Land survey result intelligent management method and system based on big data analysis

Abstract

The invention relates to the technical field of big data storage, and particularly discloses a land survey result intelligent management method and system based on big data analysis, a data processing center receives land survey result data, records the data as a land survey data column, calls a data similarity algorithm, compares the data with an existing storage data column, and judges a pre-storage library; extracting data feature information, processing to obtain a data feature value, and determining a data migration monitoring period; monitoring and extracting data use information in the period, evaluating a data use degree value, and judging whether to adjust the period or not; and according to the data utilization degree value in the period, whether data migration occurs is judged, an adaptive storage library is obtained, and then intelligent management is performed on the land survey data column.

Description

Technical Field

[0001] The present invention relates to the field of big data storage technology, and in particular to a method and system for intelligent management of land survey results based on big data analysis. Background Art

[0002] Land surveys are crucial for comprehensively assessing land resources and utilization, and for obtaining accurate and reliable basic land data. Traditional methods for processing massive amounts of land survey data rely on manual data entry, organization, and analysis, which is slow and error-prone. Furthermore, land survey data encompasses multiple dimensions, such as topography, soil, and land use types. Traditional analytical methods struggle to comprehensively analyze this multidimensional data, hindering the in-depth exploration of the inherent connections and patterns within the data. In the field of land surveys, the introduction of big data analysis technology can effectively address the challenges faced by traditional management methods.

[0003] For example, the invention patent with announcement number CN117893171A announces a rural land information investigation and processing method, system, equipment and storage medium. The rural land information investigation and processing method includes obtaining rural land plot line data, generating rural land images and land information codes based on the rural land plot line data; generating a land QR code based on the land information code, and obtaining rural land registration information based on the land QR code; inputting the rural land registration information and rural land image into a preset land registration table for storage; obtaining the rural land status according to the land registration table, and generating a rural land registration report based on the rural land status.

[0004] Combining the above technical solutions, it is found that due to the diversity and dynamic changes of land information, most of the existing land data management technical solutions adopt a fixed storage method and a single storage structure, resulting in insufficient flexibility in land data management, limiting the scalability and compatibility of land data, and ultimately affecting the use of land data. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides an intelligent management method and system for land survey results based on big data analysis, which can effectively solve the problems involved in the above-mentioned background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: In the first aspect, the present invention provides an intelligent management method for land survey results based on big data analysis, including: a data processing center receives land survey results data that needs to be stored, and records it as a land survey data column, calls a data similarity algorithm, outputs the similarity between the land survey data column and each existing stored data column, and determines the pre-storage storage of the land survey data column; extracts data feature information of the land survey data column, comprehensively determines the data feature value of the land survey data column, and finally obtains a data migration monitoring cycle; monitors the land survey data column under the data migration monitoring cycle, extracts data usage information of the land survey data column, evaluates the data utilization value of the land survey data column, and determines whether to adjust the data migration monitoring cycle; extracts the data utilization value of the land survey data column in the data migration monitoring cycle, determines whether to perform data migration on the land survey data column, obtains an adaptation storage library for the land survey data column, and intelligently manages the land survey data column.

[0007] As a further method, a pre-storage repository of land survey data columns is determined. The specific determination process is: calling a data similarity algorithm, comparing the land survey data columns with each existing stored data column in terms of similarity, outputting the similarity between the land survey data column and each existing stored data column, comparing it with a predefined data similarity threshold, counting a number of existing stored data columns whose similarity is greater than or equal to the similarity threshold, recording them as each comparison historical data column, counting data usage information of each comparison historical data column, and determining the data usage indicator parameter of the comparison historical data column; matching the data usage indicator parameter of the comparison historical data column with the storage repository corresponding to each predefined data usage indicator parameter interval according to the data usage indicator parameter of the comparison historical data column, determining the interval to which the data usage indicator parameter of the comparison historical data column belongs, and obtaining the storage repository corresponding to the interval, which is recorded as the pre-storage repository of land survey data columns.

[0008] As a further method, the data migration monitoring cycle is finally obtained. The specific analysis process is: collect data feature information of the land survey data column, and determine the data feature sub-value of the land survey data column; perform weighted aggregation processing on the data feature sub-value of the land survey data column and the data usage indicator parameter of the comparison historical data column to obtain the data feature value of the land survey data column; according to the data feature value of the land survey data column, match and obtain the first adjustment ratio value of the data migration cycle, adjust the default data migration cycle, and obtain the data migration monitoring cycle.

[0009] As a further method, it is determined whether to adjust the data migration monitoring period. The specific determination process is: obtain the time length of the data migration monitoring period, match it with the time length of the first monitoring sub-period corresponding to each predefined time length interval, and obtain the time length of the first monitoring sub-period of data migration; count the data usage information of the land survey data column in the first monitoring sub-period of data migration, evaluate the data utilization value of the land survey data column, and compare it with the predefined data utilization adaptation value; if the data utilization value of the land survey data column is greater than the data utilization adaptation value, shorten the data migration monitoring period according to the preset data migration period second adjustment ratio value; if the data utilization value of the land survey data column is less than or equal to the data utilization adaptation value, there is no need to adjust the data migration monitoring period.

[0010] As a further method, it is determined whether to perform data migration on the land survey data column. The specific analysis process is as follows: extract the data utilization value of the land survey data column in the data migration monitoring period, match it with the storage repository corresponding to each predefined data utilization value interval, determine the interval to which the data utilization value of the land survey data column in the data migration monitoring period belongs, and obtain the storage repository corresponding to the interval, which is recorded as the adaptation storage repository of the land survey data column; verify the adaptation storage repository of the land survey data column with the pre-storage repository. If the adaptation storage repository is the same as the pre-storage repository, then do not perform data migration on the land survey data column. Otherwise, migrate the land survey data column to the adaptation storage repository.

[0011] As a further method, the land survey data column is intelligently managed. The specific management process is as follows: matching the data usage value of the land survey data column in the data migration monitoring period with the authority granularity corresponding to each predefined data usage value interval, determining the interval to which the data usage value of the land survey data column in the data migration monitoring period belongs, assigning the authority granularity corresponding to the interval to the land survey data column, and obtaining the initial authority granularity of the land survey data column; extracting the data usage value of the land survey data column in the preset period, matching it with the number of inspection executions corresponding to each predefined data usage value interval, determining the interval to which the data usage value of the land survey data column in the preset period belongs, assigning the number of inspection executions corresponding to the interval to the land survey data column, obtaining the number of inspection executions of the land survey data column, and performing the corresponding number of inspection operations within the preset period.

[0012] The second aspect of the present invention provides an intelligent management system for land survey results based on big data analysis, including: a pre-storage storage determination module, which is used for a data processing center to receive land survey results data that needs to be stored and record it as a land survey data column, call a data similarity algorithm, output the similarity between the land survey data column and each existing stored data column, and determine the pre-storage storage of the land survey data column; a monitoring period determination module, which is used to extract data feature information of the land survey data column, comprehensively determine the data feature value of the land survey data column, and finally obtain a data migration monitoring period; a monitoring period adjustment module, which is used to monitor the land survey data column under the data migration monitoring period, extract data usage information of the land survey data column, evaluate the data utilization value of the land survey data column, and determine whether to adjust the data migration monitoring period; a data intelligent management module, which is used to extract the data utilization value of the land survey data column in the data migration monitoring period, determine whether to perform data migration on the land survey data column, obtain an adaptation storage repository for the land survey data column, and intelligently manage the land survey data column.

[0013] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0014] (1) The present invention provides a method and system for intelligent management of land survey results based on big data analysis. The data processing center receives land survey results data, records it as a land survey data column, calls a data similarity algorithm, compares it with the existing stored data column, and determines the pre-storage storage; extracts data feature information, obtains data feature values ​​after processing, and determines the data migration monitoring cycle; monitors under the cycle, extracts data usage information, evaluates the data utilization value, and determines whether to adjust the cycle; determines whether to migrate data based on the data utilization value within the cycle, obtains the adaptation storage library, and then intelligently manages the land survey data column.

[0015] (2) The present invention determines the data usage indicator parameters of the reference historical data column by collecting statistics on the data usage information of each reference historical data column, which can accurately match the potential usage pattern of the current data column (for example, the pre-storage storage corresponding to the high-frequency access historical data is hot storage), avoiding the blindness of "one-size-fits-all" storage allocation and improving the accuracy of initial positioning. The future activity of historical data can be predicted by its usage intensity, and the storage path can be planned in advance (such as pre-storing it in hot storage and setting a short monitoring cycle), laying the foundation for subsequent dynamic migration.

[0016] (3) The present invention evaluates the data utilization value of the land survey data column by statistically analyzing the data usage information of the land survey data column in the first monitoring sub-cycle of data migration, thereby avoiding the performance bottleneck caused by the long-term retention of high-frequency data in cold storage, or the waste of hot storage resources occupied by low-frequency data. Through dynamic evaluation within the sub-cycle, the storage monitoring resources are “allocated on demand”, with intensive monitoring of high-frequency data and sparse monitoring of low-frequency data, thus reducing the system computing overhead. In combination with parameters such as the number of edits and user coverage, not only the storage location can be determined, but also the potential risks of the data can be identified (for example, a high number of edits may be accompanied by data integrity risks), and the permission granularity and inspection frequency can be adjusted synchronously to form a “monitoring-assessment-response” closed loop. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0018] Figure 1 Schematic diagram of the method steps of the present invention.

[0019] Figure 2 This is a schematic diagram of system module connections of the present invention.

[0020] Figure 3 This is the architecture diagram of the survey results interactive platform. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0022] Reference Figure 1 As shown, the first aspect of the present invention provides an intelligent management method for land survey results based on big data analysis, including: a data processing center receives land survey results data to be stored, and records it as a land survey data column, calls a data similarity algorithm, outputs the similarity between the land survey data column and each existing stored data column, and determines the pre-storage storage of the land survey data column.

[0023] The above-mentioned data processing center specifically provides an interactive platform for land survey results based on three-dimensional visualization, such as Figure 3 As shown, Figure 3This is the architecture diagram of the survey results interaction platform, including data acquisition, multi-source data access and fusion, 3D modeling engine, for building high-precision 3D real-scene models based on oblique photography, laser point cloud and BIM data, and multi-source data precise alignment and visual rendering based on spatial registration algorithm. It is used to divide layers according to business needs (such as terrain, buildings, planning red lines, real-time sensor data), support unified rendering and interactive analysis of 3D models (OBJ, GLTF), point clouds (LAS / LAZ), vector data (GeoJSON), and raster images (GeoTIFF), support multi-mode interaction, support natural language instructions and data management, build a multi-source data storage architecture, store land survey data with timestamps, collaborative sharing, support multi-user collaborative work, realize multi-user real-time editing of 3D scenes (such as plot annotation, planning line adjustment), and broadcast operations to all participants in real time.

[0024] It should be explained that the present invention mainly describes data management, and adopts the method of separating hot and cold data for storage to realize intelligent management of land survey results. The following storage repository is specifically a hot data storage repository or a cold data storage repository.

[0025] Specifically, the pre-storage repository for land survey data columns is determined by the following process:

[0026] The data similarity algorithm is called to compare the land survey data column with each existing stored data column in terms of similarity. The similarity between the land survey data column and each existing stored data column is output and compared with a predefined data similarity threshold. A number of existing stored data columns whose similarity is greater than or equal to the similarity threshold are counted and recorded as each comparison historical data column. The data usage information of each comparison historical data column is counted and the data usage indicator parameters of the comparison historical data column are determined.

[0027] The above-mentioned calling data similarity algorithm may use a hash algorithm to compare the land survey data column with each existing stored data column for similarity.

[0028] According to the data usage indicator parameters of the control historical data column, the storage libraries corresponding to the predefined data usage indicator parameter intervals are matched to determine the interval to which the data usage indicator parameters of the control historical data column belong, and the storage library corresponding to the interval is obtained, which is recorded as the pre-storage library of the land survey data column.

[0029] The essence of matching historical data columns through data similarity algorithms (such as hash algorithms) is to find the "data sample" that is closest to the current land survey data column in terms of structure, usage, access mode, etc. The data usage indicator parameters of the historical data columns directly reflect the usage intensity and activity of similar data in actual business, and can be used as a "reference template" for the future use of the current land survey data column. In the early stage of data entry (pre-storage stage), by predicting the usage pattern of historical data, the storage path for the data can be "planned" in advance, greatly improving management efficiency. High-frequency data is prioritized for hot storage, and low-frequency data is automatically returned to cold storage to avoid the waste of resources caused by a "one-size-fits-all" approach.

[0030] Furthermore, the data usage indicator parameters of the comparison historical data column are determined. The specific analysis process is as follows:

[0031] The data usage information of each comparison historical data column includes the total number of accesses to each comparison historical data column, the user coverage of each comparison historical data column, the average access time of each comparison historical data column, and the number of update operations of each comparison historical data column. The data usage information of each comparison historical data column can be specifically extracted from the repository operation log.

[0032] The total number of visits to each comparison historical data column, the user coverage of each comparison historical data column, the average visit duration of each comparison historical data column, and the number of update operations of each comparison historical data column are respectively de-unitized and normalized to obtain the normalized value of the total number of visits to each comparison historical data column, the normalized value of the user coverage of each comparison historical data column, the normalized value of the average visit duration of each comparison historical data column, and the normalized value of the number of update operations of each comparison historical data column. The weighted average processing is then performed in sequence to obtain the data usage indicator parameters of the comparison historical data column. The specific analysis method is as follows:

[0033]

[0034] Where, δ is the data usage indicator parameter for the comparison historical data column, CD i is the normalized value of the total number of visits to the i-th control history data column, i is the number of each control history data column, i = 1, 2, 3, ..., M, M is the total number of control history data columns, GH i is the normalized value of user coverage of the i-th comparison historical data column, AR i is the normalized value of the average access time of the i-th control historical data column, CV iis the normalized value of the number of update operations of the i-th control historical data column, g1 is the weight factor corresponding to the total number of visits predefined in the land survey results database, g2 is the weight factor corresponding to the user coverage predefined in the land survey results database, g3 is the weight factor corresponding to the average visit time predefined in the land survey results database, and g4 is the weight factor corresponding to the number of update operations predefined in the land survey results database.

[0035] The data usage indicator parameter of the above-mentioned comparison historical data column is expressed as the usage intensity and activity level of the comparison historical data column in actual business.

[0036] It's important to note that the user coverage mentioned above specifically refers to the number of unique users who have accessed the comparison historical data column. A high user coverage indicates that the data covers a wide range of users and may represent fundamental data for multiple business processes or departments, resulting in high usage.

[0037] The weight factors corresponding to the total number of visits, the weight factors corresponding to the user coverage, the weight factors corresponding to the average visit time and the weight factors corresponding to the number of update operations are all extracted from the land survey results library, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. For example, the total number of visits, the user coverage, the average visit time and the update operation number are respectively mapped with the weight factors corresponding to the total number of visits, the user coverage, the average visit time and the update operation number preset in the land survey results library to form a mapping set. The real-time total number of visits, user coverage, average visit time and update operation number are brought into the mapping set to obtain the weight factors corresponding to the total number of visits, the user coverage, the average visit time and the update operation number.

[0038] In this embodiment, the correlation between these parameters is considered through multivariate analysis of total access counts, user coverage, average access duration, and update operation counts. A high user coverage indicates a larger potential access group, directly leading to an increase in the total access count. Conversely, if the user coverage is low (e.g., visible only to a few administrators), even if a single user frequently accesses the data, the total access count is capped, affecting the use of data in the comparison history data column. A high total access count but low user coverage may indicate abnormally high access by a small number of users (e.g., automated script calls), necessitating vigilance against permission abuse or system vulnerabilities. A long average access duration (e.g., 30 minutes) typically corresponds to complex operations (e.g., spatial data editing and multi-dimensional statistical analysis). Such operations are often accompanied by data updates (e.g., modifying attribute fields and adding spatial features), leading to an increase in the number of update operations. Furthermore, frequently updated data (e.g., real-time land change data) is highly time-sensitive and attracts users to conduct longer analyses (e.g., comparing new and old data), forming a cycle of "update → in-depth access → further update," significantly increasing the use of data in the comparison history data column.

[0039] The data feature information of the land survey data column is extracted, the data feature value of the land survey data column is comprehensively determined, and finally the data migration monitoring cycle is obtained.

[0040] Specifically, the data migration monitoring cycle is finally obtained. The specific analysis process is as follows:

[0041] Collect data feature information of the land survey data column and determine the data feature sub-value of the land survey data column. The data feature information of the land survey data column includes the memory usage of the land survey data column, the total number of fields in the land survey data column, the time window of the land survey data column, and the completeness value of the land survey data column.

[0042] The completeness value of the above-mentioned land survey data column can be specifically obtained by the ratio of the number of fields without missing values ​​in the land survey data column to the total number of fields.

[0043] The memory usage of the land survey data column, the total number of fields in the land survey data column, the time window of the land survey data column, and the completeness value of the land survey data column are normalized and weighted aggregation is performed in sequence to obtain the data feature sub-values ​​of the land survey data column. The specific analysis process is as follows:

[0044] TZ′=NC×n1+ZD×n2+TK×n3+WZ×n4;

[0045] Where TZ' is the data characteristic sub-value of the land survey data column, NC is the memory usage of the land survey data column, ZD is the total number of fields in the land survey data column, TK is the time window of the land survey data column, WZ is the completeness value of the land survey data column, n1 is the weight factor corresponding to the memory usage predefined in the land survey results library, n2 is the weight factor corresponding to the total number of fields predefined in the land survey results library, n3 is the weight factor corresponding to the time window predefined in the land survey results library, and n4 is the weight factor corresponding to the completeness value predefined in the land survey results library.

[0046] The weight factors corresponding to the memory usage, the weight factors corresponding to the total number of fields, the weight factors corresponding to the time window, and the weight factors corresponding to the completeness value are all extracted from the land survey results library, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. For example, the memory usage, the total number of fields, the time window, and the completeness value respectively form a mapping set with the preset weight factors corresponding to the memory usage, the total number of fields, the time window, and the completeness value in the land survey results library. The real-time memory usage, the total number of fields, the time window, and the completeness value are brought into the mapping set to obtain the weight factors corresponding to the memory usage, the total number of fields, the time window, and the completeness value.

[0047] In this embodiment, the data characteristic sub-value comprehensively considers multiple factors of the land survey data series. By analyzing these factors, the diverse characteristics of the data can be converted into a quantitative value, thereby more accurately reflecting the inherent characteristics of the data. Furthermore, the data characteristic sub-value is an important basis for determining the data migration monitoring cycle. It is combined with the data usage indicator parameter of the historical data series to jointly determine the data characteristic value, which in turn influences the adjustment of the data migration monitoring cycle.

[0048] The data characteristic sub-values ​​of the land survey data column and the data of the comparison historical data column are weighted and aggregated using the indicator parameters to obtain the data characteristic values ​​of the land survey data column. The specific analysis method is as follows:

[0049] TZ=TZ'×h1+δ×h2;

[0050] Where TZ is the data characteristic value of the land survey data column, TZ' is the data characteristic sub-value of the land survey data column, δ is the data usage indicator parameter for the comparison historical data column, h1 is the weight factor corresponding to the data characteristic sub-value predefined in the land survey results database, and h2 is the weight factor corresponding to the data usage indicator parameter predefined in the land survey results database.

[0051] The weight factors corresponding to the data feature sub-values ​​and the weight factors corresponding to the data usage indicator parameters are both extracted from the land survey results library, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. For example, the data feature sub-values ​​and the data usage indicator parameters respectively form a mapping set with the weight factors corresponding to the data feature sub-values ​​and the weight factors corresponding to the data usage indicator parameters preset in the land survey results library. The real-time data feature sub-values ​​and data usage indicator parameters are brought into the mapping set to obtain the weight factors corresponding to the data feature sub-values ​​and the weight factors corresponding to the data usage indicator parameters.

[0052] In this embodiment, the data characteristic value combines the data characteristic subvalues ​​and the data usage indicator parameters compared to historical data columns, more comprehensively reflecting the actual value and potential usage patterns of the data. Based on the first adjustment ratio value for the data migration cycle obtained by matching the data characteristic value, the default data migration cycle can be rationally adjusted to optimize the allocation of storage resources. Furthermore, data migration decisions based on the characteristic value ensure that data is always stored in the most appropriate location, improving the flexibility and adaptability of data management, and further enhancing the efficiency and value of data usage.

[0053] Based on the data characteristic values ​​of the land survey data column, a first adjustment ratio value of the data migration period is matched and the default data migration period is adjusted to obtain the data migration monitoring period. Specifically, the default data migration period is adjusted by multiplying the first adjustment ratio value of the data migration period by the default data migration period to obtain the data migration monitoring period.

[0054] The land survey data column is monitored under the data migration monitoring cycle, data usage information of the land survey data column is extracted, the data utilization value of the land survey data column is evaluated, and it is determined whether the data migration monitoring cycle is adjusted.

[0055] Furthermore, it is determined whether to adjust the data migration monitoring period. The specific determination process is as follows:

[0056] The time length of the data migration monitoring period is obtained, and matched with the time lengths of the first monitoring sub-periods corresponding to the predefined time length intervals to obtain the time length of the first monitoring sub-period of the data migration.

[0057] Collect data usage information of the land survey data column within the first monitoring sub-period of data migration, evaluate the data utilization value of the land survey data column, and compare it with the predefined data utilization adaptation value; if the data utilization value of the land survey data column is greater than the data utilization adaptation value, shorten the data migration monitoring period according to the second adjustment ratio value of the preset data migration period; if the data utilization value of the land survey data column is less than or equal to the data utilization adaptation value, there is no need to adjust the data migration monitoring period.

[0058] The shortening of the data migration monitoring period is specifically performed by multiplying the preset second adjustment ratio of the data migration period by the data migration monitoring period, thereby adjusting the data migration monitoring period.

[0059] When the data utilization value of a land survey data column is high (e.g., frequently accessed, frequently updated), it indicates that the data is in the core business process (e.g., real-time land change data, frequently queried basic geographic information), and its storage requirements may change dynamically with business peaks (e.g., a sudden increase in access volume on weekday mornings). By shortening the data migration monitoring cycle (e.g., from weekly to daily), such changes can be captured in real time, avoiding the lag problem of "data being frequently used but still stored in cold storage" caused by excessively long cycles, and ensuring that the data is always in the optimal storage location (e.g., promptly migrated to hot storage). When data is subject to short-term high utilization due to temporary business needs (e.g., peak land approval periods, emergency surveying and mapping tasks), shortening the cycle can quickly identify abnormal access patterns, trigger emergency migration, and avoid system performance bottlenecks caused by delayed migration.

[0060] Specifically, the data utilization value of the land survey data column is evaluated. The specific evaluation process is as follows:

[0061] The data usage information of the land survey data column includes the data access frequency of the land survey data column, the number of edits of the land survey data column, and the user coverage of the land survey data column. The data usage information of the land survey data column can be specifically extracted from the repository operation log.

[0062] The data access frequency, editing times and user coverage of the land survey data column are normalized to obtain the normalized values ​​of the data access frequency, editing times and user coverage of the land survey data column, and then weighted aggregation is performed in turn to obtain the data utilization value of the land survey data column. The specific analysis method is as follows:

[0063] β=AC×f1+EF×f2+LAI×f3;

[0064] where β is the data utilization value of the land survey data column, AC is the normalized value of the data access frequency of the land survey data column, EF is the normalized value of the number of edits of the land survey data column, LAI is the normalized value of the user coverage of the land survey data column, f1 is the weight factor corresponding to the data access frequency predefined in the land survey results database, f2 is the weight factor corresponding to the number of edits predefined in the land survey results database, and f3 is the weight factor corresponding to the user coverage predefined in the land survey results database.

[0065] The data utilization value of the above land survey data column represents the usage intensity and activity level of the land survey data column in actual business.

[0066] The weight factors corresponding to the data access frequency, the weight factors corresponding to the number of edits, and the weight factors corresponding to the user coverage are all extracted from the land survey results library, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. For example, the data access frequency, the number of edits, and the user coverage respectively form a mapping set with the weight factors corresponding to the data access frequency, the weight factors corresponding to the number of edits, and the weight factors corresponding to the user coverage preset in the land survey results library. The real-time data access frequency, the number of edits, and the user coverage are brought into the mapping set to obtain the weight factors corresponding to the data access frequency, the weight factors corresponding to the number of edits, and the weight factors corresponding to the user coverage.

[0067] In this embodiment, a multivariate analysis of data access frequency, edit count, and user coverage is performed, specifically considering the correlation between these parameters. A high data access frequency generally indicates that the data is widely used. When users discover data problems (such as attribute errors or missing spatial elements) or need to supplement information during use, they will trigger edit operations (such as correction, addition, and deletion), resulting in an increase in the number of edits, thereby increasing the data utilization of the land survey data column. Conversely, low-frequency access data (such as historical archived data) is rarely accessed and has low editing needs, so the number of edits is generally maintained at a low level. User coverage (i.e., the number of independent users accessing the data) directly determines the upper limit of the access frequency. The higher the coverage (such as if the data is open to all grassroots units in the province), the higher the total access frequency may be, even if the access frequency of a single user is low, which also increases the data utilization of the land survey data column. However, if the user coverage includes a large number of ordinary users (with only read-only permissions), even if the coverage is high, the number of edits may be low (because editing operations are concentrated in a few high-authority users, such as data administrators), resulting in a "high coverage, low editing" phenomenon, which affects the data utilization of the land survey data column.

[0068] The data utilization value of the land survey data column in the data migration monitoring cycle is extracted to determine whether to perform data migration on the land survey data column, obtain the adaptation storage library of the land survey data column, and intelligently manage the land survey data column.

[0069] Furthermore, it is determined whether to perform data migration on the land survey data column. The specific analysis process is as follows:

[0070] The data utilization value of the land survey data column in the data migration monitoring period is extracted and matched with the storage repositories corresponding to the predefined data utilization value intervals. The interval to which the data utilization value of the land survey data column in the data migration monitoring period belongs is determined, and the storage repositories corresponding to the interval are obtained, which are recorded as the adaptation storage repositories of the land survey data column.

[0071] The adaptation storage library and the pre-storage library of the land survey data column are verified. If the adaptation storage library is the same as the pre-storage library, the land survey data column will not be migrated. Otherwise, the land survey data column will be migrated to the adaptation storage library.

[0072] Specifically, the land survey data columns are managed intelligently. The specific management process is as follows:

[0073] The data usage value of the land survey data column in the data migration monitoring period is matched with the permission granularity corresponding to each predefined data usage value interval, the interval to which the data usage value of the land survey data column in the data migration monitoring period belongs is determined, and the permission granularity corresponding to the interval is assigned to the land survey data column to obtain the initial permission granularity of the land survey data column.

[0074] Extract the data usage level value of the land survey data column in the preset period, match it with the inspection execution times corresponding to the predefined data usage level value intervals, determine the interval to which the data usage level value of the land survey data column in the preset period belongs, assign the inspection execution times corresponding to the interval to the land survey data column, obtain the inspection execution times of the land survey data column, and perform the corresponding number of inspection operations within the preset period.

[0075] Based on the data utilization level of land survey data columns, the initial permissions are accurately matched to avoid "excessive permissions" or "insufficient permissions". The traditional fixed permission model cannot be dynamically adjusted with the data utilization level (for example, temporary high-utilization data is not promptly upgraded, resulting in business obstruction, or long-term low-utilization data is not reclaimed, resulting in security loopholes). Intelligent management realizes "on-demand allocation" of permissions by real-time monitoring of data utilization. High data utilization is often accompanied by high-frequency editing and multi-user access, resulting in an increased risk of data damage (such as concurrent editing conflicts, malicious tampering, and accidental deletion). Intelligent management implements more intensive inspections on high-utilization data through the "data usage value-inspection execution number" mapping, which can timely detect data anomalies (such as a sudden increase in the number of edits but a decrease in the frequency of access, which may indicate batch data tampering), and control risks in the bud. When the inspection finds signs of data damage, it immediately triggers an early warning and freezes suspicious operation permissions.

[0076] Furthermore, it also includes temporary privilege escalation and privilege revoke of users. The specific analysis method is as follows:

[0077] Obtain the user's operation execution data during the evaluation period to determine the user's permission score.

[0078] The user's operation execution data during the evaluation period, including the user's cross-permission execution times, the number of data columns the user accesses, and the user's maximum cross-permission access duration; the operation execution data can be extracted from the operation report of the data processing center.

[0079] The user's cross-permission execution times, the number of data columns accessed by the user, and the maximum cross-permission access duration are normalized and weighted and aggregated to obtain the user's permission score. The specific analysis method is as follows:

[0080] QX=CS×r1+WF×r2+FT×r3;

[0081] Where QX is the user's permission score, CS is the user's cross-permission execution count, WF is the number of data columns accessed by the user, FT is the user's maximum cross-permission access duration, r1 is the weight factor corresponding to the predefined cross-permission execution count in the land survey results database, r2 is the weight factor corresponding to the predefined number of data columns accessed in the land survey results database, and r3 is the weight factor corresponding to the predefined maximum cross-permission access duration in the land survey results database.

[0082] The weight factors corresponding to the number of cross-authority executions, the weight factors corresponding to the number of data column accesses, and the weight factors corresponding to the maximum cross-authority access duration are all extracted from the land survey results library, and the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. For example, the number of cross-authority executions, the number of data column accesses, and the maximum cross-authority access duration are respectively mapped with the weight factors corresponding to the number of cross-authority executions, the weight factors corresponding to the number of data column accesses, and the weight factors corresponding to the maximum cross-authority access duration preset in the land survey results library to form a mapping set. The real-time number of cross-authority executions, the number of data column accesses, and the maximum cross-authority access duration are brought into the mapping set to obtain the weight factors corresponding to the number of cross-authority executions, the weight factors corresponding to the number of data column accesses, and the weight factors corresponding to the maximum cross-authority access duration.

[0083] In this embodiment, by determining the user's permission score, the system can make user permission management more flexible, efficient, and secure. It breaks the limitations of the traditional fixed permission model, not only meeting the user's permission needs in complex business scenarios, but also promptly preventing potential risks, improving the overall management level and operational efficiency of the system. It can also achieve automatic temporary permission escalation, ensuring that business can be carried out smoothly and efficiently, and improving the system's adaptability to complex business scenarios.

[0084] If the user's permission score is greater than the first permission score threshold, a temporary permission escalation is triggered.

[0085] Under the traditional fixed permission mode, users need to apply for temporary permissions through manual approval (such as the need to access data columns with non-default permissions when collaborating across departments), and the process may take up to several hours or even days. In this embodiment, by calculating the permission score in real time, when the user frequently crosses the permission due to business needs (such as temporarily participating in land change data verification and needing to edit non-job data columns), and the score exceeds the threshold, the system automatically triggers a temporary permission increase without manual intervention, shortening the permission acquisition time from "hours" to "seconds", avoiding process blockages caused by "permission barriers". When a user accesses multiple data columns in a short period of time or performs complex cross-permission operations (such as calling land use data and ownership data for correlation analysis at the same time), the permission score increases due to "a large number of data columns accessed" and "a reasonable number of cross-permission executions". Temporary permission increase can avoid repeated verification and improve the efficiency of collaborative operations of multiple data columns.

[0086] If the user's permission score is less than the second threshold of the permission score, the user's operation execution data will be continuously monitored to determine the user's permission score. If the user's permission score is less than the second threshold of the permission score and continues for a preset period of time, an early warning will be issued for the user's execution and permission recovery will be triggered. Otherwise, the basic permissions of the role will be maintained.

[0087] If the user's permission score is between the first permission score threshold and the second permission score threshold, the basic permission of the role is maintained.

[0088] Reference Figure 2 As shown, the second aspect of the present invention provides an intelligent management system for land survey results based on big data analysis, including a pre-storage repository determination module, a monitoring cycle determination module, a monitoring cycle adjustment module, a data intelligent management module and a land survey results library.

[0089] The land survey results database is used to store preset values ​​of various factors.

[0090] The pre-storage repository determination module is connected to the monitoring period determination module, the monitoring period determination module is connected to the monitoring period adjustment module, the monitoring period adjustment module is connected to the data intelligent management module, and the pre-storage repository determination module, the monitoring period determination module, the monitoring period adjustment module and the data intelligent management module are all connected to the land survey results database.

[0091] The pre-storage repository determination module is used by the data processing center to receive the land survey results data that needs to be stored, and record it as a land survey data column, call the data similarity algorithm, output the similarity between the land survey data column and each existing stored data column, and determine the pre-storage repository of the land survey data column.

[0092] The monitoring cycle determination module is used to extract data feature information of the land survey data column, comprehensively determine the data feature values ​​of the land survey data column, and finally obtain the data migration monitoring cycle.

[0093] The monitoring cycle adjustment module is used to monitor the land survey data column under the data migration monitoring cycle, extract data usage information of the land survey data column, evaluate the data utilization value of the land survey data column, and determine whether to adjust the data migration monitoring cycle.

[0094] The data intelligent management module is used to extract the data utilization value of the land survey data column in the data migration monitoring cycle, determine whether to perform data migration on the land survey data column, obtain the adaptation storage library of the land survey data column, and perform intelligent management on the land survey data column.

[0095] The above content is merely an example and explanation of the structure of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the scope of protection of the present invention.

Claims

1. An intelligent management method for land survey results based on big data analysis, characterized in that: include: The data processing center receives the land survey results data to be stored and records it as a land survey data column, calls the data similarity algorithm, outputs the similarity between the land survey data column and each existing stored data column, and determines the pre-storage storage of the land survey data column; Extract the data feature information of the land survey data column, comprehensively determine the data feature value of the land survey data column, and finally obtain the data migration monitoring cycle; Monitor the land survey data column during the data migration monitoring cycle, extract data usage information of the land survey data column, evaluate the data utilization value of the land survey data column, and determine whether to adjust the data migration monitoring cycle; The data utilization value of the land survey data column in the data migration monitoring cycle is extracted to determine whether to perform data migration on the land survey data column, obtain the adaptation storage library of the land survey data column, and intelligently manage the land survey data column.

2. The intelligent management method for land survey results based on big data analysis according to claim 1 is characterized by: The specific determination process of the pre-storage repository for determining land survey data columns is as follows: Calling a data similarity algorithm, performing a similarity comparison between the land survey data column and each existing stored data column, outputting the similarity between the land survey data column and each existing stored data column, comparing the similarity with a predefined data similarity threshold, counting a number of existing stored data columns whose similarity is greater than or equal to the similarity threshold, recording them as reference historical data columns, counting data usage information of each reference historical data column, and determining a data usage indicator parameter of the reference historical data column; According to the data usage indicator parameters of the control historical data column, the storage libraries corresponding to the predefined data usage indicator parameter intervals are matched to determine the interval to which the data usage indicator parameters of the control historical data column belong, and the storage library corresponding to the interval is obtained, which is recorded as the pre-storage library of the land survey data column.

3. The intelligent management method for land survey results based on big data analysis according to claim 2 is characterized by: The determination and comparison of the data in the historical data column uses indicator parameters, and the specific analysis process is as follows: The data usage information of each comparison history data column includes the total number of accesses to each comparison history data column, the user coverage of each comparison history data column, the average access time of each comparison history data column, and the number of update operations for each comparison history data column; The total number of visits to each comparison historical data column, the user coverage of each comparison historical data column, the average visit time of each comparison historical data column, and the number of update operations of each comparison historical data column are respectively de-unitized and normalized to obtain the normalized value of the total number of visits to each comparison historical data column, the normalized value of the user coverage of each comparison historical data column, the normalized value of the average visit time of each comparison historical data column, and the normalized value of the number of update operations of each comparison historical data column, and weighted average processing is performed in sequence to obtain the data usage indicator parameters of the comparison historical data column.

4. The method for intelligent management of land survey results based on big data analysis according to claim 1 is characterized by: The data migration monitoring cycle is finally obtained, and the specific analysis process is as follows: Collecting data characteristic information of land survey data columns and determining data characteristic sub-values ​​of land survey data columns; Perform weighted aggregation processing on the data characteristic sub-values ​​of the land survey data column and the data usage indicator parameters of the comparison historical data column to obtain the data characteristic values ​​of the land survey data column; According to the data characteristic values ​​of the land survey data column, the first adjustment ratio value of the data migration period is matched and the default data migration period is adjusted to obtain the data migration monitoring period.

5. The method for intelligent management of land survey results based on big data analysis according to claim 1 is characterized by: The specific process of determining whether to adjust the data migration monitoring period is as follows: Obtaining the duration of the data migration monitoring period, and matching it with the duration of the first monitoring sub-period corresponding to each predefined time interval to obtain the duration of the first monitoring sub-period of the data migration; Collect data usage information of the land survey data column within the first monitoring sub-period of data migration, evaluate the data utilization value of the land survey data column, and compare it with the predefined data utilization adaptation value; if the data utilization value of the land survey data column is greater than the data utilization adaptation value, shorten the data migration monitoring period according to the second adjustment ratio value of the preset data migration period; if the data utilization value of the land survey data column is less than or equal to the data utilization adaptation value, there is no need to adjust the data migration monitoring period.

6. The method for intelligent management of land survey results based on big data analysis according to claim 5 is characterized by: The specific evaluation process for evaluating the data utilization value of the land survey data column is as follows: The data usage information of the land survey data column includes the data access frequency of the land survey data column, the number of edits of the land survey data column, and the user coverage of the land survey data column; The data access frequency of the land survey data column, the number of edits of the land survey data column and the user coverage of the land survey data column are respectively normalized to obtain the normalized value of the data access frequency of the land survey data column, the normalized value of the number of edits of the land survey data column and the normalized value of the user coverage of the land survey data column, and then weighted aggregation processing is performed in sequence to obtain the data utilization value of the land survey data column.

7. The method for intelligent management of land survey results based on big data analysis according to claim 1 is characterized by: The specific analysis process for determining whether to perform data migration on the land survey data column is as follows: Extract the data utilization value of the land survey data column in the data migration monitoring period, match it with the storage banks corresponding to the predefined data utilization value intervals, determine the interval to which the data utilization value of the land survey data column in the data migration monitoring period belongs, obtain the storage bank corresponding to the interval, and record it as the adaptation storage bank of the land survey data column; The adaptation storage library and the pre-storage library of the land survey data column are verified. If the adaptation storage library is the same as the pre-storage library, the land survey data column will not be migrated. Otherwise, the land survey data column will be migrated to the adaptation storage library.

8. The method for intelligent management of land survey results based on big data analysis according to claim 1 is characterized by: The specific management process of the land survey data column is as follows: Matching the data usage level value of the land survey data column in the data migration monitoring period with the permission granularity corresponding to each predefined data usage level value interval, determining the interval to which the data usage level value of the land survey data column in the data migration monitoring period belongs, and assigning the permission granularity corresponding to the interval to the land survey data column, thereby obtaining the initial permission granularity of the land survey data column; Extract the data usage level value of the land survey data column in the preset period, match it with the inspection execution times corresponding to the predefined data usage level value intervals, determine the interval to which the data usage level value of the land survey data column in the preset period belongs, assign the inspection execution times corresponding to the interval to the land survey data column, obtain the inspection execution times of the land survey data column, and perform the corresponding number of inspection operations within the preset period.

9. The method for intelligent management of land survey results based on big data analysis according to claim 1 is characterized by: It also includes temporary privilege escalation and privilege recovery. The specific analysis method is as follows: Obtain the user's operation execution data during the evaluation period and determine the user's permission score; The user's operation execution data during the evaluation period, including the user's cross-permission execution times, the user's number of data columns accessed, and the user's maximum cross-permission access duration; The user's cross-permission execution times, the number of data columns accessed by the user, and the maximum cross-permission access duration of the user are normalized and weighted and aggregated to obtain the user's permission score. If the user's permission score is greater than the first permission score threshold, a temporary permission escalation is triggered; If the user's permission score is less than the second permission score threshold, the user's operation execution data will be continuously monitored to determine the user's permission score. If the user's permission score is less than the second permission score threshold and remains less than the second permission score threshold for a preset period of time, an early warning will be issued for the user's execution and permission revocation will be triggered. Otherwise, the basic permissions of the role will be maintained. If the user's permission score is between the first permission score threshold and the second permission score threshold, the basic permission of the role is maintained.

10. A system using the method for intelligent management of land survey results based on big data analysis as claimed in any one of claims 1 to 9, characterized in that: include: The pre-storage storage determination module is used for the data processing center to receive the land survey results data to be stored, record it as a land survey data column, call the data similarity algorithm, output the similarity between the land survey data column and each existing stored data column, and determine the pre-storage storage of the land survey data column; The monitoring cycle determination module is used to extract the data feature information of the land survey data column, comprehensively determine the data feature values ​​of the land survey data column, and finally obtain the data migration monitoring cycle; A monitoring cycle adjustment module is used to monitor the land survey data column under the data migration monitoring cycle, extract data usage information of the land survey data column, evaluate the data utilization value of the land survey data column, and determine whether to adjust the data migration monitoring cycle; The data intelligent management module is used to extract the data utilization value of the land survey data column in the data migration monitoring cycle, determine whether to migrate the data of the land survey data column, obtain the adaptation storage library of the land survey data column, and perform intelligent management of the land survey data column.

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