A method, device and equipment for evaluating the potential of land resources

Abstract

The present invention provides a method, device and equipment for evaluating the potential of land resources, and relates to the field of agricultural land resource evaluation. The method for evaluating the potential of land resources comprises: determining multiple evaluation factors that affect agricultural land in the target area according to the geographical conditions of the target area; segmenting the target area through a preset segmentation line to obtain a first area and a second area; assigning points to each evaluation factor based on a preset standard to obtain a scoring result for each evaluation factor; assigning weights to the evaluation factors of the first area to obtain a first weight ratio; and determining the potential of agricultural land resources in the target area based on the scoring result and the first weight ratio. The scheme of the present invention realizes the accurate evaluation of the potential of cultivated land in high-latitude areas or subarctic areas with a large latitude span, and improves the utilization rate of cultivated land.

Description

Technical Field

[0001] The present invention relates to the field of agricultural land resource assessment, and in particular to a method, device and equipment for assessing the potential of land resources. Background Art

[0002] Food security is one of the problems facing the world, and has therefore become one of the important contents of the United Nations Sustainable Development Goals (SDGs). Cultivated land resources are an important guarantee for maintaining national food security and promoting sustainable social and economic development. One of the core issues in solving the food security problem is to ensure the security of cultivated land resources, and the development and utilization of reserve cultivated land resources is an important means to ensure food security and the cultivated land red line. However, in the process of evaluating reserve cultivated land resources, the existing land assessment software can only be applied to the development and assessment of land under conditions such as low latitudes, small latitude spans, and small temperature differences. It is not applicable to cultivated land in high latitudes or subarctic regions with large latitude spans, and it is impossible to accurately assess cultivated land in high latitudes or subarctic regions with large latitude spans. Summary of the invention

[0003] The present invention provides a method, device and equipment for evaluating the potential of land resources, which solves the problem that the traditional cultivated land evaluation method cannot accurately evaluate the cultivated land potential in high-latitude areas or sub-arctic areas with a large latitude span.

[0004] In order to solve the above technical problems, the technical solution of the present invention is as follows:

[0005] An embodiment of the present invention provides a method for evaluating the potential of land resources, comprising:

[0006] According to the geographical conditions of the target area, determine multiple evaluation factors affecting the agricultural land in the target area;

[0007] Segmenting the target area using a preset segmentation line to obtain a first area and a second area;

[0008] Based on the preset standard, each evaluation factor is scored respectively to obtain the scoring result of each evaluation factor;

[0009] Performing weight allocation on the evaluation factors of the first region to obtain a first weight ratio;

[0010] Based on the scoring result and the first weight ratio, the potential of agricultural land resources in the target area is determined.

[0011] Optionally, the evaluation factors include:

[0012] The target area’s altitude evaluation factor, terrain slope evaluation factor, temperature evaluation factor, precipitation evaluation factor, soil type evaluation factor, organic matter content evaluation factor, and pH evaluation factor.

[0013] Optionally, based on a preset standard, each of the evaluation factors is scored respectively to obtain a scoring result for each evaluation factor, including:

[0014] Based on the evaluation factor, obtaining geographic data of a first region;

[0015] Based on the geographic data of the first region and preset standards, each of the evaluation factors is scored to obtain a scoring result for each evaluation factor.

[0016] Optionally, weighting the evaluation factors of the first region to obtain a first weight ratio includes:

[0017] Performing a first weight allocation on the evaluation factors of the first region by a first preset method to obtain first allocated weights of the evaluation factors of the first region;

[0018] Performing a second weight allocation on the evaluation factors of the first region by a second preset method to obtain second allocation weights of the evaluation factors of the first region;

[0019] A first weight ratio is acquired based on the first allocation weight and the second allocation weight.

[0020] Optionally, determining the potential of agricultural land resources in the target area based on the scoring result and the first weight ratio includes:

[0021] Obtaining a second weight ratio according to the first weight ratio and a first preset formula;

[0022] According to the scoring result and the first weight ratio, the planting suitability index of the first region is obtained;

[0023] According to the scoring result and the second weight ratio, the planting suitability index of the second region is obtained;

[0024] Based on the suitability index of the first area and the suitability index of the second area, the potential of agricultural land resources in the target area is determined.

[0025] Optionally, obtaining a second weight ratio according to the first weight ratio and a first preset formula includes:

[0026] Based on the temperature difference between the first region and the second region, determining a weight value of a temperature evaluation factor of the second region by a first preset method;

[0027] According to the weight value of the temperature evaluation factor of the second region, the first weight ratio and the first preset formula, weights are allocated to the evaluation factors other than the temperature evaluation factor in the second region, and a second weight ratio is obtained;

[0028] Wherein, the first preset formula is: W k1 represents the new weight of the kth evaluation factor in the second region, W k0 represents the original weight of the kth evaluation factor in the first region; W T1 represents the new weight of the temperature evaluation factor in the second region, W T0 Represents the original weight of the temperature evaluation factor in the first region.

[0029] Optionally, obtaining a suitable planting index for the first region according to the scoring result and the first weight ratio includes:

[0030] Based on the scoring result and the first weight ratio, obtaining the planting suitability index of the first region through a second preset formula;

[0031] Among them, the second preset formula is: S is the suitability index of the first region; n is the number of evaluation factors in the first region; W k is the weight of the kth evaluation factor in the first region; V k is the scoring result of the kth evaluation factor in the first region.

[0032] The embodiment of the present invention further provides a land resource potential assessment device, comprising:

[0033] An acquisition module, for determining a plurality of evaluation factors affecting agricultural land in a target area according to geographical conditions of the target area;

[0034] A processing module is used to divide the target area through a preset dividing line to obtain a first area and a second area; based on a preset standard, each of the evaluation factors is scored to obtain a scoring result of each evaluation factor; weights are assigned to the evaluation factors of the first area to obtain a first weight ratio; based on the scoring result and the first weight ratio, the potential of the agricultural land resources in the target area is determined.

[0035] An embodiment of the present invention further provides a computing device, comprising: a processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method for evaluating the potential of land resources as described above is executed.

[0036] An embodiment of the present invention further provides a computer-readable storage medium, comprising: storing instructions, which, when executed on a computer, enable the computer to execute the land resource potential assessment method as described above.

[0037] The above solution of the present invention includes at least the following beneficial effects:

[0038] The land resource potential assessment method of the present invention determines multiple evaluation factors that affect the agricultural land in the target area according to the geographical conditions of the target area; divides the target area through a preset dividing line to obtain a first area and a second area; assigns points to each evaluation factor based on a preset standard to obtain a scoring result of each evaluation factor; assigns weights to the evaluation factors of the first area to obtain a first weight ratio; and determines the potential of agricultural land resources in the target area based on the scoring result and the first weight ratio. The method realizes an accurate assessment of the potential of cultivated land in high-latitude areas or sub-arctic areas with a large latitude span, thereby improving the utilization rate of cultivated land. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a schematic flow chart of the land resource potential assessment method of the present invention;

[0040] Figure 2 It is a module schematic diagram of the land resource potential assessment device of the present invention. DETAILED DESCRIPTION

[0041] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0042] like Figure 1 As shown, an embodiment of the present invention provides a method for evaluating the potential of land resources, comprising:

[0043] Step 11, determining multiple evaluation factors affecting the agricultural land in the target area according to the geographical conditions of the target area;

[0044] Step 12, segmenting the target area using a preset segmentation line to obtain a first area and a second area;

[0045] Step 13, based on the preset standard, each of the evaluation factors is scored respectively to obtain the scoring result of each evaluation factor;

[0046] Step 14, weighting the evaluation factors of the first region to obtain a first weight ratio;

[0047] Step 15: Determine the potential of agricultural land resources in the target area based on the scoring result and the first weight ratio.

[0048] In this embodiment, the evaluation factors of the first region and the second region are the same, including: the altitude evaluation factor of the target region, the terrain slope evaluation factor, the temperature evaluation factor, the precipitation evaluation factor, the soil type evaluation factor, the organic matter content evaluation factor and the pH evaluation factor; the land resource potential evaluation method described in this embodiment can be used in high-latitude regions or subarctic regions with a large latitude span; it can be mainly applied to regions with a latitude exceeding 60°N. When the latitude exceeds 60°N, permafrost will occur, and with the continuous increase in latitude, the burial depth of the frozen soil layer will continue to increase. In addition, at 60° The annual average temperature in the area north of 60°N is below minus 5°C, and the slope is relatively high, which is not suitable for crop growth. Therefore, when evaluating the target area including the area north of 60°N and the area south of 60°N, the present invention adopts a method of setting 60°N as a preset dividing line, dividing the target area by the preset dividing line, obtaining a first area and a second area, setting the area north of 60°N as the second area, and setting the area south of 60°N as the first area to evaluate the entire target area; achieving accurate evaluation of the potential of arable land in high-latitude areas or subarctic areas with a large latitude span, and improving the utilization rate of arable land.

[0049] In this embodiment, the selection of the evaluation factors can determine the evaluation factors of the suitability of agricultural production in the target area according to the geographical conditions that affect the target area, and screen out the evaluation factors that can affect the agricultural land in the target area by listening to expert opinions, reading materials, etc.; commonly used factors are temperature, precipitation, altitude, slope, soil type, soil organic matter and pH value, etc.; for example: when the precipitation in the target area has obvious zonality, the difference between land and sea is obvious, the annual precipitation in the south is more, the annual precipitation in the north is less, and the precipitation in the east is greater than that in the west. The annual precipitation in the northern part of the study area is concentrated between 0 and 300 mm, and the southern part is between 600 mm and 1200 mm. The target area is dominated by a monsoon climate with warm and humid summers and cold and dry winters. At the same time, according to the climate map of the target area, it is concluded that the annual average temperature in the northern part of the Far East is less than -5°C, and the areas with an annual average temperature greater than 0°C are mainly concentrated in some areas south of 55°N. The temperature difference between the north and the south is large, and the annual average temperature difference is 15°C. Therefore, in this case, for the large temperature difference between the north and the south and the large difference in precipitation, it is necessary to select the temperature evaluation factor and the precipitation evaluation factor;

[0050] When the target area is mostly between 400 and 1200 m in elevation, and areas with elevations below 200 m only exist in the northeast, the target area has a high overall slope, and areas with more obvious slope fluctuations account for about half of the target area, in this case, it is necessary to select the altitude evaluation factor and the terrain slope evaluation factor;

[0051] According to the soil property map of the target area, among the main soil types in the target area, leaching soil, gray soil and germinating soil (including embryonic soil) account for 50% of the entire Far East region. Organic soil, gray soil, chernozem soil, etc. also account for a relatively large proportion, and the remaining types account for a small proportion. Organic matter is an important part of the soil formation process, and its content affects every process of the soil's ecological function. Organic matter is a reflection of the health of the soil. Whether it is suitable for the growth of crops is determined by its fertility, and the fertility of the soil is determined by organic matter. In most areas of the target area, the content of soil organic matter is less than 6g·kg-1, followed by 6 to 10g·kg-1. Another important factor affecting soil fertility is the pH value of the soil. Different climates form different soil acidity and alkalinity. The most widely distributed soils are basically acidic but rich in organic matter and minerals. Whether they are suitable for agriculture depends on their nutrient content. Production; for example, coniferous forest soil is poor and easy to freeze, so it is more suitable for forestry; forest soil is fertile and easy to cultivate, so it can be used for agricultural reclamation; the most suitable black grassland soil is relatively neutral and alkaline, rich in organic matter and calcium, deep and water-retaining and fertilizer-retaining, and is also high-quality agricultural soil. The pH value between 6.0 and 8.0 is generally within the normal range suitable for crop growth. Outside this range, a scientific and reasonable plan for adjusting the pH value needs to be formulated. If the soil pH value in the target area is acidic according to the soil pH map of the target area, and the planting range needs to be expanded to between 5.0 and 9.0, it is necessary to select soil type evaluation factors, organic matter evaluation factors and pH evaluation factors.

[0052] In this embodiment, the preset dividing line can be determined by whether the average temperature of crops in the target area during the growing season is higher than 0°C, and whether the crop growth conditions are met as an important basis. The present invention selects the isotherm with an annual average temperature of -5°C to divide the target area, and the closest integer latitude is the dividing line.

[0053] In an optional embodiment of the present invention, step 13 may include:

[0054] Step 131, based on the evaluation factor, obtaining geographic data of the first region;

[0055] Step 132: Based on the geographic data of the first region and preset standards, score each evaluation factor to obtain a score result for each evaluation factor.

[0056] In this embodiment, the geographical data of the first region corresponds to the evaluation factors, which are the actual altitude data, terrain slope data, temperature data, precipitation data, soil type data, organic matter content data and pH data of the first region; wherein the terrain slope data can be obtained by preprocessing DEM data; the temperature data can be obtained by using the Global seamless and high-resolution temperature dataset (GSHTD) dataset, which is obtained from the GSOD official website (Global Surface Summary of the The daily data of the Mountain Environment Mapping Project (FRMM) is collected from the World Soil Database (HWSD) and the daily data of the Mountain Environment Mapping Project (FRMM) is used to obtain the daily data of the Mountain Environment Mapping Project (FRMM) based on ERA5-Land. The data is corrected and spatially downscaled to produce a high-resolution annual precipitation data set. The soil type data, organic matter data and pH data can all be obtained through the World Soil Database (HWSD). In this embodiment, the preset standard is based on the data in Table 1, the weights, scores and grading index table of adaptability evaluation factors of suitable arable and planting areas. Specifically, according to the actual geographical data of the first region and then combined with the preset standard, each of the evaluation factors is scored and the scoring result of each evaluation factor is obtained. The preset standard can also define the score interval according to the grading standard of each indicator in the natural geographical pattern, terrain conditions and soil conditions, and set the score from high to low.

[0057] Table 1. Weights, scores and classification indicators of adaptability evaluation factors in suitable arable and crop-producing areas

[0058]

[0059] In an optional embodiment of the present invention, step 14 may include:

[0060] Step 141, performing a first weight allocation on the evaluation factors of the first region by a first preset method, and obtaining a first allocation weight of each evaluation factor of the first region;

[0061] Step 142, performing a second weight allocation on the evaluation factors of the first region by a second preset method, and obtaining a second allocation weight of each evaluation factor of the first region;

[0062] Step 143, based on the first allocation weight and the second allocation weight, obtain a first weight ratio. In this embodiment, the first preset method is the AHP analytic hierarchy process; the second preset method is the CRITIC weight method; Step 143 can be based on the first allocation weight and the second allocation weight, through the formula Get the first weight ratio; where Wm Represents the final output weight of the current evaluation factor; W i is the first allocation weight of the current evaluation factor; W j is the second allocation weight of the current evaluation factor; n is the number of evaluation factors; wherein step 141 may include: classifying the evaluation factors to obtain the classification processing results; then performing the first-level indicator weight allocation processing on the classification processing results to obtain the first-level indicator allocation results; performing the second-level indicator weight allocation processing on the original evaluation factors to obtain the second-level indicator allocation results; according to the first-level indicator allocation results and the second-level indicator allocation results, obtaining the first allocation weights of each evaluation factor in the first region; specifically, it may include: firstly obtaining a multi-level ladder structure model, classifying the evaluation factors of the first region, and dividing the evaluation factors into annual average temperature, annual precipitation, terrain conditions, etc. The four categories of conditions are classified into four types: annual average temperature, annual precipitation, terrain conditions, and soil conditions. A 4×4 matrix including the four types of indicators, namely, annual average temperature, annual precipitation, terrain conditions, and soil conditions, is constructed. The specific classification results are shown in Table 2. The natural evaluation indicators of suitable arable and planting areas are used to perform subjective weighting and expert scoring to improve the comparative judgment matrix. During this period, the literature analysis method is integrated to summarize relevant thematic papers. The average value is obtained based on the conclusions of existing authoritative data, and the migration application is appropriately adjusted. The approximate value of the eigenvector of the judgment matrix is ​​calculated by the root-finding method, and the weight vector is obtained after the eigenvector is standardized. After passing the consistency test, the weight results W of the first-level indicators: annual average temperature, annual precipitation, terrain conditions, and soil conditions are obtained. A ; Similarly, the weighted results W of the secondary indicators are calculated: altitude, terrain slope, soil type, pH, and organic matter content B ; Among them, the formula for judging the matrix characteristics by the root-finding method is as follows: Among them, W i ′ is the component of the total weight, which refers to the weight value, that is, the weight result W of the first-level indicator. A Or the weight result of the secondary index W B ; a ij is the comparison result of the importance of factor i and factor j; secondly, the first-level index weight W A and the secondary index weight W B Multiplying them together gives the AHP output weight W i ;

[0063] Table 2. Natural evaluation indicators of areas suitable for cultivation and planting

[0064]

[0065]

[0066] Table 3, Ratio scale table

[0067] Scale meaning 1 Indicates that two elements have the same importance. 3 Indicates that compared with two elements, the former is slightly more important than the latter 5 Indicates that compared with two elements, the former is obviously more important than the latter 7 Indicates that compared with two elements, the former is extremely important than the latter 9 Indicates that compared with two elements, the former is more important than the latter 2,4,6,8 Indicates the middle value of the above adjacent judgments Countdown from 1 to 9 Indicates the importance of comparing the corresponding two factors in exchange order

[0068] In this embodiment, step 142 specifically uses the CRITIC weight method to extract 8-15 areas suitable for cultivation and planting, sample and record the data of each factor, and then process and calculate the data to list the original indicator data matrix: Among them, X ij represents the value of the jth evaluation index of the ith sample; each index is dimensionless, and here the positive and normalized processing is used; the index variability is expressed in the form of standard deviation, that is, the difference in value fluctuation. The larger the standard deviation, the more information can be displayed, and the stronger the evaluation strength of the index itself, and the more weight should be assigned to the index; the specific formula can be used Where S j Represents the standard deviation of the jth indicator; indicator conflict R j By calculating the correlation coefficient, the stronger the correlation with other indicators, the less conflict this indicator has with other indicators. The more the same information is reflected, the more repetitive the evaluation content can be, which weakens the evaluation strength of this indicator to a certain extent, and the weight assigned to this indicator should be reduced. Among them, r ij represents the correlation coefficient between evaluation indexes i and j; information C j The larger the value is, the greater the role of the jth evaluation index in the entire evaluation index system, and more weight should be assigned to it. j =S j ×R j , and obtain the objective weight W of the jth indicator output by the CRITIC weight method j , Step 143, specifically, since the above two weights use different calculation principles and use different data features, one is a subjective weight assignment and the other is an objective weight assignment. Combining the two methods makes the obtained data more reflective of the actual situation and ensures that the determination of the indicator weight is objective, fair and scientific. Finally, the comprehensive weight W obtained by the two methods is m This is the first weight ratio required, and the formula is Among them, W m Represents the final output weight of the current evaluation factor; W i is the first allocation weight of the current evaluation factor; W j is the second allocation weight of the current evaluation factor; n is the number of evaluation factors.

[0069] In an optional embodiment of the present invention, step 15 may include:

[0070] Step 151, obtaining a second weight ratio according to the first weight ratio and a first preset formula;

[0071] Step 152, obtaining a suitable planting index for the first region according to the scoring result and the first weight ratio;

[0072] Step 153, obtaining a suitable planting index for the second region according to the scoring result and the second weight ratio;

[0073] Step 154, determining the potential of agricultural land resources in the target area based on the suitability index of the first area and the suitability index of the second area.

[0074] In an optional embodiment of the present invention, step 151 may include:

[0075] Step 1511, based on the temperature difference between the first region and the second region, determine the weight value of the temperature evaluation factor of the second region by a first preset method;

[0076] Step 1512, according to the weight value of the temperature evaluation factor of the second region, the first weight ratio and the first preset formula, weight the evaluation factors other than the temperature evaluation factor in the second region, and obtain a second weight ratio;

[0077] Wherein, the first preset formula is: W k1 represents the new weight of the kth evaluation factor in the second region, W k0 represents the original weight of the kth evaluation factor in the first region; W T1 represents the new weight of the temperature evaluation factor in the second region, W T0 Represents the original weight of the temperature evaluation factor in the first region.

[0078] In this embodiment, since the target areas are mostly high-latitude areas or subarctic areas with a large latitude span, there is a large temperature difference between the first area and the second area after being divided by the preset dividing line. At the same time, since the temperature is a hard condition for crop growth, it becomes a dominant factor in the impact on agricultural planting in high-latitude areas. Therefore, the temperature weight of the second area should be greatly increased in the calculation of the comprehensive planting index. Therefore, for areas with a large temperature difference between the two places caused by latitude, a higher weight can be given through the first preset method, and other evaluation factors can be obtained in turn through the first preset formula, and finally all the results are converged to obtain the second weight ratio; for example, taking the temperature difference between the first area and the second area as -5 degrees Celsius, when the first weight ratio is 0.11:0.2:0.13:0.1 2: 0.15: 0.17: 0.12 (altitude: terrain slope: temperature: precipitation: soil type: organic matter content: pH), the weight value of the temperature evaluation factor in the second area can be determined first by the temperature difference between the first area and the second area and the first preset method, and the result is 0.7. Then the weights of other evaluation factors are obtained by the first preset formula, which are 0.04, 0.07, 0.04, 0.05, 0.06, and 0.04 (altitude, terrain slope, precipitation, soil type, organic matter content, pH); that is, the second weight ratio is: 0.04: 0.07: 0.7: 0.04: 0.05: 0.06: 0.04 (altitude: terrain slope: temperature: precipitation: soil type: organic matter content: pH).

[0079] In an optional embodiment of the present invention, step 152 may include:

[0080] Based on the scoring result and the first weight ratio, obtaining the planting suitability index of the first region through a second preset formula;

[0081] Among them, the second preset formula is: S is the suitability index of the first region; n is the number of evaluation factors in the first region; W k is the weight of the kth evaluation factor in the first region; V k is the scoring result of the kth evaluation factor in the first region.

[0082] In this embodiment, the suitable planting index of the second region is obtained in the same manner as the suitable planting index of the first region, both of which are obtained through the second preset formula; in this embodiment, the potential of the agricultural land resources in the target region is determined based on the suitable planting index of the first region and the suitable planting index of the second region, specifically, based on the suitable planting index of the first region and the suitable planting index of the second region, the potential of the resources of each block of the agricultural land in the target region is evaluated through Table 4, the classification standard of suitable planting and suitable cultivation areas;

[0083] Table 4: Classification standards for areas suitable for cultivation and planting

[0084]

[0085]

[0086] In this embodiment, the second preset formula can be used in combination with Table 4 to calculate the suitability score of each region. The higher the score, the more suitable it is for planting. The more suitable region can be used as reserve farmland for reclamation. A score of 0 is defined as a completely unsuitable farming area. Among them, the extremely high suitability area means: under special natural environmental conditions, various conditions are good, the main food crops can mature naturally during the growth period, and the yield is high, that is, the economic benefit of planting crops is the highest; the relatively high suitability area means: the general conditions are good, and some conditions are not so good, which can meet the growth conditions of some crops, and the yield can be used as a reserve farmland resource. The remaining crops can be remedied by transformation measures, and the economic benefit is still high; the general suitability area means: the natural conditions only meet the basic conditions for crop growth, and it can be barely planted, but the transformation measures are not good enough. The investment in construction is large, the yield is low or the quality is poor, and the economic benefits are not high; the lower suitable area means: the conditions are not very good, and it is only suitable for sowing a small number of special crops. There are many uncontrollable factors in future yields, and the cost and difficulty of human intervention are high, and it cannot bring equivalent economic benefits; the extremely low suitable area means: the natural environment is harsh, human resources are insufficient, some key conditions of crops cannot meet the requirements, and it is not easy to remedy, and crop growth is poor, such as insufficient heat and inability to mature; according to the suitable zoning for crops to be cultivated and planted, the corresponding grids are classified, and the broken small patches are ignored. Then the area of ​​each part is merged and calculated, and the extremely high suitable area is selected to estimate the grain production potential of reserve agricultural land with the yield per unit area, so as to provide a scientific basis for the reasonable layout of crops and achieve the greatest economic benefits.

[0087] The land resource potential assessment method described in the present invention solves the problem that traditional cultivated land assessment methods are unable to accurately assess cultivated land in high-latitude areas or subarctic regions with a large latitudinal span by designing a segmentation process for the target area and a scoring process for the evaluation factors. It achieves an accurate assessment of cultivated land in high-latitude areas or subarctic regions with a large latitudinal span and improves the utilization rate of cultivated land.

[0088] like Figure 2 As shown, an embodiment of the present invention provides a land resource potential assessment device 20, comprising:

[0089] An acquisition module 21 is used to determine a plurality of evaluation factors affecting the agricultural land in the target area according to the geographical conditions of the target area;

[0090] The processing module 22 is used to divide the target area through a preset dividing line to obtain a first area and a second area; based on a preset standard, each of the evaluation factors is scored to obtain a scoring result for each evaluation factor; weights are assigned to the evaluation factors of the first area to obtain a first weight ratio; based on the scoring result and the first weight ratio, the potential of the agricultural land resources in the target area is determined.

[0091] Optionally, the evaluation factors include:

[0092] The target area’s altitude evaluation factor, terrain slope evaluation factor, temperature evaluation factor, precipitation evaluation factor, soil type evaluation factor, organic matter evaluation factor, and pH evaluation factor.

[0093] Optionally, based on a preset standard, each of the evaluation factors is scored respectively to obtain a scoring result for each evaluation factor, including:

[0094] Based on the evaluation factor, obtaining geographic data of a first region;

[0095] Based on the geographic data of the first region and preset standards, each of the evaluation factors is scored to obtain a scoring result for each evaluation factor.

[0096] Optionally, weighting the evaluation factors of the first region to obtain a first weight ratio includes:

[0097] Performing a first weight allocation on the evaluation factors of the first region by a first preset method to obtain first allocated weights of the evaluation factors of the first region;

[0098] Performing a second weight allocation on the evaluation factors of the first region by a second preset method to obtain second allocation weights of the evaluation factors of the first region;

[0099] A first weight ratio is acquired based on the first allocation weight and the second allocation weight.

[0100] Optionally, determining the potential of agricultural land resources in the target area based on the scoring result and the first weight ratio includes:

[0101] Obtaining a second weight ratio according to the first weight ratio and a first preset formula;

[0102] According to the scoring result and the first weight ratio, the planting suitability index of the first region is obtained;

[0103] According to the scoring result and the second weight ratio, the planting suitability index of the second region is obtained;

[0104] Based on the suitability index of the first area and the suitability index of the second area, the potential of agricultural land resources in the target area is determined.

[0105] Optionally, obtaining a second weight ratio according to the first weight ratio and a first preset formula includes:

[0106] Based on the temperature difference between the first region and the second region, determining a weight value of a temperature evaluation factor of the second region by a first preset method;

[0107] According to the weight value of the temperature evaluation factor of the second region, the first weight ratio and the first preset formula, weights are allocated to the evaluation factors other than the temperature evaluation factor in the second region, and a second weight ratio is obtained;

[0108] Wherein, the first preset formula is: W k1 represents the new weight of the kth evaluation factor in the second region, W k0 represents the original weight of the kth evaluation factor in the first region; W T1 represents the new weight of the temperature evaluation factor in the second region, W T0 Represents the original weight of the temperature evaluation factor in the first region.

[0109] Optionally, obtaining a suitable planting index for the first region according to the scoring result and the first weight ratio includes:

[0110] Based on the scoring result and the first weight ratio, obtaining the planting suitability index of the first region through a second preset formula;

[0111] Among them, the second preset formula is: S is the suitability index of the first region; n is the number of evaluation factors in the first region; W k is the weight of the kth evaluation factor in the first region; V k is the scoring result of the kth evaluation factor in the first region.

[0112] It should be noted that the device is a device corresponding to the above-mentioned land resource potential assessment method. All implementation methods in the above-mentioned method are applicable to the embodiments of the device and can achieve the same technical effects.

[0113] An embodiment of the present invention also provides a computing device, comprising: a processor, a memory, and a program or instruction stored in the memory and executable on the processor, wherein the program or instruction, when executed by the processor, implements the steps of the land resource potential assessment method as described above.

[0114] The embodiment of the present invention further provides a computer-readable storage medium, including instructions, which, when executed on a computer, enable the computer to execute the land resource potential assessment method as described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0115] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0117] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0118] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0119] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0120] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0121] In addition, it should be noted that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it is understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in hardware, firmware, software or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0122] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general device. Therefore, the purpose of the present invention can also be achieved by simply providing a program product containing a program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order. Some steps can be performed in parallel or independently of each other.

[0123] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for evaluating the potential of land resources, characterized in that: include: According to the geographical conditions of the target area, determine multiple evaluation factors affecting the agricultural land in the target area; Segmenting the target area using a preset segmentation line to obtain a first area and a second area; Based on the preset standard, each evaluation factor is scored respectively to obtain the scoring result of each evaluation factor; Performing weight allocation on the evaluation factors of the first region to obtain a first weight ratio; Determining the potential of agricultural land resources in the target area based on the scoring result and the first weight ratio; The evaluation factors include: Evaluation factors for altitude, terrain slope, temperature, precipitation, soil type, organic matter content, and pH in the target area; Performing weight allocation on the evaluation factors of the first region to obtain a first weight ratio includes: Performing a first weight allocation on the evaluation factors of the first region by a first preset method to obtain first allocated weights of the evaluation factors of the first region; Performing a second weight allocation on the evaluation factors of the first region by a second preset method to obtain second allocation weights of the evaluation factors of the first region; Based on the first allocation weight and the second allocation weight, obtaining a first weight ratio; Based on the scoring result and the first weight ratio, the potential of agricultural land resources in the target area is determined, including: Obtaining a second weight ratio according to the first weight ratio and a first preset formula; According to the scoring result and the first weight ratio, the planting suitability index of the first region is obtained; According to the scoring result and the second weight ratio, the planting suitability index of the second region is obtained; Determine the potential of agricultural land resources in the target area based on the suitability index of the first area and the suitability index of the second area; Obtaining a second weight ratio according to the first weight ratio and a first preset formula includes: Based on the temperature difference between the first region and the second region, determining a weight value of a temperature evaluation factor of the second region by a first preset method; According to the weight value of the temperature evaluation factor of the second region, the first weight ratio and the first preset formula, weights are allocated to the evaluation factors other than the temperature evaluation factor in the second region, and a second weight ratio is obtained; Wherein, the first preset formula is: W k1 represents the new weight of the kth evaluation factor in the second region, W k0 represents the original weight of the kth evaluation factor in the first region; W T1 represents the new weight of the temperature evaluation factor in the second region, W T0 It represents the original weight of the temperature evaluation factor in the first region; According to the scoring result and the first weight ratio, the planting suitability index of the first region is obtained, including: Based on the scoring result and the first weight ratio, obtaining the planting suitability index of the first region through a second preset formula; Among them, the second preset formula is: S is the suitability index of the first region; n is the number of evaluation factors in the first region; W k is the weight of the kth evaluation factor in the first region; V k is the scoring result of the kth evaluation factor in the first region; Wherein, the target area is a high-latitude area or a subarctic area with a large latitude span; The preset dividing line is 60°N, and the target area is divided by the preset dividing line to obtain a first area and a second area, wherein the first area is an area south of 60°N, and the second area is an area north of 60°N; The preset standard defines the score range according to the grading standards of each indicator in the natural geographical pattern, terrain conditions, and soil conditions, and sets the scores from high to low; The first preset method is the AHP hierarchical analysis method, and the second preset method is the CRITIC weight method.

2. The land resource potential assessment method according to claim 1, characterized in that: Based on the preset standard, each of the evaluation factors is scored respectively to obtain the scoring result of each evaluation factor, including: Based on the evaluation factor, obtaining geographic data of a first region; Based on the geographic data of the first region and preset standards, each of the evaluation factors is scored to obtain a scoring result for each evaluation factor.

3. A land resource potential assessment device, characterized in that: include: An acquisition module, for determining a plurality of evaluation factors affecting agricultural land in a target area according to geographical conditions of the target area; A processing module, used for segmenting the target area through a preset segmentation line to obtain a first area and a second area; Based on the preset standard, each evaluation factor is scored respectively to obtain the scoring result of each evaluation factor; Performing weight allocation on the evaluation factors of the first region to obtain a first weight ratio; Determining the potential of agricultural land resources in the target area based on the scoring result and the first weight ratio; The evaluation factors include: Evaluation factors for altitude, terrain slope, temperature, precipitation, soil type, organic matter content, and pH in the target area; Performing weight allocation on the evaluation factors of the first region to obtain a first weight ratio includes: Performing a first weight allocation on the evaluation factors of the first region by a first preset method to obtain first allocated weights of the evaluation factors of the first region; Performing a second weight allocation on the evaluation factors of the first region by a second preset method to obtain second allocation weights of the evaluation factors of the first region; Based on the first allocation weight and the second allocation weight, obtaining a first weight ratio; Based on the scoring result and the first weight ratio, the potential of agricultural land resources in the target area is determined, including: Obtaining a second weight ratio according to the first weight ratio and a first preset formula; According to the scoring result and the first weight ratio, the planting suitability index of the first region is obtained; According to the scoring result and the second weight ratio, the planting suitability index of the second region is obtained; Determine the potential of agricultural land resources in the target area based on the suitability index of the first area and the suitability index of the second area; Obtaining a second weight ratio according to the first weight ratio and a first preset formula includes: Based on the temperature difference between the first region and the second region, determining a weight value of a temperature evaluation factor of the second region by a first preset method; According to the weight value of the temperature evaluation factor of the second region, the first weight ratio and the first preset formula, weights are allocated to the evaluation factors other than the temperature evaluation factor in the second region, and a second weight ratio is obtained; Wherein, the first preset formula is: W k1 represents the new weight of the kth evaluation factor in the second region, W k0 represents the original weight of the kth evaluation factor in the first region; W T1 represents the new weight of the temperature evaluation factor in the second region, W T0 It represents the original weight of the temperature evaluation factor in the first region; According to the scoring result and the first weight ratio, the planting suitability index of the first region is obtained, including: Based on the scoring result and the first weight ratio, obtaining the planting suitability index of the first region through a second preset formula; Among them, the second preset formula is: S is the suitability index of the first region; n is the number of evaluation factors in the first region; W k is the weight of the kth evaluation factor in the first region; V k is the scoring result of the kth evaluation factor in the first region; Wherein, the target area is a high-latitude area or a subarctic area with a large latitude span; The preset dividing line is 60°N, and the target area is divided by the preset dividing line to obtain a first area and a second area, wherein the first area is an area south of 60°N, and the second area is an area north of 60°N; The preset standard defines the score range according to the grading standards of each indicator in the natural geographical pattern, terrain conditions, and soil conditions, and sets the scores from high to low; The first preset method is the AHP hierarchical analysis method, and the second preset method is the CRITIC weight method.

4. A computing device, characterized in that include: A processor and a memory storing a computer program, wherein when the computer program is executed by the processor, the method according to any one of claims 1 to 2 is performed.

5. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 2.

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