A farmland non-grain monitoring method, device, equipment and storage medium

Abstract

The application discloses a farmland non-grain monitoring method, device, equipment and storage medium. The method comprises the following steps: determining the non-grain level of a target area according to the non-grain crop sowing area and the total crop sowing area of the target area; performing spatio-temporal transition analysis of non-grain by using an exploratory spatio-temporal analysis method according to the non-grain level, and determining the transition mode of the target area; obtaining the population non-agricultural rate of the target area, and predicting the future non-grain level and population non-agricultural rate according to the historical statistical data of the non-grain level and the population non-agricultural rate; and determining the non-grain level safety index according to the transition mode of the target area and the predicted non-grain level and population non-agricultural rate, so as to monitor the non-grain safety situation of the target area. The application can effectively improve the accuracy of farmland non-grain monitoring.

Description

Technical Field

[0001] This application relates to the field of land management technology, and in particular to a method, device, equipment and storage medium for monitoring the non-grain conversion of arable land. Background Technology

[0002] Current research on the phenomenon of farmland conversion to non-grain crops typically treats this as a cross-sectional state rather than a dynamic process. Consequently, existing methods for monitoring farmland conversion to non-grain crops have some limitations: the level of non-grain conversion in existing methods is generally characterized by the proportion of crop planting area, which mainly describes the non-grain crop planting status at a certain time, rather than considering non-grain conversion as a changing process. Such methods are difficult to capture the proportion fluctuations caused by seasonal changes in planting structure in multi-cropping agricultural areas, thus affecting the continuity and accuracy of non-grain conversion assessment. Furthermore, existing methods rely on area proportion indicators, ignoring the heterogeneity and evolution trajectory of non-grain conversion in spatial and temporal dimensions, resulting in low accuracy in monitoring farmland conversion to non-grain crops. Summary of the Invention

[0003] To address the above technical issues, this application provides a method, device, equipment, and storage medium for monitoring the conversion of arable land to non-grain crops. This method can more accurately assess the level of non-grain conversion, analyze the spatiotemporal transitions of non-grain conversion, and predict the safety index of non-grain conversion levels, thereby helping to shorten the time lag in macro-control of land use.

[0004] This application provides a method for monitoring the conversion of arable land to non-grain crops, including: The level of non-grain crop conversion in the target area is determined based on the non-grain crop planting area and the total crop planting area in the target area. Based on the level of degrainization, an exploratory spatiotemporal analysis method is used to conduct spatiotemporal transition analysis of degrainization to determine the transition mode of the target region. Obtain the non-agriculturalization rate of the population in the target area, and predict the future non-agriculturalization level and population non-agriculturalization rate based on the historical statistical data of the non-grain level and the population non-agriculturalization rate; Based on the transition pattern of the target area and the predicted level of non-grain production and the non-agricultural population rate, a non-grain production level security index is determined to monitor the non-grain production security situation of the target area.

[0005] As an improvement to the above scheme, determining the non-grain crop conversion level of the target area based on the non-grain crop planting area and the total crop planting area of ​​the target area includes: Obtain the sown area of ​​non-grain crops and the total sown area of ​​crops in the target area; The proportion of non-grain crops is obtained by the ratio of the non-grain crop planting area to the total crop planting area; The level of non-grain crop conversion in the target area is determined based on the change in the proportion of non-grain crops before and after a preset time period.

[0006] As an improvement to the above scheme, the step of conducting spatiotemporal transition analysis of degrainization using an exploratory spatiotemporal analysis method based on the degrainization level to determine the transition mode of the target region includes: Based on the level of degrazing in the target area, an exploratory spatiotemporal analysis method is used to generate LISA coordinates of the degrazing level at different times; If the quadrant in which the LISA coordinates are located remains unchanged, then the transition mode of the target region is determined to be the first mode; If the LISA coordinates transition from the first quadrant to the second quadrant, from the second quadrant to the first quadrant, from the third quadrant to the fourth quadrant, or from the fourth quadrant to the third quadrant, then the transition mode of the target region is determined to be the second mode. If the LISA coordinates transition from the first quadrant to the fourth quadrant, from the fourth quadrant to the first quadrant, from the second quadrant to the third quadrant, or from the third quadrant to the second quadrant, then the transition mode of the target region is determined to be the third mode. If the LISA coordinates transition from the first quadrant to the third quadrant, from the third quadrant to the first quadrant, from the second quadrant to the fourth quadrant, or from the fourth quadrant to the second quadrant, then the transition mode of the target region is determined to be the fourth mode.

[0007] As an improvement to the above scheme, the step of determining the non-grain level security index based on the transition pattern of the target area and the predicted non-grain level and population non-agriculturalization rate includes: The diffusion coefficient is determined based on the transition pattern of the target region; the diffusion coefficient characterizes the ability of the current region's non-grain level to transfer to other regions. Obtain the prices of non-grain crops and grains, and calculate the grain revenue ratio based on the ratio of the non-grain crop price to the grain price. The predicted level of non-grain consumption is weighted and added to the non-agricultural population rate, and then multiplied by the diffusion coefficient and the grain yield ratio to calculate the non-grain consumption level safety index of the target area.

[0008] As an improvement to the above scheme, determining the diffusion coefficient based on the transition mode of the target region includes: When the transition mode of the target region is the first mode, the diffusion coefficient is set to 1; When the transition mode of the target region is the second mode, the diffusion coefficient is set to 2; When the transition mode of the target region is the third mode, the diffusion coefficient is set to 1.5; When the transition mode of the target region is the fourth mode, the diffusion coefficient is set to 3.

[0009] As an improvement to the above scheme, the step of predicting the future level of non-grain production and the population non-agriculturalization rate based on historical statistical data of the non-grain production level and the population non-agriculturalization rate includes: Based on the historical statistical data of the non-grain level and the non-agricultural population rate, time series data were compiled. The time series data is accumulated to generate accumulated data; A grey prediction model is established based on the accumulated data, and the model parameters of the grey prediction model are solved using the least squares method. Based on the model parameters, the grey prediction model is used to predict the future level of non-grain consumption and the rate of non-agricultural population.

[0010] As an improvement to the above scheme, after determining the non-grain level safety index, the method further includes: If the safety index of non-grain level is less than a preset first threshold, the target area will be classified as a low-risk area for non-grain production. If the non-grain level safety index is less than a preset second threshold and not less than the first threshold, then the target area is classified as a medium-risk area for non-grain production. If the safety index of non-grain level is not less than the second threshold, the target area is divided into a high-risk area of ​​non-grain, and early warning information is output for the high-risk area.

[0011] This application also provides a monitoring device for the non-grain conversion of arable land, comprising: The non-grain crop level module is used to determine the non-grain crop level of the target area based on the non-grain crop planting area and the total crop planting area of ​​the target area. The spatiotemporal transition module is used to perform spatiotemporal transition analysis of degraining based on the degraining level using an exploratory spatiotemporal analysis method, and to determine the transition mode of the target region. The data prediction module is used to obtain the non-agriculturalization rate of the population in the target area, and predict the future non-agriculturalization level and population non-agriculturalization rate based on the historical statistical data of the non-grain level and the non-agriculturalization rate. The safety monitoring module is used to determine the safety index of the non-grain level based on the transition pattern of the target area and the predicted non-grain level and population non-agricultural rate, so as to monitor the non-grain security situation of the target area.

[0012] This application also provides a computer device, including a processor and a memory, wherein the memory stores a computer program and the computer program is configured to be executed by the processor, wherein the processor executes the computer program to implement the monitoring method for non-grain conversion of arable land as described above.

[0013] This application also provides a computer-readable storage medium storing a computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the monitoring method for non-grain conversion of arable land as described above.

[0014] Compared to existing technologies, the beneficial effects of the method, apparatus, equipment, and storage medium for monitoring farmland non-grain conversion provided in this application are as follows: By acquiring the non-grain crop planting area and the total crop planting area of ​​the target area, the non-grain conversion level of the target area can be determined based on the changes in the proportion of non-grain crops before and after a preset time period, thus improving the accuracy of farmland non-grain conversion level assessment; based on the non-grain conversion level, an exploratory spatiotemporal analysis method is used to conduct spatiotemporal transition analysis of non-grain conversion, determining the transition mode of the target area, further improving the accuracy of subsequent generation of a non-grain conversion level safety index; and the non-agricultural population of the target area is obtained. The application comprehensively considers the influence of various factors, which can effectively improve the accuracy of farmland non-grain conversion detection, provide early warning of non-grain conversion safety levels, and help shorten the time lag of macro-control of land use and implement more effective farmland use control measures. Based on the historical statistical data of the non-grain conversion level and the population non-agricultural conversion rate, the application predicts the future non-grain conversion level and population non-agricultural conversion rate, enabling early warning and intervention for non-grain conversion. Furthermore, based on the transition pattern of the target area and the predicted non-grain conversion level and population non-agricultural conversion rate, a non-grain conversion level safety index is determined to monitor the non-grain conversion safety situation of the target area. Attached Figure Description

[0015] Figure 1 This is a flowchart illustrating a method for monitoring the non-grain conversion of arable land provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a monitoring device for the non-grain conversion of arable land provided in an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0017] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for monitoring the conversion of arable land to non-grain crops, as provided in an embodiment of this application. The method includes: S1: Determine the non-grain crop planting level of the target area based on the non-grain crop planting area and the total crop planting area of ​​the target area; S2: Based on the level of degrainization, an exploratory spatiotemporal analysis method is used to conduct spatiotemporal transition analysis of degrainization to determine the transition mode of the target region; S3: Obtain the non-agriculturalization rate of the population in the target area, and predict the future non-agriculturalization level and population non-agriculturalization rate based on the historical statistical data of the non-grain level and the population non-agriculturalization rate; S4: Based on the transition pattern of the target area and the predicted level of non-grain production and the non-agricultural population rate, determine the non-grain production level security index to monitor the non-grain production security situation of the target area.

[0018] As one optional embodiment, determining the non-grain crop level of the target area based on the non-grain crop planting area and the total crop planting area of ​​the target area includes: Obtain the sown area of ​​non-grain crops and the total sown area of ​​crops in the target area; The proportion of non-grain crops is obtained by the ratio of the non-grain crop planting area to the total crop planting area; The level of non-grain crop conversion in the target area is determined based on the change in the proportion of non-grain crops before and after a preset time period.

[0019] Specifically, considering that the level of non-grain crop conversion should reflect the changes in crop planting, and that non-grain conversion is not merely an absolute increase or decrease in the planting area of ​​non-grain crops, but also needs to consider changes in planting ratio and planting structure, this embodiment characterizes the level of non-grain crop conversion by the change in the proportion of non-grain crops. First, the sown area of ​​non-grain crops and the total sown area of ​​crops in the target area are obtained in different years. The ratio of the sown area of ​​non-grain crops to the total sown area of ​​crops in different years is calculated to obtain the proportion of non-grain crops in different years. Then, the proportions of non-grain crops in the two consecutive years are subtracted to obtain the change in the non-grain planting rate between the two years, which is used as the level of non-grain crop conversion in the target area. The specific formula for the level of non-grain crop conversion is:

[0020] in, The level of non-grain consumption during the period from t1 to t2; and The non-grain crop sown areas for years t1 and t2 are respectively. and These represent the total sown area of ​​crops in year t1 and year t2, respectively; year t1 precedes year t2.

[0021] Furthermore, this application embodiment also classifies non-grain crop types, using the type with the largest increase in the ratio of planting area to total non-grain crop planting area during the study period as the non-grain crop type for the target area. The non-grain crop type can be used for subsequent calculations of the grain yield ratio.

[0022] Specifically, the screening formula for non-grain types is as follows:

[0023] in, The variation in planting rate corresponding to the non-grain type during the period from t2 to t1 is used to determine the non-grain type in the target area. and These represent the total sown area of ​​non-grain crops in year t1 and year t2, respectively. and These represent the sown area of ​​the i-th non-grain crop in years t1 and t2, respectively.

[0024] As one optional embodiment, the step of conducting spatiotemporal transition analysis of degrazing based on the degrazing level using an exploratory spatiotemporal analysis method to determine the transition mode of the target region includes: Based on the level of degrazing in the target area, an exploratory spatiotemporal analysis method is used to generate LISA coordinates of the degrazing level at different times; If the quadrant in which the LISA coordinates are located remains unchanged, then the transition mode of the target region is determined to be the first mode; If the LISA coordinates transition from the first quadrant to the second quadrant, from the second quadrant to the first quadrant, from the third quadrant to the fourth quadrant, or from the fourth quadrant to the third quadrant, then the transition mode of the target region is determined to be the second mode. If the LISA coordinates transition from the first quadrant to the fourth quadrant, from the fourth quadrant to the first quadrant, from the second quadrant to the third quadrant, or from the third quadrant to the second quadrant, then the transition mode of the target region is determined to be the third mode. If the LISA coordinates transition from the first quadrant to the third quadrant, from the third quadrant to the first quadrant, from the second quadrant to the fourth quadrant, or from the fourth quadrant to the second quadrant, then the transition mode of the target region is determined to be the fourth mode.

[0025] Specifically, based on the calculated levels of non-grain conversion in different years, the ESTDA (Exploratory Time-space Data Analysis) method is used to comprehensively analyze the spatiotemporal patterns and evolutionary characteristics of non-grain conversion. The ESTDA method includes the analysis of LISA (Local Indicators of Spatial Association) time paths and LISA spatiotemporal transitions. LISA time paths can effectively combine temporal and spatial attributes by leveraging the spatiotemporal migration patterns of local spatial association indices in Moran scatter plots, enabling a dynamic representation of static LISA. The LISA time paths are combined with traditional Markov chains, and spatiotemporal transitions are divided into four modes according to their transition forms: The first mode, Type 0, indicates that neither the target region itself nor its neighboring units undergo morphological transfer. The second mode, Type 1, indicates that the target region itself jumps, while the neighboring units remain unchanged; The third mode, Type 2, indicates that the target region itself remains unchanged, while neighboring units transition. The fourth mode, Type 3, indicates that both the target region itself and its neighboring units undergo transitions.

[0026] When implementing the ESTDA method, the Geoda software was used to calculate the LISA coordinates for different years with different levels of non-grain production. Then, based on the LISA coordinates, if the coordinate point corresponding to the non-grain production level is in the first quadrant, it is denoted as HH; if it is in the second quadrant, it is denoted as LH; if it is in the third quadrant, it is denoted as LL; and if it is in the fourth quadrant, it is denoted as HL. The LISA transition from year t to year t+1 is shown in Table 1 below: Table 1

[0027] After obtaining the LISA coordinates corresponding to the level of non-grain production, the transition pattern of the target area from year t to year t+1 can be determined based on the table above, which can be used for a comprehensive assessment of the safety of the level of non-grain production in the future.

[0028] As one optional embodiment, predicting the future level of non-grain consumption and the population non-agriculturalization rate based on historical statistical data of the non-grain consumption level and the population non-agriculturalization rate includes: Based on the historical statistical data of the non-grain level and the non-agricultural population rate, time series data were compiled. The time series data is accumulated to generate accumulated data; A grey prediction model is established based on the accumulated data, and the model parameters of the grey prediction model are solved using the least squares method. Based on the model parameters, the grey prediction model is used to predict the future level of non-grain consumption and the rate of non-agricultural population.

[0029] Specifically, in order to achieve early warning of non-grain land use security, this embodiment predicts the level of non-grain land use and the non-agricultural rate of the population, so as to use the predicted level of non-grain land use and the non-agricultural rate of the population to calculate the non-grain land use security index, thereby providing timely early warning and intervention for non-grain land use.

[0030] This application employs a grey prediction model to predict the level of non-grain production and the rate of non-agricultural population. Specifically, a GM(1,1) model can be used to predict the level of non-grain production and the rate of non-agricultural population separately, or a multivariate grey prediction model can be used to jointly predict the level of non-grain production and the rate of non-agricultural population. The essence of the grey prediction model lies in reducing the influence of random disturbance factors by accumulating the original data sequence, discovering its exponential growth law, simulating it using an exponential curve, and solving the model parameters using the least squares method. This is also an exponential fitting model based on cumulative generation and the least squares method.

[0031] First, based on historical statistical data of the non-grain consumption level and non-agricultural population rate in the target area over different years, the data is organized and preprocessed to obtain time series data. The time series data is then accumulated to generate cumulative data, and a background value sequence is constructed based on the cumulative values ​​at adjacent time points. A grey prediction model is established based on the cumulative data and the background value sequence, and the model parameters are solved using the least squares method. The grey prediction model based on the model parameters is used to predict the cumulative sequence for future time points. Then, the obtained predicted cumulative data is inversely accumulated to generate predicted values ​​for the non-grain consumption level and the non-agricultural population rate. Furthermore, the accuracy of the prediction results is verified based on the deviation between the predicted values ​​and historical statistical data to obtain an evaluation of the model's predictive effectiveness. This allows for remodeling if the model is deemed unsuitable, leading to more accurate prediction results.

[0032] As one optional embodiment, determining the non-grain level security index based on the transition pattern of the target region and the predicted non-grain level and population non-agriculturalization rate includes: The diffusion coefficient is determined based on the transition pattern of the target region; the diffusion coefficient characterizes the ability of the current region's non-grain level to transfer to other regions. Obtain the prices of non-grain crops and grains, and calculate the grain revenue ratio based on the ratio of the non-grain crop price to the grain price. The predicted level of non-grain consumption is weighted and added to the non-agricultural population rate, and then multiplied by the diffusion coefficient and the grain yield ratio to calculate the non-grain consumption level safety index of the target area.

[0033] Specifically, this embodiment uses a non-grain conversion level safety index to assess and monitor the conversion of arable land to non-grain uses. It reflects the safety of the non-grain conversion level in the target area by comprehensively considering four dimensions: the level of non-grain conversion, the non-agricultural population rate, the grain yield ratio, and the ability of non-grain conversion levels to be transferred to other areas. The specific formula for the non-grain conversion level safety index is as follows:

[0034] in, The non-grain level safety index for year t; The diffusion coefficient represents the ability of the current non-grain level to transfer to other regions, and is determined by the transition mode of the target region. At the level of non-grain production; The non-agriculturalization rate of the population; The ratio of grain yield; and As the weight, the sum of the two is 1. Since the urban population still engages in food production to a certain extent, and the level of non-food consumption is a direct core indicator compared to the non-agricultural population rate, the level of non-food consumption should be given a higher weight. Therefore, this embodiment sets... It is 0.6. It is 0.4.

[0035] The formula for calculating the non-agriculturalization rate of the population is as follows:

[0036] in, Let N be the non-agriculturalization rate of the population, N be the number of non-agricultural workers, and Z be the total resident population of the region.

[0037] The formula for calculating the grain yield ratio is as follows:

[0038] in, and These represent the prices of non-grain crops and grain crops in year t, respectively. Specifically, the price of non-grain crops can be obtained by calculating the average price of various non-grain crops, or by using the crop price corresponding to the non-grain crop type in the region as the non-grain crop price.

[0039] As one optional embodiment, determining the diffusion coefficient based on the transition mode of the target region includes: When the transition mode of the target region is the first mode, the diffusion coefficient is set to 1; When the transition mode of the target region is the second mode, the diffusion coefficient is set to 2; When the transition mode of the target region is the third mode, the diffusion coefficient is set to 1.5; When the transition mode of the target region is the fourth mode, the diffusion coefficient is set to 3.

[0040] Specifically, by observing the transition patterns of regional degrainization levels, we can understand the transfer capacity of degrainization to other cities. The stronger this transfer capacity, the higher the resulting security risk of degrainization. A diffusion coefficient is used to characterize this transfer capacity. When the transition pattern of a region is the first pattern (Type 0), it indicates that neither the region itself nor its neighboring areas undergo morphological transformation, and its diffusion coefficient is set to 1. When the transition pattern is the second pattern (Type 1), it indicates that the region itself undergoes a transition while its neighboring areas remain unchanged, and its diffusion coefficient is set to 2. When the transition pattern is the third pattern (Type 2), it indicates that the region itself remains unchanged while its neighboring areas undergo a transition, and its diffusion coefficient is set to 1.5. When the transition pattern is the fourth pattern (Type 3), it indicates that both the region itself and its neighboring areas undergo transitions, and its diffusion coefficient is set to 3, corresponding to a higher risk.

[0041] As one optional embodiment, after determining the non-grain level safety index, the method further includes: If the safety index of non-grain level is less than a preset first threshold, the target area will be classified as a low-risk area for non-grain production. If the non-grain level safety index is less than a preset second threshold and not less than the first threshold, then the target area is classified as a medium-risk area for non-grain production. If the safety index of non-grain level is not less than the second threshold, the target area is divided into a high-risk area of ​​non-grain, and early warning information is output for the high-risk area.

[0042] Specifically, after calculating the non-grain conversion level security index by comprehensively considering the non-grain conversion level, the non-agricultural population rate, the grain yield ratio, and the transfer capacity of non-grain conversion to other regions, a classification system is established by setting thresholds, such as using the natural breakpoint method, to classify the risk into high-risk, medium-risk, and low-risk categories. The higher the non-grain conversion level security index, the higher the food security risk. When the non-grain conversion level security index exceeds the set value, a corresponding early warning message is output to indicate that intervention in non-grain conversion of arable land is needed in that region. This helps to shorten the time lag of macro-control of land use and implement more effective arable land use control measures.

[0043] In a specific example, we monitored the conversion of arable land to non-grain crops in various cities of a province. First, we calculated the non-grain crop conversion level for each city based on the non-grain crop planting area and the total crop planting area. The calculation results for some cities are shown in Table 2 below: Table 2

[0044] Furthermore, the FLL was calculated based on the non-grain crop planting area of ​​each city to determine the dominant non-grain crop type in each city. The results for some cities are shown in Table 3. The non-grain crop type of city A was vegetables during 2008-2013, and sugarcane during 2013-2018 and 2018-2023.

[0045] Table 3

[0046] Furthermore, the ESTDA method was used to conduct spatiotemporal transition analysis of the non-grainization level. The spatiotemporal transition matrices of the non-grainization level for some cities are shown in Table 4 below: Table 4

[0047] By calculating the LISA coordinates corresponding to the non-grain conversion levels in different years, the spatiotemporal transition types were determined based on these coordinates. The results showed that among the four spatiotemporal transition types involved in the study period, the type where both the city itself and neighboring cities experienced transitions was the most numerous, indicating that the local spatial correlation structure of non-grain conversion ratio changes in this region has strong instability and transfer potential. Seven cities exhibited transition patterns of Type 1, Type 2, and Type 3: cities A, C, D, E, F, G, and H. These cities demonstrated strong spatial driving forces in the changes in non-grain conversion ratios and may become key areas influencing the overall evolution of the non-grain conversion pattern in the province, thus requiring close attention in farmland protection policy formulation. Further analysis showed that the LISA coordinates of all cities in the region underwent a certain degree of spatiotemporal transition during the study period. The probability that the coordinate points are distributed in the same quadrant and the transition mode is Type 0 is 22.22%, which means that the proportion of non-grain production in each city is unlikely to remain unchanged. On the contrary, the probability of a mode transition is as high as 77.78%, reflecting that the non-grain production pattern in this region has obvious dynamic evolution characteristics.

[0048] Furthermore, taking city A as an example, city A's migration pattern is Type 2, with a diffusion coefficient of 1.5 for the four time periods: 2008-2013, 2013-2018, 2018-2023, and 2023-2028. During the 2008-2013 period, the non-grain level (FL) was -0.02%, and the non-agricultural population rate was 1.20%. The main type of non-grain consumption during this period was vegetables; therefore, the grain yield ratio was the ratio of vegetable prices to grain prices. Finally, the non-grain level safety index for city A during the 2008-2013 period was calculated. The risk zone classification for city A is determined by comparing it with a preset threshold, and an early warning for farmland conversion to non-grain crops is issued based on its risk level. Similarly, a safety index for the level of non-grain conversion is calculated for each city. Risk classification and early warning are conducted to monitor the non-grain security situation in the region.

[0049] This application improves the accuracy of farmland non-grain crop conversion assessment by obtaining the non-grain crop planting area and total crop planting area of ​​the target area, and then determining the non-grain crop conversion level of the target area based on the changes in the proportion of non-grain crops before and after a preset time period. Based on the non-grain crop conversion level, an exploratory spatiotemporal analysis method is used to perform spatiotemporal transition analysis of non-grain crop conversion, determining the transition pattern of the target area, further improving the accuracy of the subsequent generation of a non-grain crop conversion level safety index. The application also obtains the non-agricultural conversion rate of the population in the target area and predicts the future non-grain crop conversion level and population non-agricultural conversion rate based on historical statistical data of the non-grain crop conversion level and population non-agricultural conversion rate, enabling early warning and intervention for non-grain crop conversion. Finally, based on the transition pattern of the target area and the predicted non-grain crop conversion level and population non-agricultural conversion rate, a non-grain crop conversion level safety index is determined to monitor the non-grain crop conversion safety situation of the target area. This application comprehensively considers the influence of multiple factors, effectively improving the accuracy of farmland non-grain crop conversion detection, providing early warning of non-grain crop conversion safety levels, helping to shorten the time lag of macro-control of land use, and implementing more effective farmland use control measures.

[0050] Accordingly, this application also provides a monitoring device for the non-grain conversion of arable land, which can realize all the processes of the monitoring method for the non-grain conversion of arable land in the above embodiments.

[0051] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a monitoring device for farmland non-grain conversion provided in an embodiment of this application. The monitoring device for farmland non-grain conversion includes: The non-grain crop level module 201 is used to determine the non-grain crop level of the target area based on the non-grain crop planting area and the total crop planting area of ​​the target area. The spatiotemporal transition module 202 is used to perform spatiotemporal transition analysis of degraining based on the degraining level using an exploratory spatiotemporal analysis method, and to determine the transition mode of the target region. The data prediction module 203 is used to obtain the non-agriculturalization rate of the population in the target area, and predict the future non-agriculturalization level and population non-agriculturalization rate based on the historical statistical data of the non-grain level and the non-agriculturalization rate. The safety monitoring module 204 is used to determine the non-grain level safety index based on the transition mode of the target area and the predicted non-grain level and population non-agricultural rate, so as to monitor the non-grain security situation of the target area.

[0052] Preferably, determining the non-grain crop conversion level of the target area based on the non-grain crop planting area and the total crop planting area of ​​the target area includes: Obtain the sown area of ​​non-grain crops and the total sown area of ​​crops in the target area; The proportion of non-grain crops is obtained by the ratio of the non-grain crop planting area to the total crop planting area; The level of non-grain crop conversion in the target area is determined based on the change in the proportion of non-grain crops before and after a preset time period.

[0053] Preferably, the step of conducting spatiotemporal transition analysis of degrainization using an exploratory spatiotemporal analysis method based on the degrainization level to determine the transition mode of the target region includes: Based on the level of degrazing in the target area, an exploratory spatiotemporal analysis method is used to generate LISA coordinates of the degrazing level at different times; If the quadrant in which the LISA coordinates are located remains unchanged, then the transition mode of the target region is determined to be the first mode; If the LISA coordinates transition from the first quadrant to the second quadrant, from the second quadrant to the first quadrant, from the third quadrant to the fourth quadrant, or from the fourth quadrant to the third quadrant, then the transition mode of the target region is determined to be the second mode. If the LISA coordinates transition from the first quadrant to the fourth quadrant, from the fourth quadrant to the first quadrant, from the second quadrant to the third quadrant, or from the third quadrant to the second quadrant, then the transition mode of the target region is determined to be the third mode. If the LISA coordinates transition from the first quadrant to the third quadrant, from the third quadrant to the first quadrant, from the second quadrant to the fourth quadrant, or from the fourth quadrant to the second quadrant, then the transition mode of the target region is determined to be the fourth mode.

[0054] Preferably, determining the non-grain level security index based on the transition pattern of the target area and the predicted non-grain level and population non-agriculturalization rate includes: The diffusion coefficient is determined based on the transition pattern of the target region; the diffusion coefficient characterizes the ability of the current region's non-grain level to transfer to other regions. Obtain the prices of non-grain crops and grains, and calculate the grain revenue ratio based on the ratio of the non-grain crop price to the grain price. The predicted level of non-grain consumption is weighted and added to the non-agricultural population rate, and then multiplied by the diffusion coefficient and the grain yield ratio to calculate the non-grain consumption level safety index of the target area.

[0055] Preferably, determining the diffusion coefficient based on the transition mode of the target region includes: When the transition mode of the target region is the first mode, the diffusion coefficient is set to 1; When the transition mode of the target region is the second mode, the diffusion coefficient is set to 2; When the transition mode of the target region is the third mode, the diffusion coefficient is set to 1.5; When the transition mode of the target region is the fourth mode, the diffusion coefficient is set to 3.

[0056] Preferably, predicting the future level of non-grain production and the population non-agriculturalization rate based on historical statistical data of the non-grain production level and the population non-agriculturalization rate includes: Based on the historical statistical data of the non-grain level and the non-agricultural population rate, time series data were compiled. The time series data is accumulated to generate accumulated data; A grey prediction model is established based on the accumulated data, and the model parameters of the grey prediction model are solved using the least squares method. Based on the model parameters, the grey prediction model is used to predict the future level of non-grain consumption and the rate of non-agricultural population.

[0057] Preferably, the arable land non-grain conversion monitoring device further includes a risk early warning module, which is specifically used for: If the safety index of non-grain level is less than a preset first threshold, the target area will be classified as a low-risk area for non-grain production. If the non-grain level safety index is less than a preset second threshold and not less than the first threshold, then the target area is classified as a medium-risk area for non-grain production. If the safety index of non-grain level is not less than the second threshold, the target area is divided into a high-risk area of ​​non-grain, and early warning information is output for the high-risk area.

[0058] In specific implementation, the working principle, control process and technical effects of the farmland non-grain monitoring device provided in this application are the same as those of the farmland non-grain monitoring method in the above embodiments, and will not be repeated here.

[0059] See Figure 3 , Figure 3 This is a structural block diagram of a computer device provided in an embodiment of this application. The computer device includes: a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program, it implements the steps in the above-described embodiments of the method for monitoring the non-grain conversion of arable land. Alternatively, when the processor 301 executes the computer program, it implements the functions of each module / unit in the above-described device embodiments.

[0060] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 302 and executed by the processor 301 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the computer device.

[0061] The computer device may include, but is not limited to, processor 301 and memory 302. Those skilled in the art will understand that the schematic diagram is merely an example of a computer device and does not constitute a limitation on the computer device. It may include more or fewer components than illustrated, or combine certain components, or different components. For example, the computer device may also include input / output devices, network access devices, buses, etc.

[0062] The processor 301 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor. The processor 301 is the control center of the computer device, connecting various parts of the entire computer device through various interfaces and lines.

[0063] The memory 302 can be used to store the computer programs and / or modules. The processor 301 implements various functions of the computer device by running or executing the computer programs and / or modules stored in the memory 302 and calling the data stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory 302 may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0064] Wherein, if the modules / units integrated into the computer device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by the processor 301, it can implement the steps of the various method embodiments described above. Wherein, the computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0065] This application also provides a computer-readable storage medium, which includes a stored computer program, wherein the computer program, when running, controls the device where the computer-readable storage medium is located to execute the monitoring method for non-grain conversion of arable land as described in any of the above embodiments.

[0066] This application provides a method, apparatus, equipment, and storage medium for monitoring the non-grain conversion of arable land. Its beneficial effects are as follows: By obtaining the sown area of ​​non-grain crops and the total sown area of ​​crops in the target area, the non-grain conversion level of the target area can be determined based on the changes in the proportion of non-grain crops before and after a preset time period, thus improving the accuracy of arable land non-grain conversion level assessment; based on the non-grain conversion level, an exploratory spatiotemporal analysis method is used to conduct spatiotemporal transition analysis of non-grain conversion, determining the transition mode of the target area, further improving the accuracy of subsequently generating a non-grain conversion level safety index; the non-agricultural conversion rate of the population in the target area is obtained, and... Based on historical statistical data of the non-grain conversion level and the non-agricultural conversion rate of the population, the future non-grain conversion level and the non-agricultural conversion rate of the population can be predicted, enabling early warning and intervention for non-grain conversion. Based on the transition pattern of the target area and the predicted non-grain conversion level and the non-agricultural conversion rate of the population, a non-grain conversion level safety index is determined to monitor the non-grain conversion safety situation of the target area. This application comprehensively considers the influence of multiple factors, which can effectively improve the accuracy of farmland non-grain conversion detection, provide early warning of the safety level of non-grain conversion, help shorten the time lag of macro-control of land use, and implement more effective farmland use control measures.

[0067] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for monitoring the conversion of arable land to non-grain crops, characterized in that, include: The level of non-grain crop conversion in the target area is determined based on the non-grain crop planting area and the total crop planting area in the target area. Based on the level of degrainization, an exploratory spatiotemporal analysis method is used to conduct spatiotemporal transition analysis of degrainization to determine the transition mode of the target region. Obtain the non-agriculturalization rate of the population in the target area, and predict the future non-agriculturalization level and population non-agriculturalization rate based on the historical statistical data of the non-grain level and the population non-agriculturalization rate; Based on the transition pattern of the target area and the predicted level of non-grain production and the non-agricultural population rate, a non-grain production level security index is determined to monitor the non-grain production security situation of the target area.

2. The method for monitoring the conversion of arable land to non-grain crops as described in claim 1, characterized in that, The determination of the non-grain crop conversion level of the target area based on the non-grain crop planting area and the total crop planting area of ​​the target area includes: Obtain the sown area of ​​non-grain crops and the total sown area of ​​crops in the target area; The proportion of non-grain crops is obtained by the ratio of the non-grain crop planting area to the total crop planting area; The level of non-grain crop conversion in the target area is determined based on the change in the proportion of non-grain crops before and after a preset time period.

3. The method for monitoring the conversion of arable land to non-grain crops as described in claim 1, characterized in that, The step involves using exploratory spatiotemporal analysis methods to perform spatiotemporal transition analysis of degraining based on the degraining level, and determining the transition mode of the target region, including: Based on the level of degrazing in the target area, an exploratory spatiotemporal analysis method is used to generate LISA coordinates of the degrazing level at different times; If the quadrant in which the LISA coordinates are located remains unchanged, then the transition mode of the target region is determined to be the first mode; If the LISA coordinates transition from the first quadrant to the second quadrant, from the second quadrant to the first quadrant, from the third quadrant to the fourth quadrant, or from the fourth quadrant to the third quadrant, then the transition mode of the target region is determined to be the second mode. If the LISA coordinates transition from the first quadrant to the fourth quadrant, from the fourth quadrant to the first quadrant, from the second quadrant to the third quadrant, or from the third quadrant to the second quadrant, then the transition mode of the target region is determined to be the third mode. If the LISA coordinates transition from the first quadrant to the third quadrant, from the third quadrant to the first quadrant, from the second quadrant to the fourth quadrant, or from the fourth quadrant to the second quadrant, then the transition mode of the target region is determined to be the fourth mode.

4. The method for monitoring the conversion of arable land to non-grain crops as described in claim 1, characterized in that, The step of determining the non-grain level security index based on the transition pattern of the target area and the predicted non-grain level and population non-agriculturalization rate includes: The diffusion coefficient is determined based on the transition pattern of the target region; the diffusion coefficient characterizes the ability of the current region's non-grain level to transfer to other regions. Obtain the prices of non-grain crops and grains, and calculate the grain revenue ratio based on the ratio of the non-grain crop price to the grain price. The predicted level of non-grain consumption is weighted and added to the non-agricultural population rate, and then multiplied by the diffusion coefficient and the grain yield ratio to calculate the non-grain consumption level safety index of the target area.

5. The method for monitoring the conversion of arable land to non-grain crops as described in claim 4, characterized in that, Determining the diffusion coefficient based on the transition mode of the target region includes: When the transition mode of the target region is the first mode, the diffusion coefficient is set to 1; When the transition mode of the target region is the second mode, the diffusion coefficient is set to 2; When the transition mode of the target region is the third mode, the diffusion coefficient is set to 1.5; When the transition mode of the target region is the fourth mode, the diffusion coefficient is set to 3.

6. The method for monitoring the conversion of arable land to non-grain crops as described in claim 1, characterized in that, The method of predicting future non-grain consumption levels and population non-agriculturalization rates based on historical statistical data of the non-grain consumption level and the population non-agriculturalization rate includes: Based on the historical statistical data of the non-grain level and the non-agricultural population rate, time series data were compiled. The time series data is accumulated to generate accumulated data; A grey prediction model is established based on the accumulated data, and the model parameters of the grey prediction model are solved using the least squares method. Based on the model parameters, the grey prediction model is used to predict the future level of non-grain consumption and the rate of non-agricultural population.

7. The method for monitoring the conversion of arable land to non-grain crops as described in claim 1, characterized in that, After determining the non-grain level safety index, the method further includes: If the safety index of non-grain level is less than a preset first threshold, the target area will be classified as a low-risk area for non-grain production. If the non-grain level safety index is less than a preset second threshold and not less than the first threshold, then the target area is classified as a medium-risk area for non-grain production. If the safety index of non-grain level is not less than the second threshold, the target area is divided into a high-risk area of ​​non-grain, and early warning information is output for the high-risk area.

8. A monitoring device for the non-grain conversion of arable land, characterized in that, include: The non-grain crop level module is used to determine the non-grain crop level of the target area based on the non-grain crop planting area and the total crop planting area of ​​the target area. The spatiotemporal transition module is used to perform spatiotemporal transition analysis of degraining based on the degraining level using an exploratory spatiotemporal analysis method, and to determine the transition mode of the target region. The data prediction module is used to obtain the non-agriculturalization rate of the population in the target area, and predict the future non-agriculturalization level and population non-agriculturalization rate based on the historical statistical data of the non-grain level and the non-agriculturalization rate. The safety monitoring module is used to determine the safety index of the non-grain level based on the transition pattern of the target area and the predicted non-grain level and population non-agricultural rate, so as to monitor the non-grain security situation of the target area.

9. A computer device, characterized in that, The method includes a processor and a memory, wherein the memory stores a computer program and the computer program is configured to be executed by the processor, wherein the processor executes the computer program to implement the method for monitoring the non-grain conversion of arable land as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the device containing the computer-readable storage medium executes the computer program, it implements the method for monitoring the non-grain conversion of arable land as described in any one of claims 1 to 7.

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