A device for tracking and managing data changes in land spatial planning and implementation

Abstract

This invention relates to a data tracking and management device for changes in land spatial planning and implementation, particularly in the field of land spatial planning technology. It includes: an image generation module for generating a plan view based on demarcated ecological protection red lines, permanent basic farmland, and urban development boundaries; a modeling module for modeling based on the generated plan view; a data acquisition module for acquiring the change in feature data of a single model map, the number of related features in two related model maps, and the spacing between the models; a data analysis module including interconnected first, second, third, and fourth computational units; and a parameter adjustment module for determining the comprehensive tracking frequency of several feature data based on a comparison of the comprehensive change parameters calculated by the first computational unit with the change parameter standard. This further improves the efficiency of land spatial planning.

Description

Technical Field

[0001] This invention relates to the field of land spatial planning technology, and in particular to a land spatial planning and implementation change data tracking and management device. Background Technology

[0002] The planning and implementation of national land space is an important policy for people's livelihood. Among them, the delineation of the three control lines—ecological protection red line, permanent basic farmland, and urban development boundary—is of paramount importance. Only by delineating and safeguarding these three lines can we better manage the spatial relationship between life, production, and ecology, and promote sustainable economic and environmental development.

[0003] Chinese Patent Publication No. CN111445116A discloses a land spatial planning auxiliary compilation system, comprising an application layer for providing land spatial planning compilation functions, a basic configuration layer for system basic configuration, a database for storing basic data for spatial planning compilation and spatial planning projects, a model library for providing spatial planning compilation model algorithms, and an indicator library for storing planning indicator data at all levels. The model library includes a dual evaluation model, a boundary prediction algorithm model, and a thematic map generation model. The application layer includes an engineering management module, a data management module, a current situation analysis module, a dual evaluation module, a spatial pattern module, a planning layout module, a mapping and illustration module, and a statistical report module. This invention can manage planning compilation results, provide important functions such as dual evaluation analysis and urban-rural boundary prediction, and improve the efficiency of planning compilation. However, it is evident that the aforementioned land spatial planning auxiliary compilation system suffers from low control precision in the data processing of land spatial planning, resulting in low planning efficiency. Summary of the Invention

[0004] To address this issue, the present invention provides a data tracking and management device for changes in land spatial planning and implementation, which overcomes the problem of low control accuracy and low planning efficiency in the data processing of land spatial planning in the prior art.

[0005] To achieve the above objectives, the present invention provides a land spatial planning and implementation change data tracking and management device, comprising:

[0006] The image generation module is used to generate a plan map based on the delineated ecological protection red line, permanent basic farmland and urban development boundary. The plan map includes an ecological plan map, an agricultural plan map and an urban plan map.

[0007] The modeling module, which is connected to the image generation module, is used to model the generated planar map based on the delineated ecological protection red line, permanent basic farmland and urban development boundary information, and generate ecological model map, agricultural model map and urban model map.

[0008] The data acquisition module is connected to the image generation module and the modeling module respectively, and is used to acquire the amount of change of feature data in a single model image, the number of related features in two related model images, and the spacing between the models.

[0009] The data analysis module, connected to the data acquisition module, includes a first processing unit, a second processing unit, a third processing unit, and a fourth processing unit that are interconnected.

[0010] The first calculation unit is used to determine the amount of data change of a single feature data in a single model graph, and to determine the tracking frequency of the single feature data based on the comparison result of the amount of data change and the amount of change threshold, and to calculate the comprehensive change parameter of the several feature data based on the amount of data change of several feature data of a single model graph obtained by the data acquisition module.

[0011] The second calculation unit is used to determine whether the two model graphs affect each other based on the comparison result of the number of associated features and the standard number of associated features, and to determine the adjustment method of the integrated tracking frequency based on the difference between the number of associated features and the standard number of associated features.

[0012] The third calculation unit is used to determine whether the integrated tracking frequency needs to be corrected based on the comparison result between the spacing and the standard spacing, and to determine the correction method for the integrated tracking frequency based on the spacing difference between the spacing and the standard spacing.

[0013] The fourth calculation unit is used to determine whether the tracking frequency of a single feature data should be adjusted based on the comparison result between the adjusted comprehensive tracking frequency and the minimum comprehensive tracking frequency, and to determine the adjustment method of the tracking frequency of a single feature data based on the frequency difference between the adjusted comprehensive tracking frequency and the minimum comprehensive tracking frequency.

[0014] The parameter adjustment module is connected to the data acquisition module and the data analysis module respectively, and is used to determine the comprehensive tracking frequency of several feature data based on the comparison result of the comprehensive change parameter calculated by the first calculation unit and the change parameter standard.

[0015] Further, the first processing unit determines the data change amount B of a single feature data in a single model graph, compares the data change amount with a change amount threshold, and determines the tracking frequency of the single feature data based on the comparison result, wherein,

[0016] Under the first threshold comparison condition, the first processing unit determines the tracking frequency for a single feature data as the first tracking frequency V1;

[0017] Under the second threshold comparison condition, the first computing unit determines the tracking frequency for a single feature data as the second tracking frequency V2;

[0018] Under the third threshold comparison condition, the first calculation unit determines the tracking frequency for a single feature data as the third tracking frequency V3;

[0019] The first threshold comparison condition must satisfy B≤B1, the second threshold comparison condition must satisfy B1<B≤B2, and the third threshold comparison condition must satisfy B>B2 and V1>V2>V3.

[0020] Further, the first calculation unit calculates a comprehensive change parameter C of several feature data based on the data change amount of several feature data of a single model graph acquired by the data acquisition module, and sets:

[0021]

[0022] Wherein, Ra1 is the data change amount of the first feature data of a single model graph, R1 is the standard data change amount of the first feature data of a single model graph, f1 is the weight of the first feature data, Ra2 is the data change amount of the second feature data of a single model graph, R2 is the standard data change amount of the second feature data of a single model graph, f2 is the weight of the second feature data, Ran is the data change amount of the nth feature data of a single model graph, Rn is the standard data change amount of the nth feature data of a single model graph, and fn is the weight of the nth feature data.

[0023] Furthermore, the parameter adjustment module compares the comprehensive change parameter C calculated by the first calculation unit with the standard value of the change parameter, and determines the comprehensive tracking frequency for several of the feature data based on the comparison result, wherein...

[0024] Under the condition of comparing the first standard value, the parameter adjustment module determines the comprehensive tracking frequency of the several feature data to be tracked at the first comprehensive tracking frequency Vz1;

[0025] Under the condition of comparison with the second standard value, the parameter adjustment module determines the comprehensive tracking frequency of the several feature data to be tracked at the second comprehensive tracking frequency Vz2;

[0026] Under the condition of comparison with the third standard value, the parameter adjustment module determines the comprehensive tracking frequency of several feature data to be tracked at the third comprehensive tracking frequency Vz3;

[0027] The first standard value comparison condition must satisfy C≤C1, the second standard value comparison condition must satisfy C1<C≤C2, and the third standard value comparison condition must satisfy C>C2, Vz1>Vz2>Vz3.

[0028] Furthermore, the second processing unit determines the number S of related features in the two related model graphs, compares the number S of related features with the standard S1 of related features, and determines whether the two model graphs influence each other based on the comparison result.

[0029] Based on the first correlation quantity comparison result, the second operation unit determines that the two model graphs do not affect each other, and the third operation unit determines the spacing between each model graph to determine whether the integrated tracking frequency needs to be adjusted.

[0030] Based on the second correlation quantity comparison result, the second calculation unit determines that the two model graphs influence each other, and at the same time determines the adjustment method of the comprehensive tracking frequency according to the difference between the correlation feature quantity and the correlation feature quantity standard.

[0031] The first correlation count comparison result is S < S1, and the second correlation count comparison result is S ≥ S1.

[0032] Further, the second processing unit calculates the difference ΔS between the number of associated features in the model graph and the standard number of associated features, compares the difference with a preset threshold, and determines the adjustment method for the integrated tracking frequency based on the comparison result.

[0033] The first adjustment method is to adjust the integrated tracking frequency to the fourth integrated tracking frequency Vz4 according to the first adjustment threshold X1.

[0034] The second adjustment method is to adjust the integrated tracking frequency to the fifth integrated tracking frequency Vz5 according to the second adjustment threshold X2.

[0035] The third adjustment method is to adjust the integrated tracking frequency to the sixth integrated tracking frequency Vz6 according to the first adjustment threshold X1.

[0036] The fourth adjustment method is to adjust the integrated tracking frequency to the seventh integrated tracking frequency Vz7 according to the second adjustment threshold X2;

[0037] The first adjustment method requires ΔS < 0 and |ΔS| < ΔS1; the second adjustment method requires ΔS < 0 and |ΔS| > ΔS1; the third adjustment method requires ΔS > 0 and |ΔS| < ΔS1; the fourth adjustment method requires ΔS > 0 and |ΔS| > ΔS1, X1 < X2, Vz5 > Vz4 > Vz6 > Vz7.

[0038] Furthermore, the third computing unit determines the spacing L between each model and compares the spacing L with the standard spacing L1. Based on the comparison result, it determines whether the integrated tracking frequency needs to be corrected.

[0039] Based on the first spacing comparison result, the third processing unit determines that it will not correct the integrated tracking frequency;

[0040] Based on the second spacing comparison result, the third calculation unit determines to correct the integrated tracking frequency, and at the same time determines the correction method for the integrated tracking frequency based on the spacing difference between the spacing and the standard spacing.

[0041] The first spacing comparison result is L < L1, and the second spacing comparison result is L ≥ L1.

[0042] Further, the third calculation unit calculates the spacing difference ΔL between the spacing and the standard spacing, compares the spacing difference ΔL with the spacing difference threshold ΔL1, and determines the correction method for the integrated tracking frequency based on the comparison result, wherein...

[0043] The first correction method is to adjust the integrated tracking frequency to the fourth integrated tracking frequency Vz4 according to the first correction threshold Z1.

[0044] The second correction method is to adjust the integrated tracking frequency to the fifth integrated tracking frequency Vz5 according to the second correction threshold Z2.

[0045] The third correction method is to adjust the integrated tracking frequency to the sixth integrated tracking frequency Vz6 according to the first correction threshold Z1.

[0046] The fourth correction method is to adjust the integrated tracking frequency to the seventh integrated tracking frequency Vz7 according to the second correction threshold Z2;

[0047] The first adjustment method requires ΔL < 0 and |ΔL| < ΔL1; the second adjustment method requires ΔL < 0 and |ΔL| > ΔL1; the third adjustment method requires ΔL > 0 and |ΔL| < ΔL1; the fourth adjustment method requires ΔL > 0 and |ΔL| > ΔL1, and Z1 < Z2.

[0048] Further, the fourth processing unit determines the adjusted integrated tracking frequency Vzj and compares this integrated tracking frequency Vzj with the minimum integrated tracking frequency Vx. Based on the comparison result, it determines whether the tracking frequency of a single feature data point has been adjusted.

[0049] If Vzj < Vx, the fourth operation unit determines to adjust the tracking frequency of a single feature data, and adjusts the tracking frequency of a single feature data according to the frequency difference between the adjusted comprehensive tracking frequency Vzj and the minimum comprehensive tracking frequency Vx.

[0050] If Vzj≥Vx, the fourth operation unit determines that it will not adjust the tracking frequency of a single feature data.

[0051] Further, the fourth arithmetic unit calculates the frequency difference ΔV between the adjusted integrated tracking frequency Vzj and the minimum integrated tracking frequency Vx, compares the frequency difference with a frequency difference threshold, and determines the adjustment method for the tracking frequency of a single feature data based on the comparison result.

[0052] The first adjustment method is to adjust the tracking frequency of a single feature data to a fourth tracking frequency V4 according to a first adjustment threshold K1.

[0053] The second adjustment method is to adjust the tracking frequency of a single feature data to the fifth tracking frequency V5 according to the second adjustment threshold K2.

[0054] The third adjustment method is to adjust the tracking frequency of a single feature data to the sixth tracking frequency V6 according to the first adjustment threshold K1.

[0055] The fourth adjustment method is to adjust the tracking frequency of a single feature data to the seventh tracking frequency V7 according to the second adjustment threshold K2;

[0056] The first adjustment method requires ΔV < 0 and |ΔV| < ΔV1; the second adjustment method requires ΔV < 0 and |ΔV| > ΔV1; the third adjustment method requires ΔV > 0 and |ΔV| < ΔV1; the fourth adjustment method requires ΔV > 0 and |ΔV| > ΔV1; K1 < K2; V5 > V4 > V6 > V7.

[0057] Compared with the prior art, the beneficial effect of the present invention is that by obtaining the data change amount of a single feature data of a single model diagram and comparing the data change amount with a preset change amount, the tracking frequency of the single feature data is determined based on the comparison result, thereby improving the control accuracy in the data processing process and further improving the planning efficiency.

[0058] Furthermore, this invention analyzes the data changes of several feature data, calculates the comprehensive change parameters of the several feature data, compares the comprehensive change parameters with the comparison parameters, and determines the comprehensive tracking frequency of the several feature data based on the comparison results, thereby further precisely controlling the changes of the several feature data and further improving planning efficiency.

[0059] Furthermore, this invention determines the number of associated features in two related model diagrams and compares this number with a preset number of associated features. Based on the comparison result, it determines whether the two model diagrams influence each other. If the two model diagrams are determined to influence each other, the difference between the number of associated features and the preset number of associated features is calculated and compared with the preset number difference. Based on the comparison result, the comprehensive frequency is adjusted, thereby further precisely controlling the changes in several feature data and further improving planning efficiency.

[0060] In particular, when it is determined that the two model diagrams do not affect each other, the distance between each model diagram is calculated and compared with the preset distance. The comprehensive tracking frequency is adjusted according to the comparison result. When the distance difference is larger, the tracking frequency is smaller, thereby accurately controlling the changes of several feature data and further improving planning efficiency.

[0061] Furthermore, when the overall tracking frequency adjustment is completed, the adjusted overall tracking frequency is compared with the minimum tracking frequency, and the comparison result determines whether to correct the tracking frequency of individual feature data. When it is determined that the tracking frequency of individual feature data should be corrected, the frequency difference is calculated and compared with the preset frequency difference. The tracking frequency of individual feature data is corrected according to the comparison result. When the frequency difference is larger, the tracking frequency of individual feature data is smaller, thereby further improving the planning efficiency. Attached Figure Description

[0062] Figure 1 This is a logic block diagram of the land spatial planning and implementation change data tracking and management device described in this invention;

[0063] Figure 2 This is a logic block diagram of the data analysis module of the land spatial planning and implementation change data tracking and management device described in this invention. Detailed Implementation

[0064] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0065] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0066] Please see Figure 1 and Figure 2 As shown, Figure 1This is a logic block diagram of the land spatial planning and implementation change data tracking and management device described in this invention; Figure 2 This is a logic block diagram of the data analysis module of the land spatial planning and implementation change data tracking and management device described in this invention.

[0067] In this embodiment of the invention, a land spatial planning and implementation change data tracking and management device includes:

[0068] The image generation module is used to generate a plan map based on the delineated ecological protection red line, permanent basic farmland and urban development boundary. The plan map includes an ecological plan map, an agricultural plan map and an urban plan map.

[0069] The modeling module, which is connected to the image generation module, is used to model the generated planar map based on the delineated ecological protection red line, permanent basic farmland and urban development boundary information, and generate ecological model map, agricultural model map and urban model map.

[0070] The data acquisition module is connected to the image generation module and the modeling module respectively, and is used to acquire the amount of change of feature data in a single model image, the number of related features in two related model images, and the spacing between the models.

[0071] The data analysis module, connected to the data acquisition module, includes a first processing unit, a second processing unit, a third processing unit, and a fourth processing unit that are interconnected.

[0072] The first calculation unit is used to determine the amount of data change of a single feature data in a single model graph, and to determine the tracking frequency of the single feature data based on the comparison result of the amount of data change and the amount of change threshold, and to calculate the comprehensive change parameter of the several feature data based on the amount of data change of several feature data of a single model graph obtained by the data acquisition module.

[0073] The second calculation unit is used to determine whether the two model graphs affect each other based on the comparison result of the number of associated features and the standard number of associated features, and to determine the adjustment method of the integrated tracking frequency based on the difference between the number of associated features and the standard number of associated features.

[0074] The third calculation unit is used to determine whether the integrated tracking frequency needs to be corrected based on the comparison result between the spacing and the standard spacing, and to determine the correction method for the integrated tracking frequency based on the spacing difference between the spacing and the standard spacing.

[0075] The fourth calculation unit is used to determine whether the tracking frequency of a single feature data should be adjusted based on the comparison result between the adjusted comprehensive tracking frequency and the minimum comprehensive tracking frequency, and to determine the adjustment method of the tracking frequency of a single feature data based on the frequency difference between the adjusted comprehensive tracking frequency and the minimum comprehensive tracking frequency.

[0076] The parameter adjustment module is connected to the data acquisition module and the data analysis module respectively, and is used to determine the comprehensive tracking frequency of several feature data based on the comparison result of the comprehensive change parameter calculated by the first calculation unit and the change parameter standard.

[0077] In this embodiment of the invention, the feature data of the ecological model map includes river area, forest area, desert area, number of green plants, etc.; the feature data of the agricultural model map includes farmland area, fruit tree planting area, etc.; and the feature data of the urban model map includes population flow, building area, underground parking lot, above-ground parking lot, etc.

[0078] Specifically, the first processing unit determines the data change amount B of a single feature data in a single model graph, compares the data change amount with a change amount threshold, and determines the tracking frequency of the single feature data based on the comparison result.

[0079] Under the first threshold comparison condition, the first processing unit determines the tracking frequency for a single feature data as the first tracking frequency V1;

[0080] Under the second threshold comparison condition, the first computing unit determines the tracking frequency for a single feature data as the second tracking frequency V2;

[0081] Under the third threshold comparison condition, the first calculation unit determines the tracking frequency for a single feature data as the third tracking frequency V3;

[0082] The first threshold comparison condition must satisfy B≤B1, the second threshold comparison condition must satisfy B1<B≤B2, and the third threshold comparison condition must satisfy B>B2 and V1>V2>V3.

[0083] Specifically, the first calculation unit calculates a comprehensive change parameter C of several feature data based on the data change amount of a single model graph acquired by the data acquisition module, and sets:

[0084]

[0085] Wherein, Ra1 is the data change amount of the first feature data of a single model graph, R1 is the standard data change amount of the first feature data of a single model graph, f1 is the weight of the first feature data, Ra2 is the data change amount of the second feature data of a single model graph, R2 is the standard data change amount of the second feature data of a single model graph, f2 is the weight of the second feature data, Ran is the data change amount of the nth feature data of a single model graph, Rn is the standard data change amount of the nth feature data of a single model graph, and fn is the weight of the nth feature data.

[0086] In this embodiment of the invention, the data changes in the feature data in the ecological model diagram include changes in river area, forest area, desert area, and the number of green plants, etc.; the data changes in the feature data in the agricultural model diagram include changes in farmland area and fruit tree planting area, etc.; and the data changes in the feature data in the urban model diagram include changes in population flow, building area, underground parking lot, and above-ground parking lot, etc.

[0087] Specifically, the parameter adjustment module compares the comprehensive change parameter C calculated by the first calculation unit with the standard value of the change parameter, and determines the comprehensive tracking frequency of several feature data based on the comparison result, wherein...

[0088] Under the condition of comparing the first standard value, the parameter adjustment module determines the comprehensive tracking frequency of the several feature data to be tracked at the first comprehensive tracking frequency Vz1;

[0089] Under the condition of comparison with the second standard value, the parameter adjustment module determines the comprehensive tracking frequency of the several feature data to be tracked at the second comprehensive tracking frequency Vz2;

[0090] Under the condition of comparison with the third standard value, the parameter adjustment module determines the comprehensive tracking frequency of several feature data to be tracked at the third comprehensive tracking frequency Vz3;

[0091] The first standard value comparison condition must satisfy C≤C1, the second standard value comparison condition must satisfy C1<C≤C2, and the third standard value comparison condition must satisfy C>C2, Vz1>Vz2>Vz3.

[0092] Specifically, the second processing unit determines the number S of related features in two related model graphs, compares the number S of related features with the standard S1 of related features, and determines whether the two model graphs influence each other based on the comparison result.

[0093] Based on the first correlation quantity comparison result, the second operation unit determines that the two model graphs do not affect each other, and the third operation unit determines the spacing between each model graph to determine whether the integrated tracking frequency needs to be adjusted.

[0094] Based on the second correlation quantity comparison result, the second calculation unit determines that the two model graphs influence each other, and at the same time determines the adjustment method of the comprehensive tracking frequency according to the difference between the correlation feature quantity and the correlation feature quantity standard.

[0095] The first correlation count comparison result is S < S1, and the second correlation count comparison result is S ≥ S1.

[0096] In this embodiment of the invention, the number of associated features in the two model diagrams includes the changes in the planting area of ​​fruit trees and / or farmland in the agricultural model diagram when the forest area in the ecological model diagram changes, and the changes in population flow and / or building area and number of parking lots in the urban model diagram.

[0097] Specifically, the second processing unit calculates the difference ΔS between the number of associated features in the model graph and the standard number of associated features, compares the difference with a preset threshold, and determines the adjustment method for the integrated tracking frequency based on the comparison result.

[0098] The first adjustment method is to adjust the integrated tracking frequency to the fourth integrated tracking frequency Vz4 according to the first adjustment threshold X1, and set Vz4 = Vzi + X1;

[0099] The second adjustment method is to adjust the integrated tracking frequency to the fifth integrated tracking frequency Vz5 according to the second adjustment threshold X2, and set Vz5 = Vzi + X2;

[0100] The third adjustment method is to adjust the integrated tracking frequency to the sixth integrated tracking frequency Vz6 according to the first adjustment threshold X1, and set Vz6 = Vzi - X1;

[0101] The fourth adjustment method is to adjust the integrated tracking frequency to the seventh integrated tracking frequency Vz7 according to the second adjustment threshold X2, and set Vz7 = Vzi - X2;

[0102] The first adjustment method requires ΔS < 0 and |ΔS| < ΔS1; the second adjustment method requires ΔS < 0 and |ΔS| > ΔS1; the third adjustment method requires ΔS > 0 and |ΔS| < ΔS1; the fourth adjustment method requires ΔS > 0 and |ΔS| > ΔS1, X1 < X2, Vz5 > Vz4 > Vz6 > Vz7, i = 1, 2, 3.

[0103] Specifically, the third computing unit determines the spacing L between each model, compares the spacing L with the standard spacing L1, and determines whether the integrated tracking frequency needs to be corrected based on the comparison result.

[0104] Based on the first spacing comparison result, the third processing unit determines that it will not correct the integrated tracking frequency;

[0105] Based on the second spacing comparison result, the third calculation unit determines to correct the integrated tracking frequency, and at the same time determines the correction method for the integrated tracking frequency based on the spacing difference between the spacing and the standard spacing.

[0106] The first spacing comparison result is L < L1, and the second spacing comparison result is L ≥ L1.

[0107] Specifically, the third processing unit calculates the spacing difference ΔL between the spacing and the standard spacing, compares the spacing difference ΔL with a spacing difference threshold ΔL1, and determines the correction method for the integrated tracking frequency based on the comparison result.

[0108] The first correction method is to adjust the integrated tracking frequency to the fourth integrated tracking frequency Vz4 according to the first correction threshold Z1, and set Vz4 = Vzi + Z1;

[0109] The second correction method is to adjust the integrated tracking frequency to the fifth integrated tracking frequency Vz5 according to the second correction threshold Z2, and set Vz5 = Vzi + Z2;

[0110] The third correction method is to adjust the integrated tracking frequency to the sixth integrated tracking frequency Vz6 according to the first correction threshold Z1, and set Vz6 = Vzi - Z1;

[0111] The fourth correction method is to adjust the integrated tracking frequency to the seventh integrated tracking frequency Vz7 according to the second correction threshold Z2, and set Vz7 = Vzi - Z2;

[0112] The first adjustment method requires ΔL < 0 and |ΔL| < ΔL1; the second adjustment method requires ΔL < 0 and |ΔL| > ΔL1; the third adjustment method requires ΔL > 0 and |ΔL| < ΔL1; the fourth adjustment method requires ΔL > 0 and |ΔL| > ΔL1, and Z1 < Z2.

[0113] Specifically, the fourth processing unit determines the adjusted integrated tracking frequency Vzj and compares it with the minimum integrated tracking frequency Vx. Based on the comparison result, it determines whether the tracking frequency of a single feature data point has been adjusted.

[0114] If Vzj < Vx, the fourth operation unit determines to adjust the tracking frequency of a single feature data, and adjusts the tracking frequency of a single feature data according to the frequency difference between the adjusted comprehensive tracking frequency Vzj and the minimum comprehensive tracking frequency Vx.

[0115] If Vzj≥Vx, the fourth operation unit determines that it will not adjust the tracking frequency of a single feature data.

[0116] Specifically, the fourth arithmetic unit calculates the frequency difference ΔV between the adjusted integrated tracking frequency Vzj and the minimum integrated tracking frequency Vx, compares the frequency difference with a frequency difference threshold, and determines the adjustment method for the tracking frequency of a single feature data based on the comparison result.

[0117] The first adjustment method is to adjust the tracking frequency of a single feature data to a fourth tracking frequency V4 according to the first adjustment threshold K1, and set V4 = Vi + K1;

[0118] The second adjustment method is to adjust the tracking frequency of a single feature data to the fifth tracking frequency V5 according to the second adjustment threshold K2, and set V5 = Vi + K2;

[0119] The third adjustment method is to adjust the tracking frequency of a single feature data to the sixth tracking frequency V6 according to the first adjustment threshold K1, and set V6 = Vi - K1;

[0120] The fourth adjustment method is to adjust the tracking frequency of a single feature data to the seventh tracking frequency V7 according to the second adjustment threshold K2, and set V7 = Vi - K2;

[0121] The first adjustment method requires ΔV < 0 and |ΔV| < ΔV1; the second adjustment method requires ΔV < 0 and |ΔV| > ΔV1; the third adjustment method requires ΔV > 0 and |ΔV| < ΔV1; the fourth adjustment method requires ΔV > 0 and |ΔV| > ΔV1; K1 < K2; V5 > V4 > V6 > V7.

[0122] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0123] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A land space planning and implementation change data tracking management apparatus, characterized by, The image generation module is configured to generate planar images including ecological planar images, agricultural planar images and urban planar images according to the completed ecological protection red line, permanent basic farmland and urban development boundary; The modeling module is connected with the image generation module and configured to model the generated planar images according to the completed ecological protection red line, permanent basic farmland and urban development boundary information, and generate ecological model images, agricultural model images and urban model images; The data acquisition module is connected with the image generation module and the modeling module and configured to acquire a variation of feature data in a single model image, a number of associated features in two model images associated with each other and a distance between the model images; The data analysis module is connected with the data acquisition module and includes first, second, third and fourth operation units connected with each other, The first operation unit is configured to determine a data variation of a single feature data in a single model image, determine a tracking frequency of the single feature data according to a comparison result of the data variation and a variation threshold, and calculate a comprehensive variation parameter of a plurality of feature data in the single model image according to the data variation acquired by the data acquisition module; The second operation unit is configured to determine whether the two model images affect each other according to a comparison result of the number of associated features and a number of associated features standard, and determine an adjustment mode of the comprehensive tracking frequency according to a number difference between the number of associated features and the number of associated features standard; The third operation unit is configured to determine whether the comprehensive tracking frequency is modified according to a comparison result of the distance and a standard distance, and determine a modification mode of the comprehensive tracking frequency according to a distance difference between the distance and the standard distance; The fourth operation unit is configured to determine whether the tracking frequency of the single feature data is adjusted according to a comparison result of the adjusted comprehensive tracking frequency and a minimum comprehensive tracking frequency, and determine an adjustment mode of the tracking frequency of the single feature data according to a frequency difference between the adjusted comprehensive tracking frequency and the minimum comprehensive tracking frequency; The parameter adjustment module is connected with the data acquisition module and the data analysis module and configured to determine the comprehensive tracking frequency of the plurality of feature data according to a comparison result of the comprehensive variation parameter calculated by the first operation unit and a variation parameter standard. The first operation unit calculates a comprehensive variation parameter C of a plurality of feature data in a single model image according to the data variation acquired by the data acquisition module, and sets 2. The land space planning and implementation change data tracking management device of claim 1, wherein, ​ ； Wherein, Ra1 is the data variation of the first characteristic data of a single model graph, R1 is the standard data variation of the first characteristic data of a single model graph, f1 is the weight of the first characteristic data, Ra2 is the data variation of the second characteristic data of a single model graph, R2 is the standard data variation of the second characteristic data of a single model graph, f2 is the weight of the second characteristic data, Ran is the data variation of the n-th characteristic data of a single model graph, Rn is the standard data variation of the n-th characteristic data of a single model graph, and fn is the weight of the n-th characteristic data.

3. The land space planning and implementation change data tracking management device of claim 2, wherein, The second operation unit determines the number of associated features S in the two model graphs associated with each other, compares the number of associated features S with the standard number of associated features S1, and determines whether the two model graphs affect each other according to the comparison result. In the first comparison result of the number of associations, the second operation unit determines that the two model graphs do not affect each other, and the third operation unit determines the distance between the model graphs to determine whether the comprehensive tracking frequency needs to be adjusted. In the second comparison result of the number of associations, the second operation unit determines that the two model graphs affect each other, and determines the adjustment mode of the comprehensive tracking frequency according to the difference between the number of associated features and the standard number of associated features. The first comparison result of the number of associations is S < S1, and the second comparison result of the number of associations is S ≥ S1.

4. The land space planning and implementation change data tracking management device of claim 3, wherein, The second operation unit calculates the number difference ΔS between the number of associated features of the model graph and the standard number of associated features, compares the number difference with a preset number threshold, and determines the adjustment mode of the comprehensive tracking frequency according to the comparison result, wherein, The first adjustment mode is to adjust the comprehensive tracking frequency to a fourth comprehensive tracking frequency Vz4 according to a first adjustment threshold X1. The second adjustment mode is to adjust the comprehensive tracking frequency to a fifth comprehensive tracking frequency Vz5 according to a second adjustment threshold X2. The third adjustment mode is to adjust the comprehensive tracking frequency to a sixth comprehensive tracking frequency Vz6 according to the first adjustment threshold X1. The fourth adjustment mode is to adjust the comprehensive tracking frequency to a seventh comprehensive tracking frequency Vz7 according to the second adjustment threshold X2. The first adjustment mode needs to satisfy ΔS < 0 and |ΔS| < ΔS1, the second adjustment mode needs to satisfy ΔS < 0 and |ΔS| > ΔS1, the third adjustment mode needs to satisfy ΔS > 0 and |ΔS| < ΔS1, the fourth adjustment mode needs to satisfy ΔS > 0 and |ΔS| > ΔS1, X1 < X2, and Vz5 > Vz4 > Vz6 > Vz7.

5. The land space planning and implementation change data tracking management device of claim 4, wherein, The third operation unit calculates the distance difference ΔL between the distance and the standard distance, compares the distance difference ΔL with a distance difference threshold ΔL1, and determines the correction mode of the comprehensive tracking frequency according to the comparison result, wherein, The first correction mode is to adjust the comprehensive tracking frequency to a fourth comprehensive tracking frequency Vz4 according to a first correction threshold Z1. The second correction mode is to adjust the comprehensive tracking frequency to a fifth comprehensive tracking frequency Vz5 according to a second correction threshold Z2. The third correction mode is to adjust the comprehensive tracking frequency to a sixth comprehensive tracking frequency Vz6 according to a first correction threshold Z1; The fourth correction mode is to adjust the comprehensive tracking frequency to a seventh comprehensive tracking frequency Vz7 according to a second correction threshold Z2; The first adjustment mode needs to satisfy ΔL < 0 and |ΔL| < ΔL1, The second adjustment mode needs to satisfy ΔL < 0 and |ΔL| > ΔL1, The third adjustment mode needs to satisfy ΔL > 0 and |ΔL| < ΔL1, The fourth adjustment mode needs to satisfy ΔL > 0 and |ΔL| > ΔL1, Z1 < Z2.

6. The land space planning and implementation change data tracking management device of claim 5, wherein, The fourth operation unit determines the adjusted comprehensive tracking frequency Vzj, compares the comprehensive tracking frequency Vzj with the minimum comprehensive tracking frequency Vx, and determines whether to adjust the tracking frequency of the single feature data according to the comparison result, wherein, If Vzj < Vx, the fourth operation unit determines to adjust the tracking frequency of the single feature data, and adjusts the tracking frequency of the single feature data according to the frequency difference between the adjusted comprehensive tracking frequency Vzj and the minimum comprehensive tracking frequency Vx; If Vzj ≥ Vx, the fourth operation unit determines not to adjust the tracking frequency of the single feature data.

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