Land resource optimization utilization system and method based on Internet of Things

Abstract

The invention discloses a land resource optimal utilization system and method based on the Internet of Things, relates to the technical field of land resource optimal utilization, and solves the problem that in regional optimization of land resources, the influence of geographical height and wind speed on crops is neglected, and then regional optimization is influenced. And the technical problem of mutual productivity interference between the optimized areas is neglected. The method comprises the following steps: dividing an intelligent planning region into a plurality of regulation and control regions, and obtaining basic data and target data in each regulation and control region; respectively analyzing the basic data and the target data to obtain a basic environment factor and a space influence factor of each regulation and control region; inputting the basic environment factors and the space influence factors into a region planning model to obtain a preliminary planning condition of each regulation and control region; performing secondary adjustment on each regulation and control region based on the initial planning condition to obtain a final planning condition; according to the method, the scientificity of planning and the sustainability of regional development can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of smart agriculture and relates to land resource optimization utilization technology, specifically to a land resource optimization utilization system and method based on the Internet of Things. Background Art

[0002] With the continuous growth of the global population and the acceleration of urbanization, land resources, as one of the important resources for human survival and development, are becoming increasingly scarce, especially in agricultural production and urban expansion. How to efficiently and reasonably utilize limited land resources has become an urgent problem to be solved. Internet of Things technology provides new means and tools for the optimal utilization of land resources. It can realize real-time monitoring of land resources, provide data support for subsequent intelligent analysis and management, and realize the optimal allocation and efficient utilization of land resources.

[0003] At present, most of the land resource optimization and utilization systems and methods based on the Internet of Things ignore the impact of geographical altitude and wind speed on crops and thus the impact on regional optimization in the regional optimization of land resources. They only plan and regulate the region according to soil moisture, soil temperature and light intensity, without considering the impact of wind speed on crops in the region, thereby reducing production efficiency and economic benefits; at the same time, they ignore the fact that there may be more of the same planned areas in the optimized region, resulting in mutual interference of production capacity between regions, resulting in economic losses caused by reduced regional production capacity.

[0004] Therefore, the present invention discloses a land resource optimization utilization system and method based on the Internet of Things, which are used to solve the above technical problems. Summary of the invention

[0005] The present invention aims to solve at least one of the technical problems existing in the prior art; to this end, the present invention proposes a land resource optimization utilization system and method based on the Internet of Things, which is used to solve the technical problems of ignoring the influence of geographical height and wind speed on crops and thus affecting regional optimization in the regional optimization of land resources, and ignoring the mutual interference of production capacity between optimized regions. The present invention divides the intelligent planning area into several control areas, obtains basic data and target data in each control area; analyzes the basic data and target data respectively to obtain basic environmental factors and spatial influencing factors of each control area; inputs the basic environmental factors and spatial influencing factors into the regional planning model to obtain the preliminary planning situation of each control area; and performs secondary adjustments on each control area based on the preliminary planning situation to obtain the final planning situation to solve the above problems.

[0006] To achieve the above-mentioned object, the first aspect of the present invention provides a land resource optimization utilization system based on the Internet of Things, comprising: an intelligent analysis module, and a data collection module, a regional optimization module and a database connected thereto;

[0007] The data collection module is used to divide the total area into an intelligent planning area and a manually defined area, and divide the intelligent planning area into a number of control areas; obtain basic data and target data in each control area; wherein the basic data includes characteristic values ​​of soil moisture, soil temperature and light intensity; the target data includes characteristic values ​​of altitude and wind speed;

[0008] The intelligent analysis module is used to analyze the basic data to obtain the basic environmental factors of each control area; analyze the target data to obtain the spatial impact factors of each control area;

[0009] The regional optimization module is used to establish a regional planning model, input basic environmental factors and spatial influencing factors into the regional planning model to obtain the preliminary planning situation of each control area; based on the preliminary planning situation, each control area is adjusted twice to obtain the final planning situation, and the final planning situation is output to the user end; wherein the planning situation includes planning for cultivated land, forest land, and garden land.

[0010] Preferably, the intelligent analysis module communicates and / or is electrically connected to the data collection module, the regional optimization module and the database respectively; the database communicates and / or is electrically connected to the data collection module, the intelligent analysis module and the regional optimization module respectively.

[0011] Preferably, the dividing of the total area into intelligently planned areas and artificially defined areas comprises:

[0012] Extract the remote sensing topographic map of the total area and determine whether there are undemarcable areas in the remote sensing topographic map; if yes, mark the undemarcable areas as artificially defined areas and mark the non-undemarcable areas as intelligently planned areas; wherein the undemarcable areas include rivers and built-up areas.

[0013] Preferably, the intelligent planning area is divided into several control areas, including:

[0014] A1: Obtain the area of ​​the standard control area from the database, and divide the intelligent planning area into several grid areas based on the area of ​​the standard control area; determine whether the grid areas are complete grids in turn; if yes, mark the area where the grid area is located as a control area; if no, mark the area where the grid area is located as an edge area, and jump to A2;

[0015] A2: sequentially obtain the area Si of each edge area in the intelligent planning area, obtain the area ratio MBi of each edge area to the standard control area based on the formula MBi=Si / BTS, and sequentially determine whether the area ratio MBi exceeds the set ratio; if yes, mark the corresponding edge area as the control area; if no, mark the corresponding edge area as the decomposition area, and jump to A3; wherein i is the number corresponding to the edge area, BTS is the area of ​​the standard control area, and the set ratio is obtained manually;

[0016] A3: Extract the control areas contacted by each decomposition area in turn, and mark the control areas as the corresponding decomposition areas to be allocated to the control areas; obtain the contact length Lj between the decomposition area and the corresponding to-be-allocated control area, and obtain the to-be-allocated proportion PBLj of the to-be-allocated control area numbered j corresponding to the current decomposition area based on the formula PBLj=Lj / ∑(Lj); allocate the decomposition area to the corresponding to-be-allocated control area in proportion based on the area of ​​the decomposition area and the to-be-allocated proportion PBLj, and combine the to-be-allocated control area and the allocated decomposition area into the control area; wherein j is the number of the to-be-allocated control area, and the range of j is [1,n]; ∑ is the summation symbol for summing Lj, and the summation range is [1,n]; n represents the number of to-be-allocated control areas corresponding to the current decomposition area.

[0017] It is worth noting that the present invention divides the intelligent planning area into several control areas, which is used to accurately divide the intelligent planning area. This enables the system to better adapt to the diverse needs in complex areas, enables the system to perform personalized optimization according to the characteristics of different areas, and enables the system to more accurately identify and process specific needs and characteristics in each area, thereby improving the accuracy and efficiency of the overall planning.

[0018] Preferably, the obtaining of basic data and target data in each regulated region includes:

[0019] Based on the soil moisture sensor, several soil moistures at different times and locations in each control area are obtained; based on the soil temperature sensor, several soil temperatures at different times and locations in each control area are obtained; based on the light intensity sensor, several light intensities at different times and locations in each control area are obtained; based on the wind speed sensor, several wind speeds at different times and locations in each control area are obtained; based on the altitude measuring instrument, several altitudes at different locations in each control area are obtained;

[0020] The mode Zm, maximum value Dm and minimum value Xm of various data of several soil moistures, several soil temperatures, several light intensities, several wind speeds and several altitudes in each control area are obtained in turn; the characteristic value Tm of various data in the current control area is obtained based on the formula Tm=(B1m×Dm+B2m×Xm+Zm) / 2; wherein, when m is 1, it represents the data corresponding to the soil moisture, when m is 2, it represents the data corresponding to the soil temperature, when m is 3, it represents the data corresponding to the light intensity, when m is 4, it represents the data corresponding to the wind speed, and when m is 5, it represents the data corresponding to the altitude; B1m and B2m are both proportional adjustment coefficients greater than 0, and B1m+B2m=1, B1m>B2m.

[0021] It is worth noting that the present invention calculates the mode, maximum value and minimum value of each data in the control area in proportion to obtain the characteristic value of the corresponding data. The use of the mode, maximum value and minimum value can reflect the distribution characteristics of the data and the centralization trend of the data, and can reduce the impact of a single data point or extreme value on the overall analysis results.

[0022] Preferably, the basic environmental factors of each regulated area obtained by analyzing the basic data include:

[0023] B1: Extract the characteristic value T1 of soil moisture, the characteristic value T2 of soil temperature, and the characteristic value T3 of light intensity in each control area in turn;

[0024] B2: Determine whether the characteristic value T1 of the soil moisture exceeds the standard humidity range; if yes, set the value of the basic environmental factor JHZ of the current control area to 0; if no, jump to B3;

[0025] B3: Determine whether the characteristic value T2 of the soil temperature exceeds the standard temperature range; if yes, set the value of the basic environmental factor JHZ of the current control area to 0; if no, jump to B4;

[0026] B4: Determine whether the characteristic value T3 of the light intensity exceeds the standard light intensity range; if yes, set the value of the basic environmental factor JHZ of the current control area to 0; if no, jump to B5; wherein the standard humidity range, standard temperature range and standard light intensity range are all obtained through experience;

[0027] B5: Based on the formula JHZ = α1×exp(-|T1-BT1|)+α2×exp(-|T2-BT2|)+α3×exp(-|T3-BT3|), the basic environmental factor JHZ of the current control area is obtained; wherein α1, α2 and α3 are all amplitude adjustment coefficients greater than 0, and the value ranges of α1, α2 and α3 are all (0,1]; BT1 is the middle value of the standard humidity range, BT2 is the middle value of the standard temperature range, and BT3 is the middle value of the standard light intensity range.

[0028] It is worth noting that the present invention calculates the characteristic value T1 of soil moisture, the characteristic value T2 of soil temperature, and the characteristic value T3 of light intensity to obtain a basic environmental factor that can express the suitability of plant growth in the current regulated area, and integrates multiple environmental factors into one factor, which can comprehensively reflect the environmental conditions provided by the current regulated area for plant growth, rather than a single condition, thereby providing a more accurate assessment for the preliminary planning of the regulated area, improving resource utilization efficiency, and reducing unnecessary waste of resources.

[0029] Preferably, the analysis of the target data to obtain the spatial impact factor of each regulatory region includes:

[0030] The characteristic value T4 of the altitude and the characteristic value T5 of the wind speed in each control area are extracted to determine whether the characteristic value T5 of the wind speed is greater than the wind speed threshold BT5; if yes, the value of the spatial influence factor KYZ of the current control area is set to 0; if no, the spatial influence factor KYZ of the current control area is obtained based on the formula KYZ=β / exp((T4-BT4) / BT4)×ln(|T5-BT5| / BT5+1); wherein β is an amplitude adjustment coefficient greater than 0, and BT4 is an artificially set standard altitude.

[0031] It is worth noting that wind speed has a certain impact on the growth of crops. For example, excessive wind speed will disrupt the growth of crops, making it difficult for crops to form a healthy root system, resulting in crop yield reduction. Therefore, when planning the control area, the present invention takes into account the interference of wind speed on regional planning; at the same time, the higher the altitude on the earth, the greater the probability of being affected by high wind speed, so the present invention also takes into account the superimposed impact of altitude on the probability of high wind speed; the two are analyzed together to obtain a spatial impact factor that can express the impact of the current wind speed and the possible high wind speed on each control area, which provides data support for the preliminary planning of the subsequent control area and can improve the scientificity and effectiveness of regional planning.

[0032] Preferably, the establishing of the regional planning model includes:

[0033] Obtain basic environmental factors and spatial influencing factors of several historical control areas, as well as corresponding preliminary planning situations through the database; integrate the basic environmental factors, spatial influencing factors and preliminary planning situations into several groups of training data and test data; use the training data to train the artificial intelligence model; use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a regional planning model with basic environmental factors and spatial influencing factors as input and preliminary planning situations as output; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0034] Specifically, the basic environmental factors and spatial influencing factors in the test data are input into the trained artificial intelligence model to obtain the corresponding preliminary planning situation, and the preliminary planning situation is compared with the corresponding preliminary planning situation in the test data; when the two are the same, no parameter adjustment is required, and the next set of test data is tested; when the two are different, the corresponding parameters are adjusted until the two are the same, and then the next set of test data is tested; when the number of the two that is the same accounts for 95% or more of the total number of tests, a regional planning model is obtained with the basic environmental factors and spatial influencing factors as input and the preliminary planning situation as output.

[0035] Preferably, the secondary adjustment of each control area based on the preliminary planning situation to obtain the final planning situation includes:

[0036] C1: Obtain the planning importance ranking of cultivated land, forest land and garden land in the current intelligent planning area by managers, and mark each control area as LD1 control area, LD2 control area and LD3 control area according to the planning situation in descending order of importance;

[0037] C2: Extract each LD3 control region in turn, obtain the length CD31 of the contact between the LD3 control region and the LD1 control region, obtain the contact ratio ZB1 based on the formula ZB1=CD31 / TCP, and judge whether the contact ratio is greater than the preset ratio; if yes, plan the current LD3 control region as the LD1 control region; if no, jump to C3; wherein the preset ratio is obtained through experience, and TCP is the average value of the perimeter of each control region;

[0038] C3: Obtain the contact length CD32 between the LD3 control region and the LD2 control region, obtain the contact ratio ZB2 based on the formula ZB2=CD32 / TCP, and determine whether the contact ratio ZB2 is greater than a preset ratio; if yes, plan the current LD3 control region as the LD2 control region; if no, do nothing;

[0039] C4: Extract each LD2 control region in turn, obtain the length CD21 of the contact between the LD2 control region and the LD1 control region, obtain the contact ratio ZB3 based on the formula ZB3=CD21 / TCP, and judge whether the contact ratio ZB3 is greater than the preset ratio; if yes, plan the current LD2 control region as the LD1 control region; if no, jump to C5;

[0040] C5: Obtain the contact length CD23 between the LD2 control region and the LD3 control region, obtain the contact ratio four ZB4 based on the formula ZB4=CD23 / TCP, and determine whether the contact ratio four is greater than the preset ratio; if yes, plan the current LD2 control region as the LD3 control region; if no, do nothing.

[0041] It is worth noting that the planning situation of an area is obtained by analyzing the environmental characteristics of the area. Such a processing method may cause another planned area to appear among more similar planned areas, such as a piece of cultivated land appearing in the middle of an area surrounded by woodlands; this may cause mutual interference between the two planned areas. For example, the existence of woodlands will cause a reduction in the yield of cultivated land, and the existence of cultivated land will cause damage to the ecological environment of the forest. Therefore, after the total area is planned into different control areas, the present invention conducts another planning. The purpose is to avoid the situation where another planned area appears among more similar planned areas. Through this two-layer planning strategy, the mutual interference between different planned areas can be effectively reduced, and the scientific nature of the planning and the sustainability of regional development can be improved.

[0042] The second aspect of the present invention provides a method for optimizing land resource utilization based on the Internet of Things, comprising the following steps:

[0043] S1: Divide the total area into an intelligent planning area and an artificially defined area, and divide the intelligent planning area into several control areas; obtain basic data and target data in each control area;

[0044] S2: Analyze the basic data to obtain the basic environmental factors of each control area; analyze the target data to obtain the spatial impact factors of each control area;

[0045] S3: Establish a regional planning model, input basic environmental factors and spatial influencing factors into the regional planning model to obtain the preliminary planning situation of each control area; based on the preliminary planning situation, make secondary adjustments to each control area to obtain the final planning situation, and output the final planning situation to the user end.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The present invention divides the intelligent planning area into several control areas, obtains basic data and target data in each control area; analyzes the basic data and target data respectively to obtain basic environmental factors and spatial influencing factors of each control area; inputs the basic environmental factors and spatial influencing factors into the regional planning model to obtain the preliminary planning situation of each control area; and makes secondary adjustments to each control area based on the preliminary planning situation to obtain the final planning situation, thereby solving the technical problems of ignoring the influence of geographical height and wind speed on crops and thus the influence on regional optimization in the regional optimization of land resources, and ignoring the mutual interference of production capacity between optimized areas; the present invention improves the scientificity and rationality of regional planning.

[0048] 2. Wind speed has a certain impact on the growth of crops. For example, excessive wind speed will disrupt the growth of crops, making it difficult for crops to form a healthy root system, resulting in reduced crop yields. Therefore, when planning the control area, the present invention takes into account the interference of wind speed on regional planning; at the same time, the higher the altitude on the earth, the greater the probability of being affected by high wind speeds, so the present invention also takes into account the superimposed impact of altitude on the probability of high wind speeds; the two are analyzed together to obtain a spatial impact factor that can express the impact of the current wind speed and the possible high wind speed on each control area, providing data support for the preliminary planning of the subsequent control area, which can improve the scientificity and effectiveness of regional planning.

[0049] 3. The planning situation of an area is obtained by analyzing the environmental characteristics of the area. This processing method may cause another planned area to appear in more similar planned areas, such as a piece of cultivated land appears in the middle of an area surrounded by woodlands; this may cause mutual interference between the two planned areas. For example, the existence of woodlands will cause a reduction in the yield of cultivated land, and the existence of cultivated land will cause damage to the ecological environment of the forest. Therefore, after the total area is planned into different control areas, the present invention conducts another planning. The purpose is to avoid the situation where another planned area appears in more similar planned areas. Through this two-layer planning strategy, the mutual interference between different planned areas can be effectively reduced, and the scientific nature of the planning and the sustainability of regional development can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 It is a schematic diagram of the operation steps of the present invention;

[0052] Figure 2 It is a schematic diagram of the system module of the present invention;

[0053] Figure 3 A schematic diagram of the operation steps for obtaining the regulation region of the present invention;

[0054] Figure 4 The figure is a schematic diagram of the operation steps for obtaining the final planning situation of the present invention. DETAILED DESCRIPTION

[0055] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1-Figure 2 , the first aspect of the present invention provides a land resource optimization utilization system based on the Internet of Things, including: an intelligent analysis module, and a data collection module, a regional optimization module and a database connected thereto;

[0057] Data collection module: used to divide the total area into intelligent planning areas and artificially defined areas, and divide the intelligent planning areas into several control areas; obtain basic data and target data in each control area; the basic data includes characteristic values ​​of soil moisture, soil temperature and light intensity; the target data includes characteristic values ​​of altitude and wind speed;

[0058] Intelligent analysis module: used to analyze basic data to obtain basic environmental factors of each control area; analyze target data to obtain spatial impact factors of each control area;

[0059] Regional optimization module: used to establish a regional planning model, input basic environmental factors and spatial influencing factors into the regional planning model to obtain the preliminary planning situation of each control area; based on the preliminary planning situation, make secondary adjustments to each control area to obtain the final planning situation, and output the final planning situation to the user end; among which, the planning situation includes planning for cultivated land, forest land, and garden land.

[0060] Exemplarily, in this embodiment, the area of ​​the standard control area is 1 square kilometer;

[0061] Extract the remote sensing topographic map of the total area. Since there are undemarcable areas in the remote sensing topographic map, the undemarcable areas are marked as artificially defined areas, and the non-undemarcable areas are marked as intelligent planning areas;

[0062] Based on the area of ​​the standard control area, the intelligent planning area is divided into several grid areas, and the area where the complete grid area is located is marked as the control area, and the area where the incomplete grid area is located is marked as the edge area;

[0063] The ratio MBi of the area of ​​each edge area in the intelligent planning area to the area of ​​the standard control area is obtained in sequence. When the area ratio MBi exceeds the set ratio, the corresponding edge area is marked as a control area; when the area ratio MBi does not exceed the set ratio, the corresponding edge area is marked as a decomposition area;

[0064] Perform the following operations on each decomposition area:

[0065] Extract the control area that the decomposition area contacts, mark the contacted control area as the control area to be allocated to the current decomposition area, obtain the contact length Lj between the decomposition area and each control area to be allocated, and obtain the proportion PBLj of the control area to be allocated to the current decomposition area numbered j based on the formula PBLj=Lj / ∑(Lj);

[0066] Based on the area of ​​the decomposed region and the proportion to be allocated PBLj, the decomposed region is proportionally allocated to the corresponding control region to be allocated, such as:

[0067] If the area of ​​the decomposed region is 0.6 square kilometers, the current decomposed region has two waiting to be allocated to the control area, namely, waiting to be allocated to the control area 1 and waiting to be allocated to the control area 2; and the waiting allocation ratio PBL1 to be allocated to the control area 1 is 0.4, and the waiting allocation ratio PBL2 to be allocated to the control area 2 is 0.6;

[0068] Therefore, the area of ​​the 4-component decomposition area near the to-be-allocated control area 1 is allocated to the to-be-allocated control area 1 according to the ratio of 4:6, and the area of ​​the 6-component decomposition area near the to-be-allocated control area 2 is allocated to the to-be-allocated control area 2;

[0069] Obtain the mode Zm, maximum value Dm and minimum value Xm of various data in several soil moistures, several soil temperatures, several light intensities, several wind speeds and several altitudes in each control area; obtain the characteristic value Tm of various data in the current control area based on the formula Tm=(B1m×Dm+B2m×Xm+Zm) / 2; wherein, when m is 1, it indicates the data corresponding to soil moisture, when m is 2, it indicates the data corresponding to soil temperature, when m is 3, it indicates the data corresponding to light intensity, when m is 4, it indicates the data corresponding to wind speed, and when m is 5, it indicates the data corresponding to altitude;

[0070] For example, the mode Z2 of several soil temperatures in the control area is 26°C, the maximum value D2 is 36°C, and the minimum value X2 is 15°C. Based on the formula T2=(B12×D2+B22×X2+Z2) / 2=(0.6×36+0.4×15+26) / 2=26.8°C, the characteristic value T2 of the soil temperature in the current control area is obtained to be 26.8°C; wherein, in this embodiment, the value of the proportional adjustment coefficient B1m is 0.6, and the value of the proportional adjustment coefficient B2m is 0.4;

[0071] The characteristic value T1 of soil moisture, the characteristic value T2 of soil temperature, and the characteristic value T3 of light intensity are extracted in each control area in turn;

[0072] When any of the eigenvalues ​​T1, T2, and T3 exceeds the corresponding standard range, the value of the basic environmental factor JHZ of the current control area is set to 0; wherein the standard range includes the standard humidity range, the standard temperature range, and the standard light intensity;

[0073] When there is no eigenvalue exceeding the corresponding standard range among the eigenvalues ​​T1, T2, and T3, the basic environmental factor JHZ of the current control area is obtained based on the formula JHZ=α1×exp(-|T1-BT1|)+α2×exp(-|T2-BT2|)+α3×exp(-|T3-BT3|); wherein BT1 is the middle value of the standard humidity range, BT2 is the middle value of the standard temperature range, and BT3 is the middle value of the standard light intensity range;

[0074] Extract the characteristic value T4 of the altitude and the characteristic value T5 of the wind speed in each control area;

[0075] When the characteristic value T5 is greater than the wind speed threshold BT5, the value of the spatial influence factor KYZ of the current control area is set to 0;

[0076] When the characteristic value T5 is not greater than the wind speed threshold BT5, the spatial influence factor KYZ of the current control area is obtained based on the formula KYZ=β / exp((T4-BT4) / BT4)×ln(|T5-BT5| / BT5+1); wherein BT4 is the manually set standard altitude;

[0077] Obtain the basic environmental factors and spatial influencing factors of several historical control areas, as well as the corresponding preliminary planning conditions through the database; integrate the basic environmental factors, spatial influencing factors and preliminary planning conditions into several groups of training data and test data; use the training data to train the artificial intelligence model; use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a regional planning model with basic environmental factors and spatial influencing factors as input and preliminary planning conditions as output;

[0078] Input the basic environmental factors and spatial influencing factors into the regional planning model to obtain the preliminary planning situation of each control area;

[0079] Obtain the planning importance ranking of cultivated land, forest land and garden land in the current intelligent planning area by managers, and mark each control area as LD1 control area, LD2 control area and LD3 control area according to the planning situation in descending order of importance;

[0080] In this embodiment, the management personnel rank the planning importance of cultivated land, forest land, and garden land in the current intelligent planning area from large to small as follows: cultivated land, forest land, and garden land; therefore, the control area planned as cultivated land is marked as LD1 control area, the control area planned as forest land is marked as LD2 control area, and the control area planned as garden land is marked as LD3 control area;

[0081] Extract each LD3 control region in turn, obtain the length CD31 of the contact between the LD3 control region and the LD1 control region, and obtain the contact ratio ZB1 based on the formula ZB1=CD31 / TCP. When the contact ratio is greater than the preset ratio, plan the current LD3 control region as the LD1 control region.

[0082] When the contact ratio 1 is not greater than the preset ratio, the length CD32 of the contact between the LD3 control area and the LD2 control area is obtained, and the contact ratio 2 ZB2 is obtained based on the formula ZB2=CD32 / TCP. When the contact ratio 2 is greater than the preset ratio, the current LD3 control area is planned as the LD2 control area.

[0083] Extract each LD2 control region in turn, obtain the length CD21 of the contact between the LD2 control region and the LD1 control region, and obtain the contact ratio ZB3 based on the formula ZB3=CD21 / TCP. When the contact ratio ZB3 is greater than the preset ratio, the current LD2 control region is planned as the LD1 control region.

[0084] When the contact ratio three is not greater than the preset ratio, the length CD23 of the contact between the LD2 control area and the LD3 control area is obtained, and the contact ratio four ZB4 is obtained based on the formula ZB4=CD23 / TCP. When the contact ratio four is greater than the preset ratio, the current LD2 control area is planned as the LD3 control area.

[0085] In this application, the total area is divided into intelligent planning areas and artificially defined areas, including:

[0086] Extract the remote sensing topographic map of the total area and determine whether there are undemarcable areas in the remote sensing topographic map; if yes, mark the undemarcable areas as artificially defined areas and mark the non-undemarcable areas as intelligently planned areas; among them, the undemarcable areas include rivers and built-up areas.

[0087] It should be noted that before optimizing land resources, areas that cannot be planned, such as rivers and building areas, should be removed. This can reduce the amount of computation in the system and improve computational efficiency.

[0088] See also Figure 3 In this application, the intelligent planning area is divided into several control areas, including:

[0089] A1: Obtain the area of ​​the standard control area from the database, and divide the intelligent planning area into several grid areas based on the area of ​​the standard control area; determine whether the grid area is a complete grid in turn; if yes, mark the area where the grid area is located as a control area; if no, mark the area where the grid area is located as an edge area, and jump to A2;

[0090] A2: sequentially obtain the area Si of each edge area in the intelligent planning area, obtain the area ratio MBi of each edge area to the standard control area based on the formula MBi=Si / BTS, and sequentially determine whether the area ratio MBi exceeds the set ratio; if yes, mark the corresponding edge area as the control area; if no, mark the corresponding edge area as the decomposition area, and jump to A3; wherein i is the number corresponding to the edge area, BTS is the area of ​​the standard control area, and the set ratio is obtained manually;

[0091] A3: Extract the control areas contacted by each decomposition area in turn, and mark the control areas as the corresponding decomposition areas to be allocated to the control areas; obtain the contact length Lj between the decomposition area and the corresponding to-be-allocated control area, and obtain the to-be-allocated proportion PBLj of the to-be-allocated control area numbered j corresponding to the current decomposition area based on the formula PBLj=Lj / ∑(Lj); allocate the decomposition areas to the corresponding to-be-allocated control areas in proportion based on the area of ​​the decomposition areas and the to-be-allocated proportion PBLj, and combine the to-be-allocated control areas and the allocated decomposition areas into the control areas; wherein j is the number of the to-be-allocated control area, and the range of j is [1,n]; ∑ is the summation symbol for summing Lj, and the summation range is [1,n]; n represents the number of to-be-allocated control areas corresponding to the current decomposition area.

[0092] It should be noted that the grids used in the grid division of the intelligent planning area may be square grids, rectangular grids, triangular grids, or other grids with certain rules.

[0093] It should be noted that determining in sequence whether the grid areas are complete grids in the complete grid can be understood as: all planned grid areas are within the intelligent planning area, such as the grids divided at the edge of the intelligent planning area may be incomplete.

[0094] It should be noted that when the area ratio MBi exceeds the set ratio, marking the corresponding edge area as a control area in the control area can be understood as: an edge area with a larger area. The present invention does not decompose this edge area, but controls it as a control area.

[0095] It should be noted that based on the area of ​​the decomposed region and the proportion to be allocated PBLj, the decomposed region is proportionally allocated to the corresponding control region to be allocated. The specific calculation steps of this step are:

[0096] If the area of ​​the decomposed region is 0.6 square kilometers, the current decomposed region has two waiting to be allocated to the control area, namely, waiting to be allocated to the control area 1 and waiting to be allocated to the control area 2; and the waiting allocation ratio PBL1 to be allocated to the control area 1 is 0.4, and the waiting allocation ratio PBL2 to be allocated to the control area 2 is 0.6;

[0097] Therefore, the area of ​​the 4-component decomposition area close to the control area 1 to be allocated to the control area 1 is allocated to the control area 1 according to the ratio of 4:6, and the area of ​​the 6-component decomposition area close to the control area 2 to be allocated to the control area 2.

[0098] It should be noted that the area to be allocated to the control area and the allocated decomposition area are combined into the control area. For example, if the area to be allocated to the control area is 1 square kilometer and the area of ​​the allocated decomposition area is 0.2 square kilometers, the area of ​​the combined control area is 1.2 square kilometers.

[0099] In this application, basic data and target data in each regulatory area are obtained, including:

[0100] Based on the soil moisture sensor, several soil moistures at different times and locations in each control area are obtained; based on the soil temperature sensor, several soil temperatures at different times and locations in each control area are obtained; based on the light intensity sensor, several light intensities at different times and locations in each control area are obtained; based on the wind speed sensor, several wind speeds at different times and locations in each control area are obtained; based on the altitude measuring instrument, several altitudes at different locations in each control area are obtained;

[0101] The mode Zm, maximum value Dm and minimum value Xm of various data of several soil moistures, several soil temperatures, several light intensities, several wind speeds and several altitudes in each control area are obtained in turn; the characteristic value Tm of various data in the current control area is obtained based on the formula Tm=(B1m×Dm+B2m×Xm+Zm) / 2; wherein, when m is 1, it represents the data corresponding to the soil moisture, when m is 2, it represents the data corresponding to the soil temperature, when m is 3, it represents the data corresponding to the light intensity, when m is 4, it represents the data corresponding to the wind speed, and when m is 5, it represents the data corresponding to the altitude; B1m and B2m are both proportional adjustment coefficients greater than 0, and B1m+B2m=1, B1m>B2m.

[0102] It should be noted that the values ​​of the proportional adjustment coefficients B11, B12, B13, B14, and B15 are set by the staff according to the characteristics of the data, and the set values ​​are not necessarily the same; similarly, the values ​​set for the proportional adjustment coefficients B21, B22, B23, B24, and B25 are not necessarily the same.

[0103] It should be noted that the data obtained at different times and locations are data at several locations within a region obtained at several time points set based on experience.

[0104] In this application, the basic data are analyzed to obtain the basic environmental factors of each regulatory area, including:

[0105] B1: Extract the characteristic value T1 of soil moisture, the characteristic value T2 of soil temperature, and the characteristic value T3 of light intensity in each control area in turn;

[0106] B2: Determine whether the characteristic value T1 of soil moisture exceeds the standard humidity range; if yes, set the value of the basic environmental factor JHZ of the current control area to 0; if no, jump to B3;

[0107] B3: Determine whether the characteristic value T2 of soil temperature exceeds the standard temperature range; if yes, set the value of the basic environmental factor JHZ of the current control area to 0; if no, jump to B4;

[0108] B4: Determine whether the characteristic value T3 of light intensity exceeds the standard light intensity range; if yes, set the value of the basic environmental factor JHZ of the current control area to 0; if no, jump to B5; among which, the standard humidity range, standard temperature range and standard light intensity range are all obtained through experience;

[0109] B5: Based on the formula JHZ = α1×exp(-|T1-BT1|)+α2×exp(-|T2-BT2|)+α3×exp(-|T3-BT3|), the basic environmental factor JHZ of the current control area is obtained; wherein α1, α2 and α3 are all amplitude adjustment coefficients greater than 0, and the value ranges of α1, α2 and α3 are all (0,1]; BT1 is the middle value of the standard humidity range, BT2 is the middle value of the standard temperature range, and BT3 is the middle value of the standard light intensity range.

[0110] It should be noted that the standard range judgment of the characteristic value T1 of soil moisture, the characteristic value T2 of soil temperature, and the characteristic value T3 of light intensity is used to analyze whether the basic environment in the current control area is suitable for plant growth. If it is not suitable, the basic environmental factor is marked as 0, providing data support for the preliminary planning of subsequent control areas.

[0111] In this application, the target data is analyzed to obtain the spatial impact factors of each regulatory region, including:

[0112] The characteristic value T4 of the altitude and the characteristic value T5 of the wind speed in each control area are extracted to determine whether the characteristic value T5 of the wind speed is greater than the wind speed threshold BT5; if yes, the value of the spatial influence factor KYZ of the current control area is set to 0; if no, the spatial influence factor KYZ of the current control area is obtained based on the formula KYZ=β / exp((T4-BT4) / BT4)×ln(|T5-BT5| / BT5+1); wherein β is an amplitude adjustment coefficient greater than 0, and BT4 is an artificially set standard altitude.

[0113] The regional planning model established in this application includes:

[0114] Obtain basic environmental factors and spatial influencing factors of several historical control areas, as well as corresponding preliminary planning situations through the database; integrate the basic environmental factors, spatial influencing factors and preliminary planning situations into several groups of training data and test data; use the training data to train the artificial intelligence model; use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a regional planning model with basic environmental factors and spatial influencing factors as input and preliminary planning situations as output; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0115] Specifically, the basic environmental factors and spatial influencing factors in the test data are input into the trained artificial intelligence model to obtain the corresponding preliminary planning situation, and the preliminary planning situation is compared with the corresponding preliminary planning situation in the test data; when the two are the same, no parameter adjustment is required, and the next set of test data is tested; when the two are different, the corresponding parameters are adjusted until the two are the same, and then the next set of test data is tested; when the number of the two that is the same accounts for 95% or more of the total number of tests, a regional planning model with basic environmental factors and spatial influencing factors as input and preliminary planning situations as output is obtained.

[0116] It should be noted that the preliminary planning situation used in the training data is to first calculate the basic environmental factors and spatial influencing factors to obtain the planning factors, and then manually analyze the planning factors to obtain the preliminary planning situation; among them, the calculation formula for the planning factor is: GHZ = δ1 × JHZ + δ2 × KYZ to obtain the planning factor GHZ; among them, δ1 and δ2 are both proportional adjustment factors greater than 0, and δ1 + δ2 = 1.

[0117] See also Figure 4 In this application, the final planning situation is obtained by making secondary adjustments to each control area based on the preliminary planning situation, including:

[0118] C1: Obtain the planning importance ranking of cultivated land, forest land and garden land in the current intelligent planning area by managers, and mark each control area as LD1 control area, LD2 control area and LD3 control area according to the planning situation in descending order of importance;

[0119] C2: Extract each LD3 control region in turn, obtain the length CD31 of the contact between the LD3 control region and the LD1 control region, obtain the contact ratio ZB1 based on the formula ZB1=CD31 / TCP, and judge whether the contact ratio is greater than the preset ratio; if yes, plan the current LD3 control region as the LD1 control region; if no, jump to C3; wherein the preset ratio is obtained through experience, and TCP is the average value of the perimeter of each control region;

[0120] C3: Obtain the contact length CD32 between the LD3 control area and the LD2 control area, obtain the contact ratio ZB2 based on the formula ZB2=CD32 / TCP, and determine whether the contact ratio ZB2 is greater than the preset ratio; if yes, plan the current LD3 control area as the LD2 control area; if no, do nothing;

[0121] C4: Extract each LD2 control region in turn, obtain the length CD21 of the contact between the LD2 control region and the LD1 control region, obtain the contact ratio ZB3 based on the formula ZB3=CD21 / TCP, and judge whether the contact ratio ZB3 is greater than the preset ratio; if yes, plan the current LD2 control region as the LD1 control region; if no, jump to C5;

[0122] C5: Obtain the contact length CD23 between the LD2 control region and the LD3 control region, obtain the contact ratio four ZB4 based on the formula ZB4=CD23 / TCP, and determine whether the contact ratio four is greater than the preset ratio; if yes, plan the current LD2 control region as the LD3 control region; if no, do nothing.

[0123] It should be noted that the planning importance ranking of cultivated land, forest land and garden land in the current intelligent planning area by managers is obtained, and each control area is marked as LD1 control area, LD2 control area and LD3 control area in descending order of importance. It can be understood as follows:

[0124] If the management personnel rank the planning importance of cultivated land, forest land and garden in the current intelligent planning area from large to small as follows: cultivated land, forest land and garden; then the control area planned as cultivated land will be marked as LD1 control area, the control area planned as forest land will be marked as LD2 control area, and the control area planned as garden will be marked as LD3 control area.

[0125] It should be noted that steps C2 and C3 are steps for obtaining the final plan of the LD3 control area, and steps C4 and C5 are steps for obtaining the final plan of the LD2 control area; for example: when the contact ratio ZB1 of the extracted LD3 control area is greater than the preset ratio, the current LD3 control area is planned as the LD1 control area; when the contact ratio ZB1 of the extracted LD3 control area is not greater than the preset ratio, jump to C3 for the next step of judgment.

[0126] A second aspect of the present invention provides a method for optimizing land resource utilization based on the Internet of Things, comprising the following steps:

[0127] S1: Divide the total area into an intelligent planning area and an artificially defined area, and divide the intelligent planning area into several control areas; obtain basic data and target data in each control area;

[0128] S2: Analyze the basic data to obtain the basic environmental factors of each control area; analyze the target data to obtain the spatial impact factors of each control area;

[0129] S3: Establish a regional planning model, input basic environmental factors and spatial influencing factors into the regional planning model to obtain the preliminary planning situation of each control area; based on the preliminary planning situation, make secondary adjustments to each control area to obtain the final planning situation, and output the final planning situation to the user end.

[0130] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0131] Working principle of the present invention:

[0132] The present invention first divides the total area into an intelligent planning area and an artificially defined area, and divides the intelligent planning area into several control areas; in this way, the system can better adapt to the diversified needs in complex areas, so that the system can be personalized and optimized according to the characteristics of different areas; obtain basic data and target data in each control area; analyze the basic data to obtain the basic environmental factors of each control area, this step obtains a basic environmental factor that can express the suitability of plant growth in the current control area, and can provide a more accurate evaluation for the preliminary planning of the control area; analyze the target data to obtain the spatial influence factor of each control area, this step jointly analyzes the superimposed influence of wind speed and altitude on the probability of high wind speed, and obtains the spatial influence factor that can express the influence of the current wind speed and the possible high wind speed on each control area, which provides data support for the preliminary planning of subsequent control areas; establish a regional planning model, input the basic environmental factors and spatial influence factors into the regional planning model to obtain the preliminary planning situation of each control area; make secondary adjustments to each control area based on the preliminary planning situation to obtain the final planning situation, and output the final planning situation to the user end.

[0133] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The land resource optimization utilization system based on the Internet of Things is characterized by: include: Intelligent analysis module, and the data collection module, regional optimization module and database connected thereto; The data collection module is used to divide the total area into an intelligent planning area and a manually defined area, and to divide the intelligent planning area into a number of control areas; Obtain basic data and target data in each control area; wherein the basic data includes characteristic values ​​of soil moisture, soil temperature and light intensity; the target data includes characteristic values ​​of altitude and wind speed; The intelligent analysis module is used to analyze the basic data to obtain the basic environmental factors of each control area; analyze the target data to obtain the spatial impact factors of each control area; The regional optimization module is used to establish a regional planning model, input basic environmental factors and spatial influencing factors into the regional planning model to obtain the preliminary planning situation of each control area; based on the preliminary planning situation, each control area is adjusted twice to obtain the final planning situation, and the final planning situation is output to the user end; wherein the planning situation includes planning for cultivated land, forest land, and garden land.

2. The land resource optimization utilization system based on the Internet of Things according to claim 1 is characterized in that: The intelligent analysis module communicates and / or is electrically connected to the data collection module, the regional optimization module and the database respectively; the database communicates and / or is electrically connected to the data collection module, the intelligent analysis module and the regional optimization module respectively.

3. The land resource optimization utilization system based on the Internet of Things according to claim 1 is characterized in that: The division of the total area into intelligently planned areas and artificially defined areas includes: Extract the remote sensing topographic map of the total area and determine whether there are undemarcable areas in the remote sensing topographic map; if yes, mark the undemarcable areas as artificially defined areas and mark the non-undemarcable areas as intelligently planned areas; wherein the undemarcable areas include rivers and built-up areas.

4. The land resource optimization utilization system based on the Internet of Things according to claim 3 is characterized in that: The intelligent planning area is divided into several control areas, including: A1: Obtain the area of ​​the standard control area from the database, and divide the intelligent planning area into several grid areas based on the area of ​​the standard control area; determine whether the grid areas are complete grids in turn; if yes, mark the area where the grid area is located as a control area; if no, mark the area where the grid area is located as an edge area, and jump to A2; A2: sequentially obtain the area Si of each edge area in the intelligent planning area, obtain the area ratio MBi of each edge area to the standard control area based on the formula MBi=Si / BTS, and sequentially determine whether the area ratio MBi exceeds the set ratio; if yes, mark the corresponding edge area as the control area; if no, mark the corresponding edge area as the decomposition area, and jump to A3; wherein i is the number corresponding to the edge area, and BTS is the area of ​​the standard control area; A3: Extract the control areas contacted by each decomposition area in turn, and mark the control areas as the corresponding decomposition areas to be allocated to the control areas; obtain the contact length Lj between the decomposition area and the corresponding to-be-allocated control area, and obtain the to-be-allocated proportion PBLj of the to-be-allocated control area numbered j corresponding to the current decomposition area based on the formula PBLj=Lj / ∑(Lj); allocate the decomposition area to the corresponding to-be-allocated control area in proportion based on the area of ​​the decomposition area and the to-be-allocated proportion PBLj, and combine the to-be-allocated control area and the allocated decomposition area into the control area; wherein j is the number of the to-be-allocated control area, and the range of j is [1,n]; ∑ is the summation symbol for summing Lj, and the summation range is [1,n]; n represents the number of to-be-allocated control areas corresponding to the current decomposition area.

5. The land resource optimization utilization system based on the Internet of Things according to claim 1 is characterized in that: The acquisition of basic data and target data in each control area includes: Based on the soil moisture sensor, several soil moistures at different times and locations in each control area are obtained; based on the soil temperature sensor, several soil temperatures at different times and locations in each control area are obtained; based on the light intensity sensor, several light intensities at different times and locations in each control area are obtained; based on the wind speed sensor, several wind speeds at different times and locations in each control area are obtained; based on the altitude measuring instrument, several altitudes at different locations in each control area are obtained; The mode Zm, maximum value Dm and minimum value Xm of various data of several soil moistures, several soil temperatures, several light intensities, several wind speeds and several altitudes in each control area are obtained in turn; the characteristic value Tm of various data in the current control area is obtained based on the formula Tm=(B1m×Dm+B2m×Xm+Zm) / 2; wherein, when m is 1, it represents the data corresponding to the soil moisture, when m is 2, it represents the data corresponding to the soil temperature, when m is 3, it represents the data corresponding to the light intensity, when m is 4, it represents the data corresponding to the wind speed, and when m is 5, it represents the data corresponding to the altitude; B1m and B2m are both proportional adjustment coefficients greater than 0, and B1m+B2m=1, B1m>B2m.

6. The land resource optimization utilization system based on the Internet of Things according to claim 5 is characterized in that: The basic environmental factors of each regulated area obtained by analyzing the basic data include: B1: Extract the characteristic value T1 of soil moisture, the characteristic value T2 of soil temperature, and the characteristic value T3 of light intensity in each control area in turn; B2: Determine whether the characteristic value T1 of the soil moisture exceeds the standard humidity range; if yes, set the value of the basic environmental factor JHZ of the current control area to 0; if no, jump to B3; B3: Determine whether the characteristic value T2 of the soil temperature exceeds the standard temperature range; if yes, set the value of the basic environmental factor JHZ of the current control area to 0; if no, jump to B4; B4: Determine whether the characteristic value T3 of the light intensity exceeds the standard light intensity range; if yes, set the value of the basic environmental factor JHZ of the current control area to 0; if no, jump to B5; B5: Based on the formula JHZ = α1×exp(-|T1-BT1|)+α2×exp(-|T2-BT2|)+α3×exp(-|T3-BT3|), the basic environmental factor JHZ of the current control area is obtained; wherein α1, α2 and α3 are all amplitude adjustment coefficients greater than 0, and the value ranges of α1, α2 and α3 are all (0,1]; BT1 is the middle value of the standard humidity range, BT2 is the middle value of the standard temperature range, and BT3 is the middle value of the standard light intensity range.

7. The land resource optimization utilization system based on the Internet of Things according to claim 6 is characterized in that: The target data is analyzed to obtain the spatial impact factors of each control area, including: The characteristic value T4 of the altitude and the characteristic value T5 of the wind speed in each control area are extracted to determine whether the characteristic value T5 of the wind speed is greater than the wind speed threshold BT5; if yes, the value of the spatial influence factor KYZ of the current control area is set to 0; if no, the spatial influence factor KYZ of the current control area is obtained based on the formula KYZ=β / exp((T4-BT4) / BT4)×ln(|T5-BT5| / BT5+1); wherein β is an amplitude adjustment coefficient greater than 0, and BT4 is the standard altitude.

8. The land resource optimization utilization system based on the Internet of Things according to claim 7 is characterized in that: The establishing of the regional planning model comprises: Obtain basic environmental factors and spatial influencing factors of several historical control areas, as well as corresponding preliminary planning situations through the database; integrate the basic environmental factors, spatial influencing factors and preliminary planning situations into several groups of training data and test data; use the training data to train the artificial intelligence model; use the test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally obtain a regional planning model with basic environmental factors and spatial influencing factors as input and preliminary planning situations as output; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

9. The land resource optimization utilization system based on the Internet of Things according to claim 1 is characterized in that: The final planning situation is obtained by making secondary adjustments to each control area based on the preliminary planning situation, including: C1: Obtain the planning importance ranking of cultivated land, forest land and garden land in the current intelligent planning area by managers, and mark each control area as LD1 control area, LD2 control area and LD3 control area according to the planning situation in descending order of importance; C2: Extract each LD3 control region in turn, obtain the length CD31 of the contact between the LD3 control region and the LD1 control region, obtain the contact ratio ZB1 based on the formula ZB1=CD31 / TCP, and judge whether the contact ratio is greater than the preset ratio; if yes, plan the current LD3 control region as the LD1 control region; if no, jump to C3; wherein TCP is the average value of the perimeter of each control region; C3: Obtain the contact length CD32 between the LD3 control region and the LD2 control region, obtain the contact ratio ZB2 based on the formula ZB2=CD32 / TCP, and determine whether the contact ratio ZB2 is greater than a preset ratio; if yes, plan the current LD3 control region as the LD2 control region; if no, do nothing; C4: Extract each LD2 control region in turn, obtain the length CD21 of the contact between the LD2 control region and the LD1 control region, obtain the contact ratio ZB3 based on the formula ZB3=CD21 / TCP, and judge whether the contact ratio ZB3 is greater than the preset ratio; if yes, plan the current LD2 control region as the LD1 control region; if no, jump to C5; C5: Obtain the contact length CD23 between the LD2 control region and the LD3 control region, obtain the contact ratio four ZB4 based on the formula ZB4=CD23 / TCP, and determine whether the contact ratio four is greater than the preset ratio; if yes, plan the current LD2 control region as the LD3 control region; if no, do nothing.

10. A land resource optimization utilization method based on the Internet of Things, based on the operation of the land resource optimization utilization system based on the Internet of Things according to any one of claims 1 to 9, characterized in that: S1: Divide the total area into intelligent planning areas and artificially defined areas, and divide the intelligent planning areas into several control areas; Obtain basic data and target data in each regulated area; S2: Analyze the basic data to obtain the basic environmental factors of each regulatory area; Analyze the target data to obtain the spatial impact factors of each regulatory area; S3: Establish a regional planning model, input basic environmental factors and spatial influencing factors into the regional planning model to obtain the preliminary planning situation of each control area; Based on the preliminary planning situation, each control area is adjusted twice to obtain the final planning situation, and the final planning situation is output to the user end.

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