time" multiplied by the weight of "Average Processing Time"). Let's assume the final calculated value is S. At this point, a sub-comprehensive indicator is obtained, named "Efficiency of Department XX in Month X," with a value of S. If other sub-comprehensive indicators are needed (such as the efficiency of the "Household Registration" department in June, or the efficiency of the "Academic Certificate Processing" department in May), the above steps can be repeated.

[0063] Sub-indicators allow users to understand the performance of a department in a certain aspect over a certain period of time.

[0064] Furthermore, in this embodiment, obtaining the parent comprehensive index based on the sub-comprehensive index specifically involves: Determine the parent composite index that needs to be calculated, and determine the child composite index required for the corresponding parent composite index calculation; The value of the corresponding parent comprehensive index is calculated based on all sub-comprehensive indices.

[0065] The parent comprehensive indicator can be the total score of department X in month X.

[0066] Assume the parent composite indicator to be calculated is the total score of the "Household Registration" department in May, and further assume the sub-composite indicators required for this parent composite indicator calculation include: Sub-indicator A: The efficiency of the "Household Registration" department in May of XX year was 88.

[0067] Sub-indicator B: The satisfaction rate of the "Household Registration" department in May of XX year was 66%.

[0068] So, the total score for the "Household Registration" department in May is equivalent to value a (i.e., efficiency value multiplied by efficiency weight) plus value b (i.e., satisfaction value multiplied by satisfaction weight), assuming the final calculated value is Y. At this point, a parent comprehensive indicator is obtained, named "Total Score for Department X in Month X," and its value is Y. If other parent comprehensive indicators are needed (e.g., the total score for the "Household Registration" department in June, or the total score for the "Academic Certificate Processing" department in May), the above steps can be repeated.

[0069] Parental composite metrics allow users to understand the overall performance of a department over a specific period of time.

[0070] Figure 1 A flowchart of a time-series-based government data processing method 100 according to some embodiments of the present disclosure is shown. The method 100 further includes: 106. Construct an indicator system based on basic indicators, sub-comprehensive indicators, and parent comprehensive indicators.

[0071] This embodiment specifically constructs the indicator system in the form of an indicator tree: the sub-comprehensive indicator is used as the child node of the parent comprehensive indicator, and the basic indicator is used as the child node of the sub-comprehensive indicator.

[0072] When viewing the indicator system, users first select the query time on the "Indicator System" visualization interface, such as June of XX year, or the second quarter of XX year, or XX year, or XX year, month X, 8th, etc.

[0073] Assuming the user selects July of year XX as the query time, then the top-level nodes of the indicator system are the parent comprehensive indicators such as the total score of the "Household Registration" department in July, the total score of the "Academic Certificate Processing" department in July, the total score of the "ID Card Processing" department in July, the total score of the "Non-motor Vehicle Registration" department in July, and the total score of the "Marriage Registration" department in July.

[0074] If a user clicks on the total score for the "ID Card Processing" department in July on the indicator system, the indicator system will display the sub-nodes of the total score for the "ID Card Processing" department in July. These sub-nodes can be sub-comprehensive indicators such as the efficiency of the "ID Card Processing" department in July and the satisfaction of the "ID Card Processing" department in July.

[0075] If a user clicks on the "ID Card Processing" department's July performance in the indicator system, the indicator system will display the sub-nodes of the "ID Card Processing" department's July performance. These sub-nodes can be basic indicators such as the number of cases processed by the "ID Card Processing" department in July, the success rate of cases processed by the "ID Card Processing" department in July, and the average processing time of cases processed by the "ID Card Processing" department in July.

[0076] This embodiment constructs an indicator system in the form of an indicator tree, enabling users to more conveniently and clearly query the various indicators they need to understand. In summary, the method of this embodiment can automatically and accurately construct an indicator system based on government data, thereby providing government decision-makers with more timely, accurate, and reliable data support, and helping to improve the scientific nature and effectiveness of policy making.

[0077] Furthermore, the time-series-based government data processing method in this example also includes: predicting the subsequent values ​​of indicators based on basic indicators, specifically: Identify the basic indicator to be predicted and obtain the historical basic indicators related to the basic indicator to be predicted; The historical index values ​​of the basic indicators are input into the index value prediction model to obtain the predicted index values ​​of the basic indicators to be predicted.

[0078] Let's assume the basic indicator to be predicted is the application success rate of department XX in month X of year XX. For example, the application success rate of the "ID Card Processing" department in September 2023, the application success rate of the "ID Card Processing" department in October 2023, and so on. Then we need to first obtain the relevant historical basic indicators, such as the application success rate of the "ID Card Processing" department in August 2023, the application success rate of the "ID Card Processing" department in July 2023, the application success rate of the "ID Card Processing" department in June 2023, the application success rate of the "ID Card Processing" department in May 2023, and so on.

[0079] Then, the historical indicator values ​​of the "ID Card Processing" department's application success rate for each month of year X are input into the indicator value prediction model. In this embodiment, the indicator value prediction model can be a pre-trained "ID Card Processing Monthly Application Success Rate" prediction model (the indicator value prediction model in this embodiment can be existing technology; in specific use, only the indicator value prediction model related to the "basic indicator to be predicted" needs to be called). Finally, the "ID Card Processing Monthly Application Success Rate" prediction model outputs the application success rate of the "ID Card Processing" department in September 2023, the application success rate of the "ID Card Processing" department in October 2023, and so on. Figure 5 As shown, the government data processing system can display both the historical and predicted indicators of the monthly success rate of the "ID card processing" department on the prediction interface.

[0080] The method in this embodiment can also predict the indicators to be understood based on basic indicators, providing government decision-makers with more timely, accurate, and reliable data support, which helps to improve the scientific nature and effectiveness of policy-making. For example, in the processing of academic certificates, the method in this embodiment can predict the periods of high application demand, allowing the academic certificate processing department to easily prepare human and material resources in advance to cope with peak processing periods, improve government efficiency, enhance residents' happiness, and further improve the image of government departments.

[0081] Figure 2 A block diagram of a time-series-based government data processing apparatus 200 according to some embodiments of the present disclosure is shown. The apparatus 200 includes: The basic government data acquisition module 202 is configured to periodically acquire raw government data, preprocess the raw government data to obtain basic government data, and store the basic government data in the aggregation database. The indicator acquisition module 204 is configured to acquire basic dimension data based on basic government data in the aggregated database. This basic dimension data includes date, item type, processing method, processing result, and median value. It then acquires basic indicators based on the median value, sub-comprehensive indicators based on the basic indicators, and parent comprehensive indicators based on the sub-comprehensive indicators. The indicator system construction module 206 is configured to construct an indicator system based on basic indicators, sub-comprehensive indicators, and parent comprehensive indicators.

[0082] Figure 3 A block diagram of an electronic device 300 according to some embodiments of the present disclosure is shown. The device 300 includes a processor 301, which performs various appropriate actions and processes based on computer program instructions loaded into random access memory (RAM) 303 according to computer program instructions stored in read-only memory (ROM) 302. Various programs and data required for the operation of the device 300 may also be stored in RAM 303. The processor 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0083] The various processes and procedures described above, such as method 100, can be executed by processor 301. For example, in some embodiments, method 100 may be implemented as a software program tangibly contained in a machine-readable medium. In some embodiments, part or all of the software program may be loaded and / or installed on device 300 via ROM 302. When the software program is loaded into RAM 303 and executed by processor 301, one or more actions of method 100 described above may be performed.

[0084] The functions described above in this document can be performed at least in part by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: field programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload programmable logic devices (CPLDs), and so on.

[0085] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0086] This disclosure can be a method, apparatus, system, and / or program product. The program product may include a machine-readable storage medium on which machine-readable program instructions for performing various aspects of this disclosure are loaded. The machine-readable program instructions described herein can be downloaded from the machine-readable storage medium to various computing / processing devices, or downloaded via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the machine-readable program instructions from the network and forwards them to the machine-readable storage medium in the respective computing / processing device.

[0087] Machine program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. Machine-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as a standalone software package, partially on a user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the machine-readable program instructions. This electronic circuitry can execute the machine-readable program instructions to implement various aspects of this disclosure.

[0088] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. Furthermore, although operations are depicted in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, while several specific implementation details are included in the foregoing discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments.

[0089] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A time-series-based method for processing government data, characterized in that, include: Regularly acquire raw government data, preprocess the raw government data to obtain basic government data, and store the basic government data in the aggregation database; Based on the basic government data in the aggregated database, basic dimension data is obtained, including date, item type, processing method, processing result, and median value. Based on the basic dimension data, basic indicators for the median value are obtained; based on the basic indicators, sub-comprehensive indicators are obtained; and based on the sub-comprehensive indicators, parent comprehensive indicators are obtained. An indicator system is constructed based on the aforementioned basic indicators, sub-comprehensive indicators, and parent comprehensive indicators.

2. The method according to claim 1, characterized in that, The basic indicators for obtaining intermediate values ​​based on the aforementioned basic dimension data are as follows: Based on the statistical analysis of the aforementioned basic dimension data, a primary basic indicator for the same median value under the required dimension is obtained; Based on the primary basic index, a secondary basic index with the same intermediate value under the required dimension is calculated.

3. The method according to claim 2, characterized in that, The sub-comprehensive indicators are obtained based on the aforementioned basic indicators as follows: Determine the sub-comprehensive index that needs to be calculated now, and determine the basic index required for the calculation of the corresponding sub-comprehensive index; Each of the aforementioned basic indicators is input into its corresponding indicator score to obtain the model and thus its corresponding indicator score. The value of the corresponding sub-comprehensive index is calculated based on the scores of all the aforementioned indicators.

4. The method according to claim 3, characterized in that, The specific steps for obtaining the parent comprehensive index based on the sub-comprehensive index are as follows: Determine the parent comprehensive index that needs to be calculated, and determine the child comprehensive index required for the calculation of the corresponding parent comprehensive index; The value of the corresponding parent comprehensive index is calculated based on all the sub-comprehensive indices.

5. The method according to claim 1, characterized in that, The indicator system is constructed based on the aforementioned basic indicators, sub-comprehensive indicators, and parent comprehensive indicators as follows: The indicator system is constructed in the form of an indicator tree: the sub-comprehensive indicator is used as a child node of the parent comprehensive indicator, and the basic indicator is used as a child node of the sub-comprehensive indicator.

6. The method according to claim 1, characterized in that, Also includes: Based on the aforementioned basic indicators, subsequent predictions of indicator values ​​are made, specifically as follows: Identify the basic indicator to be predicted and obtain the historical basic indicators related to the basic indicator to be predicted; The historical index values ​​of the historical basic index are input into the index value prediction model to obtain the predicted index values ​​of the basic index to be predicted.

7. The method according to claim 1, characterized in that, The original government data is time-series data.

8. A time-series-based government data processing device, characterized in that, include: The basic government data acquisition module is configured to periodically acquire raw government data, preprocess the raw government data to obtain basic government data, and store the basic government data in the aggregation database. The indicator acquisition module is configured to acquire basic dimension data based on the basic government data in the aggregation library. The basic dimension data includes date, item type, processing method, processing result and median value. Based on the basic dimension data, the module acquires basic indicators of the median value, sub-comprehensive indicators based on the basic indicators, and parent comprehensive indicators based on the sub-comprehensive indicators. as well as The indicator system construction module is configured to construct an indicator system based on the basic indicator, sub-comprehensive indicator, and parent comprehensive indicator.

9. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-7.

10. An electronic device, characterized in that, include: One or more processors, and A memory associated with the one or more processors, the memory being used to store program instructions that, when read and executed by the one or more processors, perform the steps of the method according to any one of claims 1-7.

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