Geographic information system (GIS) and big data fused territorial space planning implementation method and system

Abstract

The invention provides a territorial space planning implementation method and system fusing GIS and big data, and belongs to the technical field of territorial space planning. The method comprises the following steps: acquiring a planning implementation graph and a planning implementation period; based on the planning implementation diagram, a historical planning implementation diagram database and a preset Jaccard coefficient algorithm, historical implementation guidance information is determined, a prefabricated planning implementation guidance matrix is generated according to the planning implementation period, and planning implementation plan items are determined based on the prefabricated planning implementation guidance matrix and a preset planning time window and sent to the user terminal; determining a guidance implementation evaluation value corresponding to planning implementation based on the prefabricated planning implementation guidance matrix and an implementation acquisition image from the monitoring terminal, and updating a planning implementation plan item or generating to-be-treated risk information according to the guidance implementation evaluation value; and under the condition that the to-be-treated risk information is generated, determining second guidance text information through the implementation guidance model, and updating the second guidance text information to a plan implementation item.

Description

Technical Field

[0001] The present application relates to the technical field of national land space planning, and in particular to a national land space planning implementation method and system integrating GIS and big data. Background Art

[0002] In the field of national space planning, traditional methods often rely on manual review and judgment, which makes it difficult to efficiently and accurately predict and respond to various problems that may be encountered during the implementation of the plan. With the rapid development of Geographic Information System (GIS) and big data technology, new technical means and solutions have been provided for the implementation of national space planning.

[0003] However, although some existing technologies have attempted to apply GIS data to the implementation of national spatial planning, these methods are usually limited to simple spatial location annotation and attribute information query, and lack in-depth mining and utilization of historical planning implementation data. At the same time, these methods have also failed to effectively combine the advantages of big data technology to achieve comprehensive and dynamic monitoring and evaluation of the planning implementation process.

[0004] In addition, the traditional planning implementation guidance method often relies on experienced experts or teams to obtain implementation guidance through manual analysis of planning implementation diagrams and implementation schedules. This method is not only inefficient, but also easily affected by human factors, resulting in the inability to guarantee the accuracy and reliability of guidance information.

[0005] Based on this, how to use GIS technology and big data technology to efficiently, flexibly and intelligently assist the implementation of national land space planning has become a technical problem that needs to be solved urgently. Summary of the invention

[0006] The embodiments of the present application provide a method and system for implementing land and space planning that integrates GIS and big data, which is used to solve the technical problems existing in the current implementation of land and space planning, such as excessive reliance on manual labor, low intelligence level and planning implementation efficiency, insufficient flexibility and accuracy in planning implementation, and the resulting difficulty in ensuring implementation results and poor user experience.

[0007] On the one hand, the embodiment of the present application provides a land space planning implementation method integrating GIS and big data, and the method is applied to a building implementation preview platform; the method includes:

[0008] Obtaining a planning implementation map of the national land space planning and its corresponding planning implementation period; wherein the planning implementation map includes at least the following GIS data: spatial coordinates, land use nature, and building intensity;

[0009] Based on the planning implementation map, the historical planning implementation map database and the preset Jaccard coefficient algorithm, the corresponding historical implementation guidance information is determined; wherein the historical planning implementation map database includes a plurality of historical planning implementation maps and their corresponding implementation guidance text sets;

[0010] According to the historical implementation guidance information and the planning implementation period, a prefabricated planning implementation guidance matrix is ​​generated to determine planning implementation plan items based on the prefabricated planning implementation guidance matrix and the preset planning time window, and send it to the user terminal; wherein the prefabricated planning implementation guidance matrix includes first guidance text information of each building implementation unit corresponding to the planning implementation diagram in different planning implementation periods;

[0011] Based on the prefabricated planning implementation guidance matrix and the implementation acquisition images from the monitoring terminal, determine the guidance implementation evaluation value corresponding to the planning implementation, so as to update the planning implementation plan items or generate risk information to be managed according to the guidance implementation evaluation value;

[0012] When the risk information to be managed is generated, the implementation acquisition image is input into a pre-trained implementation guidance model to determine the corresponding second guidance text information and update it to the planning implementation plan items.

[0013] In one implementation of the present application, based on the planning implementation map, the historical planning implementation map database and the preset Jaccard coefficient algorithm, the corresponding historical implementation guidance information is determined, specifically including:

[0014] According to the preset spatial division attributes, the planning implementation map is divided into several types of spatial units; wherein the preset spatial division attributes at least include residential land, commercial land, industrial land, transportation network, and green space system; one type of the spatial unit corresponds to one of the preset spatial division attributes and the corresponding unique spatial coordinates;

[0015] Encoding the spatial units of the same type into first vectors respectively, so as to determine the first vectors respectively corresponding to the preset spatial division attributes;

[0016] According to the preset Jaccard coefficient algorithm and the historical planning implementation map database, the Jaccard coefficients of the second vectors corresponding to the historical planning implementation map of each first vector are calculated on the cloud server to generate a set of Jaccard coefficients corresponding to each type of the spatial unit and the corresponding historical planning implementation map; wherein the second vector is a spatial unit coding vector extracted from the historical planning implementation map and corresponding to the first vector;

[0017] According to each of the Jaccard coefficient sets and the preset weight sets corresponding to each of the preset space division attributes, weighted normalization processing is performed on each of the Jaccard coefficients to determine the planning similarity between the planning implementation map and each of the historical planning implementation maps;

[0018] When the planning similarity is greater than a preset similarity threshold, the corresponding one or more historical planning implementation diagrams are used as similar planning implementation diagrams, and the implementation guidance text set corresponding to the similar planning implementation diagrams is used as the historical implementation guidance information.

[0019] In one implementation of the present application, a prefabricated planning implementation guidance matrix is ​​generated according to the historical implementation guidance information and the planning implementation period, specifically including:

[0020] Performing cluster analysis on each historical space unit of each historical implementation guidance text in the historical implementation guidance information and the POI distribution information within a preset range corresponding to each historical space unit as cluster features, so as to divide each historical space unit into a corresponding cluster cluster according to the clustering result;

[0021] Compare the feature vectors corresponding to each of the spatial units with each of the clusters to determine each of the building implementation units according to a first comparison result; wherein the feature vectors at least include the preset spatial division attributes and the corresponding POI distribution information; and one of the building implementation units includes one or more of the spatial units corresponding to the same cluster;

[0022] Based on the clusters and the preset implementation period comparison table, each of the historical implementation guidance texts is divided into a historical implementation guidance text sequence group; one of the historical implementation guidance text sequence groups includes a first guidance text group of the same building implementation unit at different planning implementation period stages; the first guidance text group includes one or more of the first guidance texts corresponding to the preset guidance dimension;

[0023] The prefabricated planning implementation guidance matrix is ​​constructed according to each of the historical implementation guidance text sequence groups; wherein the behavior of the prefabricated planning implementation guidance matrix is ​​the building implementation unit, which is listed as the planning implementation period stage, and the matrix elements represent the first guidance text information of the building implementation unit at the planning implementation period stage.

[0024] In one implementation of the present application, the planning implementation plan items are determined based on the prefabricated planning implementation guidance matrix and the preset planning time window, specifically including:

[0025] In response to the preset planning time window or the plan making instruction from the user terminal, determining the corresponding first guidance text group according to the correspondence between the preset planning time window and the planning implementation period;

[0026] The first guidance text group is sent to the cloud server, so that the cloud server adjusts the first guidance text group according to the big data prediction model and the corresponding preset guidance dimension change data and the expert preset adjustment coefficient, and obtains the first revised guidance text group, so as to add each first revised guidance text in the first revised guidance text group to the planning implementation plan in chronological order.

[0027] In one implementation of the present application, based on the prefabricated planning implementation guidance matrix and the implementation acquisition image from the monitoring terminal, determining the guidance implementation evaluation value corresponding to the planning implementation specifically includes:

[0028] After receiving the implementation acquisition image from the monitoring terminal, feature extraction is performed on the implementation acquisition image through a preset image recognition model to determine corresponding information of each planning implementation object; wherein the planning implementation object information includes a guidance implementation state representing a corresponding preset guidance dimension; the corresponding preset guidance dimension is obtained based on identifying the implementation acquisition image;

[0029] Matching the planning implementation object information with the prefabricated planning implementation guidance matrix to determine a corresponding guidance implementation difference value; wherein the guidance implementation difference value is obtained based on quantifying the similarity between the guidance implementation state and the corresponding first guidance text information;

[0030] The guidance implementation evaluation value is calculated based on the evaluation weights of each of the preset guidance dimensions and each of the guidance implementation difference values.

[0031] In one implementation of the present application, the monitoring terminal includes at least an image acquisition device at the planning implementation site, an aerial photography drone, and a user terminal within the buffer zone analysis area of ​​each of the building implementation units.

[0032] In one implementation of the present application, updating the planning implementation plan items or generating risk information to be managed according to the guidance implementation evaluation value specifically includes:

[0033] Matching the guidance implementation evaluation value with a preset evaluation value threshold interval [T1, T2];

[0034] When the guidance implementation evaluation value is less than or equal to T1, updating the implemented mark in the planning implementation plan; wherein the implemented mark is a mark indicating that the corresponding preset guidance dimension has been guided and implemented according to the first guidance text information;

[0035] When the guidance implementation evaluation value is greater than T1 and less than T2, updating the reminder implementation mark in the planning implementation plan item so as to remind the user to implement the planning implementation plan item;

[0036] When the guidance implementation evaluation value is greater than or equal to T2, the implementation acquisition image is sent to the user terminal to generate the risk information to be managed based on the selection operation from the user terminal.

[0037] In one implementation of the present application, the implementation acquisition image is input into a pre-trained implementation guidance model to determine the corresponding second guidance text information and update it to the planning implementation plan items, specifically including:

[0038] Determining one or more historical monitoring images corresponding to the collection location according to the collection location of the image collection;

[0039] Comparing the implemented acquisition image with the background features of each of the historical monitoring images respectively, to determine whether the first comparison image exists according to the second comparison result;

[0040] If so, determining a corresponding second comparison image in the digital twin model of the building implementation preview stage according to the spatial position coordinates of the first comparison image;

[0041] Inputting the implementation acquisition image and the second comparison image into the pre-trained implementation guidance model to determine corresponding implementation deviation information according to the model output result; wherein the implementation deviation information includes deviation values ​​of one or more preset guidance dimensions;

[0042] According to the implementation deviation information and the preset implementation suggestion text set, the corresponding implementation deviation guidance text information is determined, so as to perform simulation rehearsal and optimize the implementation deviation guidance text information through the digital twin model and the implementation deviation guidance text information until the deviation meets the preset conditions, and the implementation deviation guidance text information is used as the second guidance text information, updated to the planning implementation plan matters, and a reminder implementation mark is added.

[0043] In one implementation of the present application, after the corresponding second guidance text information is determined and updated to the planning implementation plan items, the method further includes:

[0044] Accumulating the occurrence frequency of the second guidance text information;

[0045] When the occurrence frequency is greater than a preset threshold, generating implementation warning information, and sending the implementation warning information to the user terminal; the implementation warning information includes at least text information and sound information;

[0046] And storing the second guidance text information and the corresponding planning implementation map in the historical planning implementation map database.

[0047] On the other hand, the embodiment of the present application also provides a national land space planning implementation system integrating GIS and big data, the system comprising:

[0048] An acquisition module is used to acquire a planning implementation map of national land space planning and its corresponding planning implementation period; wherein the planning implementation map includes at least the following GIS data: spatial coordinates, land use nature, and building intensity;

[0049] A first determination module is used to determine corresponding historical implementation guidance information based on the planning implementation map, the historical planning implementation map database and a preset Jaccard coefficient algorithm; wherein the historical planning implementation map database includes a plurality of historical planning implementation maps and their corresponding implementation guidance text sets;

[0050] A generation module is used to generate a prefabricated planning implementation guidance matrix according to the historical implementation guidance information and the planning implementation period, so as to determine the planning implementation plan items based on the prefabricated planning implementation guidance matrix and the preset planning time window, and send it to the user terminal; wherein the prefabricated planning implementation guidance matrix includes the first guidance text information of each building implementation unit corresponding to the planning implementation diagram in different planning implementation periods;

[0051] A second determination module is used to determine the guidance implementation evaluation value corresponding to the plan implementation based on the prefabricated plan implementation guidance matrix and the implementation acquisition image from the monitoring terminal, so as to update the plan implementation plan items or generate risk information to be managed according to the guidance implementation evaluation value;

[0052] An input determination module is used to input the implementation acquisition image into a pre-trained implementation guidance model when the risk information to be managed is generated, so as to determine the corresponding second guidance text information and update it to the planning implementation plan items.

[0053] Compared with the prior art, the present invention has the following significant effects:

[0054] Through the above technical solutions, GIS and big data technologies are introduced to automatically process and analyze large amounts of spatial data, reduce manual intervention, and improve the intelligence level and efficiency of planning implementation. At the same time, it is possible to monitor and evaluate changes in the planning implementation process, adjust planning plans in a timely manner, and effectively improve the flexibility and adaptability of planning implementation. In addition, by providing real-time, accurate planning implementation plan information and personalized guidance, user satisfaction and trust can be significantly improved, and user experience can be guaranteed. It solves the current technical problems in the implementation of national land space planning, such as excessive reliance on manual labor, low intelligence level and planning implementation efficiency, insufficient flexibility and accuracy in planning implementation, and the resulting difficulty in ensuring implementation results and poor user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0056] Figure 1 A flowchart of a method for implementing land space planning that integrates GIS and big data in an embodiment of the present application;

[0057] Figure 2 This is a structural diagram of a national land space planning implementation system that integrates GIS and big data in an embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in combination with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present application.

[0059] The embodiments of the present application provide a method and system for implementing land and space planning that integrates GIS and big data, which is used to solve the technical problems existing in the current implementation of land and space planning, such as excessive reliance on manual labor, low intelligence level and planning implementation efficiency, insufficient flexibility and accuracy in planning implementation, and the resulting difficulty in ensuring implementation results and poor user experience.

[0060] The following describes in detail various embodiments of the present application in conjunction with the accompanying drawings.

[0061] The embodiment of the present application provides a method for implementing land space planning by integrating GIS and big data. The method is applied to a building implementation preview platform. The building implementation preview platform can use digital twin technology, digitization, etc. to complete the simulation preview of the implementation process of the land space planning. Users can perform various simulation operations on the platform, such as modifying the design plan, adjusting the construction process, etc., to observe and analyze the impact of these operations on the construction implementation. Figure 1 As shown, the method may include steps S101-S105:

[0062] S101, the server obtains the planning implementation map of the national land space planning and its corresponding planning implementation period.

[0063] Among them, the planning implementation map includes at least the following GIS data: spatial coordinates, land use nature, and building intensity.

[0064] It should be noted that the server, as the executor of the national land space planning implementation method integrating GIS and big data, exists only for exemplary purposes. The executor is not limited to the server, and this application does not make any specific limitations on this.

[0065] The planning implementation map of the national land space planning is a national land space planning implementation map that users are authorized to use for building construction in a certain area or plot. The planning implementation map contains at least GIS data such as spatial coordinates, land use nature, and building intensity. It can also contain other data, such as elevation data, which is not specifically limited in this application. Among them, the spatial coordinates are used to indicate the spatial position of each plot or building, the land use nature indicates the purpose of the plot or building, such as residential, commercial, industrial, etc., and the building intensity indicates the plot ratio, building density, etc. of the plot or building.

[0066] At the same time, the server can also obtain the planning implementation period of the building corresponding to the planning implementation diagram. The planning implementation period is the time range for planning implementation and can be set by the user, including at least the start time and the expected completion time.

[0067] S102, the server determines corresponding historical implementation guidance information based on the planning implementation map, the historical planning implementation map database and the preset Jaccard coefficient algorithm.

[0068] Among them, the historical planning implementation map database includes a number of historical planning implementation maps and their corresponding implementation guidance text sets.

[0069] The historical planning implementation diagram database is connected to the server, which contains several historical planning implementation diagrams and implementation guidance text sets corresponding to the historical planning implementation diagrams. The implementation guidance text set can be constructed by the implementation guidance texts of the historical planning implementation diagrams during the planning implementation process. For example, for a building A, during its planning implementation process, there is an implementation guidance text a for environmental protection management of a certain space unit construction process and an implementation guidance text b for traffic diversion during the construction period of a certain space unit. Then the implementation guidance text set of the historical planning implementation diagram corresponding to the building A is as follows {a, b}.

[0070] In the embodiment of the present application, the above-mentioned determination of corresponding historical implementation guidance information based on the planning implementation map, the historical planning implementation map database and the preset Jaccard coefficient algorithm specifically includes:

[0071] According to the preset spatial division attributes, the planning implementation map is divided into several types of spatial units. Among them, the preset spatial division attributes include at least residential land, commercial land, industrial land, transportation network, and green space system. One type of spatial unit corresponds to a preset spatial division attribute and a corresponding unique spatial coordinate. The spatial units of the same type are respectively encoded into first vectors to determine the first vectors corresponding to the preset spatial division attributes. According to the preset Jaccard coefficient algorithm and the historical planning implementation map database, the Jaccard coefficients of the second vectors corresponding to the historical planning implementation map are calculated on the cloud server respectively, so as to generate a set of Jaccard coefficients corresponding to each type of spatial unit and the corresponding historical planning implementation map. Among them, the second vector is a coding vector of the spatial unit corresponding to the first vector extracted from the historical planning implementation map. According to each Jaccard coefficient set and the preset weight set corresponding to each preset spatial division attribute, each Jaccard coefficient is weighted and normalized to determine the planning similarity between the planning implementation map and each historical planning implementation map. When the planning similarity is greater than a preset similarity threshold, the corresponding one or more historical planning implementation diagrams are used as similar planning implementation diagrams, and the implementation guidance text sets corresponding to the similar planning implementation diagrams are used as historical implementation guidance information.

[0072] In other words, the server can divide each component in the planning implementation map into spatial units according to preset spatial division attributes such as residential land, commercial land, industrial land, transportation network, green space system, etc. Each type of spatial unit corresponds to a preset spatial division attribute and contains a unique spatial coordinate, which is used to identify the position of the spatial unit in the planning implementation map. Subsequently, the server can hash the attribute information (such as spatial coordinates, spatial unit area, etc.) of the spatial units of the same type through a method such as hash function encoding to generate a unique first vector. The first vector includes encoding elements corresponding to the spatial units of the same type, and different first vectors for different preset spatial division attributes, that is, different categories, are obtained. Thus, the spatial units in the planning implementation map are classified in an orderly manner for subsequent processing.

[0073] Subsequently, the server can establish a connection with the cloud server. The cloud server calls each historical planning implementation map in the historical planning implementation map database that has established a network connection, and vector-encodes each historical planning implementation map according to the above encoding method to obtain respective second vectors corresponding to the historical planning implementation maps. The cloud server calculates the Jaccard coefficient between the first vector and the second vector of the same type through a preset Jaccard coefficient algorithm. The formula is as follows:

[0074]

[0075] where is the first vector of the i-th type and the second vector of the i-th type of the j-th historical planning implementation map is the Jaccard coefficient. 0 < i ≤ n, 0 < j ≤ m, and n and m are natural numbers.

[0076] The server adds the Jaccard coefficients of various spatial units and each historical planning implementation map to a set to construct a Jaccard coefficient set. A Jaccard coefficient set contains the Jaccard coefficients between the first vectors of different types of spatial units and the second vectors of the corresponding encoded historical spatial units of their corresponding types. Historical spatial units refer to the spatial units obtained by dividing historical planning implementation maps. The Jaccard coefficient set of the first historical planning implementation map is as follows:

[0077]

[0078] The Jaccard coefficient set of the j-th historical planning implementation map is as follows:

[0079]

[0080] Subsequently, the server will determine a preset weight set stored in advance. The preset weight set contains the weights ω corresponding to different types of spatial unitsi At this time, the server performs weighted normalization processing on a Jaccard coefficient set through the following formula to obtain the planning similarity of the corresponding j-th historical planning implementation diagram:

[0081]

[0082] Among them, S pj is the planning similarity between the planning implementation graph and the jth historical planning implementation graph, ω i To pre-set the weight, it can be determined based on expert scoring, questionnaire survey or data analysis, etc. The planning similarity represents the similarity between the planning implementation map and the historical planning implementation map in terms of the composition and distribution of spatial units.

[0083] After obtaining the similarity of each plan, the server compares the plan similarity with the preset similarity threshold. If the plan similarity is greater than the preset similarity threshold, it is judged that the historical plan implementation diagram corresponding to the plan similarity is highly similar to the plan implementation diagram, and the historical plan implementation diagram is used as a similar plan implementation diagram. At the same time, the implementation guidance text set of the similar plan implementation diagram is associated with the plan implementation diagram as its historical implementation guidance information. The above-mentioned preset similarity threshold is set according to the actual usage scenario, and this application does not make specific restrictions on this.

[0084] Furthermore, by using the above-mentioned planning similarity calculation combined with the Jaccard coefficient, the similarity between implementation diagrams can be measured efficiently and accurately, and the closest historical planning implementation diagram cases can be intelligently matched to extract relevant information of positive and negative experiences (such as successful implementation experience and failed implementation experience) to obtain historical implementation guidance information.

[0085] S103, the server generates a prefabricated planning implementation guidance matrix according to the historical implementation guidance information and the planning implementation period, determines the planning implementation plan items based on the prefabricated planning implementation guidance matrix and the preset planning time window, and sends it to the user terminal.

[0086] The prefabricated planning implementation guidance matrix includes first guidance text information of each building implementation unit corresponding to the planning implementation diagram at different planning implementation periods.

[0087] In the embodiment of the present application, a prefabricated planning implementation guidance matrix is ​​generated according to the historical implementation guidance information and the planning implementation period, specifically including:

[0088] Each historical spatial unit of each historical implementation guidance text in the historical implementation guidance information and the POI distribution information within the preset range corresponding to each of them are used as clustering features for clustering analysis, so as to divide each historical spatial unit into a corresponding cluster cluster according to the clustering result. The feature vector corresponding to each spatial unit is compared with each cluster cluster to determine each building implementation unit according to the first comparison result. Among them, the feature vector at least includes the preset space division attribute and the corresponding POI distribution information. A building implementation unit includes one or more spatial units corresponding to the same cluster cluster. Based on the cluster cluster and the preset implementation period comparison table, each historical implementation guidance text is divided into a historical implementation guidance text sequence group. A historical implementation guidance text sequence group includes the first guidance text group of the same building implementation unit at different planning implementation period stages. The first guidance text group includes one or more first guidance texts corresponding to the preset guidance dimension. According to each historical implementation guidance text sequence group, a prefabricated planning implementation guidance matrix is ​​constructed. Among them, the behavior of the prefabricated planning implementation guidance matrix is ​​the building implementation unit, which is listed as the planning implementation period stage, and the matrix elements represent the first guidance text information of the building implementation unit at the planning implementation period stage.

[0089] That is to say, the present application adopts clustering processing for historical implementation guidance information, so that historical spatial units with similar characteristics are associated. Specifically, the server adopts historical spatial units (such as their spatial attributes: spatial coordinates and area of ​​geographical location, functional attributes: residential, shops, supermarkets) and POI distribution information within a preset range (i.e., POI point of interest distribution information) as clustering features, and performs clustering analysis. Clustering analysis can adopt density-based clustering algorithms (Density-Based Spatial Clustering of Applications with Noise, DBSCAN), K-means algorithm, etc. Through clustering, the server divides each historical spatial unit into different cluster clusters, and then the server can compare the feature vector of the spatial unit with the cluster feature corresponding to the cluster cluster. The comparison can be obtained by calculating the cosine similarity, the inverse of the Euclidean distance, etc. to obtain the first comparison similarity. If the first comparison similarity is greater than the preset first comparison similarity threshold, the server determines that the spatial unit corresponding to the first comparison similarity belongs to the corresponding cluster cluster. Through the above operation, the clusters corresponding to each space unit are obtained, and the space units corresponding to the same cluster are regarded as a building implementation unit. The above preset first comparison similarity threshold is set by the user or expert according to the actual use scenario, and this application does not make any specific limitation on this.

[0090] Subsequently, the server divides the historical implementation guidance texts corresponding to different clusters into historical implementation guidance text sequence groups according to the preset implementation period comparison table. For example, a cluster contains historical space units M1, M2, and M3, where M1 corresponds to historical implementation guidance texts a1 and a2, M2 corresponds to historical implementation guidance texts a2 and a3, and M3 corresponds to historical implementation guidance text a4. The preset implementation period comparison table includes the correspondence between different historical implementation guidance texts and preset implementation period stages, such as a1 and a4 corresponding to implementation period stage one, a2 corresponding to implementation period stage two, and a3 corresponding to implementation period stage three, where implementation period stage one, implementation period stage two, and implementation period stage three can constitute a complete planning implementation period. At this time, the historical implementation guidance text sequence group is [(a1, a4), (a2), (a3)], (a1, a4), (a2), (a3) ​​are respectively a first guidance text group, wherein a1 and a4 can correspond to different guidance dimensions respectively, and the first guidance text group includes one or more first guidance texts corresponding to the preset guidance dimension. It can be understood that each first guidance text has a preset guidance dimension, and the first guidance texts contained in the first guidance text group can correspond to different preset guidance dimensions. The preset guidance dimension is generally understood as traffic dimension, environmental protection dimension, noise dimension, construction risk dimension, etc., and this application does not make specific limitations on this.

[0091] Furthermore, the server divides each historical guidance text corresponding to different clusters through the above steps to obtain each historical implementation guidance text sequence group corresponding to multiple construction implementation units. The server takes different construction implementation units as rows and planning implementation phases as columns, and adds the first guidance text to the prefabrication planning implementation guidance matrix. The first guidance text added to the prefabrication planning implementation guidance matrix can be a code, which refers to the first guidance text information of the desired element position, such as m 11 , indicating the first guidance text information (a1, a4) in the first row and the first column.

[0092] Through the above scheme, the historical implementation guidance information of the historical planning implementation map can be effectively used to build a prefabricated planning implementation guidance matrix that provides detailed and reference guidance for the current planning implementation. At the same time, through information matrixing, the planning implementation guidance information can be clearly and intuitively displayed, which is convenient for users to view, manage and modify.

[0093] Furthermore, the above-mentioned planning implementation plan items determined based on the pre-made planning implementation guidance matrix and the preset planning time window include:

[0094] In response to the preset planning time window or plan formulation instruction from the user terminal, the corresponding first guidance text group is determined according to the correspondence between the preset planning time window and the planning implementation period. The first guidance text group is sent to the cloud server, so that the cloud server adjusts the first guidance text group according to the big data prediction model and the corresponding preset guidance dimension change data and the expert preset adjustment coefficient, and obtains the first revised guidance text group, so as to add each first revised guidance text in the first revised guidance text group to the planning implementation plan items in chronological order.

[0095] That is to say, the server can establish a network connection with the user terminal, which can be understood as the mobile phones, computers and other devices of the participants participating in the implementation of the national land space planning. This application does not make specific restrictions on this. The user terminal can send a preset planning time window to the server in advance, such as a preset planning time window of 1 day, 15 days, 30 days, etc. The preset planning time window is at least less than the planning implementation period, and is used to generate planning implementation plans for the user terminal within the preset planning time window. In addition, the user terminal can also send a preset matter formulation instruction to the server, and generate planning implementation plans according to the instruction. At this time, the server uses the pre-stored default planning time window as the preset planning time window. The server determines the planning implementation period stage it is in based on the preset planning time window, so as to match each first guidance text group corresponding to the preset planning time window in the prefabricated planning implementation guidance matrix. The server sends the determined first guidance text groups to the cloud server, and the cloud server adjusts and corrects the first guidance text group according to the actual situation through the pre-trained big data prediction model, the preset guidance dimension change data, and the expert preset condition coefficient. Specifically, the cloud server adjusts the formula:

[0096] M' xy =M xy +α*P(m xy )

[0097] Among them, M' xy Indicates the first revised guidance text group of the x-th construction implementation unit in the y-th planned implementation period; M xy is the first guidance text group of the x-th construction implementation unit in the y-th planned implementation period; α is the expert preset adjustment coefficient, which can be set according to the actual use scenario and is not specifically limited in this application; P(m xy ) represents the relevant factors m of the x-th construction implementation unit in the y-th planned implementation period obtained by the big data prediction model xy The predicted value, such as construction progress, resource demand, environmental changes, etc., and related factors such as weather conditions, market demand, policy changes, technological progress, etc., affect the generation of planning implementation plans.

[0098] In another embodiment of the present application, when the user terminal sends a preset planning time window or a contingency plan instruction, it can also select a targeted building implementation unit so that the cloud server can generate a first correction guidance text group for the selected building implementation unit and send it to the server.

[0099] After obtaining the first revised guidance text group, it can be arranged in sequence according to the chronological order of the planning implementation period, from top to bottom or from left to right, and added to the planning implementation plan items. The planning implementation plan items can be presented in a table or other form, which is not specifically limited in this application. Subsequently, the server sends the planning implementation plan items to the user terminal in real time for the user to view.

[0100] Through the above plan, the foresight and accuracy of the establishment of planning implementation plans can be improved, and the availability of planning implementation plans can be increased.

[0101] S104, the server determines the guidance implementation evaluation value corresponding to the plan implementation based on the pre-made plan implementation guidance matrix and the implementation acquisition images from the monitoring terminal, so as to update the plan implementation plan items or generate risk information to be managed according to the guidance implementation evaluation value.

[0102] The monitoring terminal at least includes image acquisition equipment, aerial photography drones and user terminals in the buffer zone analysis area of ​​each building implementation unit at the planning implementation site. Image acquisition equipment includes but is not limited to cameras and infrared acquisition equipment. The buffer zone analysis area of ​​the building implementation unit can be understood as an area with a preset width range around the building implementation unit entity. This application does not specifically limit the preset width range.

[0103] In the embodiment of the present application, based on the prefabricated planning implementation guidance matrix and the implementation acquisition image from the monitoring terminal, the guidance implementation evaluation value corresponding to the planning implementation is determined, specifically including:

[0104] After receiving the implementation acquisition image from the monitoring terminal, the implementation acquisition image is feature extracted through the preset image recognition model to determine the corresponding information of each planning implementation object. Among them, the planning implementation object information includes the guidance implementation status that characterizes the corresponding preset guidance dimension. The corresponding preset guidance dimension is obtained based on the recognition of the implementation acquisition image. The planning implementation object information is matched with the prefabricated planning implementation guidance matrix to determine the corresponding guidance implementation difference value. Among them, the guidance implementation difference value is obtained based on the similarity between the quantitative guidance implementation status and the corresponding first guidance text information. According to the evaluation weights of each preset guidance dimension and each guidance implementation difference value, the guidance implementation evaluation value is calculated.

[0105] The monitoring terminal can send implementation acquisition images to the server in real time. Although the server of this application runs a building implementation rehearsal platform, the server can further realize synchronous rehearsal at each stage of the planning and implementation period. Even in the actual construction implementation process, it can also provide timely planning implementation guidance or early warning based on the analysis results of the implementation acquisition images.

[0106] In other words, after receiving the implementation acquisition image collected by the monitoring terminal, the server can use the preset image recognition model to extract features in the image (including object shape, color, position, etc.). The preset image recognition model can be a pre-trained machine learning model or a convolutional neural network CNN model. The training process uses a number of implementation acquisition image samples and planning implementation object information labels until the accuracy of the model output result is greater than the accuracy threshold, and the preset image recognition model is obtained. By extracting features from the implementation acquisition image using the preset image recognition model, the planning implementation image information contained in the implementation acquisition image can be obtained. For example, the implementation acquisition image captures the building and its surrounding environment at location A. The planning implementation object information may include text representations corresponding to the scaffolding erection status of the building, the setting status of the surrounding road guardrails, the operation status of temporary traffic signs, and the environmental dust status. This application does not make specific restrictions on this.

[0107] Subsequently, the server can compare the planning implementation object information with each first guidance text information corresponding to the prefabricated planning implementation guidance matrix. The comparison can be determined according to the preset guidance dimension and the planning implementation period stage to determine the matrix elements corresponding to the guidance implementation status of the planning implementation object information in the prefabricated planning implementation guidance matrix, and the first guidance text information corresponding to the matrix element and the text corresponding to the guidance implementation status are calculated for cosine similarity to obtain the guidance implementation difference value. Among them, before performing the cosine similarity calculation, it is necessary to vector encode the first guidance text information and the text corresponding to the guidance implementation status respectively, and perform cosine similarity calculation based on the feature vector obtained by encoding. The server also pre-stores evaluation weights for different preset guidance dimensions. The evaluation weight value can be set by the user, and this application does not make specific restrictions on this. The guidance implementation evaluation value is calculated by the following formula:

[0108]

[0109] Among them, D is the guiding implementation evaluation value, w z is the evaluation weight of the z-th preset guidance dimension, 1≤z≤r, r is a natural number; U(F z ,F Mz ) is used to calculate the feature vector F of the first guidance text information of the z-th preset guidance dimension Mz The text feature vector F corresponding to the guidance implementation status of the z-th preset guidance dimension zThe similarity function of the similarity between them. The guidance implementation evaluation value is used to characterize the degree of deviation of the planning implementation object information from the prefabricated planning implementation guidance matrix. The more it deviates from the first guidance text of the prefabricated planning implementation guidance matrix, the greater the guidance implementation evaluation value. In layman's terms, if the planning implementation is not carried out in accordance with the first guidance text, the greater the guidance implementation evaluation value.

[0110] Furthermore, the above-mentioned updating of planning implementation plans or generating risk information to be managed based on the guidance and implementation assessment values ​​specifically includes:

[0111] The guided implementation evaluation value is matched with the preset evaluation value threshold interval [T1, T2].

[0112] Wherein, when the guidance implementation evaluation value is less than or equal to T1, the implemented mark in the planning implementation plan is updated. Wherein, the implemented mark is a mark that the guidance implementation is carried out for the corresponding preset guidance dimension according to the first guidance text information.

[0113] When the guidance implementation evaluation value is greater than T1 and less than T2, the reminder implementation mark in the planning implementation plan items is updated to remind the user to implement the planning implementation plan items.

[0114] When the guidance implementation assessment value is greater than or equal to T2, the implementation acquisition image is sent to the user terminal to generate risk information to be managed based on the selection operation from the user terminal.

[0115] That is to say, the server pre-sets a preset evaluation value threshold interval for performing the next operation according to the guidance implementation evaluation value, and the preset evaluation value threshold interval [T1, T2] can be adjusted or set by the user according to the actual use scenario, and this application does not make specific restrictions on this. Among them, when the guidance implementation evaluation value is less than or equal to T1, it means that the plan has been implemented according to the first guidance text. At this time, the server adds an implemented mark to the corresponding guidance text in the planning implementation plan; and when the guidance implementation evaluation value is greater than T1 and less than T2, it means that the plan has not been implemented according to the first guidance text. At this time, the server adds a reminder implementation mark to the corresponding guidance text in the planning implementation plan, and sends a user terminal to remind the user to implement the corresponding matters; when the guidance implementation evaluation value is greater than or equal to T2, it may happen that not only the first guidance text is not planned and implemented, but there are certain risks or hidden dangers, or there are events to be guided that are not in the planning implementation plan. The server sends the implementation acquisition image to the user terminal, and the user can view the implementation acquisition image and select it, such as manually selecting the first guidance text corresponding to the implementation acquisition image, or selecting the option to generate risk information to be managed for the implementation acquisition image. This application does not make specific restrictions on this. Through the above operations, the planning implementation process can be continuously optimized, the implementation effect can be improved, the frequency of manual inspections can be reduced, and the level of intelligence can be improved.

[0116] S105, when generating risk information to be managed, the server inputs the implementation acquisition image into a pre-trained implementation guidance model to determine the corresponding second guidance text information and update it to the planned implementation plan items.

[0117] In the embodiment of the present application, the implementation acquisition image is input into the pre-trained implementation guidance model to determine the corresponding second guidance text information and update it to the planning implementation plan items, specifically including:

[0118] According to the collection location of the implementation collection image, one or more historical monitoring images corresponding to the collection location are determined. The implementation collection image is compared with the background features of each historical monitoring image respectively, so as to determine whether there is a first comparison image according to the second comparison result. In the case of determining that the first comparison image exists, the corresponding second comparison image in the digital twin model of the building implementation preview stage is determined according to the spatial position coordinates of the first comparison image. The implementation collection image and the second comparison image are input into the pre-trained implementation guidance model to determine the corresponding implementation deviation information according to the model output result. Among them, the implementation deviation information includes one or more deviation values ​​of preset guidance dimensions. According to the implementation deviation information and the preset implementation suggestion text set, the corresponding implementation deviation guidance text information is determined, so as to simulate and preview and optimize the implementation deviation guidance text information through the digital twin model and the implementation deviation guidance text information, until the deviation meets the preset conditions, and the implementation deviation guidance text information is used as the second guidance text information, updated to the planning implementation plan matters, and added with a reminder implementation mark.

[0119] That is to say, the implementation acquisition image carries its acquisition location, and the acquisition method of the acquisition location is such as obtained through the positioning of the monitoring terminal, or marked by the user, and this application does not make specific restrictions on this. The preset database connected to the server can store historical monitoring images of several acquisition locations, and the server matches the historical monitoring images at the acquisition location through the acquisition location. Subsequently, the server can also extract the first background features of the implementation acquisition image and the second background features of each historical monitoring image through the neural network model, calculate the similarity of the first background features with each second background feature to obtain the second comparison similarity, and select the historical monitoring image whose second comparison similarity is greater than the second comparison similarity threshold as the first comparison image. If there is no second comparison similarity greater than the second comparison similarity threshold, it is determined that there is no first comparison image. At this time, it means that the implementation acquisition image may not be collected at the corresponding acquisition location, and an alarm message is generated and recorded in the log for subsequent operation and maintenance personnel to view. The second comparison similarity threshold is set by the user according to the actual usage scenario, and this application does not make specific restrictions on this.

[0120] When the server determines that there is a first comparison image in the implementation acquisition image, the server will obtain a second comparison image of the building implementation object from the digital twin model based on the spatial position coordinates of the building implementation object corresponding to the first comparison image. In other words, the server pre-stores the building implementation object (i.e., building implementation unit) corresponding to the first comparison image and its corresponding spatial position coordinates, and can extract the second comparison image of the building implementation preview stage from the digital twin model through the spatial position coordinates. Among them, the first comparison image can be used to verify whether the implementation acquisition image is a real acquisition and match it with the second comparison image.

[0121] Subsequently, the server inputs the implementation acquisition image and the second comparison image into a pre-trained implementation guidance model, which can be a neural network model trained by a number of image pair samples and implementation deviation information labels. The implementation deviation information is obtained through the model results of the implementation guidance model, and the implementation deviation information is a sequence of deviation values ​​of different preset guidance dimensions, such as [0.9, 0.75, 0.6, 0.8...]. The server further matches the implementation deviation guidance text information in the preset implementation suggestion text set through the implementation deviation information, and simulates and rehearses the current planning implementation status of the implementation acquisition image in the digital twin model through the implementation deviation guidance text information, and continuously optimizes the implementation deviation guidance text information according to the changes in the implementation deviation information during the simulation and rehearsal process until the deviation meets the preset conditions, such as each deviation value in the implementation deviation information is less than a predetermined value. At this time, the server uses the obtained implementation deviation guidance text information as the second guidance text information, and updates it to the planning implementation plan items, and adds a reminder implementation mark to facilitate the user to implement the plan.

[0122] In addition, after obtaining the second guidance text information, the present application may also perform corrections through the above-mentioned adjustment and correction steps of the first guidance text group to obtain the second corrected guidance text information, and the present application does not make any specific limitation on this.

[0123] In another embodiment of the present application, after the corresponding second guidance text information is determined and updated to the planning implementation plan items, the method further includes:

[0124] The server accumulates the frequency of occurrence of the second guidance text information. When the frequency of occurrence is greater than a preset threshold, an implementation warning message is generated and sent to the user terminal. The implementation warning message includes at least text information and sound information. And the second guidance text information and the corresponding planning implementation map are stored in the historical planning implementation map database. When the frequency of occurrence is not greater than the preset threshold, only the frequency of occurrence is accumulated.

[0125] In other words, each time the second guidance text information is obtained, the server will accumulate the frequency of occurrence of the second guidance text information. If the frequency of occurrence is high and greater than the preset threshold, the server will generate an implementation warning message to remind the user terminal to check. At the same time, the second guidance text information and the planning implementation map are stored in the historical planning implementation map database for reference in the subsequent implementation of other plans. The preset threshold can be set by the user according to the actual usage scenario, and this application does not make specific restrictions on this.

[0126] Through the above technical solutions, GIS and big data technologies are introduced to automatically process and analyze large amounts of spatial data, reduce manual intervention, and improve the intelligence level and efficiency of planning implementation. At the same time, it is possible to monitor and evaluate changes in the planning implementation process, adjust planning plans in a timely manner, and effectively improve the flexibility and adaptability of planning implementation. In addition, by providing real-time, accurate planning implementation plan information and personalized guidance, user satisfaction and trust can be significantly improved, and user experience can be guaranteed. It solves the current technical problems in the implementation of national land space planning, such as excessive reliance on manual labor, low intelligence level and planning implementation efficiency, insufficient flexibility and accuracy in planning implementation, and the resulting difficulty in ensuring implementation results and poor user experience.

[0127] Figure 2 A structural diagram of a national land space planning implementation system integrating GIS and big data provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the national land space planning implementation system 200 integrating GIS and big data includes:

[0128] The acquisition module 201 is used to obtain the planning implementation map of the national land space planning and its corresponding planning implementation period. The planning implementation map includes at least the following GIS data: spatial coordinates, land use nature, and building intensity. The first determination module 202 is used to determine the corresponding historical implementation guidance information based on the planning implementation map, the historical planning implementation map database, and the preset Jaccard coefficient algorithm. The historical planning implementation map database includes a number of historical planning implementation maps and their corresponding implementation guidance text sets. The generation module 203 is used to generate a prefabricated planning implementation guidance matrix based on the historical implementation guidance information and the planning implementation period, so as to determine the planning implementation plan items based on the prefabricated planning implementation guidance matrix and the preset planning time window, and send it to the user terminal. The prefabricated planning implementation guidance matrix includes the first guidance text information of each building implementation unit corresponding to the planning implementation map in different planning implementation periods. The second determination module 204 is used to determine the guidance implementation evaluation value corresponding to the planning implementation based on the prefabricated planning implementation guidance matrix and the implementation acquisition image from the monitoring terminal, so as to update the planning implementation plan items or generate risk information to be governed according to the guidance implementation evaluation value. The input determination module 205 is used to input the implementation acquisition image into the pre-trained implementation guidance model when generating the risk information to be managed, so as to determine the corresponding second guidance text information and update it to the planned implementation plan items.

[0129] The first determining module 202 is specifically used for:

[0130] According to the preset spatial division attributes, the planning implementation map is divided into several types of spatial units. Among them, the preset spatial division attributes include at least residential land, commercial land, industrial land, transportation network, and green space system. One type of spatial unit corresponds to a preset spatial division attribute and a corresponding unique spatial coordinate. The spatial units of the same type are respectively encoded into first vectors to determine the first vectors corresponding to the preset spatial division attributes. According to the preset Jaccard coefficient algorithm and the historical planning implementation map database, the Jaccard coefficients of the second vectors corresponding to the historical planning implementation map are calculated on the cloud server respectively, so as to generate a set of Jaccard coefficients corresponding to each type of spatial unit and the corresponding historical planning implementation map. Among them, the second vector is a coding vector of the spatial unit corresponding to the first vector extracted from the historical planning implementation map. According to each Jaccard coefficient set and the preset weight set corresponding to each preset spatial division attribute, each Jaccard coefficient is weighted and normalized to determine the planning similarity between the planning implementation map and each historical planning implementation map. When the planning similarity is greater than a preset similarity threshold, the corresponding one or more historical planning implementation diagrams are used as similar planning implementation diagrams, and the implementation guidance text sets corresponding to the similar planning implementation diagrams are used as historical implementation guidance information.

[0131] The generation module 203 is specifically used for:

[0132] Each historical spatial unit of each historical implementation guidance text in the historical implementation guidance information and the POI distribution information within the preset range corresponding to each of them are used as clustering features for clustering analysis, so as to divide each historical spatial unit into a corresponding cluster cluster according to the clustering result. The feature vector corresponding to each spatial unit is compared with each cluster cluster to determine each building implementation unit according to the first comparison result. Among them, the feature vector at least includes the preset space division attribute and the corresponding POI distribution information. A building implementation unit includes one or more spatial units corresponding to the same cluster cluster. Based on the cluster cluster and the preset implementation period comparison table, each historical implementation guidance text is divided into a historical implementation guidance text sequence group. A historical implementation guidance text sequence group includes the first guidance text group of the same building implementation unit at different planning implementation period stages. The first guidance text group includes one or more first guidance texts corresponding to the preset guidance dimension. According to each historical implementation guidance text sequence group, a prefabricated planning implementation guidance matrix is ​​constructed. Among them, the behavior of the prefabricated planning implementation guidance matrix is ​​the building implementation unit, which is listed as the planning implementation period stage, and the matrix elements represent the first guidance text information of the building implementation unit at the planning implementation period stage.

[0133] The generation module 203 is also specifically used for:

[0134] In response to the preset planning time window or plan formulation instruction from the user terminal, the corresponding first guidance text group is determined according to the correspondence between the preset planning time window and the planning implementation period. The first guidance text group is sent to the cloud server, so that the cloud server adjusts the first guidance text group according to the big data prediction model and the corresponding preset guidance dimension change data and the expert preset adjustment coefficient, and obtains the first revised guidance text group, so as to add each first revised guidance text in the first revised guidance text group to the planning implementation plan items in chronological order.

[0135] The second determining module 204 is specifically used for:

[0136] After receiving the implementation acquisition image from the monitoring terminal, the implementation acquisition image is feature extracted through the preset image recognition model to determine the corresponding information of each planning implementation object. Among them, the planning implementation object information includes the guidance implementation status that characterizes the corresponding preset guidance dimension. The corresponding preset guidance dimension is obtained based on the recognition of the implementation acquisition image. The planning implementation object information is matched with the prefabricated planning implementation guidance matrix to determine the corresponding guidance implementation difference value. Among them, the guidance implementation difference value is obtained based on the similarity between the quantitative guidance implementation status and the corresponding first guidance text information. According to the evaluation weights of each preset guidance dimension and each guidance implementation difference value, the guidance implementation evaluation value is calculated.

[0137] The system also includes: the monitoring terminal includes at least image acquisition equipment at the planning implementation site, an aerial photography drone and user terminals in the buffer zone analysis area of ​​each building implementation unit.

[0138] The second determining module 204 is further specifically configured to:

[0139] Match the guidance implementation evaluation value with the preset evaluation value threshold interval [T1, T2]. When the guidance implementation evaluation value is less than or equal to T1, update the implemented mark in the planning implementation plan items. Among them, the implemented mark is a mark that the guidance implementation of the corresponding preset guidance dimension has been carried out according to the first guidance text information. When the guidance implementation evaluation value is greater than T1 and less than T2, update the reminder implementation mark in the planning implementation plan items to remind the user to implement the planning implementation plan items. When the guidance implementation evaluation value is greater than T2, the implementation acquisition image is sent to the user terminal to generate risk information to be managed based on the selection operation from the user terminal.

[0140] The input determination module 205 is specifically used for:

[0141] According to the collection location of the implementation collection image, several historical monitoring images corresponding to the collection location are determined. The implementation collection image is compared with the background features of each historical monitoring image respectively to determine whether the first comparison image exists according to the second comparison result. If so, the corresponding second comparison image in the digital twin model of the building implementation preview stage is determined according to the spatial position coordinates of the first comparison image. The implementation collection image and the second comparison image are input into the pre-trained implementation guidance model to determine the corresponding implementation deviation information according to the model output result. Among them, the implementation deviation information includes one or more deviation values ​​of preset guidance dimensions. According to the implementation deviation information and the preset implementation suggestion text set, the corresponding implementation deviation guidance text information is determined, so as to simulate and preview and optimize the implementation deviation guidance text information through the digital twin model and the implementation deviation guidance text information, until the deviation meets the preset conditions, and the implementation deviation guidance text information is used as the second guidance text information, updated to the planning implementation plan matters, and added with a reminder implementation mark.

[0142] The above system also includes:

[0143] The occurrence frequency of the second guidance text information is accumulated. When the occurrence frequency is greater than a preset threshold, implementation warning information is generated and sent to the user terminal. The implementation warning information includes at least text information and sound information. And the second guidance text information and the corresponding planning implementation map are stored in the historical planning implementation map database.

[0144] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0145] The system and method provided in the embodiment of the present application correspond one to one, and therefore the system also has similar beneficial technical effects as its corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system will not be repeated here.

[0146] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0147] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for implementing national land space planning integrating GIS and big data, characterized in that: The method is applied to a building implementation preview platform; the method comprises: Obtaining a planning implementation map of the national land space planning and its corresponding planning implementation period; wherein the planning implementation map includes at least the following GIS data: spatial coordinates, land use nature, and building intensity; Based on the planning implementation map, the historical planning implementation map database and the preset Jaccard coefficient algorithm, the corresponding historical implementation guidance information is determined; wherein the historical planning implementation map database includes a plurality of historical planning implementation maps and their corresponding implementation guidance text sets; According to the historical implementation guidance information and the planning implementation period, a prefabricated planning implementation guidance matrix is ​​generated to determine planning implementation plan items based on the prefabricated planning implementation guidance matrix and the preset planning time window, and send it to the user terminal; wherein the prefabricated planning implementation guidance matrix includes first guidance text information of each building implementation unit corresponding to the planning implementation diagram in different planning implementation periods; Based on the prefabricated planning implementation guidance matrix and the implementation acquisition images from the monitoring terminal, determine the guidance implementation evaluation value corresponding to the planning implementation, so as to update the planning implementation plan items or generate risk information to be managed according to the guidance implementation evaluation value; When the risk information to be managed is generated, the implementation acquisition image is input into a pre-trained implementation guidance model to determine the corresponding second guidance text information and update it to the planning implementation plan items.

2. The method for implementing land space planning integrating GIS and big data according to claim 1, characterized in that: Based on the planning implementation map, the historical planning implementation map database and the preset Jaccard coefficient algorithm, the corresponding historical implementation guidance information is determined, specifically including: According to the preset spatial division attributes, the planning implementation map is divided into several types of spatial units; wherein the preset spatial division attributes at least include residential land, commercial land, industrial land, transportation network, and green space system; one type of the spatial unit corresponds to one of the preset spatial division attributes and the corresponding unique spatial coordinates; Encoding the spatial units of the same type into first vectors respectively, so as to determine the first vectors respectively corresponding to the preset spatial division attributes; According to the preset Jaccard coefficient algorithm and the historical planning implementation map database, the Jaccard coefficients of the second vectors corresponding to the historical planning implementation map of each first vector are calculated on the cloud server to generate a set of Jaccard coefficients corresponding to each type of the spatial unit and the corresponding historical planning implementation map; wherein the second vector is a spatial unit coding vector extracted from the historical planning implementation map and corresponding to the first vector; According to each of the Jaccard coefficient sets and the preset weight sets corresponding to each of the preset space division attributes, weighted normalization processing is performed on each of the Jaccard coefficients to determine the planning similarity between the planning implementation map and each of the historical planning implementation maps; When the planning similarity is greater than a preset similarity threshold, the corresponding one or more historical planning implementation diagrams are used as similar planning implementation diagrams, and the implementation guidance text set corresponding to the similar planning implementation diagrams is used as the historical implementation guidance information.

3. The method for implementing land space planning integrating GIS and big data according to claim 2 is characterized in that: According to the historical implementation guidance information and the planned implementation period, a prefabricated planning implementation guidance matrix is ​​generated, specifically including: Performing cluster analysis on each historical space unit of each historical implementation guidance text in the historical implementation guidance information and the POI distribution information within a preset range corresponding to each historical space unit as cluster features, so as to divide each historical space unit into a corresponding cluster cluster according to the clustering result; Compare the feature vectors corresponding to each of the spatial units with each of the clusters to determine each of the building implementation units according to a first comparison result; wherein the feature vectors at least include the preset spatial division attributes and the corresponding POI distribution information; and one of the building implementation units includes one or more of the spatial units corresponding to the same cluster; Based on the clusters and the preset implementation period comparison table, each of the historical implementation guidance texts is divided into a historical implementation guidance text sequence group; one of the historical implementation guidance text sequence groups includes a first guidance text group of the same building implementation unit at different planning implementation period stages; the first guidance text group includes one or more of the first guidance texts corresponding to the preset guidance dimension; The prefabricated planning implementation guidance matrix is ​​constructed according to each of the historical implementation guidance text sequence groups; wherein the behavior of the prefabricated planning implementation guidance matrix is ​​the building implementation unit, which is listed as the planning implementation period stage, and the matrix elements represent the first guidance text information of the building implementation unit at the planning implementation period stage.

4. The method for implementing land space planning integrating GIS and big data according to claim 3 is characterized in that: Based on the prefabricated planning implementation guidance matrix and the preset planning time window, the planning implementation plan items are determined, including: In response to the preset planning time window or the plan making instruction from the user terminal, determining the corresponding first guidance text group according to the correspondence between the preset planning time window and the planning implementation period; The first guidance text group is sent to the cloud server, so that the cloud server adjusts the first guidance text group according to the big data prediction model and the corresponding preset guidance dimension change data and the expert preset adjustment coefficient, and obtains the first revised guidance text group, so as to add each first revised guidance text in the first revised guidance text group to the planning implementation plan in chronological order.

5. The method for implementing land space planning integrating GIS and big data according to claim 1 is characterized in that: Based on the prefabricated planning implementation guidance matrix and the implementation acquisition image from the monitoring terminal, determining the guidance implementation evaluation value corresponding to the planning implementation, specifically including: After receiving the implementation acquisition image from the monitoring terminal, feature extraction is performed on the implementation acquisition image through a preset image recognition model to determine corresponding information of each planning implementation object; wherein the planning implementation object information includes a guidance implementation state representing a corresponding preset guidance dimension; the corresponding preset guidance dimension is obtained based on identifying the implementation acquisition image; Matching the planning implementation object information with the prefabricated planning implementation guidance matrix to determine a corresponding guidance implementation difference value; wherein the guidance implementation difference value is obtained based on quantifying the similarity between the guidance implementation state and the corresponding first guidance text information; The guidance implementation evaluation value is calculated based on the evaluation weights of each of the preset guidance dimensions and each of the guidance implementation difference values.

6. The method for implementing land space planning integrating GIS and big data according to claim 5 is characterized in that: The monitoring terminal at least includes an image acquisition device at the planning implementation site, an aerial photography drone, and a user terminal in the buffer zone analysis area of ​​each of the building implementation units.

7. The method for implementing land space planning integrating GIS and big data according to claim 5 is characterized in that: According to the guidance implementation assessment value, the planning implementation plan items are updated or risk information to be managed is generated, specifically including: Matching the guidance implementation evaluation value with a preset evaluation value threshold interval [T1, T2]; When the guidance implementation evaluation value is less than or equal to T1, updating the implemented mark in the planning implementation plan; wherein the implemented mark is a mark indicating that the corresponding preset guidance dimension has been guided and implemented according to the first guidance text information; When the guidance implementation evaluation value is greater than T1 and less than T2, updating the reminder implementation mark in the planning implementation plan item so as to remind the user to implement the planning implementation plan item; When the guidance implementation evaluation value is greater than or equal to T2, the implementation acquisition image is sent to the user terminal to generate the risk information to be managed based on the selection operation from the user terminal.

8. The method for implementing land space planning integrating GIS and big data according to claim 1, characterized in that: The implementation acquisition image is input into a pre-trained implementation guidance model to determine the corresponding second guidance text information and update it to the planning implementation plan items, specifically including: According to the acquisition location of the image acquisition, determining one or more historical monitoring images corresponding to the acquisition location; Comparing the implemented acquisition image with the background features of each of the historical monitoring images respectively, to determine whether the first comparison image exists according to the second comparison result; If so, determining a corresponding second comparison image in the digital twin model of the building implementation preview stage according to the spatial position coordinates of the first comparison image; Inputting the implementation acquisition image and the second comparison image into the pre-trained implementation guidance model to determine corresponding implementation deviation information according to the model output result; wherein the implementation deviation information includes deviation values ​​of one or more preset guidance dimensions; According to the implementation deviation information and the preset implementation suggestion text set, the corresponding implementation deviation guidance text information is determined, so as to perform simulation rehearsal and optimize the implementation deviation guidance text information through the digital twin model and the implementation deviation guidance text information until the deviation meets the preset conditions, and the implementation deviation guidance text information is used as the second guidance text information, updated to the planning implementation plan matters, and a reminder implementation mark is added.

9. The method for implementing land space planning integrating GIS and big data according to claim 1, characterized in that: After determining the corresponding second guidance text information and updating it to the planning implementation plan items, the method further includes: Accumulating the occurrence frequency of the second guidance text information; When the occurrence frequency is greater than a preset threshold, generating implementation warning information, and sending the implementation warning information to the user terminal; the implementation warning information includes at least text information and sound information; And storing the second guidance text information and the corresponding planning implementation map in the historical planning implementation map database.

10. A national land space planning implementation system integrating GIS and big data, characterized in that: The system comprises: An acquisition module is used to acquire a planning implementation map of national land space planning and its corresponding planning implementation period; wherein the planning implementation map includes at least the following GIS data: spatial coordinates, land use nature, and building intensity; A first determination module is used to determine corresponding historical implementation guidance information based on the planning implementation map, the historical planning implementation map database and a preset Jaccard coefficient algorithm; wherein the historical planning implementation map database includes a plurality of historical planning implementation maps and their corresponding implementation guidance text sets; A generation module is used to generate a prefabricated planning implementation guidance matrix according to the historical implementation guidance information and the planning implementation period, so as to determine the planning implementation plan items based on the prefabricated planning implementation guidance matrix and the preset planning time window, and send it to the user terminal; wherein the prefabricated planning implementation guidance matrix includes the first guidance text information of each building implementation unit corresponding to the planning implementation diagram in different planning implementation periods; A second determination module is used to determine the guidance implementation evaluation value corresponding to the plan implementation based on the prefabricated plan implementation guidance matrix and the implementation acquisition image from the monitoring terminal, so as to update the plan implementation plan items or generate risk information to be managed according to the guidance implementation evaluation value; An input determination module is used to input the implementation acquisition image into a pre-trained implementation guidance model when the risk information to be managed is generated, so as to determine the corresponding second guidance text information and update it to the planning implementation plan items.

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