CN117408495B

CN117408495B - Data analysis method and system based on comprehensive management of land resources

Info

Publication number: CN117408495B
Application number: CN202311697998.0A
Authority: CN
Inventors: 高春苗
Original assignee: Heze Municipal Bureau Of Natural Resources And Planning
Current assignee: Heze Municipal Bureau Of Natural Resources And Planning
Priority date: 2023-12-12
Filing date: 2023-12-12
Publication date: 2024-03-29
Anticipated expiration: 2043-12-12
Also published as: CN117408495A

Abstract

The invention relates to the technical field of land resource management, in particular to a data analysis method and system based on comprehensive land resource management. The method comprises the following steps: performing three-dimensional simulation operation of the land analysis area to generate simulated land three-dimensional data; performing abnormal land pH value division processing on the three-dimensional data of the simulated land, and respectively generating abnormal land division data and conventional land division data; performing simulation restoration processing on the abnormal land partition data to generate restoration land partition data; performing data integration on the conventional land division data and the repair land division data to generate optimized land division data; performing land use prediction on the optimized land partition data to generate land use data; and carrying out land resource management processing on the land analysis area according to the land use data to generate land resource management data. According to the invention, by analyzing the data of the land resources, the more efficient comprehensive management of the land resources is realized.

Description

Data analysis method and system based on comprehensive management of land resources

Technical Field

The invention relates to the technical field of land resource management, in particular to a data analysis method and system based on comprehensive land resource management.

Background

The data analysis of the land resource comprehensive management is to process the land data correspondingly by using a Geographic Information System (GIS), a remote sensing technology and other multi-field technologies so as to know the information of land resource management requirements and the like in depth. However, the traditional data analysis method for comprehensive management of land resources is used for manually collecting the relevant information of the soil and then matching the specific information of the soil application, so that the relevant information of the soil is processed too much with manpower, and a large amount of time is consumed for matching the specific application of the soil, so that the data analysis effect of comprehensive management of the land resources is poor.

Disclosure of Invention

Based on the above, the invention provides a data analysis method and system based on land resource comprehensive management, so as to solve at least one of the above technical problems.

In order to achieve the above purpose, a data analysis method based on land resource comprehensive management comprises the following steps:

step S1, acquiring a land analysis area, and performing three-dimensional simulation operation of the land analysis area on the land analysis area based on a GIS technology to generate simulated land three-dimensional data;

Step S2, performing grid division processing on the three-dimensional data of the simulated land to generate simulated land division data; acquiring soil pH value data according to the simulated land partition data to generate soil pH value data; performing abnormal land pH value division processing on the simulated land division data according to the soil pH value data to respectively generate abnormal land division data and conventional land division data;

s3, acquiring a historical land restoration strategy; performing simulation restoration processing of the abnormal land area on the abnormal land partition data based on a historical land restoration strategy to generate restoration land partition data; performing data integration on the conventional land division data and the repair land division data to generate optimized land division data;

s4, performing land attribute characteristic data acquisition processing on the optimized land partition data to generate land attribute characteristic data;

s5, obtaining a training sample for land use; building a relation model of the land attribute and the land use based on a decision tree algorithm and a land use training sample, and generating a land use prediction model; transmitting the land attribute characteristic data to a land use prediction model to predict land use, and generating land use data;

S6, performing land resource management processing on the land analysis area according to the land use data to generate land resource management data; performing real-time monitoring processing on the land resource management data to generate land resource monitoring data; and carrying out real-time optimization processing on the indexes of the land resources on the land resource management data according to the land resource monitoring data, and generating and optimizing the land resource management data.

According to the invention, the land analysis area is obtained, the GIS technology is utilized to perform three-dimensional simulation operation, the key geographic features such as topography, landform, water flow and the like of the land analysis area can be accurately simulated, the generated simulated land three-dimensional data not only provides highly detailed and accurate geographic information for land resource management, but also can be used for simulating the influence of different land utilization schemes, so that the optimal land resource allocation strategy is helped to be selected, potential problem areas such as dangerous species invasion and propagation hazards or flood threats can be identified early through three-dimensional simulation, necessary preventive and management measures are taken, and the safety and sustainability of land resources are improved. The three-dimensional data of the simulated land are subjected to grid division processing to generate simulated land division data, fine granularity division of the land is realized, the land characteristics are clearer and more visible, the soil pH value data acquisition is carried out according to the simulated land division data, the soil quality related information can be obtained, the abnormal land division processing is carried out on the basis of the soil pH value data, abnormal land division data and conventional land division data are generated, and the recognition of the pH value abnormality problem in the land is facilitated. The method comprises the steps of obtaining a historical land restoration strategy, providing a reference for a decision maker, helping to avoid land problems possibly occurring in history, making a land management strategy more scientifically, carrying out simulation restoration processing on abnormal land division data, generating restoration land division data, predicting and evaluating the effect of restoration measures and the restored land state through a simulation method, optimizing the sustainable availability of land, integrating conventional land division data with the restoration land division data, generating optimized land division data, helping to make a sustainable land management strategy, balancing the requirements of different land uses and improving the comprehensive benefits of land. The optimized land partition data is subjected to land attribute characteristic data acquisition processing, so that various key attributes of the land can be deeply excavated, and the utilization efficiency of land resources, environmental sustainability and the like are improved. By acquiring the soil use training sample, a relation model between the soil attribute and the soil use can be established, so that the influence of the soil attribute on the soil use is deeply understood. Based on the decision tree algorithm and the training sample, the adaptability of different land attributes to different land uses can be predicted with high accuracy, so that land planning and distribution are more scientific and reasonable, and the generated land use prediction model provides the implementation effect of simulating different land schemes for the current land use, and the sustainable management of land resources is improved. The land attribute characteristic data is transmitted to a land use prediction model to predict land use, so that the use configuration of land resources can be effectively optimized, and the comprehensive utilization rate of land is improved. The land resource management processing is carried out according to the land use data, so that land allocation and utilization can be matched with specific land use demands, and the benefit and the sustainability of the land resource are improved to the greatest extent. The land resource management data is subjected to index real-time optimization processing according to the land resource monitoring data, so that the configuration and the utilization mode of the land resource can be dynamically adjusted under the continuously-changing environmental conditions, the optimal utilization and sustainable management of the land resource are ensured, and the overall benefit and the environmental sustainability of the land resource are improved. Therefore, the data analysis method for the comprehensive management of the land resources automatically collects the relevant attributes of the soil features through the GIS technology, predicts the application of the relevant attributes suitable for the soil features by utilizing the mathematical model, analyzes the relevant information of the soil through automation, reduces a large amount of time for matching the specific application of the soil, and improves the prediction precision of the application of the soil at the same time, so that the data analysis effect of the comprehensive management of the land resources is obviously improved.

Preferably, step S1 comprises the steps of:

acquiring a land analysis area;

and performing three-dimensional scanning processing on the land analysis area by using a GIS technology to generate land three-dimensional image data, and performing simulation operation processing on the land three-dimensional image data by using a three-dimensional simulation technology to generate simulated land three-dimensional data.

The method for acquiring the land analysis area provides a clear boundary and a clear range for land resource management, and is beneficial to accurate focusing of resource management work. By utilizing the GIS technology to perform three-dimensional scanning processing on the land analysis area to generate land three-dimensional image data, high-precision capturing of geographic features is realized, simulation operation processing is performed on the land three-dimensional image data to generate simulated land three-dimensional data, the topography, the landform and the potential problems of the land can be deeply known in a visual mode, visual analysis of planning and decision processes is facilitated, comprehensive understanding of land resources is improved, high-quality data support is provided for land resource management, early recognition of potential problems is facilitated, and safety and sustainability of the land resources are improved.

Preferably, step S2 comprises the steps of:

S21, performing grid division processing on the three-dimensional data of the simulated land to generate simulated land division data;

s22, collecting soil pH value data according to the simulated land division data to generate soil pH value data;

s23, performing soil pH value interval comparison processing on the soil pH value data by using a preset soil pH value interval, and marking simulated land partition data corresponding to the soil pH value data as abnormal land partition data when the soil pH value data is not in the soil pH value interval;

and S24, performing soil pH value interval comparison processing on the soil pH value data by utilizing a preset soil pH value interval, and marking the simulated land partition data corresponding to the soil pH value data as conventional land partition data when the soil pH value data is in the soil pH value interval.

According to the invention, the three-dimensional data of the simulated land is subjected to grid division processing, so that the simulated land division data is generated, and the land resource is divided into smaller units, thereby being beneficial to better knowing the geographical distribution of the land. Soil pH value data are acquired according to the simulated land division data, so that soil pH value data are generated, and the detailed information of soil quality can be known through the soil pH value data. The preset soil pH value interval is utilized to carry out interval comparison processing on the soil pH value data, so that an abnormal region of the soil pH value can be identified, and the abnormal region is marked as abnormal soil partition data, thereby providing a basis for necessary repair; and the soil with the soil pH value data in a normal range is divided into conventional soil division data through the soil pH value interval comparison treatment, so that the effective utilization and management of the soil resources are ensured.

Preferably, step S3 comprises the steps of:

s31, acquiring a historical land restoration strategy;

s32, collecting external hazard factor data of the abnormal land partition data to generate abnormal land hazard factor data;

s33, transmitting the abnormal land hazard factor data to a historical land restoration strategy to extract land restoration data and generate land restoration data;

s34, performing simulation restoration processing of an abnormal land area on the abnormal land partition data by utilizing the land restoration data to generate restoration land partition data;

and step S35, carrying out data integration on the conventional land division data and the repair land division data to generate optimized land division data.

According to the invention, a historical land restoration strategy is obtained, and an effective land restoration strategy, such as a restoration method of solenopsis invicta invasion disasters, flood disasters and the like, is established by referring to the experience of the past land restoration strategy, so that the current and future land abnormality problems are more effectively solved. External hazard factor data acquisition is carried out on abnormal land partition data, and repair and protection measures can be adopted more pertinently by fully knowing the factors, so that the sustainability of land resources is improved. The abnormal land hazard factor data is transmitted to a historical land restoration strategy to extract the land restoration data, so that the land restoration data can be correspondingly extracted according to specific conditions, the severity of restoration measures is ensured to be matched, and the land restoration effect is maximized. The method has the advantages that the land repair data are utilized to carry out simulation repair processing on the abnormal land partition data, feasibility and effect of the repair strategy can be evaluated before actual operation through simulation repair process, the risk of the repair strategy is reduced, the repair efficiency is improved, and the quick recovery and reuse of land resources are ensured. The conventional land partition data and the repair land partition data are integrated, a comprehensive optimized land resource management view is provided, specific uses of different land resources can be comprehensively considered on land resource allocation in the subsequent steps, optimal allocation and sustainable management of the land resources are realized, and overall benefit of the land resources is improved.

Preferably, step S4 comprises the steps of:

s41, performing three-dimensional soil level data acquisition processing on the optimized land division data to generate three-dimensional soil level data;

step S42, performing soil type division on the three-dimensional soil level data according to the preset soil level type to generate soil type data;

s43, collecting the land sunshine characteristic data of the optimized land partition data to generate the land sunshine characteristic data;

and S44, carrying out data integration on the soil type data and the land sunshine characteristic data according to the data sequence of the optimized land division data to generate land attribute characteristic data.

The invention collects and processes the three-dimensional soil level data of the optimized soil division data, provides highly detailed description of soil quality and characteristics by deeply knowing the vertical distribution of the soil, and is helpful for identifying possible problems in the soil level. By dividing the soil types according to the three-dimensional soil level data, the distribution of different soil types can be accurately identified, so that the physical and chemical characteristics of the soil can be better understood, and a foundation is provided for different purposes and management strategies of the soil resources. The acquisition of the land sunshine characteristic data is beneficial to evaluating the sunshine condition of the land, including information such as sunshine irradiation time and intensity, and the like, and provides important data for crop growth, building layout and urban planning. According to the data sequence of the optimized land partition data, the data of the soil type data and the land sunshine characteristic data are subjected to data integration of the corresponding data sequence, comprehensive land attribute information is provided, the diversity and complexity of land resources can be known more accurately, various attributes of the land resources are considered more comprehensively, and therefore a more scientific and reasonable land management strategy is formulated.

Preferably, step S5 comprises the steps of:

step S51, obtaining a training sample for land use;

s52, establishing a mapping relation between the land attribute characteristics and the land use by utilizing a decision tree algorithm, and generating an initial land use prediction model;

step S53, carrying out data division processing on the land use training samples to respectively generate a land use training set and a land use test set;

s54, performing model super-parameter adjustment processing on an initial land use prediction model by using a land use training set to generate a land use prediction training model, and performing model super-parameter evaluation processing on the land use prediction training model by using a land use test set to generate a land use prediction model;

and S55, transmitting the land attribute characteristic data to a land use prediction model to predict the land use, and generating land use data.

According to the invention, the training sample for land use is obtained, the actual data is provided for land use prediction, the land attribute and the actual use of the historical data are included, an accurate prediction model is built, and various conditions under different land types and conditions are reflected. The method comprises the steps of establishing a mapping relation between land attribute characteristics and land uses by utilizing a decision tree algorithm, generating an initial land use prediction model, providing a quantitative relation between land attributes and land uses, being beneficial to more accurately predicting the optimal uses under different land attributes, and improving sustainable management and utilization of land resources. The training samples for the land use are subjected to data partitioning processing to generate a training set and a testing set, so that the generalization capability of a prediction model for the land use can be evaluated, the model can be effectively predicted on new data, and the reliability of the prediction for the land use is improved. The method comprises the steps of performing model super-parameter adjustment processing on an initial land use prediction model by using a land use training set, performing model super-parameter evaluation processing on the land use prediction training model by using a land use test set, optimizing model performance by performing super-parameter adjustment and evaluation on the model, improving prediction accuracy, and being beneficial to selecting optimal model configuration so as to adapt to the complexity of different land attributes and uses, thereby improving the precision of land use prediction. The land attribute characteristic data are transmitted to the land use prediction model to conduct actual land use prediction, land use information can be obtained rapidly, reasonable planning of land resource utilization is facilitated, decision uncertainty and decision loss time are reduced, and sustainable management and optimization of land resources are improved.

Preferably, step S6 comprises the steps of:

step S61, performing land resource management processing on the land analysis area according to the land use data to generate land resource management data;

step S62, performing real-time monitoring processing on the land resource management data to generate land resource monitoring data;

step S63, collecting the index data of the land resources according to the preset land resource index to the land resource monitoring data, and generating land resource index data;

s64, performing index comprehensive evaluation calculation processing on the land resource index data by using a land resource index comprehensive evaluation algorithm to generate index comprehensive evaluation data;

and step S65, carrying out real-time optimization processing on the land resources management data based on the index comprehensive evaluation data, thereby generating optimized land resources management data.

According to the invention, the land resource management processing is carried out according to the land use data, and the distribution of the land resources is matched with the specific land use demands, so that the coordination and consistency of the land use demands are ensured, and the benefit and sustainable management of the land resources are improved. The use condition and the state of the land resource can be known in time through the real-time monitoring of the land resource management data, so that potential problems can be responded quickly, the resource waste and the environmental risk are reduced, and the real-time monitoring is helpful for ensuring the safety and the sustainability of the land resource. And carrying out index data acquisition on the land resource monitoring data according to preset land resource indexes, thereby being beneficial to quantifying various indexes of the land resource, such as land utilization rate, land quality, environmental influence and the like, so as to evaluate the condition and benefit of the land resource more comprehensively. The comprehensive evaluation calculation processing is carried out on the data by utilizing the comprehensive evaluation algorithm of the land resource index, so that the comprehensive condition of the land resource is more comprehensively known, and the potential problem can be identified. The land resource management data is optimized in real time based on the index comprehensive evaluation data, so that the configuration and the utilization mode of the land resources can be dynamically adjusted according to actual conditions, the optimal utilization and sustainable management of the land resources are ensured, the continuous changing requirements and challenges can be met, and the overall benefit and the environmental sustainability of the land resources are improved.

Preferably, the soil resource index comprehensive evaluation algorithm in step S64 is as follows:

；

in the method, in the process of the invention,index comprehensive evaluation level expressed as land resource management data,/->Expressed as index number>Denoted as +.>Difference weight information of index data of each index and index standard threshold value, < ->Denoted as +.>Index data of individual index->Denoted as +.>Index standard threshold value of individual index,/>Weight information indicating that the index data does not reach the index standard threshold value,/or->Data amount expressed as index data not reaching the index standard threshold,/for the index data>Expressed as index mean data>Expressed as estimated sustainable time of land resource management, +.>An anomaly adjustment value expressed as an index comprehensive evaluation level.

The invention utilizes a comprehensive evaluation algorithm of land resource indexes, and the algorithm comprehensively considers the index quantityFirst->Difference weight information of index data of individual indexes and index standard threshold value->First->Index data of individual index->First->Index criterion threshold value of individual index->Weight information for index data not reaching index standard threshold value>Data amount of index data not reaching index standard threshold +.>Index mean data->Estimated sustainable time of land resource management +. >And interactions between functions to form a functional relationship:

that is to say,the functional relation is used for obtaining corresponding index comprehensive evaluation grades by calculating all indexes of the land resource management data, and carrying out data integration on the index comprehensive evaluation grades corresponding to different land areas of all the land resource management data to generate index comprehensive evaluationThe function formula can comprehensively calculate a plurality of indexes to obtain a comprehensive grade, and the independent data calculation of the plurality of indexes is avoided, so that a large amount of time is saved. First->The difference value weight information of the index data of the individual indexes and the index standard threshold value represents the weight of each index, which reflects the importance of different indexes in comprehensive evaluation, and the importance of certain indexes can be highlighted according to the specific requirements of land resource management by adjusting the weight value, so that the actual situation is better reflected, and the accuracy and the flexibility of the evaluation are improved; first->Index data of individual index +.>The standard threshold of the index reflects the degree of deviation of the index data from the standard threshold, and by comparing the difference values, whether each index reaches the standard or not can be evaluated, so that the health condition of the land resource is determined, and the recognition of the problem and the optimization of the land resource management strategy are facilitated; the weight information that the index data does not reach the index standard threshold value and the weight information that the index data does not reach the index standard threshold value can be used for adjusting the weight of the index which does not reach the standard threshold value, and the influence of the index which does not reach the standard can be emphasized by reasonably setting the weight information, so that the improvement and adjustment of land resource management are guided; the amount of data for which the index data does not reach the index standard threshold reflects the quality of the index data, and when the parameter value is large, stricter management and supervision may be required to ensure the data quality; the index mean value data are used for evaluating the overall performance of the land resource management and determining the health condition of the land resource management, so that a basis is provided for decision making. The estimated sustainable time of land resource management reflects the long-term influence of a management strategy, and a long-term planning and sustainability management strategy is formulated to ensure sustainable utilization of land resources. The functional relation accurately evaluates the health condition and sustainability of the land resource and is helpful for formulating effective land Ground resource management policies and decisions. Abnormality adjustment value +.>The functional relation is adjusted and corrected, and the error influence caused by abnormal data or error items is reduced, so that the index comprehensive evaluation grade of the land resource management data is more accurately generated>The accuracy and the reliability of the index comprehensive evaluation calculation processing of the land resource index data are improved. Meanwhile, the weight information and the adjustment value in the formula can be adjusted according to actual conditions and are applied to the land resource index data corresponding to different land resource management data, so that the flexibility and applicability of the algorithm are improved.

Preferably, step S65 comprises the steps of:

when the index comprehensive evaluation data is monitored to be smaller than a preset index comprehensive evaluation threshold value, carrying out disfavored index data extraction on the index comprehensive evaluation data to generate disfavored index data;

and carrying out disuse index optimization processing on the land resource management data according to the disuse index data, thereby generating optimized land resource management data.

When the index comprehensive evaluation data is monitored to be smaller than the preset index comprehensive evaluation threshold value, the invention can timely detect the disfavor condition of land resource management, and can rapidly identify the problem of improper land resource utilization or non-compliance with planning through monitoring and evaluation, thereby being beneficial to avoiding potential resource waste and environmental risk, timely taking measures and ensuring the sustainability and benefit of land resources. According to the method, the method comprises the steps of carrying out the disuse index optimization treatment on land resource management data according to the disuse index data, taking the disuse index as a basis of decision, adjusting a land resource management strategy, and carrying out targeted optimization, so that the use efficiency of land resources can be effectively improved, the waste of the resources can be reduced, and meanwhile, the potential environmental and ecological risks can be reduced, thereby being beneficial to realizing sustainable management and optimal utilization of the land resources.

The present disclosure provides a data analysis system based on land resource integrated management, for executing the data analysis method based on land resource integrated management described above, the data analysis system based on land resource integrated management includes:

the land resource simulation operation module is used for acquiring a land analysis area, carrying out three-dimensional simulation operation on the land analysis area based on a GIS technology, and generating simulated land three-dimensional data;

the abnormal simulation land data identification module is used for carrying out grid division processing on the three-dimensional data of the simulation land to generate simulation land division data; acquiring soil pH value data according to the simulated land partition data to generate soil pH value data; performing abnormal land pH value division processing on the simulated land division data according to the soil pH value data to respectively generate abnormal land division data and conventional land division data;

the abnormal simulation land data restoration module is used for acquiring a historical land restoration strategy; performing simulation restoration processing of the abnormal land area on the abnormal land partition data based on a historical land restoration strategy to generate restoration land partition data; performing data integration on the conventional land division data and the repair land division data to generate optimized land division data;

The simulated land attribute feature acquisition module is used for carrying out land attribute feature data acquisition processing on the optimized land partition data to generate land attribute feature data;

the simulation land use prediction module is used for obtaining a land use training sample; building a relation model of the land attribute and the land use based on a decision tree algorithm and a land use training sample, and generating a land use prediction model; transmitting the land attribute characteristic data to a land use prediction model to predict land use, and generating land use data;

the land resource management optimization module is used for carrying out land resource management processing on the land analysis area according to the land use data to generate land resource management data; performing real-time monitoring processing on the land resource management data to generate land resource monitoring data; and carrying out real-time optimization processing on the indexes of the land resources on the land resource management data according to the land resource monitoring data, and generating and optimizing the land resource management data.

The method has the beneficial effects that the method realizes the deep exploration and simulation of the land analysis area through the GIS technology and the three-dimensional simulation technology, generates high-precision simulated land three-dimensional data, provides a solid data base for land resource management and planning, and is beneficial to accurately evaluating the current situation of land resources. The space structure of the land resource is clearer and more visible through grid division processing, the land type and purpose change can be recognized, a data basis is provided for reasonable allocation of the land resource, the pH value interval comparison of the soil is favorable for quickly recognizing the pH value abnormality of the soil, abnormal land division data and conventional land division data are marked, effective tools are provided for problem diagnosis and processing of the land resource, the effectiveness and sustainability of repair work are ensured, the external hazard factor data acquisition is favorable for knowing the specific problem and hazard source of the abnormal land, the data provide root causes of the problem, the targeted repair strategy is favorable for formulating, the abnormal land hazard factor data is transmitted to the historical repair strategy to perform land repair data acquisition, and the land repair data are generated, so that the repair strategy has higher efficiency. The simulation restoration of the abnormal land area is carried out through the land restoration data, so that the health state of the land is restored, and a solid foundation is provided for sustainable management of land resources. The data integration combines the conventional land division data and the repair land division data to generate optimized land division data, which is beneficial to reasonably planning land use and improving the overall benefit and sustainable management of land resources. The three-dimensional soil level data acquisition is beneficial to deep understanding of the soil subsurface structure and soil characteristics, determining the soil type, evaluating the suitability of the soil, managing water resources, planning infrastructure and the like, reasonably planning the agricultural land and the land application, and improving the comprehensive utilization of the land resources and the agricultural sustainability of the land resources. The solar radiation, temperature, humidity and other factors of the land resources are visualized by collecting the land sunshine characteristic data, and the data has key effects on the fields of urban planning, energy management, ecological protection and the like, so that the comprehensive benefit and the sustainability of the land resources are improved. The method comprises the steps of establishing a mapping relation between land attribute characteristics and land uses through decision tree algorithm modeling, generating an initial land use prediction model, wherein the initial land use prediction model is a classification model, so that land uses are presumed according to land attribute characteristic data, intelligent support is provided for land resource planning and decision, super-parameter adjustment of the model is carried out on the initial land use prediction model through land use training samples, prediction precision of the model is improved, the land use prediction model is generated, the land attribute characteristic data are transmitted to the land use prediction model, accurate prediction of land uses is realized, scientific planning and sustainable management of land resources are facilitated, and utilization benefits of the land resources are improved. And the land resource management processing is carried out according to the land use data, so that the land use is matched with the planning requirement, the land use is ensured to be in line with the actual requirement, the land use benefit is improved, and the resource waste is reduced. By monitoring the land resource management data in real time, problems and index abnormal conditions in the resource management can be rapidly found and responded, so that measures can be taken in time, potential risks are reduced, stability and safety of land resources are guaranteed, real-time optimization is carried out according to the index abnormal conditions, optimal utilization of the land resources is guaranteed, and efficiency, sustainability and the like of the land resources are improved.

Drawings

FIG. 1 is a schematic flow chart of steps of a data analysis method based on land resource comprehensive management;

FIG. 2 is a flowchart illustrating the detailed implementation of step S3 in FIG. 1;

FIG. 3 is a flowchart illustrating the detailed implementation of step S4 in FIG. 1;

FIG. 4 is a flowchart illustrating the detailed implementation of step S6 in FIG. 1;

the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

In order to achieve the above objective, referring to fig. 1 to 4, the present invention provides a data analysis method based on land resource comprehensive management, comprising the following steps:

In the embodiment of the present invention, as described with reference to fig. 1, a step flow diagram of a data analysis method based on land resource integrated management of the present invention is provided, where in this example, the steps of the data analysis method based on land resource integrated management include:

in the embodiment of the invention, a specific farmland area is selected as a research object, for example, a farmland with a larger area. Three-dimensional simulation operation of a land analysis area is performed based on a GIS technology, for example, satellite remote sensing data such as Landsat, sentinel and the like are used for acquiring high-resolution satellite images of the farmland area, the images provide information of terrain, vegetation coverage and the like of the area, the terrain data including detailed information of ground surface elevation and ground characteristics are acquired through a LiDAR technology, a high-precision Digital Terrain Model (DTM) can be generated by LiDAR, aerial photography is performed by using an unmanned plane or an airplane to acquire aerial images of the farmland area, more detailed geographic data are provided, and digital maps of the existing geographic information such as land ownership information, cadastral data and the like are integrated to obtain more comprehensive land analysis area data. The satellite image, liDAR data, aerial photographic image, digital map and other data are integrated into a GIS platform, and the data are converted into three-dimensional geographic information by using GIS software or a special geographic information system tool, which can comprise a terrain model, a building model, a vegetation model and the like. And three-dimensional simulation operation is performed on the farmland area by using simulation software such as ESRI ArcGIS 3D analysis and Autodesk InfraWorks, and the conditions of land utilization, water resource distribution and the like under different conditions are simulated to generate simulated land three-dimensional data including a three-dimensional map, land utilization conditions, topographical features and the like.

in the embodiment of the invention, a numerical calculation method, such as a kriging interpolation method, is used to grid-divide the farmland area, which is divided into uniform small grids, each representing a land unit. In the field, soil sampling instruments are used to measure PH, conductivity, and other PH related parameters of the soil, and the collected data typically includes GPS coordinates to correlate it with a particular unit of land. The collected soil pH value data is correlated with the simulated land partition data, whether the soil pH value is abnormal or not is determined through data analysis, for example, a certain pH value interval can be set to judge whether the land is too acid or too alkali, and the land is partitioned into two types according to the analysis result, so that the abnormal land partition data and the conventional land partition data are respectively obtained.

in the embodiment of the invention, researchers can collect information about historical land restoration strategies of farmland areas through channels such as literature research, farmland management records and the like, so that the historical land restoration strategies including land improvement, pollution cleaning, vegetation restoration and the like are obtained. In the GIS platform, a historical land repair strategy is used as a reference, and repair simulation is carried out on abnormal land partition data, which can include land improvement measures, vegetation recovery plans and the like, so that the GIS tool can help simulate the influence of different repair schemes and generate repair land partition data which represents the state of the repaired land. The conventional land division data and the repair land division data are integrated to generate optimized land division data, and the process can be to combine the conventional land division data and the repair land division data in a GIS environment, replace abnormal land division data in an originally established simulation map by using the repair land division data, ensure that the repair land is fully utilized, and generate the optimized land division data.

in the embodiment of the invention, the optimized land partition data is acquired and processed by using a remote sensing technology or an automatic sensor network, and the land attribute feature data is generated by using the features such as soil compactness, soil thickness, sunlight time, sunlight intensity, preparation coverage rate and the like.

in the embodiment of the invention, a land use training sample is obtained, wherein the sample is a historical land attribute stored in a land resource management system and a corresponding land specific use, and can comprise farmlands, woodlands, wetlands, urban lands and the like, and each land block is accurately marked with the land use. And establishing a relation model between the land attribute and the land use on a land use training sample by using a machine learning technology, such as a decision tree algorithm, taking the land attribute characteristic information in the land use training sample as an input characteristic of the model, taking the land use information in the land use training sample as a label of the model, and learning the relation between the land attribute characteristic and the land use through the model training decision tree algorithm, thereby establishing a land use prediction model. The land attribute feature data of the farmland area is transmitted to the model for prediction using the established decision tree model, and the model predicts the land use of each land block according to the input of the land attribute data, for example, whether a certain land block is suitable for agriculture, forestry or other uses.

According to the simulated land use data, the land use data comprise simulated land use planning, land interval management and other data, land resource management is carried out on an actual land analysis area, the land analysis area is subjected to land resource management according to a simulation scheme, and land resource management data are generated, wherein the land resource management data are real land use, land sustainable development conditions and the like. By utilizing a sensor network and a remote monitoring technology, key parameters such as soil humidity, vegetation health, meteorological data and the like of a farmland area are continuously monitored, land resource management data are updated in real time, timeliness of the data is ensured, and land resource monitoring data are generated. And (3) comprehensively evaluating the land resources in real time by combining the monitored data of indexes such as soil humidity and vegetation health, automatically identifying a problem area if the monitored comprehensive evaluation data of the indexes is lower than a preset threshold value, extracting the index data which does not reach the index standard, and indicating that the land resources have problems, such as the problems that the pesticide standard of farmlands and the soil dryness and humidity degree do not reach the standard, and the like, so as to repair the problems of the land resources to generate the optimized land resource management data.

Preferably, step S1 comprises the steps of:

acquiring a land analysis area;

In the embodiment of the invention, a land analysis area is obtained, and the area contains a specific analysis range through an artificially set analysis area, for example, a specific farmland area is selected as a research object, and the area can be a farm, a rural area or a farmland, so that the area can comprise various different types of lands and terrains for comprehensive analysis. The method comprises the steps of acquiring the existing map and geographic data of a farmland area by using a Geographic Information System (GIS) tool, wherein the data comprise elevation data, topography, soil types, landform information and the like, converting the integrated geographic data into three-dimensional image data by using three-dimensional modeling software (such as ArcGIS 3D analysis), wherein the data comprise topography, landform, buildings, water bodies and the like, and performing simulation operation processing on the data by using a three-dimensional simulation tool (such as Unity 3D or other professional simulation software) to simulate farmland scenes under different seasons and different meteorological conditions. These simulation data will include vegetation growth, water flow simulation, etc.

Preferably, step S2 comprises the steps of:

In the embodiment of the invention, a Geographic Information System (GIS) tool is used for dividing the simulated land three-dimensional data into regular grid cells, each grid cell represents a specific land area, the grid division can be realized through the grid division function of GIS software, the farmland area is divided into grids, and a unique identifier is allocated to each grid to generate the simulated land division data. And (3) collecting soil samples corresponding to the grid cells by using soil sampling equipment in each grid cell according to the simulated land partition data, wherein the samples comprise pH value (pH value) data of soil, sampling can be performed at different depths so as to obtain data of different soil layers, and the collected data are associated with the corresponding grid cells to generate the pH value data of the soil. And presetting a reasonable soil pH value interval, comparing the acquired soil pH value data with the soil pH value interval, and marking grid cells of corresponding simulation land partition data as abnormal land partition data if the soil pH value data is not in the reasonable interval, wherein the abnormal land partition data may need special management or repair. Comparing the collected soil pH value data with a reasonable interval, and if the soil pH value data is in the reasonable interval, marking grid cells of the corresponding simulation land partition data as conventional land partition data, wherein the conventional land partition data can be regarded as being suitable for general agriculture or other land uses.

Preferably, step S3 comprises the steps of:

s31, acquiring a historical land restoration strategy;

As an example of the present invention, referring to fig. 2, a detailed implementation step flow diagram of step S3 in fig. 1 is shown, where step S3 includes:

s31, acquiring a historical land restoration strategy;

in the embodiment of the invention, the land restoration strategies and measures which are historically implemented in the farmland area are collected, wherein the land restoration strategies and measures comprise restoration methods, materials for improving land quality, restoration time and other information, and the information can be obtained from the historical records, scientific research documents and expert opinions of farmland management departments.

in the embodiment of the invention, the sensor network or satellite remote sensing technology is used for monitoring external hazard factors of farmland areas corresponding to the abnormal land partition data, such as meteorological data (rainfall, temperature, humidity and the like), land erosion degree, pest and disease conditions and the like, and the data can be acquired in real time and correlated with the abnormal land partition data.

In the embodiment of the invention, a land repair model is established based on land damage in a historical land repair strategy and a corresponding repair strategy, and the model can be a model based on machine learning, a neural network or an expert system based on rules and is trained by utilizing the historical land repair strategy. And transmitting external hazard factor data into a repair model, analyzing the data by the repair model, and extracting parameters required by land repair, such as an agent for eliminating solenopsis invicta, the dosage, the construction time and the like. And generating land repair data according to the parameters extracted by the repair model, wherein the data comprises information such as which areas need to be repaired, detailed description of a repair scheme, predicted repair time, cost estimation and the like.

in the embodiment of the invention, the affected area in the abnormal land partition data is subjected to simulation restoration processing according to the land restoration data, which relates to land improvement, vegetation restoration or other restoration measures, and the prediction effect of the restored abnormal land partition data is acquired, so that the restored land partition data is generated.

In the embodiment of the invention, the repaired land partition data and the conventional land partition data are integrated together to form a comprehensive land resource management data set, and the data set comprises the repaired land and the conventional land, thereby providing a foundation for comprehensive management of land resources.

Preferably, step S4 comprises the steps of:

As an example of the present invention, referring to fig. 3, a detailed implementation step flow diagram of step S4 in fig. 1 is shown, where step S4 includes:

in the embodiment of the invention, the soil structure of the land partition data is collected and optimized through the remote sensing technology of the steps, then the soil structure is subjected to three-dimensional hierarchical analysis to obtain the information such as soil density, soil thickness, humidity and organic matter content in the soil, and the information is subjected to data integration to generate three-dimensional soil hierarchical data;

in the embodiment of the invention, the soil level type is set by using geology and soil science knowledge, corresponding soil level parameters for constructing the soil level type are included, the three-dimensional soil level data is divided according to preset soil level types such as sandy loam, clay, loam and the like, the three-dimensional soil level data is compared with the soil level parameters in the preset soil level types, factors such as the size of soil particles, chemical property, water retention capacity and the like are compared, the nearest soil level type is obtained, and the soil level type in each grid for optimizing the soil division data is subjected to data integration to generate the soil type data.

in the embodiment of the invention, solar radiation sensors, weather stations and other devices are used for collecting and optimizing the sunshine characteristic data of the land corresponding to the land division data, wherein the sunshine characteristic data comprise sunshine hours, radiant quantity, illumination intensity and the like, the data are helpful for knowing the illumination condition of the land, and have important influence on the growth of crops, the water evaporation and other processes, so that the land sunshine characteristic data are generated.

In the embodiment of the invention, the optimized land division data is used as a data identification sequence, and the soil type data and the land insolation characteristic data are subjected to data integration of the corresponding data sequence to form the land attribute characteristic data, wherein the land attribute characteristic data comprises the land type, the soil characteristic and the insolation information.

Preferably, step S5 comprises the steps of:

step S51, obtaining a training sample for land use;

In embodiments of the present invention, historical land use data is collected from a field area, which may include land uses corresponding to previous land attributes, such as crop planting conditions, animal husbandry information, land use history, etc., which will be used as the basis for training models. And constructing a classification model by using a decision tree algorithm in a machine learning algorithm, and establishing a mapping relation between land attribute characteristic data and historical land use data, wherein the land attribute characteristic data is used as input, and the historical land use is used as target output. The land use training samples were divided into two independent data sets, 80% of the land use training samples were divided into land use training sets for model training, and 20% of the land use training samples were divided into land use test sets for model evaluation. The initial soil use prediction model is trained by using a soil use training set, the performance of the model is optimized by adjusting the super parameters (such as the depth of a tree, a splitting standard and the like) of the model, the soil use prediction training model is generated, and the performance of the soil use prediction training model is evaluated by using a soil use test set, wherein the indexes comprise accuracy, recall rate, F1 score and the like. Finally, a trained and evaluated land use prediction model is generated for subsequent land use prediction. And transmitting the land attribute characteristic data required to be subjected to land use prediction to a trained land use prediction model. The model will analyze the input land attribute characteristics and generate corresponding land use data, i.e. predict what purpose the land will be used for, generate land use data that can be used for land planning, resource management and decision making to achieve optimal utilization and sustainable management of the land resources.

Preferably, step S6 comprises the steps of:

As an example of the present invention, referring to fig. 4, a detailed implementation step flow diagram of step S6 in fig. 1 is shown, where step S6 includes:

in the embodiment of the invention, the land analysis area is divided into different application areas, such as an agricultural area, a forest area and the like, according to the predicted land application data. According to the land use requirements and the planning of each area, a corresponding land resource management strategy is formulated, including land allocation, use planning, development limitation and the like, and the generated land resource management data comprises the land use, land resource utilization condition, land distribution and other information of each area and is used for decision making of land resource management.

in the embodiment of the invention, the land resource management data is monitored in real time by means of sensors, remote sensing technology and the like, and the relevant information of land resources such as land utilization conditions, land quality, land change and the like is collected in real time to generate the land resource monitoring data which are used for monitoring the actual conditions of the land resources so as to identify potential problems and improve management strategies in time.

in the embodiment of the invention, the related index data is extracted from the real-time monitoring data according to the predefined land resource indexes, and the indexes can comprise the utilization rate of land, the ecological environment condition of land, the water resource utilization condition of land and the like, so as to generate the land resource index data. The collection of index data is helpful for quantitatively evaluating the condition of land resources, and provides scientific basis for subsequent decisions.

in the embodiment of the invention, the comprehensive evaluation algorithm is used for calculating and analyzing the land resource index data. The algorithm can comprehensively calculate each land resource index based on methods such as weight distribution, multi-index evaluation, whether index data reach index standards and the like, and generate comprehensive evaluation data which reflect the overall condition of the land resource and are helpful for understanding the complexity and the comprehensiveness of the land resource.

In the embodiment of the invention, the index comprehensive evaluation data is monitored in real time, when the index comprehensive evaluation data is too low, the land resource management development is poor, if a plurality of indexes are not up to the standard, the indexes which are not up to the standard in the index comprehensive evaluation data, such as the dry and wet degree of the land in agriculture, the soil fertility status and the like, are too low, and the indexes are correspondingly repaired and optimized, so that the optimized land resource management data with the improved index comprehensive evaluation data is generated.

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in the method, in the process of the invention,index comprehensive evaluation level expressed as land resource management data,/->Expressed as index number>Denoted as +.>Difference weight information of index data of each index and index standard threshold value, < ->Denoted as +.>Index data of individual index->Denoted as +.>Index standard threshold value of individual index,/>Weight information indicating that the index data does not reach the index standard threshold value,/or- >Data amount expressed as index data not reaching the index standard threshold,/for the index data>Expressed as index mean data>Expressed as estimated sustainable time of land resource management, +.>An anomaly adjustment value expressed as an index comprehensive evaluation level.

The invention utilizes a land resource indexComprehensive evaluation algorithm, which comprehensively considers index quantityFirst->Difference weight information of index data of individual indexes and index standard threshold value->First->Index data of individual index->First->Index criterion threshold value of individual index->Weight information for index data not reaching index standard threshold value>Data amount of index data not reaching index standard threshold +.>Index mean data->Estimated sustainable time of land resource management +.>And interactions between functions to form a functional relationship:

that is to say,the functional relation calculates all indexes of the land resource management data to obtain corresponding index comprehensive evaluation grades, and all indexes are calculatedThe function formula can comprehensively calculate a plurality of indexes to obtain a comprehensive grade, and independent data calculation of the plurality of indexes is avoided, so that a large amount of time is saved. First- >The difference value weight information of the index data of the individual indexes and the index standard threshold value represents the weight of each index, which reflects the importance of different indexes in comprehensive evaluation, and the importance of certain indexes can be highlighted according to the specific requirements of land resource management by adjusting the weight value, so that the actual situation is better reflected, and the accuracy and the flexibility of the evaluation are improved; first->Index data of individual index +.>The standard threshold of the index reflects the degree of deviation of the index data from the standard threshold, and by comparing the difference values, whether each index reaches the standard or not can be evaluated, so that the health condition of the land resource is determined, and the recognition of the problem and the optimization of the land resource management strategy are facilitated; the weight information that the index data does not reach the index standard threshold value and the weight information that the index data does not reach the index standard threshold value can be used for adjusting the weight of the index which does not reach the standard threshold value, and the influence of the index which does not reach the standard can be emphasized by reasonably setting the weight information, so that the improvement and adjustment of land resource management are guided; the amount of data for which the index data does not reach the index standard threshold reflects the quality of the index data, and when the parameter value is large, stricter management and supervision may be required to ensure the data quality; the index mean value data are used for evaluating the overall performance of the land resource management and determining the health condition of the land resource management, so that a basis is provided for decision making. The estimated sustainable time of land resource management reflects the long-term influence of management strategies, and the long-term planning and sustainability management strategies are formulated to ensure the land resource availability Continuous utilization. The functional relation accurately evaluates the health condition and sustainability of the land resources, and is helpful for making effective land resource management strategies and decisions. Abnormality adjustment value +.>The functional relation is adjusted and corrected, and the error influence caused by abnormal data or error items is reduced, so that the index comprehensive evaluation grade of the land resource management data is more accurately generated>The accuracy and the reliability of the index comprehensive evaluation calculation processing of the land resource index data are improved. Meanwhile, the weight information and the adjustment value in the formula can be adjusted according to actual conditions and are applied to the land resource index data corresponding to different land resource management data, so that the flexibility and applicability of the algorithm are improved.

Preferably, step S65 comprises the steps of:

In the embodiment of the invention, when the index comprehensive evaluation data is monitored to be lower than the threshold value, the extraction of the disuse index data is automatically triggered, wherein the disuse index data comprises specific indexes and data which lead to the unsatisfactory state of land resources, for example, the index comprehensive evaluation data of farmlands is lower than the preset threshold value, and the disuse index data can comprise related data of indexes such as the dryness and humidity degree of lands in agriculture, the soil fertility state, the land utilization efficiency and the like. According to the failure index data, corresponding optimization strategies and measures are formulated to improve land resource management, wherein the measures can comprise land re-planning, resource redistribution, agricultural technology upgrading, ecological restoration and the like, for example, if land utilization efficiency is low, measures such as improvement of a farmland irrigation system, introduction of efficient agricultural technology and the like can be proposed, and after the strategies are implemented, the generated land resource management data reflect the improved land resource utilization state, so that the optimized management and sustainable utilization of land resources are realized.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The data analysis method based on the comprehensive management of the land resources is characterized by comprising the following steps:

step S4, including:

step S44, data integration of the corresponding data sequences is carried out on the soil type data and the land sunshine characteristic data according to the data sequences of the optimized land division data, and land attribute characteristic data is generated;

step S5, including:

step S51, obtaining a training sample for land use;

step S55, transmitting the land attribute characteristic data to a land use prediction model to predict land use, and generating land use data;

step S6, including:

The comprehensive evaluation algorithm of the land resource index is as follows:

；

in the method, in the process of the invention,index comprehensive evaluation level expressed as land resource management data,/->Expressed as index number>Denoted as the firstDifference weight information of index data of each index and index standard threshold value, < ->Denoted as +.>Index data of individual index->Denoted as +.>Index standard threshold value of individual index,/>Weight information indicating that the index data does not reach the index standard threshold value,/or->Data amount expressed as index data not reaching the index standard threshold,/for the index data>Expressed as index mean data>Expressed as estimated sustainable time of land resource management, +.>An anomaly adjustment value expressed as an index comprehensive evaluation level;

step S65, including:

2. The data analysis method based on comprehensive management of land resources as claimed in claim 1, wherein step S1 comprises the steps of:

Acquiring a land analysis area;

3. The data analysis method based on comprehensive management of land resources as claimed in claim 1, wherein step S2 comprises the steps of:

4. The data analysis method based on comprehensive management of land resources as claimed in claim 1, wherein step S3 comprises the steps of:

s31, acquiring a historical land restoration strategy;

5. A data analysis system based on land resource integrated management for performing the data analysis method based on land resource integrated management according to claim 1, the data analysis system based on land resource integrated management comprising: