An intelligent management method for government data operation and maintenance

Abstract

The present invention relates to the field of data management technology, and in particular to an intelligent management method for government data operation and maintenance, comprising step one: determining a spatial boundary value based on the data processing speed of each storage node value, and setting a spatial status for the data storage space; step two: setting data tags for the collected government data, and storing them according to the data tags, while detecting the spatial status of the data storage space at this time, and generating a space management signal; step three: based on the space management signal, obtaining and analyzing the data usage records of historical storage data, and determining the inertial usage cycle and data usage value; step four: calculating the data evaluation value of the historical storage data based on the inertial usage cycle and the data usage value, determining expired data based on the data evaluation value, and performing operation and maintenance management on the expired data, thereby optimizing the utilization resources of the government data storage space, further reducing the load of the storage device, and saving energy costs.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and in particular to an intelligent management method for government data operation and maintenance. Background Art

[0002] Government data is a general term for information, situations, materials, data, charts, written materials, and audio-visual materials that reflect government work and its related activities in government activities. It is managed by government data management departments such as the Bureau of Government Information and Statistics.

[0003] Prior art CN118796647A discloses a government affairs system operation and maintenance method and system based on multi-source data fusion. The method obtains the current performance data, current application data, and current security data of each government affairs server in the government affairs system and performs a comprehensive analysis to obtain the current system performance evaluation index, current application performance evaluation index, and current security evaluation index of each government affairs server. The data is then compared and analyzed with the corresponding preset evaluation thresholds, and operation and maintenance measures are taken based on the comparison and analysis results.

[0004] However, when government data is collected, many information sources are involved. When operating and managing government data, the collected information will contain a large amount of data, which in turn increases the storage space of the data. When the data storage space is occupied in large quantities, the performance of the storage system will decline. At this time, the response time of government data may be extended from the original several seconds to several minutes, seriously affecting the efficiency of government services. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems in the background technology and to propose an intelligent management method for government data operation and maintenance.

[0006] The present invention adopts the following technical solution: a method for intelligent management of government data operation and maintenance, which specifically includes the following steps:

[0007] Step 1: Based on the basic information of the data storage space, retrieve the relevant devices and obtain the usage information of the relevant devices. First, set the storage node value, and based on the storage node value, obtain the data processing speed of the relevant devices at each storage node value in the usage information, and calculate the speed characteristic value of each storage node value. Then, based on the storage node value and the speed characteristic value, obtain the speed change curve and change function, calculate the change function, and obtain the processing rate value. Based on the processing rate value, determine the space boundary value, and set the space state for the data storage space according to the space boundary value. The space state includes free space and controlled space.

[0008] Step 2: Set data tags for the collected government data and store them according to the data tags. At the same time, when storing the government data, detect the spatial status of the data storage space at this time and generate a corresponding space management signal;

[0009] Step 3: When a space management signal is detected, historical storage data is obtained, target analysis data is set according to the data tag, the collection time and data usage record of the target analysis data are obtained, the last use time is obtained from the data usage record, and based on the last use time and collection time, the inertia usage cycle is determined. Then, within the inertia usage cycle, the data usage value of the target analysis data is determined based on the total number of times the target analysis data is used;

[0010] Step 4: The actual usage times and time intervals of the historically stored data are then collected, and the data evaluation value of the historically stored data is calculated based on the data usage value and inertial usage cycle under the corresponding data tag. The expired data and valid data are then determined based on the data evaluation value, and the expired data is managed through operation and maintenance. The operation and maintenance management methods include data deletion, data archiving, and data compression.

[0011] As a further solution of the present invention, a method for obtaining a speed change curve and a change function includes:

[0012] S1: Based on the device model of the data storage space, relevant devices are searched on the big data network and usage information of the relevant devices is obtained. The usage information includes the data processing speed of the data storage space under different data storage capacities;

[0013] S2: Set an equal interval value d for the data storage capacity. Starting from the initial capacity, the storage node value n is obtained in sequence according to the equal interval value using the formula n = CS + d × a, where CS is the initial capacity and a = 1, 2, ...;

[0014] S3: According to the storage node value, obtain the data processing speed V of the relevant device i-n , where i represents the device number of the relevant device, V i-n Indicates the data processing speed of the corresponding device i at the storage node value n;

[0015] Calculate the velocity characteristic value VTn at each storage node value n, I is the total number of related devices obtained after retrieval. Further, i∈[1, I];

[0016] S4: Using the storage node value n as the horizontal coordinate and the velocity characteristic value VTn as the vertical coordinate, a plane coordinate system is set, and the velocity characteristic value corresponding to each storage node value is plotted in the plane coordinate system to obtain coordinates of several points;

[0017] Then set the storage node value as the independent variable factor, set the speed characteristic value as the dependent variable factor, and fit the point coordinates into a linear curve based on the point coordinates in the plane coordinate system, and mark it as the speed change curve, and at the same time obtain the change function VTn=f(n) of the speed change curve.

[0018] As a further solution of the present invention, a method for setting a space state of a data storage space includes:

[0019] S5: Perform a derivative operation on the change function to obtain a processing rate value k, k=f'(n). When the absolute value of the processing rate value k is larger, it means that the slope of the speed change curve at this position is larger, that is, the change speed is faster;

[0020] According to the rate threshold, take the storage node value at the corresponding position when the absolute value of the processing speed threshold k is equal to the rate threshold, and mark this storage node value as the space boundary value;

[0021] S6: According to the space demarcation value, the data storage space with a data storage capacity less than the space demarcation value is marked as free space, and the data storage space with a data storage capacity greater than or equal to the space demarcation value is marked as controlled space.

[0022] As a further solution of the present invention, a method for generating a spatial management signal includes:

[0023] The collected government data is preprocessed and stored in the data storage space. At the same time, when storing the government data, the spatial status of the data storage space is determined. If the spatial status is free space, the government data is directly stored in the data storage space. If the spatial status is controlled space, a space management signal is generated.

[0024] As a further solution of the present invention, the data preprocessing method includes data cleaning and data integration. Data cleaning refers to identifying abnormal data in government data collected in real time and processing it accordingly. Abnormal data includes missing data, abnormal data, and duplicate data. Data integration refers to identifying corresponding data tags for government data collected from different information sources and merging and storing them according to the data tags.

[0025] Data labeling refers to marking government data according to its data source, and data source refers to the source from which government data is obtained.

[0026] As a further solution of the present invention, a method for obtaining the inertial usage cycle and the data usage value includes:

[0027] SS1: Mark the government data stored in the data storage space as historical storage data, arbitrarily select a data tag, obtain the historical storage data corresponding to this data tag, and mark it as target analysis data. The target analysis data is arranged in chronological order to obtain a position sequence;

[0028] Obtain the data update time and data usage records of the target analysis data, where the data update time refers to the collection time of the target analysis data, and the data usage record refers to the record of the target analysis data being used;

[0029] In the data usage record, identify the last usage time of the target analysis data and obtain the collection time of the target analysis data. Subtract the collection time from the last usage time to obtain the usage interval time. Then, obtain the usage interval time of multiple target analysis data, average the usage interval time of the multiple target analysis data, and mark the obtained result as the inertial usage cycle of the target analysis data.

[0030] SS2: Determine the data usage value of the target analysis data based on the inertial usage time. That is, obtain the total number of times each target analysis data is used within the inertial usage time, then take the average of the total number of times all target analysis data are used, and mark it as the data usage value of the target analysis data.

[0031] As a further solution of the present invention, when identifying the last usage time of the target analysis data, it is necessary to first determine the usage interval of the target analysis data based on the data usage record. When there is a target analysis data m1 that is used at time t1 and is not used within the next consecutive usage intervals b1, time t1 is used as the last usage time of the target analysis data m1, where b1 is a threshold.

[0032] When obtaining the total number of times target analysis data is used, only the target analysis data containing the last use time is counted.

[0033] As a further solution of the present invention, a method for determining expired data and valid data includes:

[0034] ST1: Evaluate historical stored data and determine the data evaluation value PL. The specific evaluation methods include:

[0035] Randomly select a data from the historical storage data and mark it as the analysis target data, identify the data label of the analysis target data, and determine the data usage value SY and the inertial usage period Tz based on the data label;

[0036] Using the formula Obtain the data evaluation value PL, where Ds represents the actual number of times the analysis object data is used, Dt represents the time interval between the acquisition time of the analysis object data and the current time, that is, Dt is equal to the current time minus the acquisition time of the analysis object data, u is a fixed coefficient, and 0<u<1, r1, r2 and r3 are all proportional coefficients;

[0037] ST2: Obtain the data evaluation value PL of all historical stored data and compare the data evaluation value PL with the evaluation threshold Py. If PL < Py, the corresponding historical stored data is marked as expired data. Conversely, if PL ≥ Py, the corresponding historical stored data is marked as valid data.

[0038] ST3: Obtain expired data and perform operation and maintenance management on expired data. Specific operation and maintenance management methods include data deletion, data archiving, and data compression.

[0039] During operation, the present invention detects the data processing speed of the data storage space of government data to determine the free space and the control space. When in the free space, the collected government data is directly stored. When in the control space, the data usage record of the historical storage data is obtained, the data usage value and inertial usage cycle of the government data are determined, and the data evaluation value of the historical storage data is calculated. Then, expired data is determined according to the data evaluation value, and operation and maintenance management of the expired data is performed, thereby optimizing the utilization resources of the government data storage space, further reducing the load of the storage device, and saving energy costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a schematic diagram of the method flow structure of the present invention. DETAILED DESCRIPTION

[0041] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0042] Reference Figure 1 , a method for intelligent management of government data operation and maintenance, the method specifically comprising the following steps:

[0043] Step 1: Collect basic information about the data storage space and set the space status for the data storage space. The space status includes free space and controlled space. In this embodiment, the data storage space refers to the device for storing government data. The basic information includes the device model and overall storage space of the storage device. The specific method for dividing the data storage space includes:

[0044] S1: Based on the device model of the data storage space, relevant devices are searched on the big data network, and usage information of the relevant devices is obtained, where the usage information includes the data processing speed of the data storage space under different data storage capacities. For example, when the data storage capacity in the data storage space is 5%, the data processing speed in the data storage space is 200 MB / s; when the data storage capacity in the data storage space is 85%, the data processing speed in the data storage space is 95 MB / s.

[0045] S2: Set an equal distance value d for the data storage capacity. Starting from the initial capacity, the storage node value n is obtained in sequence according to the equal distance value using the formula n = CS + d × a, where CS is the initial capacity and a = 1, 2, ... In this embodiment, the initial capacity is set to 0 and the equal distance value d is set to 5;

[0046] S3: According to the storage node value, obtain the data processing speed V of the relevant device i-n , where i represents the device number of the relevant device, V i-n Indicates the data processing speed of the corresponding device i at the storage node value n;

[0047] Then calculate the velocity characteristic value VTn at each storage node value n. Specifically, I is the total number of related devices obtained after retrieval. Further, i∈[1, I];

[0048] S4: Using the storage node value n as the horizontal coordinate and the velocity characteristic value VTn as the vertical coordinate, a plane coordinate system is set, and the velocity characteristic value corresponding to each storage node value is plotted in the plane coordinate system to obtain coordinates of several points;

[0049] Then, the storage node value is set as the independent variable factor, the speed characteristic value is set as the dependent variable factor, and the point coordinates in the plane coordinate system are fitted into a linear curve and marked as the speed change curve. At the same time, the change function of the speed change curve VTn=f(n) is obtained;

[0050] Among them, the coordinates of several points are fitted into a linear function using the curve fitting toolbox in MATLAB. The specific processing method is the existing technology and will not be described in detail here.

[0051] S5: Perform a derivative operation on the change function to obtain a processing rate value k. Specifically, k=f'(n). When the absolute value of the processing rate value k is larger, it means that the slope of the speed change curve at this position is larger, that is, the change speed is faster;

[0052] Then, according to the rate threshold, the storage node value at the corresponding position when the absolute value of the processing speed threshold k is equal to the rate threshold is taken, and this storage node value is marked as the space boundary value;

[0053] The specific value of the rate threshold is obtained by those skilled in the art after big data calculation;

[0054] S6: Then, according to the space demarcation value, the data storage space with a data storage capacity less than the space demarcation value is marked as free space, and the data storage space with a data storage capacity greater than or equal to the space demarcation value is marked as controlled space;

[0055] For example, when the space boundary value is 80%, when the data storage capacity in the data storage space is [0%, 80%), the space status at this time is free space; when the data storage capacity in the data storage space is [80%, 100%), the space status at this time is controlled space;

[0056] Step 2: The collected government data is then pre-processed and stored in the data storage space. When storing the government data, the spatial status of the data storage space is determined. If the spatial status is free space, the government data is directly stored in the data storage space. If the spatial status is controlled space, a space management signal is generated.

[0057] Among them, the data preprocessing method includes data cleaning and data integration. Data cleaning refers to identifying abnormal data in the government data collected in real time and processing it accordingly. Abnormal data includes missing data, abnormal data and duplicate data. The specific processing method is the existing technology and will not be described here. Data integration refers to identifying the corresponding data tags of government data collected from different information sources and merging and storing them according to the data tags;

[0058] Data labeling refers to labeling government data according to their data sources. Data sources refer to where government data is obtained, including service windows, online service platforms, on-site consultations, and social media platforms.

[0059] Step 3: When a space management signal is detected, the government data stored in the data storage space is subjected to inertial processing, and the government data stored in the data storage space is marked as historical storage data. Then, inertial analysis is performed on the historical storage data to determine the data usage value and inertial usage cycle of the historical storage data with different data tags.

[0060] The specific methods for determining data usage values ​​and inertial usage cycles include:

[0061] SS1: Randomly select a data tag, obtain the historical storage data corresponding to this data tag, mark it as the target analysis data, and arrange the target analysis data in chronological order to obtain a position sequence;

[0062] Obtain the data update time and data usage records of the target analysis data, where the data update time refers to the collection time of the target analysis data, and the data usage record refers to the record of the target analysis data being used;

[0063] In the data usage record, identify the last usage time of the target analysis data and obtain the collection time of the target analysis data. Subtract the collection time from the last usage time to obtain the usage interval time. Then, obtain the usage interval time of multiple target analysis data, average the usage interval time of the multiple target analysis data, and mark the obtained result as the inertial usage cycle of the target analysis data.

[0064] When identifying the last usage time of the target analysis data, it is necessary to first determine the usage interval of the target analysis data based on the data usage record. When a target analysis data m1 is used at time t1 and is not used within the next consecutive usage intervals b1, time t1 is used as the last usage time of the target analysis data m1. b1 is a threshold value, and the specific value of b1 is obtained by those skilled in the art after big data calculation.

[0065] SS2: Determine the data usage value of the target analysis data based on the inertial usage time. That is, obtain the total number of times each target analysis data is used during the inertial usage time, then take the average of the total number of times all target analysis data are used and mark it as the data usage value of the target analysis data.

[0066] It should be further explained that when obtaining the total number of times target analysis data is used, only the target analysis data containing the last use time is counted;

[0067] The historical storage data under all different tags are used as target analysis data in turn, and are processed according to the methods in steps SS1 and SS2 above to obtain the data usage value and inertial usage cycle of the historical storage data of each tag;

[0068] Step 4: Perform operation and maintenance management on historical stored data based on the data usage values ​​and inertial usage cycles of different tags. Specific operation and maintenance management methods include:

[0069] ST1: Evaluate historical stored data and determine the data evaluation value PL. The specific evaluation methods include:

[0070] Randomly select a data from the historical storage data and mark it as the analysis target data, identify the data label of the analysis target data, and determine the data usage value SY and the inertial usage period Tz based on the data label;

[0071] Then use the formula Obtain a data evaluation value PL, where Ds represents the actual number of times the analysis object data is used, Dt represents the time interval between the acquisition time of the analysis object data and the current time, i.e., Dt is equal to the current time minus the acquisition time of the analysis object data, u is a fixed coefficient, and 0 < u < 1, r1, r2, and r3 are all proportional coefficients. The specific values ​​of u, r1, r2, and r3 are obtained by those skilled in the art after big data calculation;

[0072] It should be further explained that, for the data evaluation value PL, the data evaluation value PL represents the data evaluation level. The lower the data evaluation value PL, the lower the importance level of the corresponding historical storage data at the current time. Conversely, the higher the data evaluation value PL, the higher the importance level of the corresponding historical storage data at the current time.

[0073] ST2: Obtain the data evaluation value PL of all historical stored data and compare the data evaluation value PL with the evaluation threshold Py. If PL < Py, the corresponding historical stored data is marked as expired data. Conversely, if PL ≥ Py, the corresponding historical stored data is marked as valid data.

[0074] The specific value of the evaluation threshold Py is obtained by those skilled in the art after big data calculation;

[0075] ST3: After that, the expired data is obtained and operation and maintenance management is performed on the expired data. Specific operation and maintenance management methods include data deletion, data archiving, and data compression, thereby optimizing the utilization of government data storage space resources, further reducing the load on storage devices, and saving energy costs.

[0076] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for intelligent management of government data operation and maintenance, characterized in that: The method specifically comprises the following steps: Step 1: Based on the basic information of the data storage space, retrieve the relevant devices and obtain the usage information of the relevant devices. First, set the storage node value, and based on the storage node value, obtain the data processing speed of the relevant devices at each storage node value in the usage information, and calculate the speed characteristic value of each storage node value. Then, based on the storage node value and the speed characteristic value, obtain the speed change curve and change function, calculate the change function, and obtain the processing rate value. Based on the processing rate value, determine the space boundary value, and set the space state for the data storage space according to the space boundary value. The space state includes free space and controlled space. The method for obtaining the speed change curve and the change function includes: S1: Based on the device model of the data storage space, relevant devices are searched on the big data network and usage information of the relevant devices is obtained. The usage information includes the data processing speed of the data storage space under different data storage capacities; S2: Set an equal interval value d for the data storage capacity. Starting from the initial capacity, the storage node value n is obtained in sequence according to the equal interval value using the formula n = CS + d × a, where CS is the initial capacity and a = 1, 2, ...; S3: According to the storage node value, obtain the data processing speed V of the relevant device i-n , where i represents the device number of the relevant device, V i-n Indicates the data processing speed of the corresponding device i at the storage node value n; Calculate the velocity characteristic value VTn at each storage node value n, I is the total number of related devices obtained after retrieval. Further, i∈[1, I]; S4: Using the storage node value n as the horizontal coordinate and the velocity characteristic value VTn as the vertical coordinate, a plane coordinate system is set, and the velocity characteristic value corresponding to each storage node value is plotted in the plane coordinate system to obtain coordinates of several points; Then, the storage node value is set as the independent variable factor, the speed characteristic value is set as the dependent variable factor, and the point coordinates in the plane coordinate system are fitted into a linear curve and marked as the speed change curve. At the same time, the change function of the speed change curve VTn=f(n) is obtained; Methods for setting the data storage space status include: S5: Perform a derivative operation on the change function to obtain a processing rate value k, k=f'(n). When the absolute value of the processing rate value k is larger, it means that the slope of the speed change curve at this position is larger, that is, the change speed is faster; According to the rate threshold, take the storage node value at the corresponding position when the absolute value of the processing speed threshold k is equal to the rate threshold, and mark this storage node value as the space boundary value; S6: According to the space demarcation value, the data storage space with a data storage capacity less than the space demarcation value is marked as free space, and the data storage space with a data storage capacity greater than or equal to the space demarcation value is marked as controlled space; Step 2: Set data tags for the collected government data and store them according to the data tags. At the same time, when storing the government data, detect the spatial status of the data storage space at this time and generate a corresponding space management signal; Step 3: When a space management signal is detected, historical storage data is obtained, target analysis data is set according to the data tag, the collection time and data usage record of the target analysis data are obtained, the last use time is obtained from the data usage record, and based on the last use time and collection time, the inertia usage cycle is determined. Then, within the inertia usage cycle, the data usage value of the target analysis data is determined based on the total number of times the target analysis data is used; The methods for obtaining the inertial usage cycle include: Mark the government data stored in the data storage space as historical storage data, select any data label, obtain the historical storage data corresponding to this data label, and mark it as target analysis data. Arrange the target analysis data in chronological order to obtain a position sequence; Obtain the data update time and data usage records of the target analysis data, where the data update time refers to the collection time of the target analysis data, and the data usage record refers to the record of the target analysis data being used; In the data usage record, identify the last usage time of the target analysis data and obtain the collection time of the target analysis data. Subtract the collection time from the last usage time to obtain the usage interval time. Then, obtain the usage interval time of multiple target analysis data, average the usage interval time of the multiple target analysis data, and mark the obtained result as the inertial usage cycle of the target analysis data. Step 4: The actual usage times and time intervals of the historically stored data are then collected, and the data evaluation value of the historically stored data is calculated based on the data usage value and inertial usage cycle under the corresponding data tag. The expired data and valid data are then determined based on the data evaluation value, and the expired data is managed through operation and maintenance. The operation and maintenance management methods include data deletion, data archiving, and data compression.

2. A method for intelligent management of government data operation and maintenance according to claim 1, characterized in that: The method for generating the spatial management signal includes: The collected government data is preprocessed and stored in the data storage space. At the same time, when storing the government data, the spatial status of the data storage space is determined. If the spatial status is free space, the government data is directly stored in the data storage space. If the spatial status is controlled space, a space management signal is generated.

3. The method for intelligent management of government data operation and maintenance according to claim 2, characterized in that: Data preprocessing methods include data cleaning and data integration. Data cleaning refers to identifying abnormal data in real-time government data and processing it accordingly. Abnormal data includes missing data, abnormal data, and duplicate data. Data integration refers to identifying corresponding data labels for government data collected from different information sources and merging and storing them according to data labels. Data labeling refers to marking government data according to its data source, and data source refers to the source from which government data is obtained.

4. The method for intelligent management of government data operation and maintenance according to claim 1, characterized in that: Methods for obtaining data usage values ​​include: According to the inertial usage time, the data usage value of the target analysis data is determined, that is, the total number of times each target analysis data is used within the inertial usage time is obtained, and then the total number of times all target analysis data are used is averaged and marked as the data usage value of the target analysis data.

5. The method for intelligent management of government data operation and maintenance according to claim 4 is characterized in that: When identifying the last usage time of target analysis data, it is necessary to first determine the usage interval of the target analysis data based on the data usage record. If a target analysis data m1 is used at time t1 and is not used within the next b1 consecutive usage intervals, time t1 is used as the last usage time of the target analysis data m1, where b1 is the threshold. When obtaining the total number of times target analysis data is used, only the target analysis data containing the last use time is counted.

6. A method for intelligent management of government data operation and maintenance according to claim 5, characterized in that: Methods for determining expired data and valid data include: ST1: Evaluate historical stored data and determine the data evaluation value PL. The specific evaluation methods include: Randomly select a data from the historical storage data and mark it as the analysis target data, identify the data label of the analysis target data, and determine the data usage value SY and the inertial usage period Tz based on the data label; Using the formula Obtain the data evaluation value PL, where Ds represents the actual number of times the analysis object data is used, Dt represents the time interval between the acquisition time of the analysis object data and the current time, that is, Dt is equal to the current time minus the acquisition time of the analysis object data, u is a fixed coefficient, and 0<u<1, r1, r2 and r3 are all proportional coefficients; ST2: Obtain the data evaluation value PL of all historical stored data and compare the data evaluation value PL with the evaluation threshold Py. If PL < Py, the corresponding historical stored data is marked as expired data. Conversely, if PL ≥ Py, the corresponding historical stored data is marked as valid data. ST3: Obtain expired data and perform operation and maintenance management on expired data. Specific operation and maintenance management methods include data deletion, data archiving, and data compression.

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