Real-time e-government affair platform based on big data processing

Abstract

The invention discloses a real-time e-government affair platform based on big data processing, relates to the technical field of e-government affair platforms, and solves the problem of relatively large time delay in a data integration process caused by inconsistent data conversion rates. Based on different capacities corresponding to different formats of data and the conversion rate of the whole data in the format unification process, the conversion rate interval of related data is confirmed, then subsequent correlation confirmation of related completion time is carried out in the confirmed conversion rate interval, and subsequent correlation confirmation of the related completion time is carried out based on a computing power distribution mode. The data in the corresponding format tends to be consistent gradually in the related completion time, so that it can be fully guaranteed that related data converted by multiple different conversion processes can complete the related conversion process in a relatively consistent time period, it is guaranteed that the fusion rate is relatively high when subsequent data is fused, and the data waiting time of the corresponding government affair platform is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of e-government platforms, and in particular to a real-time e-government platform based on big data processing. Background Art

[0002] The e-government platform is a modern government service system that uses modern information technology, especially Internet technology, to realize the electronic, networked and intelligent government services. It empowers advanced technologies such as artificial intelligence, innovates government services, enables the public to obtain government information and services electronically, and at the same time improves the efficiency and quality of government public services.

[0003] The application with publication number CN117575874A discloses an e-government platform security management system based on multi-source data, which specifically relates to the technical field of government platform security management, including a multi-source data collection module, a model building module, a real-time performance grading module, an outlier analysis module, and an early warning module. The module collects multi-source data when acquiring information security data in the e-government platform security management system to evaluate the real-time performance of data acquisition, builds a real-time performance evaluation model based on the multi-source data, generates a real-time performance evaluation index, evaluates the real-time performance of data acquisition, generates different real-time performance levels, takes different processing measures according to different real-time performance levels, and promptly reports to management personnel when the real-time performance is poor so that the management personnel can receive timely early warnings and deploy security systems with reduced real-time performance of data acquisition in advance, thereby reducing related information security risks and underreporting of information security incidents.

[0004] In the process of related data processing, the real-time e-government platform needs to obtain data from multiple different sources for the updated data or display data generated within each specified period, and then integrate the data from multiple different sources. During the integration process, the data from multiple different sources need to be uniformly converted into related data of the same format, and then related and integrated. However, the original processing method, in actual application, has inconsistent conversion rates of some data and inconsistent capacity of each data, which will result in some data not being completely converted after conversion. As a result, the waiting time for data integration is too long, and a good data integration analysis and management effect cannot be achieved. The timeliness of data processing needs to be enhanced. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a real-time e-government platform based on big data processing, which solves the problem of long delays in the data integration process caused by inconsistent conversion rates between data.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A real-time e-government platform based on big data processing, comprising: The periodic data acquisition terminal acquires data of different formats generated by different data sources within a specified period, and the specified period is a preset period; The data conversion analysis end performs initial conversion on several groups of data in different formats, determines the conversion rate range of data in different formats by limiting a group of initial conversion stages, and simultaneously confirms the remaining capacity to be converted of data in different formats after the initial conversion stage. Based on this conversion rate range, the time completion range of the corresponding data is identified. The specific method is as follows: A set of initial conversion stages is defined, the time period of the initial conversion stage is a preset period, the conversion rate generated within the preset period is confirmed, and from different conversion rates corresponding to different format data, based on the minimum value and the maximum value of the conversion rate, the conversion rate range belonging to the format data is directly determined; From the acquired data in different formats, the remaining capacity to be transferred of the data in different formats is marked as DZ i , where i represents different format data; Remaining transfer capacity DZ determined based on data in different formats i And the conversion rate interval of the corresponding format data, identify and confirm the time completion interval of the corresponding format data, and propose the conversion rate interval as [ZH i min,ZH i max], using DZ i ÷ZH i max=T i min and DZ i ÷ZH i min=T i max, lock its time completion interval [T i min,T i max]; The associated computing power allocation end, based on the different time completion intervals confirmed by the data in different formats, prioritizes the relevant numerical range of the time completion interval, performs initial sorting to confirm the interval sequence, and then allocates computing power based on the different conversion processing processes corresponding to different intervals in the interval sequence. The specific method is as follows: Based on the determined time completion intervals belonging to the data in different formats, determine the middle value of the corresponding interval, and based on the numerical value of the middle value, sort the groups of time completion intervals in a small to large manner to lock the interval sequence; The subsequent format conversion process is monitored in real time, and based on the remaining capacity to be converted of the corresponding format data and its conversion rate interval, the time completion interval of the corresponding format data is confirmed in real time, and the confirmed interval sequence is proposed as follows: {Q1 , Q 2 , Q 3 ,……,Q n}, give priority to Q n The associated data conversion process is processed: from Q 1 -Q n-1 In the associated conversion process, the same computing power is allocated to Q in equal proportion n During the conversion process, when Q n With Q n-1 When the maximum overlap between the two intervals reaches 90%, 90% is the maximum overlap ratio of the two intervals. n With Q n-1 The associated time intervals are merged to obtain a set of merged intervals, and then 1 -Q n-2 In the associated conversion process, the same computing power is allocated to the conversion process of the merged interval in geometric proportion until the maximum overlap between the merged interval and the previous group of intervals reaches 90%, and Q n , Q n-1 and Q n-2 The associated time intervals are merged to obtain a group of merged intervals, which are then processed in sequence in the same way to allocate the associated computing power of different conversion processes in real time; The data fusion processing end is for several groups of data with different formats that have completed the unified format. The data with different formats belongs to the data acquired within the specified period. Combined with the preset catalog table, several groups of data with unified format are fused, and the fused data after fusion processing is transmitted to the periodic database for storage. The specific method is as follows: The preset directory table is a data fusion table prepared in advance by relevant personnel. Each different data area has a different data header. The corresponding data with the same data header is filled into the specified position of the preset directory table. Based on the preset directory table, several groups of data in different formats with unified formats are fused to obtain fused data, which is stored in the periodic database.

[0007] Preferably, the periodic database associates and stores data of different formats acquired within a specified period, and the stored data is in a fused format to complete unified overall data.

[0008] Preferably, it also includes: The data sharing end associates and shares the fused data stored in the periodic database, and based on the sharing instructions, the corresponding fused data is synchronously shared to different IP ends. The sharing instructions are prepared in advance by relevant personnel. And the data sharing end includes a consistency checking unit and an abnormal node determination unit; The consistency check unit performs consistency check on the fused data that needs to be associated and shared. Multiple check nodes are set in the fused data. Each check node is set at the corresponding data segment. Based on the specific data segment capacity check process, it is identified whether there is an abnormal shared segment. The specific method is as follows: Based on the original data segmentation in the fused data, a check node is set between each data segment and marked with J k Mark the check nodes, where k=1, 2, ..., n, and n represents the total number of check nodes. When k is 1, it represents the initial position of the initial segment of the fused data. When k is 2, it represents the position between the initial segment of the fused data and the second data segment. When k is n, it represents the end position of the end segment of the fused data. Based on the specific fusion data sharing process, the capacity is checked every time the data is shared to the check node, and the capacity of the data segment between the check nodes and the data segment between the same check nodes in other sharing processes is confirmed one by one, and the confirmed capacity is marked as R k-o , where o represents different shared processes; Then confirm the original fusion data from the cycle database and confirm this R in the same way k-o Corresponding standard capacity Rb k , if R k-o =Rb k , no processing is required. If R k-o ≠Rb k , mark the corresponding shared process as an abnormal process, and mark the corresponding data segment between the check nodes as an abnormal shared segment; The abnormal node determination unit identifies the shared process rate curve of the abnormal shared segment based on the calibrated abnormal process and abnormal shared segment, identifies the mutation segment from the shared process rate curve, and calibrates part of the data associated with the mutation segment as mutation data, and then identifies whether the mutation data has missing data based on the sharing of the mutation data in other processes and displays: based on the determined abnormal shared segment, the shared process rate curve of the abnormal shared segment is determined based on the sharing rates at different times, and the change value Bz between adjacent points is identified from the shared process rate curve, where Bz=|shared rate of the next point-shared rate of the previous point|, if Bz>Y1, where Y1 is a preset value, the value segment between the adjacent points is calibrated as a mutation segment; Identify part of the data associated with the mutation segment based on the corresponding shared process, and mark the identified part of the data as mutation data; Identify whether the curve segments associated with this mutation data in other shared processes are mutation segments. If they are all mutation segments, no calibration is performed. Otherwise, this mutation data is calibrated as missing data and directly displayed in the external display terminal.

[0009] The present invention provides a real-time e-government platform based on big data processing. Compared with the prior art, it has the following beneficial effects: The present invention obtains the relevant data generated within a specified period, and based on the different capacities of the data in different formats and the conversion rate of the entire data during the format unification process, confirms the conversion rate interval of the relevant data, and then performs the associated confirmation of the subsequent relevant completion time from the confirmed conversion rate interval. Subsequently, based on the computing power allocation method, the data in the corresponding format gradually tends to be consistent in the relevant completion time, which can fully ensure that the relevant data converted by multiple different conversion processes can complete the relevant conversion process within a relatively consistent time period, and ensure that when the subsequent data is integrated, the integration rate is faster, thereby reducing the data waiting time of the corresponding government platform; Subsequently, based on the relevant sharing features of multiple different sharing processes of the corresponding fused data, the corresponding abnormal data segments are identified, and then based on the sharing rate curve of the corresponding abnormal data segments in the sharing process, the associated abnormal points in the curve are identified, and then gradually confirmed, the missing data can be quickly locked and the associated response measures can be made in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the principle framework of the present invention. DETAILED DESCRIPTION

[0011] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0012] See also Figure 1 , the present application provides a real-time e-government platform based on big data processing, including a periodic data acquisition terminal, a data conversion and analysis terminal, an associated computing power allocation terminal, a data fusion processing terminal, a periodic database, and a data sharing terminal, wherein the periodic data acquisition terminal is electrically connected to an input node of the data conversion and analysis terminal, wherein the data conversion and analysis terminal is electrically connected to an input node of the associated computing power allocation terminal, and the associated computing power allocation terminal, the data fusion processing terminal, the periodic database, and the data sharing terminal are all electrically connected from an output node to an input node, and the data sharing terminal includes a consistency check unit and an abnormal node determination unit, and the consistency check unit is electrically connected to an input node of the abnormal node determination unit; Among them, the periodic data acquisition end acquires data of different formats generated by different data sources within a specified period, and the specified period is a preset period, and transmits the acquired data of different formats to the data conversion and analysis end. Specifically, the corresponding e-government platform needs to update the data once every certain period of time. For example, a certain platform needs to regularly update the relevant interface it displays every 24 hours to make the displayed relevant content the most accurate relevant data. For example, the corresponding government platform needs to update the corresponding government information in real time every day. The government information is provided by different data sources, and the specified period is 24 hours. After every 24 hours, data acquisition is required, and the multiple sets of data acquired are data of different formats generated by different data sources. Among them, the data conversion analysis end performs initial conversion on several groups of data in different formats, determines the conversion rate interval of data in different formats by limiting a group of initial conversion stages, and synchronously confirms the remaining capacity of data in different formats to be converted after the initial conversion stage, and then identifies the time completion interval of the corresponding data based on this conversion rate interval. Specifically, the relevant data of multiple different data sources, in order to ensure that several different formats of data can be fully integrated in the later stage, it is necessary to achieve the relevant effect of unified conversion during format conversion, ensure that the conversion time can be relatively consistent, and synchronously complete the format conversion effect between different data. Among them, the specific method of identifying the time completion interval of the corresponding data is: A set of initial conversion stages is defined, and the time period of the initial conversion stage is a preset period, and its specific value is prepared in advance, generally 2 minutes or less, and the conversion rate generated within the preset period is confirmed, and from the different conversion rates corresponding to the different format data, based on the minimum and maximum values ​​of the conversion rates, the conversion rate range belonging to the format data is directly determined. In the initial conversion stage, different format data have different conversion rates, and their conversion rates have corresponding maximum and minimum values, so based on their maximum and minimum values, the corresponding conversion rate range can be locked; From the acquired data of different formats, the remaining capacity to be transferred (that is, the overall data capacity of the corresponding data) of the different formats is marked as DZ i , where i represents different format data; Remaining transfer capacity DZ determined based on data in different formats i And the conversion rate interval of the corresponding format data, identify and confirm the time completion interval of the corresponding format data, and propose the conversion rate interval as [ZH i min,ZH i max], using DZ i ÷ZH i max=T i min and DZ i ÷ZHi min=T i max, lock its time completion interval [T i min,T i max]; Specifically, in order to maintain consistency during the format conversion process, it is necessary to confirm the relevant time intervals. Through associated adjustments, the computing power allocated to the processes of different formats of data during the conversion process is associated with changes. When a slow process is allocated with more computing power, its conversion rate will be faster. When a fast process is allocated with less computing power, its conversion rate will be slower. Therefore, the computing power is reasonably allocated and adjusted to ensure that its conversion time is as synchronized as possible.

[0013] Among them, the associated computing power allocation end, based on the different time completion intervals confirmed by the data in different formats, prioritizes the relevant numerical range of the time completion interval, performs initial sorting to confirm the interval sequence, and then allocates computing power based on the different conversion processing processes corresponding to different intervals in the interval sequence, reduces the specific difference in time spent on the conversion process of data in different formats, and makes the completion time of data in different formats as consistent as possible, and ensures the consistency of the data format conversion process; The specific method of computing power allocation is as follows: Based on the determined time completion intervals belonging to the data in different formats, determine the middle value of the corresponding interval, and based on the numerical value of the middle value, sort the groups of time completion intervals in a small to large manner to lock the interval sequence; The subsequent format conversion process is monitored in real time, and based on the remaining capacity to be converted of the corresponding format data and its conversion rate interval, the time completion interval of the corresponding format data is confirmed in real time, and the confirmed interval sequence is proposed as follows: {Q 1 , Q 2 , Q 3 ,……,Q n}, give priority to Q n The associated data conversion process is processed: from Q 1 -Q n-1 In the associated conversion process, the same computing power is allocated to Q in equal proportion n During the conversion process, when Q n With Q n-1 When the maximum overlap between the two intervals reaches 90%, 90% is the maximum overlap ratio of the two intervals (for example, A is [2, 8], B is [3, 7], then the overlap area is [3, 7], then the overlap of B is 100%, then the overlap reaches more than 90%, that is, there are two overlap ratios between the two intervals, belonging to different intervals, then there is a corresponding maximum overlap ratio), Q nWith Q n-1 The associated time intervals are merged to obtain a set of merged intervals, and then 1 -Q n-2 In the associated conversion process, the same computing power is allocated to the conversion process of the merged interval in geometric proportion until the maximum overlap between the merged interval and the previous group of intervals reaches 90%, and Q n , Q n-1 and Q n-2 The associated time intervals are merged to obtain a group of merged intervals, and then they are processed in the same way in sequence, and the associated computing power of different conversion processes is allocated in real time. In the real-time allocation process, it can be fully guaranteed that the relevant data converted by multiple different conversion processes can complete the relevant conversion process within a relatively consistent time period, and ensure that the subsequent data is merged at a faster rate, thereby reducing the data waiting time of the corresponding government platform; Among them, the data fusion processing end, for several groups of data with different formats that have completed the unified format, the data with different formats belong to the data acquired within the specified period, and then combine the preset directory table to fuse several groups of data with unified format, and transmit the fused data after the fusion processing to the periodic database for storage, wherein the specific method of fusion is: The preset directory table is a data fusion table prepared in advance by relevant personnel. Each different data area has a different data header. The corresponding data with the same data header is filled in the specified position of the preset directory table. Based on the preset directory table, several groups of data in different formats with unified format are fused to obtain fused data, and stored in the periodic database. Specifically, this preset directory table can be understood as a fusion logic, and its specific fusion logic is prepared in advance by related personnel. Based on this fusion logic, the data is fused to obtain the corresponding complete data after fusion. The complete data can be used for related applications and can be shared in different APPs or terminals. In order to better integrate, each data has a preset related data header, and the preset directory table also has a corresponding header preset to facilitate data fusion.

[0014] Among them, the periodic database associates and stores the data of different formats obtained within a specified period, and the stored data is in a fused format to complete unified overall data.

[0015] Among them, the data sharing end associates and shares the fused data stored in the periodic database, and based on the sharing instructions, the corresponding fused data is synchronously shared to different IP ends. The sharing instructions are prepared in advance by relevant personnel, and during the sharing process, the consistency of the fused data is associated and checked based on the consistency check unit, and then the abnormal node determination unit is used to determine the abnormal node. The different IP ends include: APP end, device terminal or public account, etc.; Among them, the consistency check unit performs consistency check on the fused data that needs to be associated and shared, sets multiple check nodes in the fused data, each check node is set at the corresponding data segment, and identifies whether there is an abnormal shared segment based on the specific data segment capacity check process. Specifically, when the corresponding data is shared, in order to identify whether there is abnormal data loss or other situations in different sharing processes, it is necessary to perform relevant consistency checks based on the real-time sharing process to identify whether the shared data is complete. The specific method of identification is: Based on the original data segmentation in the fused data, a check node is set between each data segment and marked with J k Mark the check nodes, where k=1, 2, ..., n, and n represents the total number of check nodes. When k is 1, it represents the initial position of the initial segment of the fused data. When k is 2, it represents the position between the initial segment of the fused data and the second data segment. When k is n, it represents the end position of the end segment of the fused data. Combined with the example: suppose that a string of data has four data segments, namely A, B, C and D, then set the check nodes, and the expression after setting is: 1 A 2 B 3 C 4 D 5 ,In order to better show its effect, the check node mark k here is associated and marked in the form of ,subscript for easy identification; Based on the specific fusion data sharing process, the capacity is checked every time the data is shared to the check node, and the capacity of the data segment between the check nodes and the data segment between the same check nodes in other sharing processes is confirmed one by one, and the confirmed capacity is marked as R k-o , where o represents different shared processes; Then confirm the original fusion data from the cycle database and confirm this R in the same way k-o Corresponding standard capacity Rb k (The original fusion data is definitely standard, so the specific capacity value of the standard can be directly selected from it). k-o =Rb k , no processing is required. If R k-o ≠Rbk , mark the corresponding shared process as an abnormal process, and mark the corresponding data segment between the check nodes as an abnormal shared segment; Specifically, in order to identify whether there is data missing in each shared process, it is necessary to confirm the capacity of the data segment shared by the relevant processes in real time to identify whether there are abnormal shared segments. For abnormal shared segments, abnormal missing data should be identified and the specific missing data should be assessed.

[0016] The abnormal node determination unit identifies the shared process rate curve of the abnormal shared segment based on the marked abnormal process and abnormal shared segment, identifies the mutation segment from the shared process rate curve, and marks part of the data associated with the mutation segment as mutation data, and then identifies whether the mutation data has missing data and displays it based on the sharing of the mutation data in other processes; Based on the determined abnormal sharing segment, the sharing process rate curve of this abnormal sharing segment is determined based on the sharing rates at different times. From the sharing process rate curve, the change value Bz between adjacent points is identified, where Bz=|sharing rate of the next point-sharing rate of the previous point|. If Bz>Y1, where Y1 is a preset value, and its specific value is determined by the operator based on experience, the value segment between the adjacent points is marked as a mutation segment. Identify part of the data associated with the mutation segment based on the corresponding shared process, and mark the identified part of the data as mutation data; Identify whether the curve segments associated with this mutation data in other sharing processes are mutation segments. If they are all mutation segments (it means that during the sharing process of this data, due to specific data attributes or other problems, the corresponding sharing rate will mutate. In this case, each different sharing process has such problems. Therefore, this type of data segment, that is, the mutation segment, does not fluctuate. Basically, there will be no problem of data missing. Under normal circumstances, when there are corresponding fluctuations during the transmission of the data sharing process, corresponding data will be missing), no calibration will be performed. Otherwise, this mutation data will be calibrated as missing data and directly displayed in the external display terminal. Based on the displayed missing data, external personnel will directly conduct correlation analysis and evaluation on such missing data to confirm whether such mutation data needs to be processed and make relevant response measures in time.

[0017] Some of the data in the above formulas are dimensionless and numerically calculated. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0018] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. 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 method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A real-time e-government platform based on big data processing, characterized in that: include: The periodic data acquisition terminal acquires data of different formats generated by different data sources within a specified period, and the specified period is a preset period; Data conversion analysis end, and performs initial conversion on several groups of data in different formats, determines the conversion rate range of data in different formats by limiting a set of initial conversion stages, and simultaneously confirms the remaining capacity to be converted of data in different formats after the initial conversion stage, and then identifies the time completion range of the corresponding data based on this conversion rate range; The associated computing power allocation end performs preliminary sorting and confirming of the interval sequence based on the different time completion intervals confirmed by the data in different formats, and then allocates computing power based on the different conversion processing processes corresponding to different intervals in the interval sequence; At the data fusion processing end, for several groups of data in different formats that have completed format unification, the data in different formats belong to the data acquired within a specified period, and then combined with the preset directory table, several groups of data that have completed format unification are fused, and the fused data after fusion processing is transmitted to the periodic database for storage.

2. A real-time e-government platform based on big data processing according to claim 1, characterized in that: The specific method of the data conversion analysis end to identify the time completion interval of the corresponding data is: A set of initial conversion stages is defined, the time period of the initial conversion stage is a preset period, the conversion rate generated within the preset period is confirmed, and from different conversion rates corresponding to different format data, based on the minimum value and the maximum value of the conversion rate, the conversion rate range belonging to the format data is directly determined; From the acquired data in different formats, the remaining capacity to be transferred of the data in different formats is marked as DZ i , where i represents different format data; Remaining transfer capacity DZ determined based on data in different formats i And the conversion rate interval of the corresponding format data, identify and confirm the time completion interval of the corresponding format data, and propose the conversion rate interval as [ZH i min,ZH i max], using DZ i ÷ZH i max=T i min and DZ i ÷ZH i min=T i max, lock its time completion interval [T i min,T i max].

3. A real-time e-government platform based on big data processing according to claim 2, characterized in that: The specific method of computing power allocation by the associated computing power allocation terminal is as follows: Based on the determined time completion intervals belonging to the data in different formats, determine the middle value of the corresponding interval, and based on the numerical value of the middle value, sort the groups of time completion intervals in a small to large manner to lock the interval sequence; The subsequent format conversion process is monitored in real time, and based on the remaining capacity to be converted of the corresponding format data and its conversion rate interval, the time completion interval of the corresponding format data is confirmed in real time, and the confirmed interval sequence is proposed as: {Q1, Q2, Q3, ..., Q n }, give priority to Q n The associated data conversion process is processed: from Q1-Q n-1 In the associated conversion process, the same computing power is allocated to Q in equal proportion n During the conversion process, when Q n With Q n-1 When the maximum overlap between the two intervals reaches 90%, 90% is the maximum overlap ratio of the two intervals. n With Q n-1 The associated time intervals are merged to obtain a set of merged intervals, and then from Q1-Q n-2 In the associated conversion process, the same computing power is allocated to the conversion process of the merged interval in geometric proportion until the maximum overlap between the merged interval and the previous group of intervals reaches 90%, and Q n , Q n-1 and Q n-2 The associated time intervals are merged to obtain a group of merged intervals, which are then processed in sequence in the same way to allocate the associated computing power of different conversion processes in real time.

4. A real-time e-government platform based on big data processing according to claim 3, characterized in that: The specific method of fusing data at the data fusion processing end is as follows: The preset directory table is a data fusion table prepared in advance by relevant personnel. Each different data area has a different data header. The corresponding data with the same data header is filled into the specified position of the preset directory table. Based on the preset directory table, several groups of data in different formats with unified formats are fused to obtain fused data, which is stored in the periodic database.

5. A real-time e-government platform based on big data processing according to claim 4, characterized in that: The periodic database associates and stores the data in different formats acquired within a specified period, and the stored data is in a fused format to complete unified overall data.

6. A real-time e-government platform based on big data processing according to claim 1, characterized in that: Also includes: The data sharing end associates and shares the fused data stored in the periodic database, and based on the sharing instructions, the corresponding fused data is synchronously shared to different IP ends. The sharing instructions are prepared in advance by relevant personnel. And the data sharing end includes a consistency checking unit and an abnormal node determination unit; The consistency check unit performs consistency check on the fused data that needs to be associated and shared. Multiple check nodes are set in the fused data. Each check node is set at a corresponding data segment. Based on the specific data segment capacity check process, it is identified whether there is an abnormal shared segment. The abnormal node determination unit identifies the shared process rate curve of the abnormal shared segment based on the marked abnormal process and abnormal shared segment, identifies the mutation segment from the shared process rate curve, and marks part of the data associated with the mutation segment as mutation data. Based on the sharing of the mutation data in other processes, it identifies whether the mutation data has missing data and displays it.

7. A real-time e-government platform based on big data processing according to claim 6, characterized in that: The specific method of the consistency checking unit for identifying whether there is an abnormal shared segment is: Based on the original data segmentation in the fused data, a check node is set between each data segment and marked with J k Mark the check nodes, where k=1, 2, ..., n, and n represents the total number of check nodes. When k is 1, it represents the initial position of the initial segment of the fused data. When k is 2, it represents the position between the initial segment of the fused data and the second data segment. When k is n, it represents the end position of the end segment of the fused data. Based on the specific fusion data sharing process, the capacity is checked every time it is shared to the check node, and the capacity of the data segment between the check nodes and the data segment between the same check nodes in other sharing processes is confirmed one by one, and the confirmed capacity is marked as R k-o , where o represents different shared processes; Then confirm the original fusion data from the cycle database and confirm this R in the same way k-o Corresponding standard capacity Rb k , if R k-o =Rb k , no processing is required. If R k-o ≠Rb k , the corresponding shared process is marked as an abnormal process, and the data segment between the corresponding check nodes is marked as an abnormal shared segment.

8. A real-time e-government platform based on big data processing according to claim 7, characterized in that: The abnormal node determination unit identifies whether the mutation data has missing data and displays: Based on the determined abnormal sharing segment, the sharing process rate curve of this abnormal sharing segment is determined based on the sharing rates at different times. From the sharing process rate curve, the change value Bz between adjacent points is identified, where Bz=|sharing rate of the next point-sharing rate of the previous point|. If Bz>Y1, where Y1 is a preset value, the value segment between the adjacent points is marked as a mutation segment. Identify part of the data associated with the mutation segment based on the corresponding shared process, and mark the identified part of the data as mutation data; Identify whether the curve segments associated with this mutation data in other shared processes are mutation segments. If they are all mutation segments, no calibration is performed. Otherwise, this mutation data is calibrated as missing data and directly displayed in the external display terminal.

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