Map updating system based on cloud computing

Abstract

The invention relates to the technical field of geographic information processing, in particular to a map updating system based on cloud computing, which comprises a global offset correction module, a regional error division module, a local feature correction module, a residual error optimization fitting module and a computing resource scheduling module. According to the method, through offset calculation and hierarchical division of real-time geographic data, offset characteristics of multiple geographic areas are accurately analyzed, dynamic distribution optimization of data errors in a global range is achieved, hierarchical error division is combined with geographic boundary lines and multi-level data characteristics, difference values are accurately extracted, and area division is adjusted; the local feature correction optimizes error matching under a complex terrain by integrating a slope, a land type and a terrain parameter relationship, enhances terrain adaptability and detail precision, ensures high efficiency of large-scale data processing through nonlinear relationship fitting of residual values and optimization of a resource allocation proportion, and improves the accuracy of data processing. And meanwhile, the map updating response speed is increased.

Description

Technical Field

[0001] The present invention relates to the field of geographic information processing technology, and in particular to a map updating system based on cloud computing. Background Art

[0002] The field of geographic information processing technology includes the collection, storage, management, analysis and display of geographic spatial data. The core content of this technology is to provide accurate spatial data support for various application scenarios through the digitization and informatization of geographic information. Geographic information processing includes a series of technical methods such as data collection and updating, spatial data modeling, map making, spatial analysis and visual expression of geographic information. These technologies greatly improve the processing efficiency and accuracy of spatial information by using computer technology, remote sensing technology and satellite navigation, and are widely used in transportation navigation, urban planning, environmental monitoring and other fields.

[0003] Among them, the map update system refers to a technical solution that maintains the accuracy and timeliness of map information through real-time collection, processing and updating of geographic data. The patent subject is aimed at the problem of map data in geographic information being invalidated due to the passage of time and environmental changes, and covers geographic data collection, data comparison and analysis, change detection, and update and reconstruction of map data. Specifically, the system uses satellite remote sensing technology to obtain real-time geographic image data, extracts areas that are inconsistent with existing maps through data matching and change recognition algorithms, and combines cloud computing to quickly process and dynamically update massive geographic data, ultimately generating a digital map file containing the latest geographic information.

[0004] Existing technologies have deficiencies in the real-time performance and error correction of map data. The calculation of offsets lacks systematic global distribution analysis, which makes it difficult for data updates to accurately reflect changes. Error correction in areas with complex terrain lacks specificity and cannot handle changes in slope and land type, affecting the application effect of map data in local environments. In addition, large-scale geographic data processing has limited dynamic scheduling capabilities for computing resources, resulting in low update efficiency and difficulty in meeting the demand for rapid map updates in high-load scenarios. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a map updating system based on cloud computing.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: A map updating system based on cloud computing includes: The global offset correction module calculates the global offset of the acquisition points in multiple geographic regions based on the image acquisition time, geographic location and elevation data, extracts the offset value characteristics in combination with the latitude and longitude points, calculates the weighted average of the offset values ​​between regions, aggregates the offset ranges of multiple regions after comparing the offset value characteristics, and obtains the global offset distribution coefficient; The regional error division module calculates the difference of the multi-regional offset range based on the global offset distribution coefficient, calls the geographic boundary line to delineate the stratified range, calculates the extreme value of the offset data between the layers, compares the multi-layer data characteristics, extracts the difference value and re-divides the interval, and obtains the stratified error interval value; The local feature correction module calculates the variation of regional terrain parameters based on the stratified error interval value and the raster image data, extracts the slope and land type data for matching, calculates the relationship between the local error offset value and the terrain parameters, calls the parameter relationship result to perform correction operation on the error, and obtains the local correction error coefficient; The residual optimization fitting module extracts the regional distribution parameters of the residual value based on the local correction error coefficient, calculates the nonlinear relationship between the residual value and the spatial distribution, performs trend analysis and fitting calculation on the abnormal points, calls the fitting result data for update, regenerates the correction trend distribution, and obtains the residual fitting distribution value; The computing resource scheduling module extracts the dynamic data of the task load demand in the region based on the residual fitting distribution value, calls the geographical boundary line to divide the task boundary, allocates the load resources and then calculates the distribution balance between the regions, re-evaluates the resource allocation parameters and adjusts the allocation ratio to obtain the dynamic ratio of resource allocation.

[0007] The global offset distribution coefficient specifically includes offset value characteristics, weighted average, and multi-partition offset range. The stratified error interval value specifically refers to the inter-layer offset data extreme value and multi-layer data difference value. The local correction error coefficient includes land type data matching, terrain parameter change, and local error offset value relationship. The residual fitting distribution value specifically includes residual value regional distribution parameters, nonlinear relationship calculation results, and corrected trend distribution. The resource allocation dynamic ratio includes task load demand dynamic data, inter-regional distribution balance, and resource allocation parameter adjustment ratio.

[0008] As a further solution of the present invention, the step of obtaining the global offset distribution coefficient is specifically: Based on the image acquisition time, geographic location and elevation data, the longitude and latitude points and corresponding offsets of the acquisition points in multiple geographic areas are extracted. According to the elevation data range and the offset value distribution of abnormal points, the acquisition points that exceed the specified elevation range are screened, the offset values ​​after screening are adjusted and the elevation change trend is recalculated to construct the acquisition point offset feature matrix; Based on the acquisition point offset feature matrix, the acquisition point offset feature values ​​in multiple regions are classified and processed, the feature values ​​are classified and summarized by the regional offset data range, differentiated inter-region offset weights are set according to the classification results, the feature values ​​within the distribution range are called to recalculate the regional offset weighted results, the inter-region offset weighted data are generated and integrated into a unified table, and a regional offset weighted distribution table is obtained; Based on the regional offset weighted distribution table, the offset weighted results between regions are called, the offset characteristic value distribution of differentiated regions is compared, the regional offset range is aggregated, and the relationship between the multi-region offset mean and the global offset is calculated using the formula: ; Calculate and generate global offset distribution coefficient; in, represents the global offset distribution coefficient, Representative The offset weight of the region, Representative The offset characteristic value of the region, represents the mean of all regional offset eigenvalues, Represents the total number of regions.

[0009] As a further solution of the present invention, the step of obtaining the layered error interval value is specifically as follows: Based on the global offset distribution coefficient and geographic boundary line data, the regional offset range in the distribution coefficient is called, the hierarchical correspondence between the boundary line layered data and the offset range is analyzed, the upper and lower bounds of the offset range are extracted according to the hierarchical division rules, and the range of each layer is calculated to generate the regional hierarchical offset range; Based on the regional stratified offset range and the offset values ​​between multiple levels, the data characteristics with a large offset value range between levels are screened, the trend of the extreme value change is analyzed and the range of the difference between levels is calculated, the offset feature difference values ​​between levels are determined by the stratified difference weights, and a multi-level offset extreme value distribution matrix is ​​established; Based on the multi-level offset extreme value distribution matrix, the offset value difference characteristics and difference weight data in the matrix are called to analyze the changes in the upper and lower limits of the offset range between levels, using the formula: ; Calculate the stratification error value, re-divide the interval based on the weight and offset difference characteristics, and obtain the stratification error interval value; in, represents the stratification error interval value, Representative The difference weights of the layers, Representative The upper limit of the layer's offset range, Representative The upper limit of the layer's offset range, Representative The difference in the offset characteristics of the layer, Represents the number of layers.

[0010] As a further solution of the present invention, the step of obtaining the local correction error coefficient is specifically as follows: Based on the stratified error interval value, the raster image data is called to extract the regional terrain parameters, the land type and slope information are analyzed in combination with the image data, the matching degree between the terrain parameter changes and the slope is analyzed, and the regional terrain change data is generated; Based on the regional terrain change data, the local error offset value is calculated, the terrain parameter and offset value relationship data are called for comparison, the offset characteristics are analyzed in combination with the land type and slope matching data, and a local error offset and terrain parameter relationship matrix is ​​generated; Based on the local error offset and terrain parameter relationship matrix, combined with the terrain characteristics and offset relationship results, the error correction operation logic is called to analyze the relationship between the local correction error weight and the terrain characteristics, using the formula: ; Calculate and generate local correction error coefficients; in, represents the local correction error coefficient, Representative The land type weight of the region, Representative The slope parameter of the area, Representative The error offset value of the area, Represents the average of all regional error offset values, Represents the number of regions, Represents the total number of data points.

[0011] As a further solution of the present invention, the step of obtaining the residual fitting distribution value is specifically as follows: Based on the local correction error coefficient, the regional distribution parameter is called to extract the spatial distribution characteristics of the residual value, analyze the distribution law of the residual value in the differentiated area, establish a distribution feature matrix based on the relationship data between the regional terrain and the spatial coordinates, and generate the residual and spatial distribution relationship data; Based on the residual and spatial distribution relationship data, the nonlinear relationship between the residual value and the spatial distribution is calculated, the distribution characteristics of abnormal residual points are screened, the trend change characteristics of abnormal points are extracted through regional characteristics, the correlation between abnormal points and spatial parameters is analyzed, and preliminary trend analysis results are generated; Based on the preliminary trend analysis results, nonlinear trend data is called, combined with the weight characteristics of regional parameters, and trend fitting is optimized using the formula: ; Regenerate the corrected trend distribution and obtain the residual fitting distribution value; in, represents the residual fitted distribution value, is the base of natural logarithms, is the adjustment coefficient for trend fitting, is the fitting translation coefficient, For the The spatial coordinate parameters of the point, For the The weight parameter of the point, is the total number of data points.

[0012] As a further solution of the present invention, the step of obtaining the dynamic ratio of resource allocation is specifically: Based on the residual fitting distribution value, the task load demand data in the region is called to analyze the temporal variation and spatial distribution of the load demand, the task boundaries are divided in combination with the geographic boundary line data, the adaptability of the task load and the boundary division rule is refined, and the task load and geographic partition relationship data are generated; Based on the task load and geographical partition relationship data, calculate the load resource demand between regions, calculate the balance of load distribution between regions, judge the deviation between load distribution characteristics and supply relationship, extract the matching situation of load demand and resource supply between regions based on the evaluation results, and obtain the load balance analysis results between regions; Based on the results of the inter-region load balancing analysis, the resource allocation parameters are re-evaluated, and the task boundary conditions are called to optimize the allocation logic. The formula is: ; Adjust resource allocation ratios in multiple regions, optimize resource utilization efficiency, and obtain dynamic resource allocation ratios; in, Represents the dynamic ratio of resource allocation, Representative The load demand of the area, Representative The resource supply in the region, Representative The allocation weight of the region, Represents the total number of regions.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the offset characteristics of multiple geographical regions are accurately analyzed through the offset calculation and hierarchical division of real-time geographic data, and the dynamic distribution optimization of data errors in the global scope is realized. The hierarchical error division combines the geographic boundary lines and multi-level data characteristics to accurately extract the difference values ​​and adjust the regional division, which significantly improves the data update accuracy. Local feature correction optimizes the error matching under complex terrain by integrating the relationship between slope, land type and terrain parameters, and enhances the terrain adaptability and detail accuracy. The nonlinear relationship fitting of residual values ​​and the optimization of resource allocation ratio ensure the high efficiency of large-scale data processing, while improving the map update response speed, and comprehensively enhancing the reliability and timeliness of geographic data. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 is a system flow chart of the present invention; Figure 2 is a flow chart of steps for obtaining the global offset distribution coefficient of the present invention; Figure 3 This is a flow chart of the steps for obtaining the stratified error interval value of the present invention; Figure 4 Flow chart of the steps of obtaining the local correction error coefficient of the present invention; Figure 5 A flowchart of the steps for obtaining the residual fitting distribution value of the present invention; Figure 6 A flow chart of the steps for obtaining the dynamic ratio of resource allocation according to the present invention. DETAILED DESCRIPTION

[0015] 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.

[0016] 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.

[0017] Embodiment 1: See also Figure 1 , the cloud computing-based map update system includes: The global offset correction module calculates the global offset of the acquisition points in multiple geographic regions based on the image acquisition time, geographic location and elevation data, extracts the offset value characteristics in combination with the latitude and longitude points, calculates the weighted average of the offset values ​​between regions, aggregates the offset ranges of multiple regions after comparing the offset value characteristics, and obtains the global offset distribution coefficient; The regional error division module calculates the difference of multi-regional offset ranges based on the global offset distribution coefficient, calls the geographic boundary line to delineate the stratified range, calculates the extreme value of the offset data between the layers, compares the differences of the multi-layer data characteristics, extracts the difference value and re-divides the interval to obtain the stratified error interval value; The local feature correction module calculates the change of regional terrain parameters based on the layered error interval value and the raster image data, extracts the slope and land type data for matching, calculates the relationship between the local error offset value and the terrain parameters, calls the parameter relationship result to correct the error, and obtains the local correction error coefficient; The residual optimization fitting module extracts the regional distribution parameters of the residual value based on the local correction error coefficient, calculates the nonlinear relationship between the residual value and the spatial distribution, performs trend analysis and fitting calculations on the abnormal points, calls the fitting result data for update, regenerates the correction trend distribution, and obtains the residual fitting distribution value; The computing resource scheduling module extracts the dynamic data of task load demand in the region based on the residual fitting distribution value, divides the task boundaries by calling the geographical boundary lines, allocates load resources and then calculates the distribution balance between regions, re-evaluates the resource allocation parameters and adjusts the allocation ratio to obtain the dynamic ratio of resource allocation.

[0018] The global offset distribution coefficient specifically includes the offset value characteristics, weighted average, and multi-partition offset range. The stratified error interval value specifically refers to the extreme value of the offset data between layers and the multi-layer data difference value. The local correction error coefficient includes land type data matching, terrain parameter change, and local error offset value relationship. The residual fitting distribution value specifically includes the residual value regional distribution parameter, nonlinear relationship calculation result, and corrected trend distribution. The dynamic ratio of resource allocation includes the dynamic data of task load demand, regional distribution balance, and resource allocation parameter adjustment ratio.

[0019] See also Figure 2 , the steps for obtaining the global offset distribution coefficient are as follows: Based on the image acquisition time, geographic location and elevation data, the longitude and latitude points and corresponding offsets of the acquisition points in multiple geographic areas are extracted. According to the elevation data range and the offset value distribution of abnormal points, the acquisition points that exceed the specified elevation range are screened, the offset values ​​after screening are adjusted and the elevation change trend is recalculated to construct the acquisition point offset feature matrix; The elevation offset value of each collection point is parsed point by point based on the longitude and latitude data, and the elevation data of the collection point is compared with the elevation mean of other collection points in the region. The absolute difference between the offset value and the mean is calculated, and the points whose offset values ​​exceed the regional range are screened out. The adjustment interval of the offset data is reset according to the upper and lower quantiles of the offset value within the regional range, and the offset value of the screened outlier point is corrected according to the median value of the adjustment interval. At the same time, the corrected regional offset distribution characteristics are recalculated and compared with the original characteristics to verify the rationality of the corrected offset characteristics and distribution, and to construct the collection point offset feature matrix.

[0020] Based on the acquisition point offset feature matrix, the offset feature values ​​of the acquisition points in multiple regions are classified and processed, and the feature values ​​are classified and summarized by the regional offset data range. Differentiated inter-regional offset weights are set according to the classification results, and the feature values ​​within the distribution range are called to recalculate the regional offset weighted results. The inter-regional offset weighted data are generated and integrated into a unified table to obtain the regional offset weighted distribution table. Firstly, the normalized offset value of each acquisition point is calculated according to the mean, standard deviation and range of the elevation offset characteristic value in the region. The classification interval of the regional characteristics is divided according to the discrete degree of the normalized offset value, and the offset characteristic value is divided into multiple weight levels. The mean offset of the weight layer of each classification is calculated and classified as the weight layer offset characteristic value of the region. The regional offset weight of each weight layer is set according to the normalized offset distribution of the region. The regional offset characteristic values ​​are weighted and summed according to the weight distribution level to obtain the overall offset characteristic distribution between regions. The similarity of the weighted characteristic distribution between regions is compared, and the data of similar regions are integrated to reduce the regional classification error, and the weighted distribution table of regional offset is obtained.

[0021] Based on the regional offset weighted distribution table, call the offset weighted results between regions, compare the offset characteristic value distribution of differentiated regions, aggregate the regional offset range, and calculate the relationship between the multi-region offset mean and the global offset using the formula: ; Calculate and generate global offset distribution coefficient; in, represents the global offset distribution coefficient, Representative The offset weight of the region, Representative The offset characteristic value of the region, represents the mean of all regional offset eigenvalues, Represents the total number of regions.

[0022] formula: ; The benefit of the formula is that, by introducing the combination of regional offset weight parameters and global mean parameters, it can effectively reflect the contribution of each regional offset feature value in the global scope, and reduce the impact of regional quantity differences through square root normalization; Detailed explanation of the formula and the process of formula calculation and derivation: Take the weighted characteristic value in the regional offset weighted distribution table as , whose specific value depends on the weighted calculation result in paragraph 2, for example, the weighted eigenvalue of region 1 , the weighted eigenvalue of region 2 , the weighted eigenvalue of region 3 , bias weight Calculated according to the weight layer in paragraph 2, for example, the weight of region 1 , Region 2 weight , Region 3 weight , global offset mean It is calculated by the weighted eigenvalue mean of all regions, specifically: ; Substituting the above data into the formula: ; ; ; ; This result shows that the global offset distribution coefficient It indicates that the overall distribution uniformity of the inter-regional offset characteristic values ​​is low, and its numerical results are used to comprehensively calculate the global offset distribution characteristics and further guide the zoning adjustment decision.

[0023] See also Figure 3 , the specific steps for obtaining the stratified error interval value are: Based on the global offset distribution coefficient and geographic boundary line data, the regional offset range in the distribution coefficient is called, the hierarchical correspondence between the boundary line layered data and the offset range is analyzed, the upper and lower bounds of the offset range are extracted according to the hierarchical division rules, and the range of each layer is calculated to generate the regional hierarchical offset range; First, the geographic boundary line data is layered, and points and line segments with adjacent relationships in the boundary line are extracted. The boundary line segments are layered by calculating the interval distance and slope difference between them. The layering rules are determined according to the range of terrain slope changes and the spatial distribution characteristics of the region. Then, in the divided geographic boundary layer, the global offset distribution coefficient is called, and the range of the global offset coefficient in each layer is screened out by comparing the global offset values ​​of the sampling points in each layer area, and the range value is recorded by layer. Then, combined with the upper and lower bounds of the regional layered offset range, the distribution interval of the offset value in the boundary layer is further determined, and the offset value interval of the boundary layer is analyzed and the offset value range characteristics of each layer are extracted. For example, in areas with large terrain height differences, the range of the offset value will be constrained by the upper and lower bounds of the boundary line, and the increase in the height difference will directly affect the interval size of the offset range value. By calculating the offset value interval for different regional characteristics of the boundary line, the upper and lower bound changes of different layers can be clarified. Finally, the layered division data of the geographic boundary line and the change characteristics of the offset range in the layer are combined with the upper and lower bound offset values ​​of each layer in the region to generate the regional layered offset range.

[0024] Based on the regional stratified offset range and the offset values ​​between multiple levels, the data characteristics with large offset value range between levels are screened, the trend of extreme value changes is analyzed and the range of differences between levels is calculated. The offset feature difference values ​​between levels are determined by stratified difference weights, and a multi-level offset extreme value distribution matrix is ​​established. Firstly, the regional offset ranges between multiple levels are compared, and the offset range values ​​are calculated by extracting the adjacent upper and lower offset values ​​between levels, and the changing trend of the offset range in adjacent levels is judged; then, the offset range values ​​between levels are called, and their changing trends are analyzed to determine the level with the most significant change in the offset range, and the data distribution range with large changes is obtained by calculating its offset value standard deviation and change mean; then, according to the offset value distribution of different regional levels, a difference weight calculation model is established, and the offset value range of each layer is associated with the weight coefficient. By comparing the upper and lower bound differences of the offset value and the regional weight parameters, the levels with large differences are marked first and their characteristic values ​​are recorded; finally, by integrating the multi-level offset range values ​​and the corresponding weight characteristics, combined with the changes in regional characteristic values, a multi-level offset range value distribution matrix is ​​established.

[0025] Based on the multi-level offset range distribution matrix, the offset value difference characteristics and difference weight data in the matrix are called to analyze the changes in the upper and lower limits of the offset range between levels, using the formula: ; Calculate the stratification error value, re-divide the interval based on the weight and offset difference characteristics, and obtain the stratification error interval value; in, represents the stratification error interval value, Representative The difference weights of the layers, Representative The upper limit of the layer's offset range, Representative The upper limit of the layer's offset range, Representative The difference in the offset characteristics of the layer, Represents the number of layers.

[0026] formula: ; The benefit of the formula is that by combining the calculation of the upper and lower limit offset values ​​of the region and the offset extremes between levels, and by introducing the normalized relationship between the difference weight and the offset characteristic quantity, the offset characteristics of multi-level regions can be effectively processed and the precision of the offset error calculation can be improved.

[0027] Detailed explanation of the formula and the process of formula calculation and derivation: Let the upper limit of the offset range be and , the difference characteristic value is , the difference weight is , the number of layers is , substitute into the formula: ; First, calculate the offset range of each layer: ; Square it and divide by the difference feature value: ; Introduce difference weight calculation: ; Sum and square the three layers of data: ; The result shows that the stratified error interval value is 2.224, which is closely related to the variation range of the regional offset characteristic quantity, indicating that the inter-layer variation of the regional offset range has certain differences and can be further used for the division of stratified intervals.

[0028] See also Figure 4 , the steps for obtaining the local correction error coefficient are as follows: Based on the stratified error interval value, call the raster image data, extract the regional terrain parameters, combine the image data to analyze the land type and slope information, analyze the matching degree between the terrain parameter changes and the slope, and generate regional terrain change data; The elevation data is parsed from the image file and quantified into continuous raster values. The elevation values ​​are normalized according to the geographic spatial reference system. Then, the slope formula is used to calculate the slope. Slope data is obtained, where the elevation change value is calculated by the difference between adjacent grids. At the same time, land type information is extracted through image data classification technology, and the original non-numerical data of land classification is standardized. A weight value is set for each land type by matching its corresponding terrain characteristics. The weight value is determined based on the statistical distribution curve of multiple terrain experiments. Finally, by analyzing the interactive relationship between elevation data and slope characteristics, combined with the refined spatial scale of image grids, regional terrain change data with detailed data association is generated.

[0029] Based on the regional terrain change data, the local error offset value is calculated, the terrain parameters and offset value relationship data are called for comparison, the offset characteristics are analyzed by combining the land type and slope matching data, and the local error offset and terrain parameter relationship matrix is ​​generated; First, the terrain change data is classified by spatial location, and the abnormal data is screened according to the threshold standard of slope range and elevation change. Calculate the error offset value for each layer, where and Represent the difference values ​​of elevation and horizontal coordinates respectively. Then, call the weight parameter of land type, calculate the weighted average of the offset value of each type, analyze the offset correlation between the adjacent layers, and further establish the error offset relationship matrix between slope characteristics and land types. By combining the distribution data of slope and offset values ​​to form an overall matrix, the relationship matrix between local error offset and terrain parameters is generated.

[0030] Based on the local error offset and terrain parameter relationship matrix, combined with the terrain characteristics and offset relationship results, the error correction operation logic is called to analyze the relationship between the local correction error weight and the terrain characteristics, using the formula: ; Calculate and generate local correction error coefficients; in, represents the local correction error coefficient, Representative The land type weight of the region, Representative The slope parameter of the area, Representative The error offset value of the area, Represents the average of all regional error offset values, Represents the number of regions, Represents the total number of data points.

[0031] formula: ; Detailed explanation of the formula and the process of formula calculation: The parameters and calculation process in the formula are as follows: Local correction error coefficient : Represents the correction coefficient after taking into account the terrain and error offset, which is used to correct the errors in the model.

[0032] Land type weight :Weights are assigned to the land types in each region. Different types of land have different effects on the correction coefficient. The value is obtained from geological survey data. For example, cultivated land may be 1.0, forest land may be 0.8, and grassland may be 0.5.

[0033] Slope parameters : Indicates the slope of each area. The slope has a greater impact on the error. The greater the slope, the greater the impact of the terrain may be. This value is obtained through terrain measurement. For example, a slope of 0-5 degrees is 1.0, 5-15 degrees is 1.5, and above 15 degrees is 2.0.

[0034] Error offset value : The error value obtained by specific measurement of each area, such as +0.2m or -0.3m, etc.

[0035] Error mean : The average of all regional error offset values, calculated by statistical methods.

[0036] Number of regions : Indicates the total number of regions involved in the calculation.

[0037] Total number of data points : The number of all data points, including the sum of data points in all regions.

[0038] In the actual example, the following parameters are considered: Land type weight ; Slope parameters ; Error offset value ; Total number of data points ; Number of regions ; Error mean

[0039] Calculation steps: First, calculate the difference between the absolute value of the error offset in each area and the average: ; Calculate the weighted error offset: ; calculate : ; The numerical results It shows that according to the given data and terrain characteristics, the local correction effect of the error is 0.22. This correction factor reflects the comprehensive quantitative result of the impact of terrain and error offset on the overall correction.

[0040] See also Figure 5 , the specific steps for obtaining the residual fitting distribution value are: Based on the local correction error coefficient, the regional distribution parameters are called to extract the spatial distribution characteristics of the residual value, analyze the distribution law of the residual value in the differentiated area, and establish the distribution characteristic matrix based on the relationship data between the regional terrain and the spatial coordinates to generate the residual and spatial distribution relationship data; First, the terrain elevation parameters of each area are obtained and the elevation difference range is quantified. For example, the elevation point data is obtained through multi-point sampling, and then the elevation mean and standard deviation are calculated. The elevation range is divided into multiple gradient intervals, and the residual value distribution parameters in the interval are marked one by one. Then, the regional characteristics are extracted according to the change law of the residual value, and the discrete degree of the residual value between different regions is compared. The discrete coefficient formula is used Calculate the ratio of the mean and standard deviation of the residual values ​​in the region, screen out areas with large residual value dispersion, and generate a regional distribution matrix based on the above classification and calculation results; further combine the spatial coordinate system to extract the residual distribution characteristics, map the residual point position of each region and its spatial distance to adjacent points through the coordinate axis, calculate the mean difference and distance standard deviation between the residual points, and use the geometric calculation formula to perform a weighted average of the spatial distance between the points and the boundary in each region to generate residual and spatial distribution relationship data.

[0041] Based on the data of the relationship between residuals and spatial distribution, the nonlinear relationship between residual values ​​and spatial distribution is calculated, the distribution characteristics of abnormal residual points are screened, the trend change characteristics of abnormal points are extracted through regional characteristics, the correlation between abnormal points and spatial parameters is analyzed, and preliminary trend analysis results are generated; Firstly, the trend of regional residual value changes is extracted according to the spatial characteristic matrix, and the relationship between residual value and coordinate is fitted by piecewise linear regression formula. The regression coefficient and its significance of each segment are calculated, and the segments with lower fitting degree are selected for further analysis. The difference between residual value and local spatial coordinate is used as variable to calculate nonlinear relationship. Calculate the residual trend equation for each anomaly area, where the coefficient , , It is obtained through fitting optimization method, and the segments with larger regression errors are extracted as the outlier distribution areas. Then, the residual points in the outlier area are further extracted with the characteristic differences from the adjacent points. The outliers are grouped into aggregation areas through the geometric center calculation formula to obtain the trend distribution characteristics of the outliers. Combined with the above results, preliminary trend analysis results are generated.

[0042] Based on the preliminary trend analysis results, nonlinear trend data is called, combined with the weight characteristics of regional parameters, and trend fitting is optimized using the formula: ; Regenerate the corrected trend distribution and obtain the residual fitting distribution value; in, represents the residual fitted distribution value, is the base of natural logarithms, is the adjustment coefficient for trend fitting, is the fitting translation coefficient, For the The spatial coordinate parameters of the point, For the The weight parameter of the point, is the total number of data points.

[0043] formula: ; The formula is useful in that by introducing the regional weight , nonlinear correction parameters and The accuracy of residual fitting is optimized so that the fitting results can reflect regional characteristics and trend changes.

[0044] Detailed explanation of the formula and the process of formula calculation and derivation: Let the fitting weight parameter As the regional characteristics change, the residual value is quantified by its discrete degree. Assuming that the regional weight is determined by the discrete coefficient, the formula is used Calculate the weights, where For the The standard deviation of the regional residual values, is the mean value, and the specific value is , ,get ; Nonlinear correction parameters Represents the change in the slope of the relationship between the residual and the coordinates, and is assigned the value obtained through regression analysis , offset parameter Represents the translation amount, assigned as , space coordinates The coordinates of the center of the region are quantified and taken , , , calculated as follows: ; Calculate each term in turn: ; The sum is: ; The result shows that the residual fitting distribution value is 0.950, indicating that the trend distribution accuracy after nonlinear optimization is high and can be used for the calculation and analysis of subsequent regional correction trends.

[0045] See also Figure 6 , the steps for obtaining the dynamic ratio of resource allocation are as follows: Based on the residual fitting distribution value, the task load demand data in the region is called to analyze the temporal variation and spatial distribution of the load demand. The task boundaries are divided based on the geographic boundary line data, the adaptability of the task load and the boundary division rules is refined, and the task load and geographic partition relationship data are generated. Firstly, the task load data is decomposed into trend items, seasonal items and random items by using the time series decomposition method, the change rate of the trend items is calculated, and the significant periodic change load areas are screened out according to the standard deviation of the change amplitude of the seasonal items. Combined with the coordinate points of geographic information, the weighted average method is used to spatially match these load demand points with the geographic boundary lines, and the load demand distribution value contained in each geographic boundary line is calculated. Further, the actual number of task points and the distribution density of load points in the geographic area are used to fit the distribution density of the load within the boundary line through the least squares method, and the high-density load area is screened out. The load importance weight of each area within the task boundary is determined by the clustering radius of the regional center point and the adjacent points. Combined with actual operation data examples, such as the relationship between the task load of the logistics distribution center in the region and the distribution volume of the surrounding stations, the distribution volume is brought into the analysis as the load demand, and a partitioned load distribution model of the distribution area is established to generate task load and geographic partition relationship data.

[0046] Based on the task load and geographical partition relationship data, calculate the load resource demand between regions, calculate the balance of load distribution between regions, judge the deviation between load distribution characteristics and supply relationship, and extract the matching situation of load demand and resource supply between regions based on the evaluation results to obtain the load balance analysis results between regions; By comparing the load demand value and resource supply value of each area, the resource supply-demand ratio is calculated using the normalization formula, and the extreme value and mean of the supply-demand ratio are extracted. The regional scope with large deviations is judged according to the supply and demand balance between regions. The distribution of task loads in the region and the actual available data of resource allocation are used to perform multiple fitting calculations on the resource allocation amount in the region. Under the condition that the total amount of resources remains unchanged, the load resources in the area with uneven supply and demand are dynamically adjusted. Combined with the adjusted supply-demand ratio data, according to the actual matching of the load distribution model in the region, the correlation between the adjusted resource allocation plan and the task completion rate is evaluated. The evaluation results are used to analyze the resource demand allocation ratio of high-load areas within the task boundary. Through logistics scenario examples, such as the relationship between site resource allocation efficiency and task completion rate in the same area, the resource allocation strategy between adjacent areas is further adjusted to obtain the load balance analysis results between regions.

[0047] Based on the results of the load balancing analysis between regions, the resource allocation parameters are re-evaluated, and the allocation logic is optimized by calling the task boundary conditions. The formula is: ; Adjust resource allocation ratios in multiple regions, optimize resource utilization efficiency, and obtain dynamic resource allocation ratios; in, Represents the dynamic ratio of resource allocation, Representative The load demand of the area, Representative The resource supply in the region, Representative The allocation weight of the region, Represents the total number of regions.

[0048] formula: ; Detailed explanation of the formula and the process of formula calculation and derivation: This formula is used to calculate the dynamic ratio of resource allocation , its purpose is to optimize the allocation ratio of multi-regional resources and improve resource utilization efficiency. Each parameter in the formula has an actual measurement or calculation basis, ensuring that all values ​​are obtained based on real data.

[0049] Parameter details: : Dynamic ratio of resource allocation, which indicates the efficiency of resource allocation after integrating regional demand, resource supply and weight factors.

[0050] : No. The load demand of the region is collected through the data monitoring system within the region, such as power consumption, traffic demand, etc.

[0051] : No. The resource supply of a region is determined based on the actual supply data recorded by the regional resource management system, such as power supply, bandwidth supply, etc.

[0052] : No. The allocation weight of a region is calculated based on the region's strategic importance, historical data, and expected growth rate.

[0053] : Total number of regions, from official statistics provided by regional administrations.

[0054] Formula calculation derivation process (specific example): Assume that there are three regions of data as follows: Region 1: Unit load, Unit resources, ; Region 2: Unit load, Unit resources, ; Region 3: Unit load, Unit resources, ; The calculation of the formula is broken down as follows: 1. Calculate the ratio of load demand to resource supply in each area, and then multiply that ratio by the corresponding weight.

[0055] Region 1: ; Region 2: ; Region 3: ; Calculate the average of these values ​​to get : ; Result interpretation: Calculated This indicates that the current resource allocation efficiency exceeds the average level, and some regions have high resource utilization efficiency, which requires adjustment of resource allocation to further optimize efficiency. This result indicates that the existing weights and resource supply strategies need to be re-evaluated in specific regions, especially in areas with unbalanced resource supply.

[0056] 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 cloud computing-based map updating system, characterized in that: The system comprises: The global offset correction module calculates the global offset of the acquisition points in multiple geographic regions based on the image acquisition time, geographic location and elevation data, extracts the offset value characteristics in combination with the latitude and longitude points, calculates the weighted average of the offset values ​​between regions, aggregates the offset ranges of multiple regions after comparing the offset value characteristics, and obtains the global offset distribution coefficient; The regional error division module calculates the difference of the multi-regional offset range based on the global offset distribution coefficient, calls the geographic boundary line to delineate the stratified range, calculates the extreme value of the offset data between the layers, compares the multi-layer data characteristics, extracts the difference value and re-divides the interval, and obtains the stratified error interval value; The local feature correction module calculates the variation of regional terrain parameters based on the stratified error interval value and the raster image data, extracts the slope and land type data for matching, calculates the relationship between the local error offset value and the terrain parameters, calls the parameter relationship result to perform correction operation on the error, and obtains the local correction error coefficient; The residual optimization fitting module extracts the regional distribution parameters of the residual value based on the local correction error coefficient, calculates the nonlinear relationship between the residual value and the spatial distribution, performs trend analysis and fitting calculation on the abnormal points, calls the fitting result data for update, regenerates the correction trend distribution, and obtains the residual fitting distribution value; The computing resource scheduling module extracts the dynamic data of the task load demand in the region based on the residual fitting distribution value, calls the geographical boundary line to divide the task boundary, allocates the load resources and then calculates the distribution balance between the regions, re-evaluates the resource allocation parameters and adjusts the allocation ratio to obtain the dynamic ratio of resource allocation.

2. The cloud computing-based map updating system according to claim 1, characterized in that: The global offset distribution coefficient specifically includes offset value characteristics, weighted average, and multi-partition offset range. The stratified error interval value specifically refers to the inter-layer offset data extreme value and multi-layer data difference value. The local correction error coefficient includes land type data matching, terrain parameter change, and local error offset value relationship. The residual fitting distribution value specifically includes residual value regional distribution parameters, nonlinear relationship calculation results, and corrected trend distribution. The resource allocation dynamic ratio includes task load demand dynamic data, inter-regional distribution balance, and resource allocation parameter adjustment ratio.

3. The cloud computing-based map updating system according to claim 2, characterized in that: The steps for obtaining the global offset distribution coefficient are specifically as follows: Based on the image acquisition time, geographic location and elevation data, the longitude and latitude points and corresponding offsets of the acquisition points in multiple geographic areas are extracted. According to the elevation data range and the offset value distribution of abnormal points, the acquisition points that exceed the specified elevation range are screened, the offset values ​​after screening are adjusted and the elevation change trend is recalculated to construct the acquisition point offset feature matrix; Based on the acquisition point offset feature matrix, the acquisition point offset feature values ​​in multiple regions are classified and processed, the feature values ​​are classified and summarized by the regional offset data range, differentiated inter-region offset weights are set according to the classification results, the feature values ​​within the distribution range are called to recalculate the regional offset weighted results, the inter-region offset weighted data are generated and integrated into a unified table, and a regional offset weighted distribution table is obtained; Based on the regional offset weighted distribution table, the offset weighted results between regions are called, the offset characteristic value distribution of differentiated regions is compared, the regional offset range is aggregated, and the relationship between the multi-region offset mean and the global offset is calculated using the formula: ； Calculate and generate global offset distribution coefficient; in, represents the global offset distribution coefficient, Representative The offset weight of the region, Representative The offset characteristic value of the region, represents the mean of all regional offset eigenvalues, Represents the total number of regions.

4. The cloud computing-based map updating system according to claim 3, characterized in that: The step of obtaining the stratified error interval value is specifically as follows: Based on the global offset distribution coefficient and geographic boundary line data, the regional offset range in the distribution coefficient is called, the hierarchical correspondence between the boundary line layered data and the offset range is analyzed, the upper and lower bounds of the offset range are extracted according to the hierarchical division rules, and the range of each layer is calculated to generate the regional hierarchical offset range; Based on the regional stratified offset range and the offset values ​​between multiple levels, the data characteristics with a large offset value range between levels are screened, the trend of the extreme value change is analyzed and the range of the difference between levels is calculated, the offset feature difference values ​​between levels are determined by the stratified difference weights, and a multi-level offset extreme value distribution matrix is ​​established; Based on the multi-level offset extreme value distribution matrix, the offset value difference characteristics and difference weight data in the matrix are called to analyze the changes in the upper and lower limits of the offset range between levels, using the formula: ； Calculate the stratification error value, re-divide the interval based on the weight and offset difference characteristics, and obtain the stratification error interval value; in, represents the stratification error interval value, Representative The difference weights of the layers, Representative The upper limit of the layer's offset range, Representative The upper limit of the layer's offset range, Representative The difference in the offset characteristics of the layer, Represents the number of layers.

5. The cloud computing-based map updating system according to claim 4, characterized in that: The steps for obtaining the local correction error coefficient are specifically as follows: Based on the stratified error interval value, the raster image data is called to extract the regional terrain parameters, the land type and slope information are analyzed in combination with the image data, the matching degree between the terrain parameter changes and the slope is analyzed, and the regional terrain change data is generated; Based on the regional terrain change data, the local error offset value is calculated, the terrain parameter and offset value relationship data are called for comparison, the offset characteristics are analyzed in combination with the land type and slope matching data, and a local error offset and terrain parameter relationship matrix is ​​generated; Based on the local error offset and terrain parameter relationship matrix, combined with the terrain characteristics and offset relationship results, the error correction operation logic is called to analyze the relationship between the local correction error weight and the terrain characteristics, using the formula: ； Calculate and generate local correction error coefficients; in, represents the local correction error coefficient, Representative The land type weight of the region, Representative The slope parameter of the area, Representative The error offset value of the area, Represents the average of all regional error offset values, Represents the number of regions, Represents the total number of data points.

6. The cloud computing-based map updating system according to claim 5, characterized in that: The steps for obtaining the residual fitting distribution value are specifically as follows: Based on the local correction error coefficient, the regional distribution parameter is called to extract the spatial distribution characteristics of the residual value, analyze the distribution law of the residual value in the differentiated area, establish a distribution feature matrix based on the relationship data between the regional terrain and the spatial coordinates, and generate the residual and spatial distribution relationship data; Based on the residual and spatial distribution relationship data, the nonlinear relationship between the residual value and the spatial distribution is calculated, the distribution characteristics of abnormal residual points are screened, the trend change characteristics of abnormal points are extracted through regional characteristics, the correlation between abnormal points and spatial parameters is analyzed, and preliminary trend analysis results are generated; Based on the preliminary trend analysis results, nonlinear trend data is called, combined with the weight characteristics of regional parameters, and trend fitting is optimized using the formula: ； Regenerate the corrected trend distribution and obtain the residual fitting distribution value; in, represents the residual fitted distribution value, is the base of natural logarithms, is the adjustment coefficient for trend fitting, is the fitting translation coefficient, For the The spatial coordinate parameters of the point, For the The weight parameter of the point, is the total number of data points.

7. The cloud computing-based map updating system according to claim 6, characterized in that: The steps for obtaining the dynamic ratio of resource allocation are specifically as follows: Based on the residual fitting distribution value, the task load demand data in the region is called to analyze the temporal variation and spatial distribution of the load demand, the task boundaries are divided in combination with the geographic boundary line data, the adaptability of the task load and the boundary division rule is refined, and the task load and geographic partition relationship data are generated; Based on the task load and geographical partition relationship data, calculate the load resource demand between regions, calculate the balance of load distribution between regions, judge the deviation between load distribution characteristics and supply relationship, extract the matching situation of load demand and resource supply between regions based on the evaluation results, and obtain the load balance analysis results between regions; Based on the results of the inter-region load balancing analysis, the resource allocation parameters are re-evaluated, and the task boundary conditions are called to optimize the allocation logic. The formula is: ； Adjust resource allocation ratios in multiple regions, optimize resource utilization efficiency, and obtain dynamic resource allocation ratios; in, Represents the dynamic ratio of resource allocation, Representative The load demand of the area, Representative The resource supply in the region, Representative The allocation weight of the region, Represents the total number of regions.

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