Land resource optimization utilization system and method based on internet of things

Abstract

The application discloses a land resource optimized utilization system and method based on Internet of Things, relates to the technical field of land resource optimized utilization, and solves the technical problems that the influence of geographical height and wind speed on crops is ignored in the regional optimization of land resources, which further influences the regional optimization, and that the existence of mutual interference of production capacity between the optimized regions is ignored; the application divides intelligent planning regions into a plurality of regulation and control regions, acquires basic data and target data in each regulation and control region, analyzes the basic data and the target data respectively to obtain basic environmental factors and space influence factors of each regulation and control region, inputs the basic environmental factors and the space influence factors into a regional planning model to obtain preliminary planning conditions of each regulation and control region, and adjusts each regulation and control region based on the preliminary planning conditions to obtain final planning conditions, so that the scientificity of planning and the sustainability of regional development can be improved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of intelligent agriculture, and relates to land resource optimization utilization technology, in particular to a land resource optimization utilization system and method based on the Internet of Things. BACKGROUND

[0002] With the continuous growth of global population and the acceleration of urbanization process, land resources, as one of the important resources for human survival and development, become increasingly scarce, especially in agricultural production and urban expansion. How to efficiently and reasonably use limited land resources has become a problem to be solved. The Internet of Things technology provides a new means and tool for land resource optimization utilization, which can realize real-time monitoring of land resources, provide data support for subsequent intelligent analysis and management, and realize the optimization and efficient use of land resources.

[0003] At present, most land resource optimization utilization systems and methods based on the Internet of Things ignore the influence of geographical height and wind speed on crops in the regional optimization of land resources, and only plan and control the region according to soil humidity, soil temperature and light intensity, without considering the influence of wind speed on crops in the region, which reduces production efficiency and economic benefits. At the same time, it is ignored that there may be another kind of planning region in the optimized region, which causes the existence of mutual interference of production capacity between regions, resulting in economic losses caused by the reduction of production capacity of the region.

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

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; for this purpose, the present application 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 in the regional optimization of land resources and ignoring the mutual interference of production capacity between regions after optimization. The present application divides the intelligent planning region into several control regions, obtains the basic data and target data in each control region; analyzes the basic data and target data to obtain the basic environmental factors and spatial influence factors of each control region; inputs the basic environmental factors and spatial influence factors into the regional planning model to obtain the preliminary planning situation of each control region; and adjusts each control region based on the preliminary planning situation to obtain the final planning situation, which solves the above problems.

[0006] To achieve the above purpose, the first aspect of the present application provides a land resource optimization utilization system based on the Internet of Things, which comprises an intelligent analysis module, and a data collection module, a regional optimization module and a database connected thereto.

[0007] The data collection module is configured to divide the total area into an intelligent planning area and a man-defined area, divide the intelligent planning area into a plurality of control areas, and acquire basic data and target data in each control area; the basic data includes characteristic values of soil humidity, soil temperature, and light intensity; and the target data includes characteristic values of altitude and wind speed.

[0008] The intelligent analysis module is configured to analyze the basic data to obtain basic environmental factors of each control area, and analyze the target data to obtain spatial influence factors of each control area.

[0009] The area optimization module is configured to establish an area planning model, input the basic environmental factors and the spatial influence factors into the area planning model to obtain a preliminary planning situation of each control area, perform secondary adjustment on each control area based on the preliminary planning situation to obtain a final planning situation, and output the final planning situation to a user end; the planning situation includes planning as farmland, woodland, and garden land.

[0010] Preferably, the intelligent analysis module is in communication and / or electrical connection with the data collection module, the area optimization module, and the database; and the database is in communication and / or electrical connection with the data collection module, the intelligent analysis module, and the area optimization module.

[0011] Preferably, the division of the total area into the intelligent planning area and the man-defined area comprises:

[0012] extracting a remote sensing topographic map of the total area, judging whether there is an unclassifiable area in the remote sensing topographic map, marking the unclassifiable area as the man-defined area and marking a non-unclassifiable area as the intelligent planning area if the unclassifiable area exists; and the unclassifiable area includes a river and a building area.

[0013] Preferably, the division of the intelligent planning area into a plurality of control areas comprises:

[0014] A1: acquiring an area of a standard control area from a database, performing grid division on the intelligent planning area based on the area of the standard control area to obtain a plurality of grid areas, and judging whether the grid area is a complete grid in sequence; marking a region where the grid area is located as a control area if the grid area is the complete grid; marking a region where the grid area is located as an edge region and jumping to A2 if the grid area is not the complete grid;

[0015] A2: sequentially obtaining the area Si of each edge region in the intelligent planning area, obtaining the area ratio MBi of each edge region and the standard control region based on the formula MBi=Si / BTS, sequentially judging whether the area ratio MBi exceeds the set proportion; yes, marking the corresponding edge region as a control region; no, marking the corresponding edge region as a decomposition region, and jumping to A3; wherein i is the number corresponding to the edge region, BTS is the area of the standard control region, and the set proportion is obtained by manual operation;

[0016] A3: sequentially extracting the control region contacted by each decomposition region, marking the control region as the corresponding decomposition region to be allocated to the control region; obtaining the length Lj of the decomposition region and the corresponding to-be-allocated-to-control region, obtaining the to-be-allocated proportion PBLj of the to-be-allocated-to-control region corresponding to the current decomposition region with the number j based on the formula PBLj=Lj / ∑(Lj); allocating the decomposition region to the corresponding to-be-allocated-to-control region in proportion based on the area of the decomposition region and the to-be-allocated proportion PBLj, and integrating the to-be-allocated-to-control region and the allocated decomposition region into a control region; wherein j is the number of the to-be-allocated-to-control region, and j ranges from 1 to n; ∑ is a summation symbol for summing Lj, and the summation range is from 1 to n; n represents the number of the to-be-allocated-to-control region corresponding to the current decomposition region.

[0017] It is worth noting that the present application divides the intelligent planning area into several control regions, which is used for accurate division of the intelligent planning area, so that the system can better adapt to the diversified needs in complex areas, the system can be personalized optimized according to the characteristics of different areas, and the system can 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 obtained basic data and target data in each control region include:

[0019] Based on the soil humidity sensor, a plurality of soil humidities at different times and different locations in each control region are obtained, based on the soil temperature sensor, a plurality of soil temperatures at different times and different locations in each control region are obtained, based on the light intensity sensor, a plurality of light intensities at different times and different locations in each control region are obtained, based on the wind speed sensor, a plurality of wind speeds at different times and different locations in each control region are obtained, and based on the altitude measuring instrument, a plurality of altitudes at different locations in each control region are obtained.

[0020] Obtain the mode Zm, the maximum value Dm and the minimum value Xm of each data in the soil moisture, the soil temperature, the light intensity, the wind speed and the altitude of each regulation area in sequence; obtain the characteristic value Tm of each data of the current regulation area based on the formula Tm=(B1m*Dm+B2m*Xm+Zm) / 2; wherein, m is 1 for the data corresponding to the soil moisture, m is 2 for the data corresponding to the soil temperature, m is 3 for the data corresponding to the light intensity, m is 4 for the data corresponding to the wind speed, and m is 5 for 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 application obtains the characteristic value of the corresponding data by proportionally calculating the mode, the maximum value and the minimum value of each data in the regulation area, and the mode, the maximum value and the minimum value can reflect the distribution characteristics and the concentration trend of the data, so that the influence of single data point or extreme value on the overall analysis result can be reduced.

[0022] Preferably, the analysis of the basic data obtains the basic environmental factors of each regulation area, including:

[0023] B1: sequentially extract the characteristic value T1 of the soil moisture, the characteristic value T2 of the soil temperature and the characteristic value T3 of the light intensity in each regulation area;

[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 regulation 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 regulation 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 regulation area to 0; if no, jump to B5; wherein, the standard humidity range, the standard temperature range and the standard light intensity range are obtained by experience;

[0027] B5: obtain the basic environmental factor JHZ of the current regulation area based on the formula JHZ=α1*exp(-|T1-BT1|)+α2*exp(-|T2-BT2|)+α3*exp(-|T3-BT3|); wherein, α1, α2 and α3 are all amplitude adjustment coefficients greater than 0, and the value range of α1, α2 and α3 is 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 application calculates the characteristic value T1 of soil humidity, the characteristic value T2 of soil temperature and the characteristic value T3 of light intensity to obtain a basic environmental factor capable of expressing the suitability of plant growth in the current regulation area, and the multiple environmental factors are integrated into a factor, which can comprehensively reflect the environmental conditions provided by the current regulation area for plant growth instead of a single condition, so that more accurate evaluation is provided for the preliminary planning of the regulation area, resource utilization efficiency is improved, and unnecessary resource waste is reduced.

[0029] Preferably, the target data is analyzed to obtain the spatial influence factor of each regulation area, including:

[0030] The characteristic value T4 of the altitude and the characteristic value T5 of the wind speed in each regulation area are extracted, and it is judged 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 regulation area is set to 0; otherwise, the spatial influence factor KYZ of the current regulation 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 a standard altitude artificially set.

[0031] It is worth noting that the wind speed has a certain influence on the growth of crops, for example, excessive wind speed can disturb the growth of crops, making it difficult for crops to form healthy root systems, resulting in crop yield reduction. Therefore, the application considers the interference of wind speed on the planning of the regulation area when planning the regulation area; at the same time, the higher the altitude on the earth, the greater the probability of being affected by high wind speed, so the application also considers the superimposed influence of altitude on the probability of high wind speed; the two are analyzed together to obtain a spatial influence factor capable of expressing the influence of the current wind speed and the possible influence of high wind speed on each regulation area, which provides data support for the preliminary planning of the subsequent regulation area, and can improve the scientificity and effectiveness of the planning of the regulation area.

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

[0033] The basic environmental factor and the spatial influence factor of a plurality of historical regulation areas and the corresponding preliminary planning situation are obtained through a database; the basic environmental factor, the spatial influence factor and the preliminary planning situation are integrated into a plurality of groups of training data and detection data; the artificial intelligence model is trained using the training data; the trained artificial intelligence model is tested using the test data, and the artificial intelligence model is adjusted according to the test result; finally, a regional planning model is obtained, in which the input is the basic environmental factor and the spatial influence factor, and the output is the preliminary planning situation; wherein the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0034] Specifically, the basic environmental factors and the space influence factors in the test data are input into the trained artificial intelligence model to obtain a 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 needed, and the next set of test data is detected; 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 detected; when the number of the two that are the same accounts for 95% or more of the total number of detections, a regional planning model with the input of the basic environmental factors and the space influence factors and the output of the preliminary planning situation is obtained.

[0035] Preferably, the final planning situation is obtained by secondarily adjusting each control region based on the preliminary planning situation, comprising:

[0036] C1: Obtain the importance of the current intelligent planning region in the planning of farmland, woodland, and garden by the management personnel, and mark each control region according to the planning situation in descending order of importance as an LD1 control region, an LD2 control region, and an LD3 control region;

[0037] C2: Extract each LD3 control region in turn, obtain the length CD31 of the LD3 control region contacting the LD1 control region, obtain the contact proportion one ZB1 based on the formula ZB1=CD31 / TCP, and determine whether the contact proportion one is greater than a preset proportion; if yes, the current LD3 control region is planned as an LD1 control region; if no, jump to C3; wherein the preset proportion is obtained by experience, and TCP is the average value of the perimeter of each control region;

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

[0039] C4: Extract each LD2 control region in turn, obtain the length CD21 of the LD2 control region contacting the LD1 control region, obtain the contact proportion three ZB3 based on the formula ZB3=CD21 / TCP, and determine whether the contact proportion three is greater than a preset proportion; if yes, the current LD2 control region is planned as an LD1 control region; if no, jump to C5;

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

[0041] It is worth noting that the planning of a region is analyzed according to the environmental characteristics of the region, and such a processing manner can cause another planning region to appear in a large number of the same planning regions, for example, a cultivated land appears in a region surrounded by forest land; such can cause mutual interference between two planning regions, for example, the existence of forest land can cause the yield of cultivated land to decrease, and the existence of cultivated land can cause the ecological environment of forest land to be destroyed, so the application further plans after the total region is planned into different control regions, and the purpose is to avoid the situation that another planning region appears in a large number of the same planning regions, and through the double-layer planning strategy, the mutual interference between different planning regions can be effectively reduced, and the scientificity of planning and the sustainability of regional development are improved.

[0042] The second aspect of the application provides a land resource optimization utilization method based on the Internet of Things, comprising the following steps:

[0043] S1: dividing a total region into an intelligent planning region and a human-defined region, dividing the intelligent planning region into a plurality of control regions, and acquiring basic data and target data in each control region;

[0044] S2: analyzing the basic data to obtain basic environmental factors of each control region, and analyzing the target data to obtain spatial influence factors of each control region;

[0045] S3: establishing a regional planning model, inputting the basic environmental factors and the spatial influence factors into the regional planning model to obtain a preliminary planning situation of each control region, and based on the preliminary planning situation, performing secondary adjustment on each control region to obtain a final planning situation, and outputting the final planning situation to a user end.

[0046] Compared with the prior art, the application has the beneficial effects that:

[0047] 1. The application divides the intelligent planning region into a plurality of control regions, acquires basic data and target data in each control region, analyzes the basic data and the target data to obtain basic environmental factors and spatial influence factors of each control region, inputs the basic environmental factors and the spatial influence factors into a regional planning model to obtain a preliminary planning situation of each control region, and based on the preliminary planning situation, performs secondary adjustment on each control region to obtain a final planning situation, thereby solving the technical problems that in the regional optimization of land resources, the influence of geographical height and wind speed on crops is ignored, and the mutual interference of production capacity between optimized regions is ignored, and the scientificity and rationality of regional planning are improved.

[0048] 2. Wind speed has a certain influence on the growth of crops, such as excessive wind speed will disturb the growth of crops, make it difficult for crops to form healthy root system, leading to crop yield reduction. Therefore, the present application considers the interference of wind speed on regional planning when planning the regulation area; at the same time, the higher the altitude of the earth, the greater the probability of being affected by high wind speed, so the present application also considers the superimposed influence of altitude on the probability of high wind speed; the two are analyzed together to obtain a spatial influence factor that can express the influence of the current wind speed and the possible influence of high wind speed on each regulation area, providing data support for the preliminary planning of the subsequent regulation area, and can improve the scientificity and effectiveness of regional planning.

[0049] 3. According to the environmental characteristics of a region, the planning of this region is obtained, which may cause another kind of planning region to appear in a large number of the same kind of planning region, such as a cultivated land appearing in the middle of a region surrounded by forest land; This may cause mutual interference between the two kinds of planning regions, such as the existence of forest land will cause the yield reduction of cultivated land, and the existence of cultivated land will cause the destruction of the ecological environment of forest land, so the present application carries out a planning after the total region is planned into different regulation areas, the purpose is to avoid the situation that another kind of planning region appears in a large number of the same kind of planning region, through this double-layer planning strategy, the mutual interference between different planning regions can be effectively reduced, and the scientificity of planning and the sustainability of regional development can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0051] Figure 1 The operation steps of the present application are shown in the figure;

[0052] Figure 2 The system module diagram of the present application is shown in the figure;

[0053] Figure 3 The operation steps of the present application are shown in the figure;

[0054] Figure 4 The operation steps of the present application are shown in the figure. DETAILED DESCRIPTION

[0055] The technical solutions of the present application will be described clearly and completely below in connection with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the scope of the present application.

[0056] Please refer to Figures 1-2 The first aspect of the present application provides a land resource optimization system based on Internet of Things, comprising: an intelligent analysis module, and a data collection module, a regional optimization module and a database connected thereto.

[0057] The data collection module is configured to divide the total area into an intelligent planning area and a man-defined area, divide the intelligent planning area into a plurality of control areas, and obtain basic data and target data in each control area, wherein the basic data comprises characteristic values of soil humidity, soil temperature and light intensity, and the target data comprises characteristic values of altitude and wind speed.

[0058] The intelligent analysis module is configured to analyze the basic data to obtain basic environmental factors of each control area, and analyze the target data to obtain spatial influence factors of each control area.

[0059] The regional optimization module is configured to establish a regional planning model, input the basic environmental factors and the spatial influence factors into the regional planning model to obtain a preliminary planning situation of each control area, perform secondary adjustment on each control area based on the preliminary planning situation to obtain a final planning situation, and output the final planning situation to a user end, wherein the planning situation comprises planning as farmland, woodland or garden.

[0060] For example, in the present embodiment, the area of the standard control area is 1 square kilometer.

[0061] A remote sensing topographic map of the total area is extracted, and the areas that cannot be divided are marked as man-defined areas, and the areas that are not the areas that cannot be divided are marked as intelligent planning areas.

[0062] The intelligent planning areas are grid-divided based on the area of the standard control area to obtain a plurality of grid areas, and the areas where the complete grid areas are located are marked as control areas, and the areas where the incomplete grid areas are located are marked as edge areas.

[0063] The area ratio MBi 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 a set proportion, the corresponding edge area is marked as a control area, and when the area ratio MBi does not exceed the set proportion, the corresponding edge area is marked as a decomposition area.

[0064] The following operations are performed on each decomposition region:

[0065] The control region contacted by the decomposition region is extracted, the contacted control region is marked as a to-be-assigned control region of the current decomposition region, the length Lj of the contact between the decomposition region and each to-be-assigned control region is obtained, the to-be-assigned proportion PBLj of the to-be-assigned control region corresponding to the current decomposition region is obtained based on the formula PBLj=Lj / ∑(Lj), and the to-be-assigned proportion PBLj of the to-be-assigned control region corresponding to the current decomposition region is obtained based on the formula PBLj=Lj / ∑(Lj);

[0066] The decomposition region is proportionally assigned to the corresponding to-be-assigned control region based on the area of the decomposition region and the to-be-assigned proportion PBLj, for example:

[0067] If the area of the decomposition region is 0.6 square kilometers, the current decomposition region has two to-be-assigned control regions, namely to-be-assigned control region 1 and to-be-assigned control region 2, and the to-be-assigned proportion PBL1 of to-be-assigned control region 1 is 0.4 and the to-be-assigned proportion PBL2 of to-be-assigned control region 2 is 0.6;

[0068] Therefore, 4 / 6 of the area of the decomposition region near to-be-assigned control region 1 is assigned to to-be-assigned control region 1, and 6 / 6 of the area of the decomposition region near to-be-assigned control region 2 is assigned to to-be-assigned control region 2;

[0069] The mode Zm, the maximum value Dm, and the minimum value Xm of the soil moisture, the soil temperature, the light intensity, the wind speed, and the altitude of each control region are obtained, the characteristic value Tm of each data of the current control region is obtained based on the formula Tm=(B1m×Dm+B2m×Xm+Zm) / 2, and m is 1, 2, 3, 4, or 5, indicating the data corresponding to the soil moisture, the soil temperature, the light intensity, the wind speed, or the altitude, respectively;

[0070] For example, the mode Z2 of the soil temperature of the control region is 26℃, the maximum value D2 is 36℃, and the minimum value X2 is 15℃, the characteristic value T2 of the soil temperature of the current control region is obtained based on the formula T2=(B12×D2+B22×X2+Z2) / 2=(0.6×36+0.4×15+26) / 2=26.8℃, and the characteristic value T2 of the soil temperature of the current control region is 26.8℃. 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 values T1 of the soil moisture, the characteristic values T2 of the soil temperature, and the characteristic values T3 of the light intensity of each control region are sequentially extracted.

[0072] When there is a characteristic value exceeding the corresponding standard range among the characteristic value T1, the characteristic value T2 and the characteristic value T3, the value of the basic environmental factor JHZ of the current regulation area is set to 0; wherein the standard range includes a standard humidity range, a standard temperature range and a standard light intensity;

[0073] When there is no characteristic value exceeding the corresponding standard range among the characteristic value T1, the characteristic value T2 and the characteristic value T3, the basic environmental factor JHZ of the current regulation 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] The characteristic value T4 of the altitude and the characteristic value T5 of the wind speed in each regulation area are extracted;

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

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

[0077] The basic environmental factor and the space influence factor of a plurality of historical regulation areas, and the corresponding preliminary planning situation are obtained through a database; the basic environmental factor, the space influence factor and the preliminary planning situation are integrated into a plurality of sets of training data and detection data; the training data are used to train an artificial intelligence model; the test data are used to test the artificial intelligence model after training, and the artificial intelligence model is adjusted according to the test result; finally, a regional planning model is obtained, which inputs the basic environmental factor and the space influence factor and outputs the preliminary planning situation;

[0078] The basic environmental factor and the space influence factor are input into the regional planning model to obtain the preliminary planning situation of each regulation area;

[0079] The importance of the planning of farmland, forest land and garden land in the current intelligent planning area is obtained by the management personnel, and each regulation area is marked as an LD1 regulation area, an LD2 regulation area and an LD3 regulation area according to the planning situation in order from large to small according to the importance;

[0080] In the embodiment, the management personnel orders the importance of the planning of the farmland, the woodland and the garden in the current intelligent planning region from large to small as: the farmland, the woodland and the garden; therefore, the regulation region planned as the farmland is marked as the LD1 regulation region, the regulation region planned as the woodland is marked as the LD2 regulation region, and the regulation region planned as the garden is marked as the LD3 regulation region.

[0081] The LD3 regulation region is extracted in sequence, the length CD31 of the LD3 regulation region contacting the LD1 regulation region is obtained, the contact proportion one ZB1 is obtained based on the formula ZB1=CD31 / TCP, and when the contact proportion one is greater than the preset proportion, the current LD3 regulation region is planned as the LD1 regulation region.

[0082] When the contact proportion one is not greater than the preset proportion, the length CD32 of the LD3 regulation region contacting the LD2 regulation region is obtained, the contact proportion two ZB2 is obtained based on the formula ZB2=CD32 / TCP, and when the contact proportion two is greater than the preset proportion, the current LD3 regulation region is planned as the LD2 regulation region.

[0083] The LD2 regulation region is extracted in sequence, the length CD21 of the LD2 regulation region contacting the LD1 regulation region is obtained, the contact proportion three ZB3 is obtained based on the formula ZB3=CD21 / TCP, and when the contact proportion three is greater than the preset proportion, the current LD2 regulation region is planned as the LD1 regulation region.

[0084] When the contact proportion three is not greater than the preset proportion, the length CD23 of the LD2 regulation region contacting the LD3 regulation region is obtained, the contact proportion four ZB4 is obtained based on the formula ZB4=CD23 / TCP, and when the contact proportion four is greater than the preset proportion, the current LD2 regulation region is planned as the LD3 regulation region.

[0085] In the application, the total region is divided into the intelligent planning region and the artificial defined region, including:

[0086] The remote sensing topographic map of the total region is extracted, and it is judged whether there is an unclassifiable region in the remote sensing topographic map; if yes, the unclassifiable region is marked as the artificial defined region, and the non-unclassifiable region is marked as the intelligent planning region; wherein, the unclassifiable region includes the river and the building region.

[0087] It should be noted that the regions such as the river and the building region which cannot be planned are removed before the optimization of the land resource, so that the operation amount of the system can be reduced, and the operation efficiency can be improved.

[0088] Please refer to Figure 3 In the application, the intelligent planning region is divided into a plurality of regulation regions, including:

[0089] A1: obtaining the area of the standard regulatory region from the database, performing grid division on the smart planning region based on the area of the standard regulatory region to obtain a plurality of grid regions; sequentially judging whether the grid region is a complete grid; yes, marking the region where the grid region is located as a regulatory region; no, marking the region where the grid region is located as an edge region, and jumping to A2;

[0090] A2: sequentially obtaining the area Si of each edge region in the smart planning region, obtaining the area ratio MBi of each edge region to the standard regulatory region based on the formula MBi=Si / BTS, sequentially judging whether the area ratio MBi exceeds the set proportion; yes, marking the corresponding edge region as a regulatory region; no, marking the corresponding edge region as a decomposition region, and jumping to A3; wherein i is the number corresponding to the edge region, BTS is the area of the standard regulatory region, and the set proportion is obtained manually;

[0091] A3: sequentially extracting the regulatory region contacted by each decomposition region, marking the regulatory region as the to-be-assigned-to-regulatory region corresponding to the decomposition region; obtaining the length Lj of the contact between the decomposition region and the corresponding to-be-assigned-to-regulatory region, obtaining the to-be-assigned proportion PBLj of the to-be-assigned-to-regulatory region corresponding to the number j of the current decomposition region based on the formula PBLj=Lj / ∑(Lj); based on the area of the decomposition region and the to-be-assigned proportion PBLj, proportionally assigning the decomposition region to the corresponding to-be-assigned-to-regulatory region, and integrating the to-be-assigned-to-regulatory region and the assigned decomposition region into a regulatory region; wherein j is the number of the to-be-assigned-to-regulatory region, and j ranges from 1 to n; ∑ is a summation symbol for summing Lj, and the summation range is from 1 to n; n represents the number of the to-be-assigned-to-regulatory region corresponding to the current decomposition region.

[0092] It should be noted that the grid in the grid division of the smart planning region can be a square grid, a rectangular grid, a triangle, or any grid with a certain rule.

[0093] It should be noted that the complete grid in the sequential judgment of whether the grid region is a complete grid can be understood as: the planned grid region is entirely within the smart planning region, and the grid on the edge of the smart planning region may not be complete.

[0094] It should be noted that when the area ratio MBi exceeds the set proportion, the to-be-assigned-to-regulatory region corresponding to the regulatory region in the regulatory region can be understood as: the edge region with a larger area is not decomposed by the present application, but is regarded as a regulatory region for regulation.

[0095] It should be noted that based on the area of the decomposition region and the to-be-assigned proportion PBLj, the decomposition region is proportionally assigned to the corresponding to-be-assigned-to-regulatory region, and the specific calculation steps of this step are:

[0096] If the area of the decomposition region is 0.6 square kilometers, the current decomposition region has two to be allocated to the control region, which are to be allocated to the control region 1 and to be allocated to the control region 2; and the to-be-allocated proportion PBL1 of to-be-allocated to the control region 1 is 0.4, and the to-be-allocated proportion PBL2 of to-be-allocated to the control region 2 is 0.6;

[0097] Therefore, according to the proportion 4:6, 4 / 10 of the area of the decomposition region near to-be-allocated to the control region 1 is allocated to to-be-allocated to the control region 1, and 6 / 10 of the area of the decomposition region near to-be-allocated to the control region 2 is allocated to to-be-allocated to the control region 2.

[0098] It should be noted that the to-be-allocated to the control region and the allocated decomposition region are combined into the control region, which can be understood as follows: the area of to-be-allocated to the control region is 1 square kilometer, and the area of the allocated decomposition region is 0.2 square kilometers, so the combined area of the control region is 1.2 square kilometers.

[0099] In the present application, the basic data and target data in each control region are obtained, including:

[0100] Based on the soil humidity sensor, a plurality of soil humidities at different times and different locations in each control region are obtained, based on the soil temperature sensor, a plurality of soil temperatures at different times and different locations in each control region are obtained, based on the light intensity sensor, a plurality of light intensities at different times and different locations in each control region are obtained, based on the wind speed sensor, a plurality of wind speeds at different times and different locations in each control region are obtained, and based on the altitude measuring instrument, a plurality of altitudes at different locations in each control region are obtained.

[0101] The mode Zm, the maximum value Dm and the minimum value Xm of each data in the plurality of soil humidities, the plurality of soil temperatures, the plurality of light intensities, the plurality of wind speeds and the plurality of altitudes in each control region are obtained in turn; the characteristic value Tm of each data in the current control region 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 humidity, 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 set values of 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 different places is the data obtained at several positions in the region at several time points set by experience.

[0104] In the present application, the basic environmental factors of each control region are obtained by analyzing the basic data, including:

[0105] B1: sequentially extracting the characteristic value T1 of soil humidity, the characteristic value T2 of soil temperature, and the characteristic value T3 of light intensity in each control region;

[0106] B2: determining whether the characteristic value T1 of soil humidity exceeds the standard humidity range; if yes, setting the value of the basic environmental factor JHZ of the current control region to 0; if no, jumping to B3;

[0107] B3: determining whether the characteristic value T2 of soil temperature exceeds the standard temperature range; if yes, setting the value of the basic environmental factor JHZ of the current control region to 0; if no, jumping to B4;

[0108] B4: determining whether the characteristic value T3 of light intensity exceeds the standard light intensity range; if yes, setting the value of the basic environmental factor JHZ of the current control region to 0; if no, jumping to B5; wherein the standard humidity range, the standard temperature range, and the standard light intensity range are obtained by experience;

[0109] B5: obtaining the basic environmental factor JHZ of the current control region based on the formula JHZ = α1 × exp(-|T1-BT1|) + α2 × exp(-|T2-BT2|) + α3 × exp(-|T3-BT3|); wherein α1, α2, and α3 are amplitude adjustment coefficients greater than 0, and the value range of α1, α2, and α3 is (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 determination of the characteristic value T1 of soil humidity, 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 region can meet the growth of plants, and when it does not meet, the basic environmental factor is marked as 0, providing data support for the preliminary planning of each control region.

[0111] In the present application, the spatial influence factors of each control region are obtained by analyzing the target data, including:

[0112] Extract the characteristic value T4 of the altitude and the characteristic value T5 of the wind speed in each control region, judge whether the characteristic value T5 of the wind speed is greater than the wind speed threshold BT5; if yes, set the value of the space influence factor KYZ of the current control region to 0; if no, obtain the space influence factor KYZ of the current control region 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 a standard altitude artificially set.

[0113] The regional planning model is established in the application, including:

[0114] Obtain the basic environmental factors and the space influence factors of a plurality of historical control regions, and the corresponding preliminary planning situations through the database; integrate the basic environmental factors, the space influence factors and the preliminary planning situations into a plurality of sets of training data and detection data; train the artificial intelligence model using the training data; test the artificial intelligence model after training using the test data, and adjust the artificial intelligence model according to the test result; finally obtain the regional planning model with the input of the basic environmental factors and the space influence factors and the output of the preliminary planning situation; wherein, the artificial intelligence model includes a BP neural network model or an RBF neural network model.

[0115] Specifically, input the basic environmental factors and the space influence factors in the test data into the artificial intelligence model after training to obtain the corresponding preliminary planning situation, and compare the preliminary planning situation with the corresponding preliminary planning situation in the test data; when they are the same, no parameter adjustment is needed, and the next set of detection data is detected; when they are different, the corresponding parameters are adjusted until they are the same, and then the next set of detection data is detected; when the number of the same is 95% or more of the total number of detections, the regional planning model with the input of the basic environmental factors and the space influence factors and the output of the preliminary planning situation is obtained.

[0116] It should be noted that the preliminary planning situation used in the training data is obtained by first calculating the planning factor based on the basic environmental factors and the space influence factors, and then analyzing the preliminary planning situation based on the planning factor by artificial means; wherein, the calculation formula for obtaining the planning factor GHZ is GHZ=δ1×JHZ+δ2×KYZ; wherein, δ1 and δ2 are both proportional adjustment factors greater than 0, and δ1+δ2=1.

[0117] Please refer to Figure 4 In the application, the final planning situation is obtained by adjusting each control region based on the preliminary planning situation, including:

[0118] C1: Obtain the management personnel's importance ranking of the current intelligent planning area in farmland, woodland, and garden planning, and mark each control region as an LD1 control region, an LD2 control region, and an LD3 control region in order of importance from large to small according to the planning situation;

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

[0120] C3: Obtain the length CD32 of the LD3 control region in contact with the LD2 control region, obtain the contact proportion two ZB2 based on the formula ZB2=CD32 / TCP, and determine whether the contact proportion two is greater than a preset proportion; if yes, plan the current LD3 control region as an LD2 control region; if no, do not perform an operation;

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

[0122] C5: Obtain the length CD23 of the LD2 control region in contact with the LD3 control region, obtain the contact proportion four ZB4 based on the formula ZB4=CD23 / TCP, and determine whether the contact proportion four is greater than a preset proportion; if yes, plan the current LD2 control region as an LD3 control region; if no, do not perform an operation.

[0123] It should be noted that obtaining the management personnel's importance ranking of the current intelligent planning area in farmland, woodland, and garden planning, and marking each control region as an LD1 control region, an LD2 control region, and an LD3 control region in order of importance from large to small can be understood by example as follows:

[0124] If the management personnel's importance ranking of the current intelligent planning area in farmland, woodland, and garden planning is farmland, woodland, and garden in order of importance from large to small, then mark the control region planned as farmland as an LD1 control region, mark the control region planned as woodland as an LD2 control region, and mark the control region planned as garden as an LD3 control region.

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

[0126] The second aspect embodiment of the present application provides a land resource optimization utilization method based on the Internet of Things, comprising the following steps:

[0127] S1: dividing the total area into an intelligent planning area and a man-defined area, dividing the intelligent planning area into a plurality of control areas; obtaining the basic data and target data in each control area;

[0128] S2: analyzing the basic data to obtain the basic environmental factors of each control area; analyzing the target data to obtain the space influence factors of each control area;

[0129] S3: establishing a regional planning model, inputting the basic environmental factors and space influence factors into the regional planning model to obtain the preliminary planning of each control area; based on the preliminary planning, adjusting each control area twice to obtain the final planning, and outputting the final planning to the user end.

[0130] Part of the data in the above formula is calculated by removing the dimension, and the formula is obtained by software simulation of a large amount of collected data to obtain a formula closest to the real situation; the preset parameters and the preset threshold in the formula are set by the person skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0131] The working principle of the present application is:

[0132] The present application first divides the total area into an intelligent planning area and a man-defined area, and divides the intelligent planning area into several regulation and control areas; in this way, the system can better adapt to the diversified needs in a complex area, and the system can be individually optimized according to the characteristics of different areas; basic data and target data in each regulation and control area are obtained; the basic data are analyzed to obtain the basic environmental factors of each regulation and control area; this step obtains a basic environmental factor that can express the plant growth suitability of the current regulation and control area, and can provide more accurate evaluation for the preliminary planning of the regulation and control area; the target data are analyzed to obtain the spatial influence factors of each regulation and control area; this step analyzes the superimposed influence of wind speed and altitude on the occurrence of high wind speed probability to obtain the spatial influence factors that can express the influence of the current wind speed and the possible influence of high wind speed on each regulation and control area, and provides data support for the subsequent preliminary planning of the regulation and control area; a regional planning model is established, the basic environmental factors and the spatial influence factors are input into the regional planning model to obtain the preliminary planning situation of each regulation and control area; the regulation and control areas are secondarily adjusted based on the preliminary planning situation to obtain the final planning situation, and the final planning situation is output to the user end.

[0133] The above embodiments are only used to illustrate the technical method of the present application and not to limit it. Although the present application 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 application can be modified or replaced equivalently without departing from the spirit and scope of the technical method of the present application.

Claims

1. A land resource optimization and utilization system based on the Internet of Things, characterized in that, include: The intelligent analysis module, along with its connected data collection module, regional optimization module, and database; The data collection module is used to divide the total area into an intelligent planning area and a manually defined area, and to further divide the intelligent planning area into several control areas. Acquire basic and target data within 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. The intelligent analysis module is used to analyze basic data to obtain basic environmental factors for each control area; and to analyze target data to obtain spatial influence factors for each control area. The regional optimization module is used to establish a regional planning model, input basic environmental factors and spatial impact factors into the regional planning model to obtain the preliminary planning status of each regulated area; based on the preliminary planning status, the regulated areas are adjusted a second time to obtain the final planning status, and the final planning status is output to the user terminal; the planning status includes planning as cultivated land, forest land, and orchard land; The analysis of the target data yields spatial influence factors for each control region, including: Extract the elevation feature value T4 and wind speed feature value T5 of each control area, and determine whether the wind speed feature value T5 is greater than the wind speed threshold BT5; if yes, set the spatial influence factor KYZ of the current control area to 0; if no, obtain the spatial influence factor KYZ of the current control area based on the formula KYZ=β / exp(|T4-BT4| / BT4)×ln(|T5-BT5| / BT5+1); where β is the amplitude adjustment coefficient greater than 0, and BT4 is the standard elevation; The final planning involves secondary adjustments to each control area based on the preliminary planning, including: C1: Obtain the ranking of the planning importance of cultivated land, forest land and orchard in the current intelligent planning area by the management personnel, and mark each control area as LD1 control area, LD2 control area and LD3 control area according to the planning status in descending order of importance; C2: Sequentially extract each LD3 control region, obtain the contact length CD31 between the LD3 control region and the LD1 control region, and obtain the contact ratio ZB1 based on the formula ZB1=CD31 / TCP. Determine whether the contact ratio is greater than a preset ratio; if yes, plan the current LD3 control region as the LD1 control region; if no, jump to C3; where TCP is the average perimeter of each control region. C3: Obtain the contact length CD32 between the LD3 control region and the LD2 control region, and obtain the contact ratio ZB2 based on the formula ZB2=CD32 / TCP. 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 not perform any operation. C4: Sequentially extract each LD2 control region, obtain the contact length CD21 between the LD2 control region and the LD1 control region, and obtain the contact ratio ZB3 based on the formula ZB3=CD21 / TCP. Determine 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, and obtain the contact ratio ZB4 based on the formula ZB4=CD23 / TCP. Determine whether the contact ratio ZB4 is greater than the preset ratio; if yes, plan the current LD2 control region as the LD3 control region; if no, do not perform any operation.

2. The land resource optimization and utilization system based on the Internet of Things according to claim 1, 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 and utilization system based on the Internet of Things according to claim 1, characterized in that, The division of the total area into intelligent planning areas and manually defined areas includes: Extract the remote sensing topographic map of the total area and determine whether there are any undelineable areas in the remote sensing topographic map; if so, mark the undelineable areas as artificially defined areas and mark the non-undelineable areas as intelligent planning areas; among them, undelineable areas include river and building areas.

4. The land resource optimization and utilization system based on the Internet of Things according to claim 3, characterized in that, The division of the intelligent planning area into several control areas includes: 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 in turn whether the grid area is a complete grid; if yes, mark the area where the grid area is located as the control area; if no, mark the area where the grid area is located as the edge area, and jump to A2; A2: Sequentially obtain the area Si of each edge region within the intelligent planning area, and obtain the area ratio MBi of each edge region to the standard control region based on the formula MBi=Si / BTS. Sequentially determine whether the area ratio MBi exceeds the set ratio; if yes, mark the corresponding edge region as the control region; if no, mark the corresponding edge region as the decomposition region and jump to A3; where i is the number corresponding to the edge region, and BTS is the area of ​​the standard control region. A3: Sequentially extract the control regions that each decomposition region contacts, and mark the control regions as the control regions to be assigned to the corresponding decomposition region; obtain the length Lj of the contact between the decomposition region and the corresponding control region to be assigned, and obtain the allocation ratio PBLj of the control region corresponding to the current decomposition region with number j based on the formula PBLj=Lj / ∑(Lj); allocate the decomposition region to the corresponding control region according to the ratio based on the area of ​​the decomposition region and the allocation ratio PBLj, and combine the control region to be assigned and the allocated decomposition region into a control region; where j is the number of the control region to be assigned, 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 control regions corresponding to the current decomposition region.

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

6. The land resource optimization and utilization system based on the Internet of Things according to claim 5, characterized in that, The analysis of basic data to obtain the basic environmental factors for each control area includes: B1: Sequentially extract the characteristic values ​​of soil moisture (T1), soil temperature (T2), and light intensity (T3) in each control area; 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, proceed 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, proceed 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, proceed to B5. B5: 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|); where α1, α2 and α3 are all amplitude adjustment coefficients greater than 0, and the values ​​of α1, α2 and α3 are all in the range of (0,1]; BT1 is the median value of the standard humidity range, BT2 is the median value of the standard temperature range, and BT3 is the median value of the standard light intensity range.

7. The land resource optimization and utilization system based on the Internet of Things according to claim 1, characterized in that, The establishment of the regional planning model includes: The system obtains basic environmental factors and spatial influencing factors of several historically regulated areas from a database, along with corresponding preliminary planning information. It then integrates these factors into several sets of training and testing data. The training data is used to train an artificial intelligence (AI) model. Testing data is used to test the trained AI model, and adjustments are made based on the test results. The final result is a regional planning model with basic environmental factors and spatial influencing factors as input and preliminary planning information as output. The AI ​​model includes either a backpropagation (BP) neural network model or an RBF neural network model.

8. A method for optimizing land resource utilization based on the Internet of Things (IoT), operating based on the IoT-based land resource optimization system according to any one of claims 1 to 7, characterized in that: S1: Divide the total area into an intelligent planning area and a manually defined area, and further divide the intelligent planning area into several control areas; Acquire basic and target data within each control area; S2: Analyze the basic data to obtain the basic environmental factors for each control area; The spatial influencing factors of each control area are obtained by analyzing the target data; S3: Establish a regional planning model, input basic environmental factors and spatial impact factors into the regional planning model to obtain the preliminary planning status of each regulated area; Based on the preliminary planning, the control areas are adjusted a second time to obtain the final planning, and the final planning is output to the user terminal. The analysis of the target data yields spatial influence factors for each control region, including: Extract the elevation feature value T4 and wind speed feature value T5 of each control area, and determine whether the wind speed feature value T5 is greater than the wind speed threshold BT5; if yes, set the spatial influence factor KYZ of the current control area to 0; if no, obtain the spatial influence factor KYZ of the current control area based on the formula KYZ=β / exp(|T4-BT4| / BT4)×ln(|T5-BT5| / BT5+1); where β is the amplitude adjustment coefficient greater than 0, and BT4 is the standard elevation; The final planning involves secondary adjustments to each control area based on the preliminary planning, including: C1: Obtain the ranking of the planning importance of cultivated land, forest land and orchard in the current intelligent planning area by the management personnel, and mark each control area as LD1 control area, LD2 control area and LD3 control area according to the planning status in descending order of importance; C2: Sequentially extract each LD3 control region, obtain the contact length CD31 between the LD3 control region and the LD1 control region, and obtain the contact ratio ZB1 based on the formula ZB1=CD31 / TCP. Determine whether the contact ratio is greater than a preset ratio; if yes, plan the current LD3 control region as the LD1 control region; if no, jump to C3; where TCP is the average perimeter of each control region. C3: Obtain the contact length CD32 between the LD3 control region and the LD2 control region, and obtain the contact ratio ZB2 based on the formula ZB2=CD32 / TCP. 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 not perform any operation. C4: Sequentially extract each LD2 control region, obtain the contact length CD21 between the LD2 control region and the LD1 control region, and obtain the contact ratio ZB3 based on the formula ZB3=CD21 / TCP. Determine 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, and obtain the contact ratio ZB4 based on the formula ZB4=CD23 / TCP. Determine whether the contact ratio ZB4 is greater than the preset ratio; if yes, plan the current LD2 control region as the LD3 control region; if no, do not perform any operation.

Images