Method, device and equipment for dynamically updating and storing cadastral data and storage medium

Abstract

The invention relates to the technical field of cadastral data dynamic management, and discloses a cadastral data dynamic updating and storage method, device and equipment and a storage medium. By receiving a land law enforcement task set of a target law enforcement vehicle, a law enforcement driving route map for a plurality of law enforcement tasks is constructed; issuing cadastral data of each law enforcement task to a cadastral data edge library at different driving road sections is taken as a decision variable, and the storage capacity of a target law enforcement vehicle, the maximum communication capability of each driving road section, the cadastral data advanced issuing characteristic and the cadastral data real-time performance are considered; and controlling cadastral data scheduling of the cadastral data edge library and the cadastral data core library, and assisting law enforcement officers in land law enforcement. Therefore, by adopting the task-oriented accurate data issuing and dynamic and the space-time analysis-based bandwidth prediction and optimization strategy, organic unification of the real-time performance, the security and the access efficiency of the cadastral data is realized, and efficient and reliable data support is provided for land law enforcement.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cadastral data dynamic management, and particularly relates to a cadastral data dynamic updating and storage method, device, equipment and storage medium. BACKGROUND

[0002] Cadastral data is the core basic data of land law enforcement, land management and real estate registration, and is widely used in illegal land occupation investigation and handling, homestead right confirmation, enterprise land compliance inspection, ecological land protection and other scenes. The traditional cadastral data updating and storage system usually adopts a mode of directly accessing a remote core database by a mobile terminal, or preloading full data to a mobile device before law enforcement.

[0003] However, in actual application, due to the characteristics of large data volume (including vector data, attribute table, remote sensing image, etc.), high update frequency (such as daily changes in land ownership), strict security requirements and the like of cadastral data, the traditional mode has obvious defects. Direct remote access is seriously affected by network delay and unstable bandwidth, especially when law enforcement is carried out in remote areas, network interruption often leads to work failure; and full data pre-download faces multiple risks such as insufficient device storage space, unguaranteed data real-time performance, data leakage due to device loss and the like. The existing technology is difficult to balance the data real-time performance, security and access efficiency, and an innovative dynamic updating and storage scheme is urgently needed.

[0004] Therefore, how to improve the dynamic updating and storage of cadastral data in the process of land law enforcement and balance the contradiction between data real-time performance, security and access efficiency is a technical problem to be solved. SUMMARY

[0005] The present application provides a cadastral data dynamic updating and storage method, device, equipment and storage medium, which aims to solve at least one of the above technical problems.

[0006] To achieve the above-mentioned purpose, the present application provides a cadastral data dynamic updating and storage method, comprising the following steps: Receiving a set of land law enforcement tasks of a target law enforcement vehicle, extracting the law enforcement content, law enforcement area position and law enforcement time window of a plurality of law enforcement tasks in the set of land law enforcement tasks; wherein the target law enforcement vehicle is deployed with a cadastral data edge library; According to the law enforcement content, matching the cadastral data volume corresponding to each law enforcement task, generating a driving route of the target law enforcement vehicle according to the law enforcement area positions of the plurality of law enforcement tasks, constructing a law enforcement driving route map for the plurality of law enforcement tasks, and writing the cadastral data volume and the law enforcement time window corresponding to each law enforcement task into the corresponding law enforcement task node in the law enforcement driving route map; extract a driving section between two adjacent law enforcement task nodes in the law enforcement driving route map, and predict an upper limit value of communication data volume of each driving section according to historical network bandwidth data; Based on the cadastral data volume of each law enforcement task node, the law enforcement time window, and the upper limit value of communication data volume of each driving section, taking the cadastral data of each law enforcement task from the cadastral data core library to the cadastral data edge library in different driving sections as a decision variable, considering the storage capacity of the target law enforcement vehicle, the maximum communication capacity of each driving section, the cadastral data pre-delivery feature, and the real-time property of cadastral data, an optimal cadastral data dynamic update and storage strategy for the land law enforcement task set is generated; The cadastral data dynamic update and storage strategy is used to control the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling on the corresponding driving section, and to drive the law enforcement personnel to use the mobile terminal locally connected to the target law enforcement vehicle to perform land law enforcement.

[0007] Optionally, the land law enforcement task set of the target law enforcement vehicle is received, and the law enforcement content, the law enforcement area position, and the law enforcement time window of a plurality of law enforcement tasks in the land law enforcement task set are extracted, which specifically includes: The land law enforcement data package issued by the land law enforcement management platform is received, and the land law enforcement task set of the target law enforcement vehicle in the target time period is parsed from the land law enforcement data package; A plurality of task elements of each law enforcement task are extracted from the land law enforcement task set; wherein the plurality of task elements include a set of law enforcement content, law enforcement area position, and law enforcement time window recorded in different expression forms in each law enforcement task; The plurality of extracted task elements are converted into a unified data format, and a task and element mapping table is established.

[0008] Optionally, according to the law enforcement content, the corresponding cadastral data volume of each law enforcement task is matched, the driving route of the target law enforcement vehicle is generated according to the law enforcement area position of a plurality of law enforcement tasks, the law enforcement driving route map for a plurality of law enforcement tasks is constructed, and the cadastral data volume and the law enforcement time window corresponding to each law enforcement task are written into the corresponding law enforcement task node in the law enforcement driving route map, which specifically includes: According to the law enforcement type of a plurality of lands in the law enforcement content, the data type required by each law enforcement task and the data storage amount of each data type are queried from the cadastral data core library, and the cadastral data volume corresponding to each law enforcement task is estimated; According to the law enforcement area position of a plurality of law enforcement tasks, the driving route of the target law enforcement vehicle is generated according to a preset optimal driving path planning algorithm; The driving route of the target law enforcement vehicle is converted into a directed graph structure, and a law enforcement driving route graph containing a plurality of law enforcement task nodes and driving direction lines between adjacent two law enforcement task nodes is constructed. The cadastral data amount corresponding to each law enforcement task and the law enforcement time window are written into the corresponding law enforcement task node of the law enforcement driving route graph as node additional information.

[0009] Optionally, the driving route section of the adjacent two law enforcement task nodes in the law enforcement driving route graph is extracted, and the upper limit value of the communication data amount of each driving route section is predicted according to historical network bandwidth data. The driving section between the adjacent two law enforcement task nodes in the law enforcement driving route graph is extracted, and the entire driving route of the law enforcement driving route graph is divided into a plurality of continuous driving route sections. According to the historical performance data of the operator base station along each driving route section, the average bandwidth parameter of each operator base station under each driving route section is estimated using a time series prediction model, and the driving time length of each operator base station corresponding to the driving communication period is considered to calculate the upper limit value of the communication data amount of each driving route section.

[0010] Optionally, according to the historical performance data of the operator base station along each driving route section, the average bandwidth parameter of each operator base station under each driving route section is estimated using a time series prediction model. The historical performance data of the operator base station along each driving route section is obtained; wherein the historical performance data includes base station location, coverage range, time series data of different time periods, and the time series data includes signal strength, signal quality, base station load and user measured throughput; The geographical trajectory of each driving route section is spatially superimposed and analyzed with the coverage range of the operator base station using a geographic information system to determine the base station set associated with each driving route section, and a communication period label is bound for each driving route section according to the law enforcement time window of the adjacent two law enforcement task nodes; The historical performance data of each operator base station corresponding to each driving route section is feature extracted and model training sample constructed, the constructed model training sample is input into a SARIMA model, the bandwidth historical data of each operator base station is trained, and the periodic variation law of the bandwidth is captured; Each operator base station corresponding to each driving route section is input into the trained model to estimate the average bandwidth parameter of each operator base station under each driving route section.

[0011] Optionally, based on the cadastral data amount of each law enforcement task node, the law enforcement time window, and the upper limit value of the communication data amount of each driving section, taking the cadastral data of each law enforcement task being downloaded from the cadastral data core library to the cadastral data edge library in different driving sections as a decision variable, considering the storage capacity of the target law enforcement vehicle, the maximum communication capacity of each driving section, the cadastral data pre-download characteristic, and the cadastral data real-time, a step of generating an optimal cadastral data dynamic update and storage strategy for the land law enforcement task set is generated, specifically including: Based on the cadastral data amount of each law enforcement task node, the law enforcement time window, and the upper limit value of the communication data amount of each driving section, taking the cadastral data of each law enforcement task being downloaded from the cadastral data core library to the cadastral data edge library in different driving sections as a decision variable; Taking the total data amount of the cadastral data received by the cadastral data edge library deployed by the target law enforcement vehicle at the initial time to any time in the target period from the cadastral data core library, subtracting the data amount of the cadastral data deleted after completing the law enforcement task to obtain the cadastral data real-time storage amount, which does not exceed the maximum storage capacity of the cadastral data edge library as the first constraint condition, taking the total data amount of the cadastral data received by the cadastral data edge library from the cadastral data core library in each driving section as the second constraint condition, taking the driving section of the cadastral data of each law enforcement task being downloaded from the cadastral data core library to the cadastral data edge library being located before the corresponding law enforcement task node in the law enforcement driving route map based on the directed graph structure as the third constraint condition; Taking the minimum of the cumulative sum of the difference between the download time of the cadastral data of all law enforcement tasks from the cadastral data core library to the cadastral data edge library and the law enforcement start time of the law enforcement time window of the law enforcement task as the optimization target, using an optimization algorithm to solve the driving section of the cadastral data of each law enforcement task from the cadastral data core library to the cadastral data edge library, and generating an optimal cadastral data dynamic update and storage strategy for the land law enforcement task set.

[0012] Optionally, using the cadastral data dynamic update and storage strategy, controlling the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling in the corresponding driving section, and driving the law enforcement personnel to perform land law enforcement using the mobile terminal locally connected to the target law enforcement vehicle. Using the cadastral data dynamic update and storage strategy, controlling the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling in the corresponding driving section; Driving the law enforcement personnel to perform land law enforcement using the mobile terminal locally connected to the target law enforcement vehicle, and deleting the cadastral data corresponding to each law enforcement task from the cadastral data edge library deployed by the target law enforcement vehicle after completing the land law enforcement of each law enforcement task.

[0013] In addition, in order to achieve the above object, the application further provides a cadastral data dynamic updating and storage device, which comprises: The receiving module is configured to receive a land law enforcement task set of a target law enforcement vehicle, and extract law enforcement content, law enforcement area position and law enforcement time window of a plurality of law enforcement tasks in the land law enforcement task set; wherein the target law enforcement vehicle is deployed with a cadastral data edge library; The constructing module is configured to match cadastral data quantity corresponding to each law enforcement task according to the law enforcement content, generate a driving route of the target law enforcement vehicle according to the law enforcement area position of the plurality of law enforcement tasks, construct a law enforcement driving route map for the plurality of law enforcement tasks, and write the cadastral data quantity corresponding to each law enforcement task and the law enforcement time window into a corresponding law enforcement task node in the law enforcement driving route map. The predicting module is configured to extract a driving section of two adjacent law enforcement task nodes in the law enforcement driving route map, and predict an upper limit value of communication data quantity of each driving section according to historical network bandwidth data. The generating module is configured to generate an optimal cadastral data dynamic updating and storage strategy for the land law enforcement task set based on the cadastral data quantity of each law enforcement task node, the law enforcement time window and the upper limit value of communication data quantity of each driving section, taking cadastral data downlink from a cadastral data core library to the cadastral data edge library in different driving sections as a decision variable, and considering storage capacity of the target law enforcement vehicle, maximum communication capacity of each driving section, cadastral data pre-downlink characteristics and cadastral data real-time performance. The executing module is configured to control the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling on a corresponding driving section by using the cadastral data dynamic updating and storage strategy, and drive law enforcement personnel to perform land law enforcement by using a mobile terminal locally connected to the target law enforcement vehicle.

[0014] In addition, in order to achieve the above object, the application further provides a cadastral data dynamic updating and storage device, which comprises: a memory, a processor and a cadastral data dynamic updating and storage program stored in the memory and executable on the processor, and the cadastral data dynamic updating and storage program realizes the steps of the cadastral data dynamic updating and storage method when executed by the processor.

[0015] In addition, in order to achieve the above object, the application further provides a storage medium, which stores a cadastral data dynamic updating and storage program, and the cadastral data dynamic updating and storage program realizes the steps of the cadastral data dynamic updating and storage method when executed by a processor.

[0016] The beneficial effects of this invention are as follows: It proposes a method, apparatus, device, and storage medium for dynamic updating and storage of cadastral data. By receiving the land enforcement task set of the target enforcement vehicle, it extracts the enforcement content, enforcement area location, and enforcement time window of several enforcement tasks, matches the cadastral data volume corresponding to each enforcement task, generates the driving route of the target enforcement vehicle, and constructs an enforcement driving route map for several enforcement tasks. Taking the distribution of cadastral data of each enforcement task from the cadastral data core library to the cadastral data edge library on different driving segments as decision variables, it considers the storage capacity of the target enforcement vehicle, the maximum communication capability of each driving segment, the cadastral data pre-distribution characteristic, and the real-time nature of the cadastral data, and controls the cadastral data scheduling between the cadastral data edge library and the cadastral data core library to assist law enforcement personnel in carrying out land enforcement. Therefore, this invention, through the full-process design of task parsing, route modeling, bandwidth prediction, strategy optimization, and execution scheduling, adopts task-oriented precise data distribution and dynamic scheduling to achieve dynamic scheduling of edge and core databases, thus eliminating the impact of remote network fluctuations. It also employs a bandwidth prediction and optimization strategy based on spatiotemporal analysis to ensure timely data delivery without premature resource consumption, significantly improving data security levels. Ultimately, it achieves an organic unity of real-time performance, security, and access efficiency of cadastral data, providing efficient and reliable data support for land law enforcement. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention; Figure 2 This is a flowchart illustrating an embodiment of the method for dynamically updating and storing cadastral data according to the present invention. Figure 3 This is a structural block diagram of a cadastral data dynamic update and storage device according to an embodiment of the present invention. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] like Figure 1 As shown, Figure 1 This is a schematic diagram of the device structure of the hardware operating environment involved in the embodiments of the present invention.

[0021] like Figure 1As shown, the apparatus can include a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to realize the connection communication between these components. The user interface 1003 can include a display screen (Display), an input unit such as a keyboard (Keyboard), and the optional user interface 1003 can also include a standard wired interface, a wireless interface. The network interface 1004 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface). The memory 1005 can be a high-speed RAM memory, or a stable memory (non-volatile memory) such as a magnetic disk memory. The memory 1005 can also be an optional storage device independent of the aforementioned processor 1001.

[0022] Those skilled in the art can understand that Figure 1 The structure of the apparatus shown in the foregoing embodiments is not a limitation on the apparatus, and the apparatus can include more or fewer components than those shown, or combine certain components, or different component arrangements.

[0023] As Figure 1 As shown, the memory 1005 as a computer storage medium can include an operating system, a network communication module, a user interface module, and a cadastral data dynamic updating and storage program.

[0024] In Figure 1 In the terminal shown, the network interface 1004 is mainly used to connect to a background server and communicate data with the background server; the user interface 1003 is mainly used to connect to a client (user end) and communicate data with the client; and the processor 1001 can be used to call the cadastral data dynamic updating and storage program stored in the memory 1005 and perform the following operations: Receiving a land law enforcement task set of a target law enforcement vehicle, extracting law enforcement content, law enforcement area position, and law enforcement time window of a plurality of law enforcement tasks in the land law enforcement task set; wherein the target law enforcement vehicle is deployed with a cadastral data edge library; According to the law enforcement content, matching cadastral data amount corresponding to each law enforcement task, generating a driving route of the target law enforcement vehicle according to the law enforcement area position of the plurality of law enforcement tasks, constructing a law enforcement driving route map for the plurality of law enforcement tasks, and writing cadastral data amount and law enforcement time window corresponding to each law enforcement task into a corresponding law enforcement task node in the law enforcement driving route map; Extracting a driving route section between two adjacent law enforcement task nodes in the law enforcement driving route map, and predicting an upper limit value of communication data amount of each driving route section according to historical network bandwidth data; Based on cadastral data amount of each law enforcement task node, law enforcement time window and upper limit value of communication data amount of each driving section, cadastral data of each law enforcement task is taken as a decision variable to be issued from cadastral data core library to cadastral data edge library in different driving sections, the storage capacity of the target law enforcement vehicle, the maximum communication capacity of each driving section, the cadastral data pre-issuing characteristic and the cadastral data real-time are considered, and the optimal cadastral data dynamic updating and storage strategy for the land law enforcement task set is generated. By using the cadastral data dynamic updating and storage strategy, the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle are controlled to perform cadastral data scheduling on the corresponding driving section, and the law enforcement personnel are driven to perform land law enforcement by using the mobile terminal locally connected with the target law enforcement vehicle.

[0025] The specific embodiment of the application applied to the device is basically the same as each embodiment of the cadastral data dynamic updating and storage method described below, and will not be repeated here.

[0026] The embodiment of the application provides a cadastral data dynamic updating and storage method, referring to Figure 2 , Figure 2 The flowchart of the cadastral data dynamic updating and storage method embodiment of the application is shown.

[0027] In this embodiment, a cadastral data dynamic updating and storage method comprises the following steps: S100: receiving a land law enforcement task set of a target law enforcement vehicle, extracting law enforcement content, law enforcement area position and law enforcement time window of a plurality of law enforcement tasks in the land law enforcement task set; wherein the target law enforcement vehicle is deployed with a cadastral data edge library; S200: according to the law enforcement content, matching cadastral data amount corresponding to each law enforcement task, generating a driving route of the target law enforcement vehicle according to the law enforcement area position of the plurality of law enforcement tasks, constructing a law enforcement driving route map for the plurality of law enforcement tasks, and writing the cadastral data amount corresponding to each law enforcement task and the law enforcement time window into the corresponding law enforcement task node in the law enforcement driving route map; S300: extracting a driving section of two adjacent law enforcement task nodes in the law enforcement driving route map, and predicting an upper limit value of communication data amount of each driving section according to historical network bandwidth data; S400: based on cadastral data amount of each law enforcement task node, law enforcement time window and upper limit value of communication data amount of each driving section, taking cadastral data of each law enforcement task as a decision variable to be issued from cadastral data core library to cadastral data edge library in different driving sections, considering the storage capacity of the target law enforcement vehicle, the maximum communication capacity of each driving section, the cadastral data pre-issuing characteristic and the cadastral data real-time, and generating the optimal cadastral data dynamic updating and storage strategy for the land law enforcement task set; S500: dynamically updating and storing the cadastral data by using the cadastral data, controlling the cadastral data scheduling of the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle on the corresponding driving section, and driving the law enforcement personnel to perform land law enforcement by using the mobile terminal locally connected to the target law enforcement vehicle.

[0028] It should be noted that in actual application, due to the characteristics of large data volume (including vector data, attribute table, remote sensing image, etc.), high update frequency (such as daily change of land ownership), strict security requirements and the like of cadastral data, the traditional mode has obvious defects. Direct remote access is seriously affected by network delay and unstable bandwidth, especially when law enforcement is carried out in remote areas, network interruption often leads to work interruption; and full data pre-download faces multiple risks such as insufficient device storage space, unguaranteed data real-time performance, data leakage due to device loss and the like. The existing technology is difficult to balance data real-time performance, security and access efficiency, and an innovative dynamic updating and storing scheme is urgently needed.

[0029] In order to solve the above problems, the embodiment receives a land law enforcement task set of a target law enforcement vehicle, extracts law enforcement content, law enforcement area position and law enforcement time window of a plurality of law enforcement tasks, matches cadastral data volume corresponding to each law enforcement task, generates a driving route of the target law enforcement vehicle, and constructs a law enforcement driving route map for the plurality of law enforcement tasks, taking cadastral data of each law enforcement task being downloaded from the cadastral data core library to the cadastral data edge library on different driving sections as a decision variable, considering storage capacity of the target law enforcement vehicle, maximum communication capacity of each driving section, cadastral data pre-delivery characteristics and cadastral data real-time performance, controlling cadastral data scheduling of the cadastral data edge library and the cadastral data core library, and assisting law enforcement personnel in land law enforcement.

[0030] Therefore, the embodiment realizes dynamic scheduling of the edge library and the core library by task-oriented precise data delivery and dynamic through the whole process design of task analysis, route modeling, bandwidth prediction, strategy optimization and execution scheduling, gets rid of the influence of remote network fluctuation, adopts bandwidth prediction and optimization strategy based on space-time analysis, ensures that data is in place in time and resources are not occupied too early, significantly improves data security level, and finally realizes the organic unification of cadastral data real-time performance, security and access efficiency, and provides efficient and reliable data support for land law enforcement.

[0031] In the preferred embodiment, the step of receiving a land law enforcement task set of a target law enforcement vehicle and extracting law enforcement content, law enforcement area position and law enforcement time window of a plurality of law enforcement tasks in the land law enforcement task set specifically includes: S110: receiving a land law enforcement data package issued by a land law enforcement management platform, and analyzing a land law enforcement task set of the target law enforcement vehicle in a target period from the land law enforcement data package; S120: Extract a plurality of task elements of each law enforcement task from the land law enforcement task set; wherein the plurality of task elements include the law enforcement content, the law enforcement area position and the law enforcement time window recorded in different expression forms in each law enforcement task; S130: Convert the plurality of extracted task elements into a unified data format, and establish a task and element mapping table.

[0032] In the embodiment, the land law enforcement data packet transmitted by the land law enforcement management platform is received, all land law enforcement tasks of a specific target law enforcement vehicle in a specified period are parsed from the data packet to form a task set, and then the key elements of each law enforcement task are extracted one by one from the task set. These elements include law enforcement content, law enforcement area position and law enforcement time window recorded in different expression forms such as text, table and coordinate. Finally, the heterogeneous elements extracted are standardized and converted (such as converting "XX village east plot" into latitude and longitude coordinates and integrating scattered table data into structured fields), and a mapping relationship table of each task and corresponding standardized elements is finally established. Thus, for the data packet issued by the land law enforcement management platform, the task set of the target vehicle is parsed, the multi-form task elements are extracted and converted into a unified format, the mapping table of the task and the element is established, the corresponding relationship between the task and the element is clear, the elements are avoided to be omitted or mismatched, and the error of the subsequent process is reduced.

[0033] In the preferred embodiment, according to the law enforcement content, the cadastral data amount corresponding to each law enforcement task is matched, the driving route of the target law enforcement vehicle is generated according to the law enforcement area position of a plurality of law enforcement tasks, the law enforcement driving route map for a plurality of law enforcement tasks is constructed, and the cadastral data amount and the law enforcement time window corresponding to each law enforcement task are written into the corresponding law enforcement task node of the law enforcement driving route map. The steps specifically include: S210: According to the law enforcement type of a plurality of lands in the law enforcement content, the data type required by each law enforcement task and the data storage amount of each data type are queried from the cadastral data core library, and the cadastral data amount corresponding to each law enforcement task is estimated; S220: According to the law enforcement area position of a plurality of law enforcement tasks, a preset optimal driving path planning algorithm is used to generate the driving route of the target law enforcement vehicle; S230: The driving route of the target law enforcement vehicle is converted into a directed graph structure, and a law enforcement driving route map including a plurality of law enforcement task nodes and a driving pointing line between adjacent two law enforcement task nodes is constructed; S240: The cadastral data amount and the law enforcement time window corresponding to each law enforcement task are written into the corresponding law enforcement task node of the law enforcement driving route map as node additional information.

[0034] In this embodiment, first, based on the land law enforcement type in the law enforcement content (such as illegal land occupation handling, homestead right confirmation), the vector data, attribute table, remote sensing image and other data types required by the type task are queried from the cadastral data core library, the storage amount of each data type is counted, and then the total cadastral data amount of a single law enforcement task is estimated, the law enforcement area position coordinates of all law enforcement tasks are collected, the preset optimal path planning algorithm such as Dijkstra algorithm is used, the shortest or most efficient driving route of the target law enforcement vehicle connected with all areas is calculated, the driving route is abstracted as a directed graph, each law enforcement task is taken as a node, and the driving path between adjacent tasks is taken as a directed edge, a law enforcement driving route graph is constructed, and finally the estimated cadastral data amount of each task and the standardized law enforcement time window are taken as additional information and associated with the corresponding task node in the route graph.

[0035] Therefore, according to the present application, the data amount is estimated according to the core library query of law enforcement content, the optimal route is generated according to the law enforcement area position, the route graph is constructed by converting into a directed graph structure, the data amount and time window are written into the corresponding task node, the abstracted task data amount and time requirement are materialized into the route node, the space and time dimension reference is provided for subsequent road section data scheduling, at the same time, the optimal route is generated based on the task area, and the data requirement of each node is determined in advance, so that the disconnection between route planning and data preparation is avoided.

[0036] In a preferred embodiment, the driving road section between two adjacent law enforcement task nodes in the law enforcement driving route graph is extracted, and the upper limit value of the communication data amount of each driving road section is predicted according to historical network bandwidth data, which specifically includes: S310: Extract the driving section between two adjacent law enforcement task nodes in the law enforcement driving route graph, and divide the entire driving route of the law enforcement driving route graph into a plurality of continuous driving road sections; S320: According to the historical performance data of the operator base station along each driving road section, the average bandwidth parameter of each operator base station on each driving road section is estimated using a time series prediction model, the driving time length of each operator base station corresponding to the driving communication period is considered, and the upper limit value of the communication data amount of each driving road section is calculated.

[0037] In this embodiment, by traversing the law enforcement driving route map, the entire driving route is divided into several continuous and independent driving sections (such as node A to node B is section 1, and node B to node C is section 2) with adjacent two law enforcement task nodes as demarcation points. Then, the historical performance data of all operator base stations connected by communication along the way in each driving section are collected, including base station location, signal coverage range, signal strength in different time periods, signal quality, base station load, user measured throughput, etc. The historical bandwidth related data of each base station is trained using a time series prediction model to estimate the average bandwidth parameters of each base station in the corresponding period in the section. Finally, the average bandwidth x driving duration is used to calculate the upper limit value of the communication data volume of each base station in the corresponding period in the section, and the upper limit value of the communication data volume of each base station in the corresponding period is accumulated to obtain the upper limit value of the communication data volume of the driving section. Thus, the historical data and time series model prediction are realized to provide a quantitative basis for data distribution allocation, avoid bandwidth insufficient sections in advance, and ensure data transmission in feasible sections.

[0038] On this basis, the average bandwidth parameters of each operator base station in each driving section are estimated using a time series prediction model according to the historical performance data of the operator base stations along each driving section, and the steps specifically include: S321: Obtain the historical performance data of the operator base stations along each driving section; wherein the historical performance data includes base station location, coverage range, time series data in different time periods, and the time series data includes signal strength, signal quality, base station load, and user measured throughput; S322: Use a geographic information system to perform spatial overlay analysis on the geographic track of each driving section and the coverage range of the operator base stations to determine the base station set associated with each driving section, and bind a communication period label to each driving section according to the law enforcement time window of adjacent two law enforcement task nodes; S323: Feature extraction and model training sample construction are performed on the historical performance data of each operator base station corresponding to each driving section, the constructed model training sample is input into a SARIMA model, the bandwidth historical data of each operator base station is trained, and the periodic variation law of the bandwidth is captured; S324: Input the driving communication period of each operator base station of each driving section into the trained model to estimate the average bandwidth parameters of each operator base station in each driving section.

[0039] In practical applications, the following ideas are adopted for the estimation of the average bandwidth parameter: obtain the multi-dimensional historical performance data of all operator base stations along the target driving route, covering spatial dimensions (base station location, coverage range) and time dimensions (signal strength, base station load, user throughput, etc. time series data at different dates and different time periods), then use geographic information system (GIS) to perform spatial overlay operation on the geographic track of the driving route and the coverage range of each base station, select the base station set covering the route and exclude the non-covered base stations, then according to the law enforcement time window of the adjacent task node, calculate the specific communication period of the vehicle passing through the route, bind the corresponding time label for the route (such as route A corresponding to 9:00-9:30), then extract the features (such as periodic features, trend features) of the historical data of the associated base stations, construct model training samples, input the SARIMA model training to capture the daily / weekly periodic variation of the bandwidth, and finally input the communication period of the route into the trained model to output the average bandwidth parameter of each associated base station in the period.

[0040] Therefore, by determining the associated base station and binding the communication period through GIS overlay analysis, training the SARIMA model to capture the bandwidth rule, and inputting the period to estimate the average bandwidth parameter, the spatio-temporal accuracy of bandwidth prediction can be improved, and the dynamic change characteristics of bandwidth can be adapted.

[0041] In the preferred embodiment, based on the cadastral data volume of each law enforcement task node, the law enforcement time window, and the upper limit value of the communication data volume of each driving route, the cadastral data of each law enforcement task is downloaded from the cadastral data core library to the cadastral data edge library as the decision variable, considering the storage capacity of the target law enforcement vehicle, the maximum communication capacity of each driving route, the cadastral data pre-delivery characteristics, and the real-time of cadastral data, an optimal cadastral data dynamic update and storage strategy step for the land law enforcement task set is generated, which specifically includes: S410: based on the cadastral data volume of each law enforcement task node, the law enforcement time window, and the upper limit value of the communication data volume of each driving route, the cadastral data of each law enforcement task is downloaded from the cadastral data core library to the cadastral data edge library as the decision variable; S420: In the cadastral data edge library deployed by the target law enforcement vehicle, the real-time storage amount of cadastral data is obtained by subtracting the data amount of cadastral data deleted after completing the law enforcement task from the total data amount of cadastral data issued by the cadastral data core library at the initial time to any time of the target time period, and the first constraint condition is that the real-time storage amount of cadastral data does not exceed the maximum storage capacity of the cadastral data edge library, the second constraint condition is that the total data amount of cadastral data received by the cadastral data edge library in each driving route from the cadastral data core library does not exceed the upper limit of communication data amount of each driving route, and the third constraint condition is that the driving route of cadastral data of each law enforcement task from the cadastral data core library to the cadastral data edge library is located before the law enforcement task node corresponding to the law enforcement task in the law enforcement driving route map based on the directed graph structure; S430: The optimization algorithm is used to solve the driving route of cadastral data of each law enforcement task from the cadastral data core library to the cadastral data edge library, and the optimal cadastral data dynamic updating and storage strategy for the land law enforcement task set is generated, with the minimum sum of the difference between the issuing time of cadastral data of all law enforcement tasks from the cadastral data core library to the cadastral data edge library and the law enforcement start time of the law enforcement time window as the optimization target.

[0042] In this embodiment, the specific driving route of cadastral data of each law enforcement task from the core library to the edge library (i.e. selecting a route to complete the transmission of the task data) is defined as a decision variable, the storage constraint that the real-time storage amount of the edge library (the total amount of received data from the initial time to any time minus the amount of deleted data) does not exceed the maximum storage capacity, the communication constraint that the total amount of all data issued in a single route does not exceed the upper limit of the communication data amount of the route, and the precedence constraint that the data issuing route must be located before the corresponding task node in the directed edge of the route map are considered, and the sum of the difference between the data issuing time and the data use time of all tasks is minimized (i.e. by reducing the time difference between the data issuing time and the data use time, the real-time of cadastral data used by each law enforcement task is ensured to be higher), and finally an optimization algorithm such as integer programming is used to solve, determine the optimal issuing route of each task data, and form a complete dynamic updating and storage strategy.

[0043] Therefore, by considering the storage constraint, the communication constraint and the precedence constraint, the optimal solution is found among storage, network and real-time, the dynamic balance of multiple targets is realized, the contradictions that cannot be considered in the traditional way are solved, and at the same time, the real-time of data is higher by taking the minimum sum of time difference as the target, and the situation of law enforcement judgment error caused by different data in the land law enforcement process is avoided.

[0044] In the preferred embodiment, the cadastral data dynamic updating and storage strategy is used to control the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling on the corresponding driving route, and to drive the law enforcement personnel to perform land law enforcement steps by using the mobile terminal locally connected to the target law enforcement vehicle, specifically including: S510: The cadastral data dynamic updating and storage strategy is used to control the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling on the corresponding driving route; S520: The law enforcement personnel are driven to perform land law enforcement by using the mobile terminal locally connected to the target law enforcement vehicle, and after completing the land law enforcement of each law enforcement task, the cadastral data corresponding to the law enforcement task is deleted from the cadastral data edge library deployed by the target law enforcement vehicle.

[0045] In the embodiment, the edge library automatically establishes a connection with the core library on the specified driving route according to the generated optimal strategy, starts data transmission scheduling, and ensures that each task data is received in the corresponding route. In the law enforcement process, the law enforcement personnel operate the mobile terminal (such as a tablet computer or a mobile phone) locally connected to the vehicle edge library, directly call the current task cadastral data stored in the edge library, and carry out on-site law enforcement verification. After completing each law enforcement task, the system automatically deletes the cadastral data corresponding to the task from the edge library to release the storage space for subsequent task data. Thus, the continuity and efficiency of law enforcement are ensured, the work is not stopped due to network interruption, the data is deleted after the task is completed, the data retention time in the equipment is reduced, the data leakage risk caused by equipment loss or theft is reduced, and the data security is strengthened.

[0046] Referring to Figure 3 , Figure 3 is a structural block diagram of the cadastral data dynamic updating and storage device embodiment of the application.

[0047] As Figure 3 shown, the cadastral data dynamic updating and storage device provided by the embodiment of the application comprises: A receiving module 10 is configured to receive a set of land law enforcement tasks of a target law enforcement vehicle, and extract the law enforcement content, the law enforcement area position, and the law enforcement time window of a plurality of law enforcement tasks in the set of land law enforcement tasks. The target law enforcement vehicle is deployed with a cadastral data edge library. A construction module 20 is configured to match the cadastral data amount corresponding to each law enforcement task according to the law enforcement content, generate a driving route of the target law enforcement vehicle according to the law enforcement area positions of the plurality of law enforcement tasks, construct a law enforcement driving route map for the plurality of law enforcement tasks, and write the cadastral data amount and the law enforcement time window corresponding to each law enforcement task into the corresponding law enforcement task node in the law enforcement driving route map. The prediction module 30 is configured to extract a driving section between two adjacent law enforcement task nodes in the law enforcement driving route map, and predict an upper limit value of a communication data volume of each driving section according to historical network bandwidth data. The generation module 40 is configured to generate an optimal cadastral data dynamic update and storage strategy for the set of land law enforcement tasks based on cadastral data volume of each law enforcement task node, a law enforcement time window, and the upper limit value of the communication data volume of each driving section, taking the storage capacity of the target law enforcement vehicle, the maximum communication capacity of each driving section, the cadastral data pre-delivery characteristic, and the cadastral data real-time performance into consideration, and taking cadastral data in each law enforcement task node being delivered from a cadastral data core library to a cadastral data edge library in different driving sections as a decision variable. The execution module 50 is configured to control the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling in the corresponding driving section by using the cadastral data dynamic update and storage strategy, and drive the law enforcement personnel to perform land law enforcement by using a mobile terminal locally connected to the target law enforcement vehicle.

[0048] Other embodiments or specific implementations of the cadastral data dynamic update and storage device can refer to the above-mentioned method embodiments, and will not be described here.

[0049] In addition, the present application also provides a cadastral data dynamic update and storage device, which comprises a memory, a processor, and a cadastral data dynamic update and storage program stored in the memory and executable on the processor. When the cadastral data dynamic update and storage program is executed by the processor, the steps of the cadastral data dynamic update and storage method described above are implemented.

[0050] The specific implementation of the cadastral data dynamic update and storage device of the present application is basically the same as the above-mentioned cadastral data dynamic update and storage method, and will not be described here.

[0051] In addition, the present application also provides a readable storage medium, which comprises a computer readable storage medium, and a cadastral data dynamic update and storage program is stored on the computer readable storage medium. The readable storage medium can be a memory 1005 in a terminal, and can also be at least one of a ROM (Read-Only Memory) / RAM (Random Access Memory), a magnetic disk, and an optical disk. The readable storage medium comprises a plurality of instructions for causing a cadastral data dynamic update and storage device with a processor to execute the cadastral data dynamic update and storage method described in each embodiment of the present application. Figure 1

[0052] ​The specific embodiments in the readable storage medium of the present application are basically the same as the above-mentioned cadastral data dynamic updating and storing method, and will not be described here.

[0053] It is to be understood that the description in the specification refers to the description of the terms "one embodiment", "another embodiment", "other embodiments", or "first embodiment to Nth embodiment" and the like, which means that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0054] It should be noted that in this paper, the term "include", "contain" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, article or system. Without more limitations, the element defined by the statement "including a" does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0055] The above-mentioned embodiment number of the present application is only for description, not representing the advantages and disadvantages of the embodiments.

[0056] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment method can be realized by software and necessary general hardware platform, of course, it can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, including a plurality of instructions for making a terminal device (which can be a mobile phone, computer, server, or network device, etc.) execute the method described in each embodiment of the present application.

[0057] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation made by using the content of the specification and drawings, or directly or indirectly applied to other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for dynamic updating and storing cadastral data, characterized in that, The method comprises the following steps: Receiving a land law enforcement task set of a target law enforcement vehicle, extracting law enforcement content, law enforcement area position and law enforcement time window of a plurality of law enforcement tasks in the land law enforcement task set; wherein the target law enforcement vehicle is deployed with a cadastral data edge library; According to the law enforcement content, matching cadastral data amount corresponding to each law enforcement task, generating driving route of the target law enforcement vehicle according to law enforcement area position of a plurality of law enforcement tasks, constructing law enforcement driving route map for a plurality of law enforcement tasks, and writing cadastral data amount and law enforcement time window corresponding to each law enforcement task into corresponding law enforcement task node in the law enforcement driving route map; Extracting driving route section of adjacent two law enforcement task nodes in the law enforcement driving route map, and predicting upper limit value of communication data amount of each driving route section according to historical network bandwidth data; Based on cadastral data amount of each law enforcement task node, law enforcement time window and upper limit value of communication data amount of each driving route section, taking cadastral data of each law enforcement task from cadastral data core library to cadastral data edge library in different driving route sections as decision variable, considering storage capacity of the target law enforcement vehicle, maximum communication capacity of each driving route section, cadastral data pre-delivery characteristics and cadastral data real-time, generating optimal cadastral data dynamic update and storage strategy for the land law enforcement task set; Using the cadastral data dynamic update and storage strategy, controlling cadastral data edge library and cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling in the corresponding driving route section, and driving law enforcement personnel to perform land law enforcement by using a mobile terminal locally connected to the target law enforcement vehicle.

2. The method for dynamic updating and storing cadastral data according to claim 1, wherein, The method comprises the following steps: Receiving a land law enforcement task set of a target law enforcement vehicle, extracting law enforcement content, law enforcement area position and law enforcement time window of a plurality of law enforcement tasks in the land law enforcement task set; wherein the target law enforcement vehicle is deployed with a cadastral data edge library; Receiving land law enforcement data packet issued by a land law enforcement management platform, and parsing land law enforcement task set of the target law enforcement vehicle in a target period from the land law enforcement data packet; Extracting a plurality of task elements of each law enforcement task from the land law enforcement task set; wherein the plurality of task elements include a set of law enforcement content, law enforcement area position and law enforcement time window recorded in each law enforcement task in different expression forms; 3. The method for dynamic updating and storing cadastral data according to claim 1, wherein, Converting the extracted plurality of task elements into a unified data format, and establishing a task and element mapping table. The method comprises the following steps: According to the law enforcement content, matching cadastral data amount corresponding to each law enforcement task, generating driving route of the target law enforcement vehicle according to law enforcement area position of a plurality of law enforcement tasks, constructing law enforcement driving route map for a plurality of law enforcement tasks, and writing cadastral data amount and law enforcement time window corresponding to each law enforcement task into corresponding law enforcement task node in the law enforcement driving route map; According to the law enforcement content, matching cadastral data amount corresponding to each law enforcement task, generating driving route of the target law enforcement vehicle according to law enforcement area position of a plurality of law enforcement tasks, constructing law enforcement driving route map for a plurality of law enforcement tasks, and writing cadastral data amount and law enforcement time window corresponding to each law enforcement task into corresponding law enforcement task node in the law enforcement driving route map; According to the law enforcement content, matching cadastral data amount corresponding to each law enforcement task, generating driving route of the target law enforcement vehicle according to law enforcement area position of a plurality of law enforcement tasks, constructing law enforcement driving route map for a plurality of law enforcement tasks, and writing cadastral data amount and law enforcement time window corresponding to each law enforcement task into corresponding law enforcement task node in the law enforcement driving route map; The driving route of the target law enforcement vehicle is converted into a directed graph structure, and a law enforcement driving route graph containing a plurality of law enforcement task nodes and driving direction lines between adjacent two law enforcement task nodes is constructed; The cadastral data amount and the law enforcement time window corresponding to each law enforcement task are written into the corresponding law enforcement task node of the law enforcement driving route graph as node additional information.

4. The method for dynamic updating and storing cadastral data according to claim 1, wherein, The driving road section of the adjacent two law enforcement task nodes in the law enforcement driving route graph is extracted, and the upper limit value of the communication data amount of each driving road section is predicted according to historical network bandwidth data, and the specific steps include: The driving section between the adjacent two law enforcement task nodes in the law enforcement driving route graph is extracted, and the entire driving route of the law enforcement driving route graph is divided into a plurality of continuous driving road sections; According to the historical performance data of the operator base station along each driving road section, the average bandwidth parameter of each operator base station under each driving road section is estimated using a time series prediction model, and the driving time length of each operator base station corresponding to the driving communication period is considered, and the upper limit value of the communication data amount of each driving road section is calculated.

5. The method for dynamic updating and storing cadastral data according to claim 4, wherein, According to the historical performance data of the operator base station along each driving road section, the average bandwidth parameter of each operator base station under each driving road section is estimated using a time series prediction model, and the specific steps include: The historical performance data of the operator base station along each driving road section is obtained; wherein the historical performance data includes base station location, coverage range, time series data in different time periods, the time series data includes signal strength, signal quality, base station load and user measured throughput; The geographical trajectory of each driving road section is spatially superimposed and analyzed with the operator base station coverage range by using a geographic information system, the base station set associated with each driving road section is determined, and a communication period label is bound for each driving road section according to the law enforcement time window of the adjacent two law enforcement task nodes; The historical performance data of each operator base station corresponding to each driving road section is feature extracted and model training sample constructed, the constructed model training sample is input into a SARIMA model, the bandwidth historical data of each operator base station is trained, and the periodic change rule of the bandwidth is captured; The average bandwidth parameter of each operator base station under each driving road section is estimated by inputting the driving communication period corresponding to each operator base station of each driving road section into the trained model.

6. The method for dynamic updating and storing cadastral data according to claim 1, wherein, Based on the cadastral data amount, the law enforcement time window of each law enforcement task node, and the upper limit value of the communication data amount of each driving road section, the cadastral data of each law enforcement task is downloaded from the cadastral data core library to the cadastral data edge library in different driving road sections as the decision variable, the storage capacity of the target law enforcement vehicle, the maximum communication capacity of each driving road section, the cadastral data pre-delivery characteristic and the cadastral data real-time are considered, and the optimal cadastral data dynamic update and storage strategy for the land law enforcement task set is generated, and the specific steps include: Based on the cadastral data amount, the law enforcement time window of each law enforcement task node, and the upper limit value of the communication data amount of each driving road section, the cadastral data of each law enforcement task is downloaded from the cadastral data core library to the cadastral data edge library in different driving road sections as the decision variable; The first constraint condition is that the real-time storage amount of cadastral data obtained by subtracting the amount of cadastral data deleted after a law enforcement task is completed from the total amount of cadastral data issued by the cadastral data core library at an initial time to an arbitrary time of a target time period in the cadastral data edge library deployed by a target law enforcement vehicle does not exceed the maximum storage capacity of the cadastral data edge library; the second constraint condition is that the total amount of cadastral data received by the cadastral data edge library in each driving section from the cadastral data core library does not exceed the upper limit of communication data amount of each driving section; and the third constraint condition is that the driving section of cadastral data of each law enforcement task from the cadastral data core library to the cadastral data edge library is located before the law enforcement task node corresponding to the law enforcement task in the law enforcement driving route map based on a directed graph structure. The optimization algorithm is used to solve the driving section of cadastral data of each law enforcement task from the cadastral data core library to the cadastral data edge library, and an optimal cadastral data dynamic updating and storage strategy for the set of land law enforcement tasks is generated.

7. The method for dynamic updating and storing cadastral data according to claim 1, wherein, The cadastral data dynamic updating and storage strategy is used to control the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling in the corresponding driving section, and to drive the law enforcement personnel to perform land law enforcement steps by using the mobile terminal locally connected to the target law enforcement vehicle, specifically including: The cadastral data dynamic updating and storage strategy is used to control the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling in the corresponding driving section; The cadastral data dynamic updating and storage strategy is used to control the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling in the corresponding driving section; 8. A device for dynamic updating and storing cadastral data, characterized in that, The cadastral data dynamic updating and storage strategy is used to control the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling in the corresponding driving section; The receiving module is configured to receive a set of land law enforcement tasks of a target law enforcement vehicle, and extract law enforcement contents, law enforcement area positions, and law enforcement time windows of a plurality of law enforcement tasks in the set of land law enforcement tasks; wherein the target law enforcement vehicle is deployed with a cadastral data edge library; The construction module is configured to match a cadastral data amount corresponding to each law enforcement task according to the law enforcement contents, generate a driving route of the target law enforcement vehicle according to the law enforcement area positions of the plurality of law enforcement tasks, construct a law enforcement driving route map for the plurality of law enforcement tasks, and write the cadastral data amount corresponding to each law enforcement task and the law enforcement time window into a corresponding law enforcement task node in the law enforcement driving route map; The prediction module is configured to extract a driving section of two adjacent law enforcement task nodes in the law enforcement driving route map, and predict an upper limit of communication data amount of each driving section according to historical network bandwidth data. The generating module is configured to generate, based on the cadastral data amount of each law enforcement task node, a law enforcement time window, and a communication data amount upper limit value of each driving section, a cadastral data dynamic update and storage strategy for a set of land law enforcement tasks, taking cadastral data in each law enforcement task being downloaded from a cadastral data core library to a cadastral data edge library in different driving sections as a decision variable, and considering a storage capacity of a target law enforcement vehicle, a maximum communication capacity of each driving section, a cadastral data pre-delivery characteristic, and cadastral data real-time performance. The executing module is configured to control the cadastral data edge library and the cadastral data core library deployed by the target law enforcement vehicle to perform cadastral data scheduling on a corresponding driving section by using the cadastral data dynamic update and storage strategy, and drive a law enforcement personnel to perform land law enforcement by using a mobile terminal locally connected to the target law enforcement vehicle.

9. A cadastral data dynamic updating and storage device, characterized in that, The cadastral data dynamic update and storage device includes a memory, a processor, and a cadastral data dynamic update and storage program stored on the memory and executable on the processor, and the cadastral data dynamic update and storage program, when executed by the processor, implements the steps of the cadastral data dynamic update and storage method according to any one of claims 1 to 7.

10. A storage medium, characterized by The storage medium has a cadastral data dynamic update and storage program stored thereon, and the cadastral data dynamic update and storage program, when executed by the processor, implements the steps of the cadastral data dynamic update and storage method according to any one of claims 1 to 7.

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