Basic-level governance data analysis method and system based on cloud computing

Abstract

The invention relates to the technical field of grassroots governance data analysis, in particular to a grassroots governance data analysis method and system based on cloud computing, and the method comprises the following steps: calculating the number of events, the change rate and the abnormal value proportion in a grid based on the spatial and temporal distribution characteristics of event report data, carrying out the weight operation, and comparing with a dynamic threshold value; and marking the key grid according to the event change fluctuation, and generating a self-adaptive grid structure value. According to the method, by combining spatial adjacency relation weight calculation, capturing potential spatial correlation characteristics between grids, optimizing regional collaborative analysis of data and trend change and spatial data coupling operation, the accuracy of spatio-temporal data prediction is improved, an abnormal change region is effectively positioned, and in the dynamic monitoring process of governing a hot spot region, the dynamic monitoring accuracy of the hot spot region is improved. Through data fluctuation and policy progress deviation analysis, an abnormal area of policy execution is marked in time, treatment effect evaluation and accurate resource configuration are enhanced, and treatment weak points are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of grassroots governance data analysis, and in particular to a grassroots governance data analysis method and system based on cloud computing. Background Art

[0002] The field of grassroots governance data analysis technology refers to the use of modern information technology to collect, store, process and analyze large amounts of data generated in grassroots governance to support scientific decision-making and efficient management of grassroots affairs by government departments, social organizations and communities. This technical field includes big data technology, cloud computing, artificial intelligence and visualization tools. Through comprehensive mining and analysis of grassroots social governance-related data, it provides data-driven decision support, improves the efficiency of resource allocation, and promotes the intelligent, precise and modern development of grassroots governance. The application scenarios of this technical field cover urban management, community services, social security, people's livelihood services, emergency management and other aspects, aiming to achieve scientific governance and efficient operation of grassroots affairs.

[0003] Among them, the grassroots governance data analysis method based on cloud computing refers to the use of the powerful data processing capabilities of the cloud computing platform to analyze and mine the massive data collected in the grassroots governance process to form actionable governance insights and decision-making basis. The purpose of this method is mainly to improve the storage, management, sharing and application efficiency of grassroots governance data through the real-time analysis and processing capabilities of cloud computing, provide accurate decision-making support for grassroots governance, optimize the grassroots social governance model, and improve the overall governance effectiveness.

[0004] Existing technologies rely on static thresholds for event anomaly identification, which makes it difficult to adapt to dynamic changes in data, resulting in distorted identification of key areas. There is a lack of spatial correlation calculations between regions, and data analysis cannot fully reflect the interactive impact of regions, ignoring potential abnormal propagation paths. In terms of dynamic monitoring of governance hotspots, data trends and regional coordination are not coupled, and the effectiveness of policy implementation is relatively rough. It is impossible to accurately locate governance deviation areas, and resource allocation is prone to misjudgment, reducing overall governance efficiency. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose a grassroots governance data analysis method and system based on cloud computing.

[0006] In order to achieve the above purpose, the present invention adopts the following technical solution: a grassroots governance data analysis method based on cloud computing, comprising the following steps: S1: Based on the spatiotemporal distribution characteristics of event reporting data, the number of events, change rate and abnormal value ratio in the grid are calculated, weight calculation is performed and compared with the dynamic threshold, key grids are marked according to event change fluctuations, and adaptive grid structure values ​​are generated; S2: Based on demographic data and regional resource allocation data, define the spatial adjacency relationship between multiple grids, calculate the spatial association weights between the grid and the adjacent grids, summarize the spatial associations between differentiated grids, and generate neighborhood spatial weight matrix values; S3: Based on the adaptive grid structure value, extract the community abnormal monitoring data of the differentiated time periods in the grid, calculate the trend change of the monitoring data, couple the trend characteristics with the neighborhood space weight matrix value, obtain the spatial regression coefficient and the time trend term, and generate the spatiotemporal autoregressive prediction value; S4: Based on the spatiotemporal autoregressive prediction value, calculate the change trend of the governance hotspot area data in each grid, overlay the result with the spatiotemporal regression parameters of the adjacent grids for analysis, identify the governance abnormal change area, and generate dynamic monitoring values ​​for the hotspot area; S5: Based on the dynamic monitoring value of the hot spot area, combined with the threshold parameters in the policy implementation progress, analyze the data fluctuations and policy progress deviations in the hot spot area, mark the policy implementation abnormal areas, and generate the hot spot area evolution evaluation value.

[0007] The adaptive grid structure values ​​include the number of events, change rate, proportion of outliers, dynamic thresholds, and key grid marks. The neighborhood space weight matrix values ​​include demographic data association weights, regional resource allocation association weights, spatial adjacency weights, and differentiated grid space correlations. The spatiotemporal autoregressive prediction values ​​include spatial regression coefficients, time trend items, and trend changes in community abnormal monitoring data. The hot spot area dynamic monitoring values ​​include governance hot spot area data change trends, neighboring grid spatiotemporal regression parameter analysis results, and governance abnormal change area identification results. The hot spot area evolution assessment values ​​include hot spot area data fluctuations, policy progress deviations, and policy implementation abnormal area marks.

[0008] As a further solution of the present invention, the step of obtaining the adaptive grid structure value is specifically as follows: S111: based on the spatiotemporal distribution characteristics of the event reporting data, the number of events per unit time in the grid is calculated, and the event change rate of multiple grids is calculated by comparing the difference in the number of events in adjacent time periods before and after, thereby generating event change rate data; S112: Statistically analyzing the event change rate data, calculating the average value and standard deviation thereof, identifying the grids whose change rate exceeds two times the standard deviation by setting a threshold, and obtaining abnormal grid data; S113: Comprehensively use the abnormal grid data, assign differentiated weights according to the number of events and the degree of abnormality in the grid, and use the weighted formula: ; Calculate the tag value of the key grid and generate the key grid tag; in, represents the event change rate of grid i, represents the weight of grid i, represents the tag value of the key grid; S114: comparing the key grid mark with a set dynamic threshold to determine whether to adjust the grid structure and obtain an adaptive grid structure value.

[0009] As a further solution of the present invention, the step of obtaining the neighborhood space weight matrix value is specifically as follows: S211: Based on demographic data and regional resource allocation data, define the spatial adjacency relationship between multiple grids, and generate spatial adjacency relationship data by calculating the geographical distance and population density difference between each grid and its adjacent grids; S212: using the spatial adjacency data, evaluating the spatial correlation between grids, applying a weighted method with reference to the impact of population density and resource allocation, calculating the spatial correlation weights between grids, and obtaining spatial correlation weight data; S213: Calculate the differential impact of each grid and its adjacent grids using the entropy method according to the spatial association weight data, using the formula: ; Generate neighborhood spatial weight matrix values; in, represents the resource allocation score of grid i, reflecting the resource allocation level of the grid, represents the resource allocation score of grid j, which also reflects the resource allocation level of the grid. is the geographical distance between grid i and grid j, increasing the denominator It is used to reduce the impact of geographical distance on weights and avoid division by zero. Represents the absolute difference in resource allocation scores between grids.

[0010] As a further solution of the present invention, the step of obtaining the spatiotemporal autoregressive prediction value is specifically as follows: S311: extracting community abnormal monitoring data of differentiated time periods within the grid based on the adaptive grid structure value, determining the difference of monitoring data within each grid by time period segmentation, and generating a trend of community abnormal monitoring data; S312: Calculate the trend change of the monitoring data in the time series by using the trend of the community abnormal monitoring data, summarize the trend change characteristics by analyzing the change amplitude between time points, and generate the time trend characteristics of the monitoring data; S313: Combine the time trend characteristics of the monitoring data with the neighborhood space weight matrix value, and perform coupling operation. By comprehensively referring to the proportion of the influence of the neighborhood space weight and the time trend characteristics on the grids, the formula is adopted: ; Calculate spatial regression coefficients and time trend terms to generate spatiotemporal autoregressive prediction values; in, represents the spatiotemporal autoregressive coefficient, represents the spatial weight from the i-th grid to the j-th grid, represents the time difference weight from the i-th time point to the j-th time point, represents the time trend coefficient at time point t, represents the observed value at time point t, represents the number of samples or the total number of data points, Represents the number of time points in time series data.

[0011] As a further solution of the present invention, the step of obtaining the dynamic monitoring value of the hot spot area is specifically: S411: extracting the governance hotspot area data in each grid based on the spatiotemporal autoregressive prediction value, calculating the change trend of the hotspot area data in the grid by segmenting the time series of the hotspot area data, and generating a change trend result of the governance hotspot area data; S412: Using the change trend results of the governance hotspot area data, overlay analysis is performed with the spatiotemporal regression parameters of the adjacent grids, and the spatiotemporal association weights between grids are dynamically adjusted to identify the governance abnormal change areas, using the formula: ; Calculate the degree of abnormal governance change and generate identification results of abnormal governance change areas; in, Representative Grid and Grid The value of the degree of abnormal change in governance, Represents the change trend value of the governance hotspot area data of grid i, reflecting the dynamic changes of the governance activities of grid i within the target time. Represents the change trend value of the governance hotspot area data of grid j, reflecting the dynamic changes of the governance activities of grid j in the same time range. Indicates the absolute difference in the data change trend of the hot spot area between the two grids. is the spatiotemporal regression parameter value of grid i and grid j, is the sum of the spatiotemporal regression parameters of grid i and all adjacent grids; S413: Combined with the identification results of the abnormal change areas, the hotspot area data and the neighborhood space weight matrix values ​​are dynamically monitored, and the dynamic monitoring values ​​of the hotspot areas are combined with the superimposed spatiotemporal regression parameters to generate dynamic monitoring values ​​of the hotspot areas.

[0012] As a further solution of the present invention, the step of obtaining the hotspot area evolution evaluation value is specifically as follows: S511: Based on the dynamic monitoring value of the hotspot area, combined with the threshold parameters in the policy implementation progress, analyzing the fluctuation of the data in the hotspot area, referring to the change trend and fluctuation standard within the threshold range, generating the hotspot area data fluctuation analysis result; S512: Calculate the policy progress deviation using the hotspot area data fluctuation analysis results, determine the degree of abnormality in policy implementation by comparing the deviation between the actual data and the expected threshold, and generate a policy progress deviation analysis result; S513: Superimpose the policy progress deviation analysis result with the data fluctuation of the adjacent grid and analyze it using the formula: ; Assess policy implementation in multiple grid areas, mark areas with policy implementation anomalies, and generate hotspot area evolution assessment values; in, Indicates the policy implementation anomaly value of grid i and grid j, Represents the data fluctuation value of grid i, represents the data fluctuation value of grid j, Represents the data fluctuation difference between the two grids, represents the policy deviation value of grid i, represents the policy deviation value of grid j, The common threshold parameter representing the policy progress between grids i and j is used to measure the synchronization of policy implementation.

[0013] A grassroots governance data analysis system based on cloud computing, the grassroots governance data analysis system based on cloud computing is used to execute the above-mentioned grassroots governance data analysis method based on cloud computing, and the system includes: The event grid marking module obtains the number of events and the rate of change in each grid based on the time and space distribution of event reporting data, compares them item by item through weight calculation and dynamic threshold, marks the key grids and generates adaptive grid structure values.

[0014] The spatial association weight calculation module defines the spatial adjacency relationship of multiple grids based on demographic data and regional resource allocation data, marks the area with adaptive grid structure values, summarizes the grid spatial association and generates neighborhood spatial weight matrix values ​​through adjacency relationship and event distribution weight calculation.

[0015] The spatiotemporal autoregressive prediction module extracts the monitoring data of differentiated time periods within the grid based on the adaptive grid structure value, calculates the data trend and couples it with the neighborhood space weight matrix value, superimposes the data results of multiple time periods and generates spatiotemporal autoregressive prediction values.

[0016] The policy implementation dynamic assessment module calculates the changing trend of the governance hotspot area data based on the spatiotemporal autoregressive prediction value, superimposes the trend result with the spatial regression parameter of the adjacent grid, determines the deviation of policy implementation and generates the hotspot area evolution assessment value.

[0017] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the spatiotemporal distribution calculation of event data and the identification of outliers are used to accurately mark key grids and enhance the adaptive recognition capability of event distribution changes. Combined with the spatial adjacency weight calculation, the potential spatial correlation characteristics between grids are captured to optimize the regional collaborative analysis of data. Trend changes are coupled with spatial data to improve the accuracy of spatiotemporal data prediction and effectively locate areas of abnormal changes. In the dynamic monitoring of hotspot governance areas, data fluctuations and policy progress deviations are analyzed to timely mark abnormal areas of policy implementation, strengthen governance effect evaluation and precise resource allocation, and address governance weaknesses. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the workflow of the present invention; Figure 2 A flow chart of the steps for obtaining the adaptive grid structure value of the present invention; Figure 3 It is a flowchart of the steps of obtaining the neighborhood space weight matrix value of the present invention; Figure 4 A flow chart of the steps for obtaining spatiotemporal autoregressive prediction values ​​of the present invention; Figure 5 A flowchart of the steps for obtaining the dynamic monitoring value of the hot spot area of ​​the present invention; Figure 6 A flow chart of the steps for obtaining the hotspot area evolution evaluation value of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0020] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0021] Embodiment 1 See also Figure 1 The present invention provides a technical solution: a grassroots governance data analysis method based on cloud computing, comprising the following steps: S1: Based on the spatiotemporal distribution characteristics of event reporting data, the number of events, change rate and abnormal value ratio in the grid are calculated, weight calculation is performed and compared with the dynamic threshold, key grids are marked according to event change fluctuations, and adaptive grid structure values ​​are generated; S2: Based on demographic data and regional resource allocation data, define the spatial adjacency relationship between multiple grids, calculate the spatial association weights between the grid and the adjacent grids, summarize the spatial associations between differentiated grids, and generate neighborhood spatial weight matrix values; S3: Based on the adaptive grid structure value, extract the community abnormal monitoring data of differentiated time periods within the grid, calculate the trend change of the monitoring data, couple the trend characteristics with the neighborhood spatial weight matrix value, obtain the spatial regression coefficient and time trend term, and generate the spatiotemporal autoregressive prediction value; S4: Based on the spatiotemporal autoregressive prediction value, calculate the change trend of the governance hotspot area data in each grid, overlay the result with the spatiotemporal regression parameters of the adjacent grids for analysis, identify the area of ​​abnormal governance change, and generate dynamic monitoring values ​​for the hotspot area; S5: Based on the dynamic monitoring values ​​of hot spots and combined with the threshold parameters in the policy implementation progress, the data fluctuations and policy progress deviations in hot spots are analyzed, the policy implementation abnormal areas are marked, and the hot spots evolution assessment values ​​are generated.

[0022] The adaptive grid structure values ​​include the number of events, change rate, proportion of outliers, dynamic thresholds, and key grid markers. The neighborhood space weight matrix values ​​include the associated weights of demographic data, the associated weights of regional resource allocation, the weights of spatial adjacency, and the spatial correlation of differentiated grids. The spatiotemporal autoregressive prediction values ​​include the spatial regression coefficient, the time trend term, and the trend changes of community abnormal monitoring data. The dynamic monitoring values ​​of hot spots include the data change trends of governance hot spots, the analysis results of the spatiotemporal regression parameters of neighboring grids, and the identification results of governance abnormal change areas. The hot spot area evolution assessment values ​​include the data fluctuations in hot spots, the deviations in policy progress, and the marking of abnormal policy implementation areas.

[0023] See also Figure 2 , the specific steps for obtaining the adaptive grid structure value are: S111: based on the spatiotemporal distribution characteristics of the event reporting data, the number of events per unit time in the grid is calculated, and the event change rate of multiple grids is calculated by comparing the difference in the number of events in adjacent time periods before and after, thereby generating event change rate data; This process involves the collection and preliminary processing of raw event data. First, we must ensure the integrity and accuracy of the data. Then, we must sort the events according to timestamps in order to calculate the changes between adjacent time periods. For each grid, we need to calculate its event change rate independently. This ratio reflects the increase or decrease in event activity within the time period and is the basis for judging abnormal grids in subsequent steps. The data obtained is the event change rate data, which provides the basis for the next step of analysis.

[0024] S112: Statistically analyze the event change rate data, calculate its average value and standard deviation, and identify the grids whose change rate exceeds two times the standard deviation by setting a threshold, so as to obtain abnormal grid data; In-depth statistical analysis of the event change rate data, including the calculation of the mean and standard deviation, requires not only a comprehensive understanding of the distribution of the event change rate, but also a normality test of the data to ensure the effectiveness of the subsequent application of the standard deviation for anomaly detection. By setting a threshold, grids with a change rate exceeding two times the standard deviation can be identified. This process requires high requirements for data sensitivity and the ability to identify outliers to ensure accurate calibration of abnormal grids. The result is abnormal grid data, which lays the foundation for further analysis.

[0025] S113: Comprehensively use abnormal grid data, assign differentiated weights according to the number of events and the degree of abnormality in the grid, and use the weighted formula: ; Calculate the tag value of the key grid and generate the key grid tag; in, represents the event change rate of grid i, represents the weight of grid i, represents the tag value of the key grid; formula: ; The benefits of the formula are that it reflects the cumulative impact of abnormal events in each grid through weight adjustment, emphasizes the importance of outliers to the overall grid structure score, effectively integrates the event change rate and the influence of the grid, and improves the accuracy and practicality of key grid markings.

[0026] Detailed explanation of the formula and the process of formula calculation and derivation: Set the event change rate of grid i to , indicating a 5% growth rate, the weight of the grid , reflecting its importance in the overall network. If there are five grids, each grid and The values ​​are given as above and calculated:

[0027]

[0028]

[0029] The results show that the calculated key grid marker value is 0.053, which represents the average event change rate in the entire grid system after weighted averaging. This result can be used to compare with the preset threshold to determine whether the grid structure needs to be adjusted.

[0030] S114: comparing the key grid mark with the set dynamic threshold to determine whether to adjust the grid structure and obtain an adaptive grid structure value.

[0031] Based on the key grid markers, a comparison is made with the dynamic threshold, which is set based on previous data and expected safety standards. The comparison process not only involves a simple comparison of numerical values, but also needs to consider the volatility of the grid marker values ​​and the possible error range. This requires a high degree of flexibility and adaptability in the threshold setting to ensure that it can truly reflect changes in the grid state and generate an adaptive grid structure value. This value will guide subsequent grid adjustment and optimization strategies to adapt to the actual event distribution and dynamic changes.

[0032] See also Figure 3 , the specific steps for obtaining the neighborhood space weight matrix value are: S211: Based on demographic data and regional resource allocation data, define the spatial adjacency relationship between multiple grids, and generate spatial adjacency relationship data by calculating the geographical distance and population density difference between each grid and its adjacent grids; This process requires detailed geographic information system data processing and demographic data analysis. First, the geographic information of the region, including topography, land use and existing infrastructure layout, is imported through GIS software. Then, combined with census data, these data include but are not limited to the age distribution of residents, economic income level, housing conditions, etc. These data are aggregated and analyzed through advanced data processing tools to ensure that the population density and resource allocation of each grid can be accurately mapped. Spatial analysis tools are used to calculate the geographic distance between grids. Taking into account the accessibility and connectivity between different grids, these distance data are not only based on actual geographic paths, but also take into account the development of transportation networks to generate spatial adjacency data for grids. This data is generated based on the straight-line distance measurement from each grid center point to other grid centers, and the boundaries of the grids are adjusted to ensure that the data of each grid reflects its spatial location and the actual status of population resources.

[0033] S212: Using spatial adjacency data, evaluate the spatial correlation between grids, apply weighted methods and refer to the impact of population density and resource allocation to calculate the spatial correlation weights between grids, and obtain spatial correlation weight data; It is necessary to combine the spatial adjacency data with the resource allocation data in the grid, which includes public facilities, educational resources, medical resources, etc. Each resource is weighted according to its direct impact on the quality of life of residents. The resource data comes from government departments and public records. The resource index is calculated through advanced statistical software. The resource index of each grid reflects the richness of the resources in the grid. Considering the impact of population density on resource demand, grids with higher population density may require more resources to maintain quality of life. Spatial statistical methods are used to calculate the spatial association weights between grids. This calculation not only reflects the geographical proximity between grids, but also comprehensively considers the distribution of population and resources. The weight calculation formula includes considerations of distance and population resource differences. Through this method, the interaction and dependence between grids can be more accurately understood and evaluated. The obtained spatial association weight data can be directly applied to urban planning and resource allocation decision-making processes to ensure the rational allocation of resources among different grids.

[0034] S213: Based on the spatial association weight data, the differential impact of each grid and its adjacent grids is calculated by the entropy method using the formula: ; Generate neighborhood spatial weight matrix values; in, represents the resource allocation score of grid i, reflecting the resource allocation level of the grid, represents the resource allocation score of grid j, which also reflects the resource allocation level of the grid. is the geographical distance between grid i and grid j, increasing the denominator It is used to reduce the impact of geographical distance on weights and avoid division by zero. Represents the absolute difference in resource allocation scores between grids.

[0035] formula: ; The benefit of the formula is that by introducing a comprehensive consideration of geographical distance and resource score differences, the spatial weights between grids can be measured more accurately, which helps urban planners make more reasonable decisions when considering resource allocation and development strategies.

[0036] Detailed explanation of the formula and the process of formula calculation and derivation: Assume that the resource allocation score of grid i is =200, the resource allocation score of grid j is 150, the distance between grids i and j is 10 kilometers. Substituting these values ​​into the formula, the calculation process is as follows:

[0037]

[0038]

[0039] The results show that when considering distance and resource differences, the spatial weight between grid i and grid j is 2.14, which means that the difference in resource allocation between the two grids has a greater impact on their spatial correlation. This weight value can be used to adjust the resource allocation and planning strategy between the two grids to ensure the rational flow and allocation of resources within the region.

[0040] See also Figure 4 , the steps for obtaining the spatiotemporal autoregressive prediction value are as follows: S311: extracting community abnormal monitoring data of differentiated time periods within the grid based on the adaptive grid structure value, determining the difference of monitoring data within each grid by time period segmentation, and generating community abnormal monitoring data trends; This process requires analyzing and comparing previous data to determine which data represents abnormal behavior, which usually involves statistical analysis, pattern recognition, and trend analysis techniques. In order to more accurately identify anomalies, judgments will be made based on the fluctuations in data in each time period and preset anomaly indicators. For example, if the data of a specific grid exceeds twice the standard deviation of the normal range in three consecutive time periods, it is considered that an anomaly exists in this time period. This method can effectively screen out abnormal situations that need attention from a large amount of data, thereby better monitoring and preventing community safety.

[0041] S312: using the trend of community abnormal monitoring data, calculating the trend change of the monitoring data in the time series, summarizing the trend change characteristics by analyzing the change amplitude between time points, and generating the time trend characteristics of the monitoring data; This involves quantitative analysis of the correlation between data points. By calculating the autoregressive coefficient and moving average coefficient of each data point, a mathematical model of the time trend can be obtained. This process requires detailed mathematical calculations and model evaluation. The time series data is fitted through statistical methods such as the least squares method to determine the main trend of the data. This analysis helps understand the dynamic changes of the data and predict future trends. This method not only improves the accuracy of the prediction, but also enhances the understanding of the possible reasons behind the abnormal data.

[0042] S313: Combine the time trend characteristics of the monitoring data with the neighborhood space weight matrix value, and perform coupling operation. By comprehensively referring to the proportion of the influence of the neighborhood space weight and the time trend characteristics on the grid, the formula is adopted: ; Calculate spatial regression coefficients and time trend terms to generate spatiotemporal autoregressive prediction values; in, represents the spatiotemporal autoregressive coefficient, represents the spatial weight from the i-th grid to the j-th grid, represents the time difference weight from the i-th time point to the j-th time point, represents the time trend coefficient at time point t, represents the observed value at time point t, represents the number of samples or the total number of data points, Represents the number of time points in time series data.

[0043] formula: ; The benefit of the formula is that by comprehensively considering the combined effects of spatial weights and temporal trends, it can more accurately predict and analyze the dynamic relationship between temporal and spatial changes.

[0044] Detailed explanation of the formula and the process of formula calculation and derivation: Assume that there are three spatial locations (n=3), and the data observed at three time points (T=3) are as follows: Spatial weight : 1.0, 0.8, 0.5 (representing the spatial connection strength between adjacent grids respectively).

[0045] Time difference weight : 0.9, 0.7, 0.3 (considering the time proximity).

[0046] Time trend coefficient : 0.4, 0.6, 0.8 (reflecting the importance of each time point).

[0047] Observations : 100, 150, 200 (observation data at each time point).

[0048] The calculation process is as follows: 1. Calculate the numerator:

[0049] 2. Calculate the denominator:

[0050] 3. Calculation :

[0051] The result shows that the obtained spatiotemporal autoregressive coefficient is very small, which indicates that the spatiotemporal dependence between grids is low or the influence of the observations is greater than the influence of the spatiotemporal position relationship. Therefore, when making predictions, more attention should be paid to the changing trend of the data itself rather than relying solely on the weight of space or time.

[0052] See also Figure 5 ,The specific steps for obtaining the dynamic monitoring value of the hotspot area are: S411: extracting the governance hotspot data in each grid based on the spatiotemporal autoregressive prediction value, calculating the change trend of the hotspot data in the grid by segmenting the time series of the hotspot data, and generating the change trend result of the governance hotspot data; Based on the spatiotemporal autoregressive prediction value, the data of the hotspot areas in the grid are evaluated in detail to find the trend of change. By collecting data in each time period, the changes before and after are compared one by one, and abnormal fluctuations or trends are identified, the continuity and sporadic changes of the data are analyzed. Data collection is combined with the real-time monitoring system based on the trend to ensure the accuracy and real-time nature of the data obtained. By regularly updating the data set, the accuracy and response speed of data processing are improved. After continuous monitoring, the change trend results of the data in the hotspot areas are obtained.

[0053] S412: Using the change trend results of the governance hotspot area data, the spatiotemporal regression parameters of the adjacent grids are overlaid for analysis. By dynamically adjusting the spatiotemporal correlation weights between grids, the governance abnormal change areas are identified using the formula: ; Calculate the degree of abnormal governance change and generate identification results of abnormal governance change areas; in, Representative Grid and Grid The value of the degree of abnormal change in governance, Represents the change trend value of the governance hotspot area data of grid i, reflecting the dynamic changes of the governance activities of grid i within the target time. Represents the change trend value of the governance hotspot area data of grid j, reflecting the dynamic changes of the governance activities of grid j in the same time range. Indicates the absolute difference in the data change trend of the hot spot area between the two grids. is the spatiotemporal regression parameter value of grid i and grid j, is the sum of the spatiotemporal regression parameters of grid i and all adjacent grids;

[0054] formula: ; The benefit of the formula is that it evaluates the abnormal changes between grids by integrating the differences in time trends and spatial regression parameters, which enhances the sensitivity and response speed to regional dynamic changes.

[0055] Detailed explanation of the formula and the process of formula calculation and derivation: Set the data change trend values ​​of the hotspot area in grid i and grid j within a specific time to be and , spatiotemporal regression parameters , the number of adjacent grids is , the spatiotemporal regression parameters of each adjacent grid They are , first calculate the absolute value of the difference , then calculate the sum of the parameters

[0056] Substitute into the formula to calculate:

[0057] The result shows that the degree of abnormal change in governance between grid i and grid j is 8, which indicates that there are obvious dynamic changes in governance hot spots and further monitoring and intervention measures are needed.

[0058] S413: Combined with the identification results of the areas of abnormal governance changes, dynamic monitoring of hotspot area data and neighborhood spatial weight matrix values ​​is performed, and the dynamic monitoring values ​​of the hotspot areas are combined with the superimposed spatiotemporal regression parameters to generate dynamic monitoring values ​​of the hotspot areas.

[0059] By analyzing the differences between data points and surrounding points, the identification method can be based on statistical methods or machine learning algorithms. For example, a local anomaly factor algorithm is used to detect local deviations in density. This method can effectively identify data points that are significantly different from most data. The hotspot area data and the neighborhood space weight matrix values ​​are dynamically monitored. Dynamic monitoring involves continuous observation and analysis of data in order to promptly detect any important changes in the data. Through real-time data analysis tools, patterns and trends in data streams can be monitored. The weight matrix is ​​an important tool for adjusting the mutual influence between neighbors in this process. For example, weights can be set according to the proximity of time or events, which helps to improve the accuracy of predictions and the timeliness of responses. The dynamic monitoring values ​​of hotspot areas are combined with the superimposed spatiotemporal regression parameters to generate dynamic monitoring values ​​for hotspot areas that can be used to predict future trends or discover potential risk areas, which is extremely important for optimal allocation of resources and risk management.

[0060] See also Figure 6 ,The specific steps for obtaining the hotspot area evolution evaluation value are as follows: S511: Based on the dynamic monitoring values ​​of the hotspot areas, combined with the threshold parameters in the policy implementation progress, the fluctuation of the data in the hotspot areas is analyzed, and the fluctuation trend and fluctuation standard within the threshold range are referred to to generate the fluctuation analysis results of the hotspot area data; By investigating and analyzing data fluctuations within the region and evaluating changing trends within the threshold range, this includes monitoring the effectiveness of the implementation of specific policies or measures, as well as the implementation of these measures in different regions. This method can identify hot spots that require special attention. These areas may, for some reason, lead to substandard policy implementation or results that are not as expected. This analysis is of great significance for adjusting policy measures and ensuring maximum policy effectiveness.

[0061] S512: Calculate the policy progress deviation using the hotspot area data fluctuation analysis results, determine the degree of abnormality in policy implementation by comparing the deviation between the actual data and the expected threshold, and generate the policy progress deviation analysis results; This involves comparing actual data with expected thresholds and calculating differences in policy implementation for each grid or region. This difference analysis helps identify weaknesses or problem areas in policy implementation. By measuring the size of the deviation, the specific degree of abnormality in policy implementation can be clarified, which is crucial for subsequent policy adjustments and resource reallocation, ensuring that resources can be more effectively invested in areas that need improvement or strengthening, thereby improving the overall efficiency and effectiveness of policy implementation.

[0062] S513: Superimpose the policy progress deviation analysis results with the data fluctuations of the adjacent grids, using the formula:

[0063] Assess policy implementation in multiple grid areas, mark areas with policy implementation anomalies, and generate hotspot area evolution assessment values; in, Indicates the policy implementation anomaly value of grid i and grid j, Represents the data fluctuation value of grid i, represents the data fluctuation value of grid j, Represents the data fluctuation difference between the two grids, represents the policy deviation value of grid i, represents the policy deviation value of grid j, The common threshold parameter representing the policy progress between grids i and j is used to measure the synchronization of policy implementation.

[0064] formula:

[0065] The benefit of the formula is that by calculating the interaction between the policy execution differences and policy progress thresholds between grids, it can effectively identify key anomaly areas in policy implementation. This approach allows policy makers and implementers to understand which areas may require additional attention or policy adjustments, thereby more accurately guiding resource allocation and management decisions.

[0066] Detailed explanation of the formula and the process of formula calculation and derivation: Assume that the data fluctuation value of grid i is and the data fluctuation value of grid j is , then the difference is Assume that the policy deviation values ​​of grids i and j are and . Calculate the mean deviation and square it, and we get . Assuming a common threshold parameter for policy progress , substituting into the formula, we get:

[0067] This result shows that there is a slight difference in policy implementation between grids i and j. Since the difference is not large, it shows that the policy implementation in these two regions is relatively consistent, suggesting that the policy is implemented evenly in these regions.

[0068] A grassroots governance data analysis system based on cloud computing, the grassroots governance data analysis system based on cloud computing is used to execute the above-mentioned grassroots governance data analysis method based on cloud computing, and the system includes: The event grid marking module obtains the number of events and the rate of change in each grid based on the time and space distribution of event reporting data, compares them item by item through weight calculation and dynamic threshold, marks the key grids and generates adaptive grid structure values.

[0069] The spatial association weight calculation module defines the spatial adjacency relationship of multiple grids based on demographic data and regional resource allocation data, marks the area with adaptive grid structure values, summarizes the grid spatial association and generates neighborhood spatial weight matrix values ​​through adjacency relationship and event distribution weight calculation.

[0070] The spatiotemporal autoregressive prediction module extracts monitoring data of differentiated time periods within the grid based on the adaptive grid structure value, calculates the data trend and couples it with the neighborhood spatial weight matrix value, superimposes the data results of multiple time periods and generates spatiotemporal autoregressive prediction values.

[0071] The policy implementation dynamic assessment module calculates the changing trend of governance hotspot area data based on spatiotemporal autoregressive prediction values, superimposes the trend results with the spatial regression parameters of adjacent grids, determines the deviation of policy implementation and generates hotspot area evolution assessment values.

[0072] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A grassroots governance data analysis method based on cloud computing, characterized in that: The following steps are involved: Based on the spatiotemporal distribution characteristics of event reporting data, the number of events, change rate and abnormal value ratio in the grid are calculated, weight calculation is performed and compared with the dynamic threshold, key grids are marked according to event change fluctuations, and adaptive grid structure values ​​are generated; Based on demographic data and regional resource allocation data, the spatial adjacency relationship between multiple grids is defined, the spatial association weights between grids and adjacent grids are calculated, the spatial associations between differentiated grids are summarized, and the neighborhood spatial weight matrix values ​​are generated; Based on the adaptive grid structure value, community abnormal monitoring data of differentiated time periods within the grid are extracted, trend changes of the monitoring data are calculated, trend characteristics are coupled with the neighborhood spatial weight matrix value, spatial regression coefficients and time trend terms are obtained, and spatiotemporal autoregressive prediction values ​​are generated; Based on the spatiotemporal autoregressive prediction value, the change trend of the governance hotspot area data in each grid is calculated, and the result is superimposed and analyzed with the spatiotemporal regression parameters of the adjacent grids to identify the governance abnormal change area and generate dynamic monitoring values ​​for the hotspot area; Based on the dynamic monitoring values ​​of the hot spot areas, combined with the threshold parameters in the policy implementation progress, the data fluctuations and policy progress deviations in the hot spot areas are analyzed, the policy implementation abnormal areas are marked, and the hot spot area evolution evaluation values ​​are generated.

2. The cloud computing-based grassroots governance data analysis method according to claim 1 is characterized in that: The adaptive grid structure values ​​include the number of events, change rate, proportion of outliers, dynamic thresholds, and key grid marks. The neighborhood space weight matrix values ​​include demographic data association weights, regional resource allocation association weights, spatial adjacency weights, and differentiated grid space correlations. The spatiotemporal autoregressive prediction values ​​include spatial regression coefficients, time trend items, and trend changes in community abnormal monitoring data. The hot spot area dynamic monitoring values ​​include governance hot spot area data change trends, neighboring grid spatiotemporal regression parameter analysis results, and governance abnormal change area identification results. The hot spot area evolution assessment values ​​include hot spot area data fluctuations, policy progress deviations, and policy implementation abnormal area marks.

3. The cloud computing-based grassroots governance data analysis method according to claim 2 is characterized in that: The steps for obtaining the adaptive grid structure value are specifically as follows: Based on the spatiotemporal distribution characteristics of event reporting data, the number of events per unit time in the grid is calculated. The event change rate of multiple grids is calculated by comparing the difference in the number of events in adjacent time periods before and after, and the event change rate data is generated. Performing statistical analysis on the event change rate data, calculating the average value and standard deviation thereof, identifying the grids whose change rate exceeds two times the standard deviation by setting a threshold, and obtaining abnormal grid data; The abnormal grid data is used comprehensively, and differentiated weights are assigned according to the number of events and the degree of abnormality in the grid, using the weighted formula: ； Calculate the tag value of the key grid and generate the key grid tag; in, represents the event change rate of grid i, represents the weight of grid i, represents the tag value of the key grid; According to the key grid mark, a comparison is made with a set dynamic threshold to determine whether to adjust the grid structure and obtain an adaptive grid structure value.

4. The cloud computing-based grassroots governance data analysis method according to claim 3 is characterized in that: The steps for obtaining the neighborhood space weight matrix value are specifically as follows: Based on demographic data and regional resource allocation data, the spatial adjacency relationship between multiple grids is defined, and the spatial adjacency relationship data is generated by calculating the geographical distance and population density difference between each grid and its neighboring grids; Using the spatial adjacency data, the spatial correlation between grids is evaluated, and a weighted method is applied to calculate the spatial correlation weights between grids with reference to the influence of population density and resource allocation, thereby obtaining spatial correlation weight data; According to the spatial association weight data, the differential impact of each grid and its adjacent grids is calculated by the entropy method, using the formula: ； Generate neighborhood spatial weight matrix values; in, represents the resource allocation score of grid i, reflecting the resource allocation level of the grid, represents the resource allocation score of grid j, which also reflects the resource allocation level of the grid. is the geographical distance between grid i and grid j, and the increase in the denominator It is used to reduce the impact of geographical distance on weights and avoid division by zero. Represents the absolute difference in resource allocation scores between grids.

5. The cloud computing-based grassroots governance data analysis method according to claim 4 is characterized in that: The steps for obtaining the spatiotemporal autoregressive prediction value are specifically as follows: Based on the adaptive grid structure value, extract the community abnormal monitoring data of differentiated time periods in the grid, determine the difference of monitoring data in each grid by time period segmentation, and generate the community abnormal monitoring data trend; Utilizing the trend of the abnormal community monitoring data, calculating the trend change of the monitoring data in the time series, summarizing the trend change characteristics by analyzing the change amplitude between time points, and generating the time trend characteristics of the monitoring data; Combined with the time trend characteristics of the monitoring data and the neighborhood space weight matrix value, a coupling operation is performed, and the proportion of the influence of the neighborhood space weight and the time trend characteristics on the grid is comprehensively referred to, and the formula is adopted: ； Calculate spatial regression coefficients and time trend terms to generate spatiotemporal autoregressive prediction values; in, represents the spatiotemporal autoregressive coefficient, represents the spatial weight from the i-th grid to the j-th grid, represents the time difference weight from the i-th time point to the j-th time point, represents the time trend coefficient at time point t, represents the observed value at time point t, represents the number of samples or the total number of data points, Represents the number of time points in time series data.

6. The cloud computing-based grassroots governance data analysis method according to claim 5 is characterized in that: The steps for obtaining the dynamic monitoring value of the hot spot area are specifically as follows: Based on the spatiotemporal autoregressive prediction value, the governance hotspot area data in each grid is extracted, and the change trend of the hotspot area data in the grid is calculated by segmenting the time series of the hotspot area data, so as to generate the change trend result of the governance hotspot area data; The change trend results of the governance hotspot area data are used to perform superposition analysis with the spatiotemporal regression parameters of the adjacent grids. The spatiotemporal association weights between grids are dynamically adjusted to identify the governance abnormal change areas. The formula is used: ； Calculate the degree of abnormal governance change and generate identification results of abnormal governance change areas; in, Representative Grid and Grid The value of the degree of abnormal change in governance, Represents the change trend value of the governance hotspot area data of grid i, reflecting the dynamic changes of the governance activities of grid i within the target time. Represents the change trend value of the governance hotspot area data of grid j, reflecting the dynamic changes of the governance activities of grid j in the same time range. Indicates the absolute difference in the data change trend of the hot spot area between the two grids. is the spatiotemporal regression parameter value of grid i and grid j, is the sum of the spatiotemporal regression parameters of grid i and all adjacent grids; Combined with the identification results of the abnormal change areas, the hotspot area data and the neighborhood space weight matrix values ​​are dynamically monitored, and the dynamic monitoring values ​​of the hotspot areas are combined with the superimposed spatiotemporal regression parameters to generate dynamic monitoring values ​​of the hotspot areas.

7. The cloud computing-based grassroots governance data analysis method according to claim 6 is characterized in that: The steps for obtaining the hotspot area evolution evaluation value are specifically as follows: Based on the dynamic monitoring values ​​of the hotspot areas, combined with the threshold parameters in the policy implementation progress, the fluctuation of data in the hotspot areas is analyzed, and the fluctuation analysis results of the hotspot area data are generated by referring to the change trend and fluctuation standard within the threshold range; Utilizing the data fluctuation analysis results of the hotspot areas, the policy progress deviation is calculated, and by comparing the deviation between the actual data and the expected threshold, the degree of abnormality in the policy implementation is determined, and the policy progress deviation analysis results are generated; The policy progress deviation analysis results are superimposed on the data fluctuations of the adjacent grids and the formula is used: ； Assess policy implementation in multiple grid areas, mark areas with policy implementation anomalies, and generate hotspot area evolution assessment values; in, Indicates the policy implementation anomaly value of grid i and grid j, Represents the data fluctuation value of grid i, represents the data fluctuation value of grid j, Represents the data fluctuation difference between the two grids, represents the policy deviation value of grid i, represents the policy deviation value of grid j, The common threshold parameter representing the policy progress between grids i and j is used to measure the synchronization of policy implementation.

8. A grassroots governance data analysis system based on cloud computing, characterized in that: According to the cloud computing-based grassroots governance data analysis method according to any one of claims 1 to 7, the system comprises: The event grid marking module obtains the number of events and the rate of change in each grid based on the time and space distribution of event reporting data, compares them item by item through weight calculation and dynamic threshold, marks key grids and generates adaptive grid structure values; The spatial association weight calculation module defines the spatial adjacency of multiple grids based on demographic data and regional resource allocation data, marks the area with adaptive grid structure values, summarizes the spatial association of grids and generates neighborhood spatial weight matrix values ​​through adjacency and event distribution weight calculations; The spatiotemporal autoregressive prediction module extracts the monitoring data of the differentiated time periods in the grid based on the adaptive grid structure value, calculates the data trend and couples it with the neighborhood space weight matrix value, superimposes the data results of multiple time periods and generates the spatiotemporal autoregressive prediction value; The policy implementation dynamic assessment module calculates the changing trend of the governance hotspot area data based on the spatiotemporal autoregressive prediction value, superimposes the trend result with the spatial regression parameter of the adjacent grid, determines the deviation of policy implementation and generates the hotspot area evolution assessment value.

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