System and method for identifying and assessing freeze-thaw risks in the construction of high-standard black soil farmland

Abstract

The present application relates to the field of risk analysis technology suitable for administrative purposes, and provides a freeze-thaw risk identification and assessment system and method for the construction of black soil high-standard farmland. The system includes: a freeze-thaw area identification unit for the construction of black soil high-standard farmland, a freeze-thaw risk zone determination unit, a freeze-thaw hazard prediction unit, a construction risk warning and prevention measures recommendation unit, and a comprehensive assessment unit for the construction of black soil high-standard farmland in the freeze-thaw area. The system implements the following functions: based on meteorological data, freeze-thaw risk areas are identified and a freeze-thaw spatiotemporal distribution map is drawn; the freeze-thaw spatiotemporal distribution map, the regional black soil distribution map, and the high-standard farmland plot distribution map are superimposed and analyzed; a freeze-thaw hazard prediction model is constructed; based on the freeze-thaw hazard prediction results, freeze-thaw prevention measures are given; and a comprehensive assessment of the high-standard farmland's ability to resist freeze-thaw risks is conducted. The system can accurately identify freeze-thaw risk areas for the construction of black soil high-standard farmland and effectively assess the high-standard farmland's ability to resist freeze-thaw risks.

Description

Technical Field

[0001] The present application relates to the field of risk analysis technology suitable for administrative purposes, and in particular to a freeze-thaw risk identification and assessment system and method for the construction of high-standard black soil farmland. Background Art

[0002] Black soil plays an extremely important role in agricultural production. Through the construction of high-standard farmland, the establishment of a complete irrigation and drainage system, field roads and ecological protection system, the grain production capacity of black soil can be effectively improved, and high and stable yields can be achieved in drought and flood.

[0003] my country's black soils are primarily concentrated in the Northeast, a typical region of seasonal freeze-thaw. This freeze-thaw process significantly impacts agricultural production and farmland infrastructure construction. In particular, freeze-thaw severely damages high-standard farmland roads and irrigation canals, leading to widespread problems such as thaw subsidence, cracking, and deformation. Existing high-standard farmland construction assessment systems primarily focus on general high-standard farmland construction. Experience has shown that directly applying general high-standard farmland construction technologies to black soil high-standard farmland construction is prone to deviations and fails to meet the actual needs of high-standard farmland construction in black soil regions. Therefore, there is an urgent need to provide an improved technical solution that addresses these shortcomings of existing technologies. Summary of the Invention

[0004] The purpose of this application is to address the lack of a response mechanism for freeze-thaw risk in the planning and evaluation process of high-standard farmland construction in the black soil region of Northeast China. Based on the above objectives, a freeze-thaw risk identification and assessment system and method for high-standard farmland construction in black soil is provided. This technical solution is used to improve the planning and assessment mechanism for high-standard farmland construction in black soil areas with seasonal freeze-thaw conditions, and to meet the actual needs of high-standard farmland construction in black soil regions.

[0005] In order to achieve the above objectives, this application provides the following technical solutions:

[0006] This application provides a freeze-thaw risk identification and assessment system for the construction of high-standard black soil farmland, including:

[0007] The freeze-thaw area identification unit for high-standard black soil farmland construction is used to identify freeze-thaw risk areas based on meteorological data using a fusion index method, and then draw a freeze-thaw spatiotemporal distribution map; the fusion index method refers to comprehensively determining freeze-thaw risk areas based on multiple freeze-thaw indices;

[0008] The freeze-thaw risk zoning sub-unit is used to overlay and analyze the freeze-thaw spatiotemporal distribution map, the regional black soil distribution map, and the high-standard farmland plot distribution map to obtain the freeze-thaw risk area for the construction of black soil high-standard farmland;

[0009] A freeze-thaw hazard prediction unit is used to construct a freeze-thaw hazard prediction model, and perform freeze-thaw hazard prediction based on the freeze-thaw risk area of the black soil high-standard farmland construction to obtain a freeze-thaw hazard prediction result;

[0010] A risk warning and prevention measure suggestion unit is constructed, which is used to provide freeze-thaw prevention measures based on the freeze-thaw hazard prediction results and the freeze-thaw risk prevention measures knowledge base for black soil high-standard farmland construction;

[0011] The comprehensive assessment unit for the construction of high-standard farmland in black soil areas in freeze-thaw zones is used to comprehensively assess the ability of high-standard farmland constructed in freeze-thaw black soil areas to resist freeze-thaw risks.

[0012] Preferably, the freeze-thaw risk area is identified based on meteorological data by a fusion index method, specifically:

[0013] Calculate multiple freeze-thaw indices based on meteorological data;

[0014] According to the multiple freeze-thaw indices and preset judgment rules, the freeze-thaw risk area is divided into multiple risk levels to obtain a freeze-thaw risk area.

[0015] Preferably, the freeze-thaw index includes: freezing index, freeze-thaw ratio, and freeze-thaw daily cycle days.

[0016] Preferably, the preset judgment rules include:

[0017] If the freezing index of the target area is greater than the first threshold, or the freeze-thaw ratio of the target area is less than the second threshold, or the number of freeze-thaw daily cycles is greater than the third threshold, the target area is judged to be a freeze-thaw risk area.

[0018] Preferably, the freeze-thaw spatiotemporal distribution map is obtained by the following steps:

[0019] Based on the multi-year historical meteorological data of the study area, the historical freeze-thaw risk areas were obtained;

[0020] Based on the future meteorological data of the study area, predict the future freeze-thaw risk areas;

[0021] Comparing the historical meteorological data with the future meteorological data to obtain a comparison result;

[0022] If the comparison result is less than a preset difference threshold, the historical freeze-thaw risk area is used as the final freeze-thaw risk area delineation result;

[0023] If the comparison result is greater than a preset difference threshold, the historical freeze-thaw risk area and the future freeze-thaw risk area are combined to obtain a final freeze-thaw risk area delineation result;

[0024] Based on the delineation result of the final freeze-thaw risk zone, the freeze-thaw spatiotemporal distribution map is output.

[0025] Preferably, the freeze-thaw hazard prediction unit further comprises: a data preparation and preprocessing subunit, a model building and verification subunit, and an application and prediction subunit;

[0026] The data preparation and preprocessing subunit is used to preprocess the pre-acquired farmland environment data and farmland damage data to form modeling data, and divide the modeling data into training set data and validation set data;

[0027] The model construction and verification subunit is used to construct a freeze-thaw hazard prediction model that maps the relationship between farmland environmental variables and farmland freeze-thaw disaster damage, and use the verification set data to evaluate the accuracy of the prediction model to obtain a trained prediction model;

[0028] The application and prediction subunit is used to use the trained prediction model to predict the damage caused by freeze-thaw disasters in the target area, so as to obtain the freeze-thaw damage prediction results.

[0029] Preferably, the knowledge base of freeze-thaw risk prevention measures for black soil high-standard farmland construction is constructed based on knowledge graph technology.

[0030] This embodiment provides a method for identifying and assessing freeze-thaw risks in the construction of high-standard black soil farmland, the method comprising:

[0031] Based on meteorological data, the freeze-thaw risk areas are identified through the fusion index method, and then the freeze-thaw spatiotemporal distribution map is drawn;

[0032] The freeze-thaw spatiotemporal distribution map, regional black soil distribution map, and high-standard farmland plot distribution map were superimposed and analyzed to obtain the freeze-thaw risk areas for black soil high-standard farmland construction;

[0033] Constructing a freeze-thaw hazard prediction model, and performing freeze-thaw hazard prediction based on the freeze-thaw risk area of the black soil high-standard farmland construction, to obtain a freeze-thaw hazard prediction result;

[0034] Based on the freeze-thaw hazard prediction results and combined with the knowledge base of freeze-thaw risk prevention measures for black soil high-standard farmland construction, freeze-thaw prevention measures are given;

[0035] A comprehensive assessment is conducted on the freeze-thaw risk resistance of high-standard farmland constructed in freeze-thaw black soil areas.

[0036] This embodiment provides an electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device; a processor, which, when the processor executes the instructions in the memory, enables the electronic device to implement the steps of the freeze-thaw risk identification and assessment method for the construction of high-standard black soil farmland as described in any of the above embodiments.

[0037] This embodiment provides a computer-readable storage medium having instructions stored thereon. When the instructions are executed on a computer, the steps of the freeze-thaw risk identification and assessment method for the construction of high-standard black soil farmland described in any of the above embodiments are implemented.

[0038] The technical solution of the embodiment of the present application has the following beneficial effects:

[0039] The freeze-thaw risk identification and assessment system for the construction of high-standard farmland on black soil provided in this embodiment addresses the deviation problem existing in the application of general high-standard farmland construction assessment to the construction of high-standard farmland on black soil. The system identifies freeze-thaw risk areas and obtains accurate freeze-thaw risk areas for the construction of high-standard farmland on black soil by superimposing freeze-thaw spatiotemporal distribution maps, regional black soil distribution maps, and high-standard farmland plot distribution maps, thereby achieving effective identification of freeze-thaw risks and laying a data foundation for risk warning and management assessment. The freeze-thaw hazard prediction model is used to predict freeze-thaw hazards and obtain freeze-thaw hazard prediction results. In combination with a pre-constructed knowledge base of freeze-thaw risk prevention measures for the construction of high-standard farmland on black soil, freeze-thaw prevention measures are given. At the same time, based on the relevant data generated by freeze-thaw risk identification, hazard prediction, and prevention measures, the system accurately assesses the impact of freeze-thaw in black soil areas on the construction of high-standard farmland, thereby meeting the actual needs of high-standard farmland construction in freeze-thaw black soil areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a structural block diagram of a freeze-thaw risk identification and assessment system for the construction of high-standard black soil farmland according to some embodiments of the present application.

[0041] Figure 2 This is a logical block diagram of a freeze-thaw risk identification and assessment system for the construction of high-standard black soil farmland according to some embodiments of the present application.

[0042] Figure 3 A schematic diagram of the structure of an electronic device provided according to some embodiments of the present application. DETAILED DESCRIPTION

[0043] High-standard farmland refers to the comprehensive improvement and upgrading of the original cultivated land within a certain area according to the standards of unified planning, unified design, unified construction, and unified acceptance, so as to meet the modern agricultural production requirements of "stable production capacity, complete facilities, good ecology, strong disaster resistance, and suitability for mechanized operation".

[0044] The technical path for high-standard farmland development typically includes preliminary investigations, planning and design, construction, post-production management and maintenance, and evaluation. Hazard prediction and risk warning are key technologies for enhancing farmland system resilience and disaster adaptability, thereby improving agricultural sustainability. Comprehensive assessment of high-standard farmland development is a crucial step in closing the technical loop and promoting continuous optimization.

[0045] Currently, the construction of high-standard farmland in black soil is typically implemented by simply transplanting common high-standard farmland construction technologies. Key aspects such as construction planning, construction processes, and construction quality assessment lack mechanisms to address freeze-thaw damage. There is also no effective assessment method to characterize the impact of seasonal freeze-thaw on agricultural production and farmland infrastructure. However, due to the unique geographical location of black soil regions, seasonal freeze-thaw has a significant impact on the construction of high-standard farmland in these regions. If the existing common high-standard farmland construction technologies are still used, the grain production capacity of black soil regions may not be effectively guaranteed and improved.

[0046] In view of this, the present embodiment provides a freeze-thaw risk identification and assessment system for the construction of black soil high-standard farmland. The system accurately identifies the freeze-thaw hazard area and the degree of hazard by setting technical units such as freeze-thaw area identification, freeze-thaw risk area demarcation, freeze-thaw hazard prediction, construction risk warning and prevention measures suggestions, and comprehensive assessment of black soil high-standard farmland construction in freeze-thaw areas (comprehensive assessment of construction quality). At the same time, the freeze-thaw response capability is added to the construction quality assessment system, providing technical guarantee for the high and stable yield of freeze-thaw black soil high-standard farmland.

[0047] The following describes an embodiment of this application by taking a certain county as the research object and combining with the accompanying drawings.

[0048] This embodiment provides a freeze-thaw risk identification and assessment system for the construction of black soil high-standard farmland, including: a freeze-thaw area identification unit 10 for black soil high-standard farmland construction, a freeze-thaw risk zone determination unit 20, a freeze-thaw hazard prediction unit 30, a construction risk warning and prevention measure recommendation unit 40, and a comprehensive assessment unit 50 for the construction of black soil high-standard farmland in freeze-thaw areas. Specifically:

[0049] The freeze-thaw area identification unit 10 for the construction of black soil high-standard farmland is used to identify the freeze-thaw risk area based on meteorological data through the fusion index method, and then draw a freeze-thaw spatiotemporal distribution map; the fusion index method refers to the comprehensive determination of the freeze-thaw risk area based on multiple freeze-thaw indices.

[0050] In this embodiment, the black soil high-standard farmland construction freeze-thaw area identification unit 10 analyzes the regional freeze-thaw risk based on meteorological data, and then performs superimposed analysis with the black soil distribution and high-standard farmland plot distribution to identify the black soil high-standard farmland construction freeze-thaw area.

[0051] It should be noted that meteorological data refers to various types of data and information describing the state of the atmosphere and its changes, collected through manual observation or automatic weather stations, remote sensing satellites, etc. In this embodiment, meteorological data include both historical meteorological data of the black soil area and future meteorological data of the black soil area. Among them, historical meteorological data are meteorological data measured in the past, which are usually recorded and archived in units of hours, days, and months. These data can be obtained through relevant meteorological departments, academic research organizations or public data. Future meteorological data can be calculated based on numerical weather forecast models (such as ECMWF, GRAPES, etc.), or can be obtained from public data sets of relevant departments or interfaces provided by third parties. This embodiment does not limit the method of obtaining meteorological data.

[0052] For example, meteorological data may include air temperature, precipitation, wind speed / direction, humidity, sunshine hours, air pressure, surface temperature / evapotranspiration, etc.

[0053] In this embodiment, historical meteorological data can be, for example, historical meteorological data for the study area over the past N years (e.g., N=10), including daily average temperature and daily hourly temperature grid data. Daily average temperature refers to the average temperature over the course of a day, and daily hourly temperature refers to the measured or forecasted temperature at each hour of the day (e.g., 00:00, 1:00, 2:00, ..., 23:00). Grid data (GridData) divides the study area into regular grids (e.g., 100×100 meters, 50×50 meters), providing a meteorological value (e.g., temperature, humidity, etc.) at the center of each grid.

[0054] Future meteorological data can be long-term meteorological forecast data. In addition to containing the same indicators as historical meteorological data, such data can also include the following: future monthly average temperature, temperature deviation compared with the same period in history, and extreme temperature events, etc., taking into account the adjustment of freeze-thaw risk areas. Among them, the future monthly average temperature refers to the average temperature forecast value for a whole month in the future. The temperature deviation compared with the same period in history refers to the difference between the current or future temperature and the average temperature of the same period in history, and can also be measured by combining the average temperature and the standard deviation. Extreme temperature events refer to high or low temperature events that exceed a certain critical value. In addition to being used to identify freeze-thaw risk areas, the above-mentioned meteorological data can also be used to determine whether the freeze-thaw risk areas in the preliminary identification results of freeze-thaw risk areas need to be adjusted.

[0055] For example, the average temperature in a certain area in January is -3.4℃ (i.e. the historical average temperature for the same period), with a standard deviation of 0.5℃. In the long-term meteorological forecast data, the average temperature in January is -3.2℃. It is determined that there is no typical difference in the climate in this area, and there is no need to adjust the preliminary identification results of the freeze-thaw risk area.

[0056] In this embodiment, a fusion index method is used to comprehensively analyze multiple indices to identify risk areas. The fusion index method refers to comprehensively determining freeze-thaw risk areas based on multiple freeze-thaw indices.

[0057] Here, the freeze-thaw index refers to an indicator used to quantify the frequency and intensity of temperature crossing the critical 0°C (freezing point) in a given region. By integrating multiple freeze-thaw indices into a fusion index approach to comprehensively determine freeze-thaw risk zones, the inability of a single indicator to fully reflect freeze-thaw risk can be mitigated. This allows for precise identification of freeze-thaw disaster risk zones and provides a foundation and basis for the development of high-standard farmland in Northeast China's black soil regions.

[0058] Preferably, the freeze-thaw index includes: freezing index, freeze-thaw ratio, and freeze-thaw daily cycle days. These freeze-thaw indices can effectively characterize the duration, degree, frequency, and other characteristics of freeze-thaw, laying the foundation for subsequent analysis.

[0059] The freezing index (FI) is the sum of the number of days in a year with temperatures below 0°C multiplied by the daily average temperature. The freeze-thaw ratio (N) is the ratio of the freezing index (FI) to the thawing index, reflecting the relative heat distribution between freezing and thawing in a region. The thawing index is the sum of the number of days in a year with temperatures above 0°C multiplied by the daily average temperature. The freeze-thaw daily cycle (D) refers to the number of days within a given period in which the soil or surface experiences freezing and thawing within a single day. Specifically, it refers to the number of days with a maximum daily temperature above 0°C and a minimum daily temperature below 0°C.

[0060] In some embodiments, based on meteorological data, freeze-thaw risk areas are identified by a fusion index method, specifically:

[0061] Calculate multiple freeze-thaw indices based on meteorological data;

[0062] According to multiple freeze-thaw indexes and preset judgment rules, the freeze-thaw risk area is divided into multiple risk levels to obtain the freeze-thaw risk area.

[0063] Specifically, historical / future meteorological data (grid data) are used as input to calculate multiple freeze-thaw indices.

[0064] Furthermore, the freezing index is calculated as follows:

[0065] ,

[0066] Where, is the freezing index, No. The average daily temperature, The number of days in a year with a temperature below 0°C.

[0067] The freeze-thaw ratio (N) is calculated as follows:

[0068] ,

[0069] Where N is the freeze-thaw ratio, is the melting index.

[0070] In this embodiment, the calculation time of the freeze-thaw daily cycle days (D) is one year, that is, the number of days that the soil or surface experiences the freezing and thawing process in one day is calculated.

[0071] In this embodiment, freeze-thaw risk areas are determined using a fusion index method, based on multiple freeze-thaw indices and combined with preset judgment rules. Specifically, the preset judgment rules include: if the freezing index of the target area is greater than a first threshold, or the freeze-thaw ratio of the target area is less than a second threshold, or the number of freeze-thaw cycles is greater than a third threshold, then the target area is determined to be a freeze-thaw risk area.

[0072] Here, the target area refers to a sub-area within the study area.

[0073] In this embodiment, a freezing index greater than the first threshold indicates a strong and sustained low-temperature process, which can reveal the development of deep permafrost; a freeze-thaw ratio less than the second threshold indicates a longer thawing period or a higher thawing intensity, indicating greater structural destructiveness; a freeze-thaw daily cycle greater than the third threshold reflects repeated freezing and thawing of the surface, which can easily lead to material fatigue and soil collapse.

[0074] In this embodiment, as long as the target area meets any one of the above three conditions, it can be determined as a freeze-thaw risk area. Preferably, multiple conditions can be used to cross-reference and comprehensively determine the freeze-thaw risk area.

[0075] For example, the judgment rule of freeze-thaw risk zone can be described as follows:

[0076] If the freezing index of the area reaches 1600℃·day, the area is determined to be a freeze-thaw risk area; and / or,

[0077] If the freeze-thaw ratio is less than 1, the area is determined to be a freeze-thaw risk area; and / or,

[0078] If the number of freeze-thaw daily cycles is greater than 100 days, the area is determined to be a freeze-thaw risk area.

[0079] The judgment rule for freeze-thaw risk areas provides a quantitative and threshold-based judgment standard, which is easy to implement batch judgment in GIS, remote sensing systems or farmland evaluation models. The comprehensive judgment and cross-judgment of multiple freeze-thaw indices can avoid misjudgment caused by a single index.

[0080] Furthermore, based on the combination of the above conditions, the level of each freeze-thaw risk zone can be determined. Specifically, if the target area meets the above three conditions at the same time, that is, the freezing index of the target area is greater than the first threshold, the freeze-thaw ratio of the target area is less than the second threshold, and the number of freeze-thaw daily cycles is greater than the third threshold, then the area can be determined as a high freeze-thaw risk area; if two of the above conditions are met at the same time, the area can be determined as a medium freeze-thaw risk area; if only any one of the above conditions is met, the area will be determined as a low freeze-thaw risk area.

[0081] For example, based on historical meteorological data, the freezing index, freeze-thaw ratio and number of freeze-thaw daily cycles of each grid point can be calculated, and then combined with the above judgment rules, the freeze-thaw risk area can be judged and the level of the freeze-thaw risk area can be determined. For example, if the freezing index of a grid point = 1800, the freeze-thaw ratio = 0.76, and the number of freeze-thaw daily cycles = 110, then the grid area is judged as a high freeze-thaw risk area.

[0082] In this embodiment, a comprehensive risk assessment is performed based on the three indexes, and then the freeze-thaw risk areas are divided into levels (high, medium, and low). This can comprehensively cover all types of freeze-thaw disasters (such as shallow freeze-thaw, deep frost heave, thermal thaw subsidence, etc.), thereby improving the accuracy of freeze-thaw risk area identification.

[0083] Furthermore, the freeze-thaw risk zone classification results (high, medium, low) obtained based on historical meteorological data are called preliminary freeze-thaw risk zone identification results, and a preliminary freeze-thaw risk identification distribution map is output.

[0084] Among them, the preliminary identification distribution map of freeze-thaw risks uses a map to visualize the preliminary identification results of freeze-thaw risks. For example, red can be used to represent high-risk areas for freeze-thaw, orange can be used to represent medium-risk areas for freeze-thaw, and green can be used to represent low-risk areas for freeze-thaw.

[0085] Specifically, tools such as ArcGIS / QGIS / Python (matplotlib+geopandas) can be used to draw and output a preliminary identification distribution map of freeze-thaw risk.

[0086] Based on the preliminary identification results of the high-freeze-thaw risk areas, the freeze-thaw risk areas are further delineated and a freeze-thaw spatiotemporal distribution map is drawn. Preferably, the freeze-thaw spatiotemporal distribution map can be obtained by the following steps:

[0087] Identify historical freeze-thaw risk areas based on multi-year (e.g., 10-year) historical meteorological data of the study area;

[0088] Based on the long-term meteorological forecast data (future meteorological data) of the study area, identify future freeze-thaw risk areas;

[0089] Compare historical meteorological data with future meteorological data to obtain comparative results;

[0090] If the comparison result is less than the preset difference threshold, the historical freeze-thaw risk area will be used as the final freeze-thaw risk area delineation result;

[0091] If the comparison result is greater than a preset difference threshold, the historical freeze-thaw risk area and the future freeze-thaw risk area are combined to obtain a final freeze-thaw risk area delineation result;

[0092] Based on the final delineation results of the freeze-thaw risk area, the freeze-thaw spatiotemporal distribution map is finally output.

[0093] In this embodiment, the identification of historical freeze-thaw risk areas and future freeze-thaw risk areas both adopts the fusion index method. The specific method is described in the above embodiment and will not be repeated here.

[0094] In this embodiment, based on the historical meteorological data of the study area for many years (for example, 10 years), historical freeze-thaw risk areas are identified, and preliminary identification results of freeze-thaw risk areas are obtained.

[0095] The purpose of comparing historical and future meteorological data is to identify differences between the historical and forecast climates, thereby determining whether the initial freeze-thaw risk zone identification results need to be adjusted. If the comparison result is less than a preset difference threshold, it is determined that the future meteorological data does not differ significantly from the historical data for the same period. In this case, the historical freeze-thaw risk zone is used as the final freeze-thaw risk zone delineation result. The difference threshold can be a range value, such as the mean ± 2 standard deviations.

[0096] That is to say, if the difference in the comparison results is small (for example, it does not exceed the mean value ± 2 times the standard deviation), the historical freeze-thaw risk area will be used as the final freeze-thaw risk area delineation result; otherwise, if the long-term meteorological forecast data is significantly different from the historical meteorological data (for example, it exceeds the mean value ± 2 times the standard deviation), the freeze-thaw risk area will be adjusted. The specific adjustment method is to union the historical freeze-thaw risk area with the future freeze-thaw risk area, and finally output the freeze-thaw spatiotemporal distribution map.

[0097] For example, taking temperature as an example, historical meteorological data indicates that the average January temperature in a certain region is -3.4°C with a standard deviation of 0.5°C. Long-term forecast data indicates a January average temperature of -3.2°C for the same region. This is considered to be non-typical (small) differences. The same method is then used to determine the deviations between the long-term forecast data and the historical meteorological data for each month (e.g., February, March, ..., December). If the climate for the entire year (January to December) shows no typical differences from the historical climate, the preliminary freeze-thaw risk identification distribution results are considered the final results. The freeze-thaw risk areas identified in this step serve as key research areas for subsequent risk prediction and assessment.

[0098] If the forecast data for the entire year (January to December) shows significant differences from historical climate, the initial identification of freeze-thaw risk zones will need to be adjusted. This adjustment is done by combining the historical freeze-thaw risk zones with the future freeze-thaw risk zones to obtain the final freeze-thaw risk zone delineation results.

[0099] It should be noted that the difference between historical meteorological data and future meteorological data can also be judged based on the comparison results of meteorological data such as air pressure, humidity, wind speed / wind direction. The specific operation method is the same as the method for determining temperature, so it will not be repeated here.

[0100] On the basis of accurately identifying freeze-thaw risk areas and drawing freeze-thaw spatiotemporal distribution maps, in this embodiment, the freeze-thaw risk area demarcation subunit 20 is used to superimpose and analyze the freeze-thaw spatiotemporal distribution maps, regional black soil distribution maps, and high-standard farmland plot distribution maps to obtain freeze-thaw risk areas for black soil high-standard farmland construction.

[0101] The purpose of the freeze-thaw risk zoning stator unit 20 is to obtain the freeze-thaw risk zoning for the construction of black soil high-standard farmland.

[0102] Here, overlay analysis is one of the analysis methods of geographic information system (GIS). It refers to the overlay of two or more layers with spatial location relationships (layers, such as freeze-thaw spatiotemporal distribution maps, regional black soil distribution maps, and high-standard farmland plot distribution maps) in geographic space, and the extraction of new spatial features through spatial relationship operations (such as intersection, union, and difference).

[0103] Specifically, by overlaying and analyzing freeze-thaw spatiotemporal distribution maps, regional black soil distribution maps, and high-standard farmland plot distribution maps, the freeze-thaw risk areas for black soil high-standard farmland construction were identified. This method, through multi-layer overlay analysis, combines the spatiotemporal characteristics of the freeze-thaw process with black soil distribution and farmland planning, enabling precise localization of freeze-thaw risk in black soil high-standard farmland.

[0104] It should be noted that the freeze-thaw spatiotemporal distribution map reflects the distribution of freeze-thaw risk areas in the entire study area. These freeze-thaw risk areas may be located in the black soil distribution area or in other landform distribution areas. In the black soil distribution area, there are high-standard farmland plots and non-high-standard farmland plots. Therefore, by superimposing the three, the freeze-thaw risk areas for the construction of black soil high-standard farmland can be accurately located.

[0105] The regional black soil distribution map can be obtained as follows: a soil type map is obtained from the soil survey results data, and regions where the soil type attribute field value is black soil are extracted to generate a regional black soil distribution map.

[0106] The distribution map of high-standard farmland plots can be obtained by extracting the distribution of high-standard farmland plots. For example, if a vector map of cultivated land plots planned for a high-standard farmland project in the study area is available, the distribution map can be obtained by splicing all the vector maps of high-standard farmland plots in the area. Otherwise, the distribution map can be obtained by identifying cultivated land plots using machine learning algorithms on high-precision remote sensing images of the study area.

[0107] Based on the obtained freeze-thaw spatiotemporal distribution map, regional black soil distribution map, and high-standard farmland plot distribution map, the three were superimposed and analyzed to obtain the freeze-thaw risk areas for the construction of black soil high-standard farmland.

[0108] The delineation of freeze-thaw risk zones for high-standard black soil farmland construction provides a spatial framework for freeze-thaw hazard prediction and risk warning. In this embodiment, the freeze-thaw hazard prediction unit 30 is used to construct a freeze-thaw hazard prediction model and perform freeze-thaw hazard prediction based on the freeze-thaw risk zones for high-standard black soil farmland construction to obtain freeze-thaw hazard prediction results.

[0109] In this embodiment, the freeze-thaw hazard prediction unit 30 is used to construct a freeze-thaw hazard prediction model for black soil high-standard farmland (hereinafter referred to as a freeze-thaw hazard prediction model), and use the model to predict freeze-thaw hazards in freeze-thaw risk areas of black soil high-standard farmland construction.

[0110] Specifically, the freeze-thaw hazard prediction unit further includes: a data preparation and preprocessing subunit, a model construction and verification subunit, and an application and prediction subunit. Among them:

[0111] The data preparation and preprocessing subunit is used to preprocess the pre-acquired farmland environmental data and farmland damage data to form modeling data, and divide the modeling data into training set data and validation set data;

[0112] The model construction and verification subunit is used to construct a freeze-thaw hazard prediction model that maps the relationship between farmland environmental variables and farmland freeze-thaw disaster damage, and uses the validation set data to evaluate the accuracy of the prediction model to obtain a trained prediction model;

[0113] The application and prediction subunit is used to use the trained prediction model to predict the damage caused by freeze-thaw disasters in the target area, and thus obtain the freeze-thaw hazard prediction results.

[0114] The execution process of the above subunits can be described as follows:

[0115] First, collect farmland environmental data and farmland damage data from areas with similar climate, geography, and other environments to the study area. Farmland environmental data can include meteorological, soil, high-standard farmland construction, and crop data. Farmland damage data can include: ditch damage rate, total length and maximum width of ditch cracks, field road damage rate, total length and maximum width of field road cracks, production road damage rate, total length and maximum width of production road cracks, etc.

[0116] For example, the specific contents of the farmland environment data are as follows:

[0117] Meteorological data: Collect historical meteorological data of the study area, including average temperature, freezing index, fusion index, rainfall, etc. Climate data is the most important variable data for modeling.

[0118] Soil data: specifically refers to black soil parameters, including soil bulk density, soil texture and other data.

[0119] Crop data: including regional crop types, planting systems and other data.

[0120] High-standard farmland construction data: including construction years, farmland slope, irrigation channel type, field road width, production road width and other data.

[0121] The data preparation and preprocessing subunit then cleans and preprocesses the acquired farmland environmental and damage data to generate modeling data. This cleaning and preprocessing process includes removing outliers, unifying data units, and standardizing data. Based on the cleaning and preprocessing results, the high-standard farmland construction plots are used as statistical units to generate modeling data (i.e., modeling data).

[0122] It should be noted that in this embodiment, the statistical unit refers to the calculation unit that processes and analyzes the data, also known as the calculation unit. That is, during the modeling and subsequent evaluation steps, the units are all plots.

[0123] The model construction and verification subunit constructs a relationship model between high-standard farmland facility damage and farmland environment. Farmland facility damage refers to farmland damage data, and farmland environment refers to farmland environmental data. This model uses farmland environmental data as the independent variable and farmland damage data as the dependent variable to construct a freeze-thaw hazard prediction model that maps farmland environmental variables to farmland freeze-thaw damage.

[0124] Preferably, the freeze-thaw hazard prediction model is a machine learning model.

[0125] Specifically, the model construction and verification sub-units respectively construct machine learning models for various variables of farmland damage data (including: ditch damage rate, total length and maximum width of ditch cracks, field road damage rate, total length and maximum width of field road cracks, production road damage rate, total length and maximum width of production road cracks) and farmland environmental data.

[0126] It should be noted that the machine learning model can be any one of a random forest model, a multiple linear regression model, and an eXtremeGradient Boosting model.

[0127] Furthermore, multiple model training data can be selected for accuracy evaluation, and the model with the highest accuracy can be selected as the application model (i.e., the trained freeze-thaw hazard prediction model).

[0128] Specifically, the modeling data can be divided into training and validation sets. Models such as random forest, multivariate linear regression, and eXtreme Gradient Boosting can be selected and trained using the training set data. A freeze-thaw damage prediction model can be constructed based on farmland environmental variables (e.g., regional climate, soil, high-standard farmland construction, crop data) and freeze-thaw damage characteristics of black soil high-standard farmland (e.g., ditch damage rate, total length and maximum width of ditch cracks, field road damage rate, total length and maximum width of field road cracks, and production road damage rate, total length and maximum width of production road cracks). The validation set data can be used to evaluate model accuracy, and the most accurate model can be selected for subsequent prediction of freeze-thaw damage characteristics of black soil high-standard farmland, resulting in a trained prediction model.

[0129] Using the trained prediction model, the farmland environmental data of the predicted area (such as regional climate, soil, high-standard farmland construction, and crop data) are input to predict the freeze-thaw disaster damage in the area (such as ditch damage rate, total length and maximum width of ditch cracks, field road damage rate, total length and maximum width of field road cracks, production road damage rate, total length and maximum width of production road cracks), obtain the freeze-thaw damage prediction results, and output the prediction results.

[0130] After obtaining the freeze-thaw hazard prediction results, in this embodiment, a risk warning and prevention measure suggestion unit 40 is constructed to provide freeze-thaw prevention measures based on the freeze-thaw hazard prediction results and a freeze-thaw risk prevention measures knowledge base for black soil high-standard farmland.

[0131] The purpose of the construction risk warning and prevention measures suggestion unit 40 is to generate construction risk warning and prevention measures suggestions. Specifically, the predicted farmland damage situation is input into the freeze-thaw risk prevention knowledge base of black soil high-standard farmland construction, and prevention measures suggestions are obtained to provide guidance for the black soil high-standard farmland construction process.

[0132] Among them, the knowledge base of freeze-thaw risk prevention measures for black soil high-standard farmland construction is used to collect, organize, store, and associate prevention measures related to freeze-thaw disasters. In this embodiment, the knowledge base of freeze-thaw risk prevention measures for black soil high-standard farmland construction is constructed based on knowledge graph technology.

[0133] It should be noted that knowledge graph technology is an artificial intelligence technology that expresses entities and their relationships through graph structure (nodes + edges).

[0134] Specifically, the knowledge on freeze-thaw disasters is first analyzed and organized to form a knowledge system for freeze-thaw risk prevention in the construction of high-standard black soil farmland. Then, relevant knowledge information is extracted from multi-source heterogeneous data (such as national / local farmland construction specifications, scientific research literature, expert knowledge, etc.), integrated into the knowledge system according to preset rules, and entity content (such as freeze-thaw risk level) and relationships are extracted. Then, knowledge modeling is performed, and finally the knowledge system is stored in the knowledge base, thereby obtaining a knowledge base of freeze-thaw risk prevention measures for the construction of high-standard black soil farmland.

[0135] The freeze-thaw hazard prediction result (i.e., freeze-thaw disaster damage situation) output by the freeze-thaw hazard prediction unit 30 is input into the knowledge base of freeze-thaw risk prevention measures for black soil high-standard farmland construction, and inference is performed in the graph information to obtain knowledge output that matches the needs, and generate freeze-thaw risk prevention measures suggestions for black soil high-standard farmland construction, i.e., freeze-thaw prevention measures.

[0136] Based on the identification of freeze-thaw areas, freeze-thaw hazard prediction, construction risk warning and prevention measures for the construction of high-standard farmland in black soil, and based on relevant data, a comprehensive assessment of the construction of high-standard farmland in black soil areas in freeze-thaw areas is conducted, that is, the ability of high-standard farmland constructed in freeze-thaw black soil areas to resist freeze-thaw risks is assessed, to provide guarantees for achieving high and stable yields of high-standard farmland.

[0137] In this embodiment, the comprehensive evaluation unit 50 for the construction of high-standard farmland in black soil in freeze-thaw areas is used to comprehensively evaluate the freeze-thaw risk resistance of high-standard farmland constructed in freeze-thaw black soil areas.

[0138] It should be noted that in the evaluation of traditional general high-standard farmland construction, the high-standard farmland construction evaluation index system based on relevant technical standards (such as GB / T 33130-2024) is composed of fields (field surface conditions), soil (soil properties), water (irrigation and drainage capacity), roads (road accessibility), grain (production capacity) and other infrastructure (the degree of supporting power transmission lines, supporting high-efficiency water-saving irrigation facilities and the proportion of farmland protection area). These current standards do not take into account the impact of freeze-thaw on high-standard farmland construction.

[0139] In this embodiment, the freeze-thaw resistance risk index is supplemented on the basis of the traditional high-standard farmland construction evaluation index system, and the evaluation index system is optimized to meet the actual needs of black soil high-standard farmland construction in freeze-thaw areas.

[0140] Specifically, based on the traditional high-standard farmland construction evaluation index system, the supplementary freeze-thaw resistance risk indicators are shown in the following table:

[0141] Table 1 Supplementary freeze-thaw resistance risk indicators

[0142]

[0143] After construction is complete, an optimized evaluation index system (including freeze-thaw resistance) will be used to assess the comprehensive development of high-standard farmland in the region. Data on fields, soil, water, roads, grain, and other infrastructure will be collected. Based on the technical implementation of freeze-thaw risk area identification and delineation, as well as hazard prediction, risk warning, and preventive measures, data on freeze-thaw resistance will be collected. Conventional measurement methods can be used to obtain numerical values. Individual indicators will then be scored and accumulated to produce the evaluation results.

[0144] Specifically, the scores for each indicator are determined based on the table above. The total score (I1) is the cumulative score of all indicators, with a maximum score of 20. The evaluation score (I2) is based on the "High-standard Farmland Construction Evaluation Standard (GB / T 33130-2024)." The comprehensive evaluation score (I) for high-standard farmland in black soil in freeze-thaw zones is: I = I1 + 0.8 * I2. The evaluation results are divided into five categories: excellent (score ≥ 90), good (score 80 ≤ < 90), fair (score 70 ≤ < 80), fair (score 60 ≤ < 70), and unqualified (score < 60).

[0145] That is to say, in this embodiment, the supplementary anti-freeze-thaw risk index is used to calculate the comprehensive score, which is recorded as the first score I1, and the comprehensive score of all the original general indicators is calculated, which is recorded as the second score I2. The weighted sum of the first score and the second score is used as the total score of the comprehensive assessment of high-standard black soil farmland in the freeze-thaw zone.

[0146] In this embodiment, freeze-thaw response capability is added to the quality assessment system for high-standard farmland construction. After identifying freeze-thaw areas for black soil high-standard farmland construction, freeze-thaw risk indicators such as the freeze-thaw resistance of ditches and the freeze-thaw resistance of road materials are added, and relevant data are collected and measured during the design, construction, and management of high-standard farmland, an accurate assessment of the comprehensive construction status of high-standard farmland in freeze-thaw risk areas can be achieved.

[0147] As an example, the following combines Figure 2 The system provided in this embodiment is further described.

[0148] like Figure 2As shown in the figure, the system consists of four parts: identification of freeze-thaw zones for high-standard farmland construction, freeze-thaw hazard prediction, construction risk warning and preventive measures recommendations, and comprehensive assessment of black soil high-standard farmland construction in freeze-thaw zones. Frozen-thaw zone identification includes preliminary identification of high-risk freeze-thaw zones and delineation of freeze-thaw risk zones. Frozen-thaw hazard prediction includes data preparation and preprocessing, model construction and validation, and application and prediction. Construction risk warning and preventive measures recommendations include the construction of a freeze-thaw risk prevention knowledge system (i.e., a knowledge base), knowledge information extraction, and knowledge output. Comprehensive assessment of black soil high-standard farmland construction in freeze-thaw zones includes the refinement of evaluation indicators and the generation and feedback of evaluation results.

[0149] In summary, the system provided in this embodiment has the following beneficial effects:

[0150] Improve the quality of high-standard farmland construction. Timely identify freeze-thaw risks in the construction of black soil high-standard farmland, and provide early warning and prevention recommendations to reduce damage to high-standard farmland roads and water conservancy facilities due to freeze-thaw hazards, reduce farmland construction and maintenance costs, and ensure the long-term and stable use of farmland.

[0151] Improved agricultural production capacity. The technical solution provided in this embodiment can not only improve the comprehensive construction quality of high-standard farmland, but also take advantage of the characteristics of high-standard farmland, such as complete infrastructure, fertile soil, and strong disaster resistance, to increase agricultural production capacity and provide a solid guarantee for food security.

[0152] Improve the ecological environment of black soil. Identifying and preventing freeze-thaw risks can also reduce the impact of freeze-thaw erosion on black soil and promote the sustainable development of the black soil ecological environment.

[0153] Based on the same inventive concept, this embodiment provides a method for identifying and assessing freeze-thaw risks in the construction of high-standard black soil farmland. The method includes the following steps:

[0154] Based on meteorological data, the freeze-thaw risk areas are identified through the fusion index method, and then the freeze-thaw spatiotemporal distribution map is drawn;

[0155] The freeze-thaw spatiotemporal distribution map, regional black soil distribution map, and high-standard farmland plot distribution map were superimposed and analyzed to obtain the freeze-thaw risk areas for black soil high-standard farmland construction;

[0156] Constructing a freeze-thaw hazard prediction model, and performing freeze-thaw hazard prediction based on the freeze-thaw risk area of the black soil high-standard farmland construction, to obtain a freeze-thaw hazard prediction result;

[0157] Based on the freeze-thaw hazard prediction results and combined with the knowledge base of freeze-thaw risk prevention measures for black soil high-standard farmland construction, freeze-thaw prevention measures are given;

[0158] A comprehensive assessment is conducted on the freeze-thaw risk resistance of high-standard farmland constructed in freeze-thaw black soil areas.

[0159] Based on the same inventive concept, this embodiment also provides an electronic device, including: a memory for storing instructions executed by one or more processors of the electronic device; a processor, when the processor executes the instructions in the memory, it can enable the electronic device to implement the steps of the freeze-thaw risk identification and assessment method for the construction of high-standard black soil farmland provided in any of the above embodiments.

[0160] The present application also provides a computer program product comprising computer-executable instructions. In one embodiment, the computer-executable instructions are used to cause a computer to execute the functions of the above-mentioned freeze-thaw risk identification and assessment method for black soil high-standard farmland construction.

[0161] Computer-executable instructions can be stored in a computer-readable storage medium. Embodiments of the present application also provide a computer-readable storage medium storing executable instructions. In one embodiment, the computer-executable instructions are used to cause a computer to execute the functions of the aforementioned embodiment of the freeze-thaw risk identification and assessment method for black soil high-standard farmland construction.

[0162] Figure 3 This is a schematic diagram of the structure of an electronic device provided according to some embodiments of the present application. The electronic device may be, but is not limited to, mobile terminals such as mobile phones, tablet computers, handheld computers, personal digital assistants (PDAs), smart home devices such as smart TVs and smart cameras, wearable devices such as smart bracelets, smart watches, and smart glasses, or other computer devices such as desktops, laptops, notebook computers, ultra-mobile personal computers (UMPCs), netbooks, and smart screens.

[0163] like Figure 3 As shown, the electronic device 200 may include one or more of the following components: a processor 201, a memory 203, a communication interface 202, and a communication bus 204. The memory 203 may be connected to the processor 201 via the bus 204. The bus can transmit data between the processor 201 and the memory 203. The bus can be divided into an address bus, a data bus, a control bus, and the like.

[0164] The processor 201 may include one or more processing cores. The processor 201 may utilize various interfaces and lines to connect various components within the entire electronic device 200. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 203, and calling data stored in the memory 203, the processor 201 performs various functions of the electronic device 200 and processes data. For example, the processor 201 may include an application processor (AP), a modem processor, a CPU, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a video codec, a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic array (PLA), and / or a neural network processing unit (NPU). Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed; the NPU is used to implement artificial intelligence (AI) functions; and the modem is used to handle wireless communications. Different processing units can be independent devices or integrated into one or more processors. For example, the multiple processing units shown above are all integrated into a SoC, or the AP is a separate semiconductor chip and the other processing units are integrated into a SoC. This application is not limited to this.

[0165] Memory 203 (also known as a computer-readable storage medium) can include random access memory (RAM), read-only memory (ROM), and non-transitory computer-readable storage medium. Memory 203 can be used to store instructions, programs, code, code sets, or instruction sets. Memory 203 can include a program storage area and a data storage area. The program storage area can store instructions for implementing an operating system and instructions for at least one function, such as a method for identifying and assessing freeze-thaw risks in the construction of high-standard black soil farmland. The data storage area can store data generated by the use of electronic device 200, such as meteorological data and intermediate model output results.

[0166] In addition, those skilled in the art will appreciate that the structure of the electronic device 200 shown in the above figures does not limit the electronic device 200. The electronic device may include more or fewer components than shown, or may combine certain components, or arrange the components differently. For example, the electronic device 200 may also include a microphone, a speaker, a radio frequency circuit, a sensor, an audio circuit, a power supply, a Bluetooth module, and other components, which will not be described in detail here.

[0167] The foregoing description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A freeze-thaw risk identification and assessment system for the construction of high-standard black soil farmland, characterized by: include: The freeze-thaw area identification unit for high-standard black soil farmland construction is used to identify freeze-thaw risk areas based on meteorological data and the fusion index method, and then draw a freeze-thaw spatiotemporal distribution map; The fusion index method refers to comprehensively determining the freeze-thaw risk area based on multiple freeze-thaw indices; The freeze-thaw risk zoning sub-unit is used to overlay and analyze the freeze-thaw spatiotemporal distribution map, the regional black soil distribution map, and the high-standard farmland plot distribution map to obtain the freeze-thaw risk area for the construction of black soil high-standard farmland; A freeze-thaw hazard prediction unit is used to construct a freeze-thaw hazard prediction model, and perform freeze-thaw hazard prediction based on the freeze-thaw risk area of the black soil high-standard farmland construction to obtain a freeze-thaw hazard prediction result; The freeze-thaw hazard prediction unit includes a model construction and verification subunit; the model construction and verification subunit is used to construct a freeze-thaw hazard prediction model that maps the relationship between farmland environmental variables and farmland freeze-thaw disaster damage using plots in the freeze-thaw risk area of black soil high-standard farmland construction as statistical units, and use the validation set data to evaluate the accuracy of the prediction model to obtain a trained prediction model; the farmland freeze-thaw disaster damage refers to damage to farmland facilities; A risk warning and prevention measure suggestion unit is constructed, which is used to provide freeze-thaw prevention measures based on the freeze-thaw hazard prediction results and the freeze-thaw risk prevention measures knowledge base for black soil high-standard farmland construction; The comprehensive assessment unit for the construction of high-standard farmland in black soil areas prone to freezing and thawing is used to supplement the freeze-thaw risk resistance index on the basis of the traditional high-standard farmland construction evaluation index system, and to conduct a comprehensive assessment of the freeze-thaw risk resistance of high-standard farmland constructed in freeze-thaw black soil areas.

2. The system according to claim 1, wherein: Based on meteorological data, the freeze-thaw risk areas are identified through the fusion index method, specifically: Calculate multiple freeze-thaw indices based on meteorological data; According to the multiple freeze-thaw indices and preset judgment rules, the freeze-thaw risk area is divided into multiple risk levels to obtain a freeze-thaw risk area.

3. The system according to claim 2, characterized in that The freeze-thaw index includes: freezing index, freeze-thaw ratio, and freeze-thaw daily cycle days.

4. The system according to claim 3, characterized in that The preset judgment rules include: If the freezing index of the target area is greater than the first threshold, or the freeze-thaw ratio of the target area is less than the second threshold, or the number of freeze-thaw daily cycles is greater than the third threshold, the target area is judged to be a freeze-thaw risk area.

5. The system according to claim 2, wherein: The freeze-thaw spatiotemporal distribution map is obtained by the following steps: Based on the multi-year historical meteorological data of the study area, the historical freeze-thaw risk areas were obtained; Based on the future meteorological data of the study area, predict the future freeze-thaw risk areas; Comparing the historical meteorological data with the future meteorological data to obtain a comparison result; If the comparison result is less than a preset difference threshold, the historical freeze-thaw risk area is used as the final freeze-thaw risk area delineation result; If the comparison result is greater than a preset difference threshold, the historical freeze-thaw risk area and the future freeze-thaw risk area are combined to obtain a final freeze-thaw risk area delineation result; Based on the delineation result of the final freeze-thaw risk zone, the freeze-thaw spatiotemporal distribution map is output.

6. The system according to claim 1, wherein: The freeze-thaw hazard prediction unit further includes: a data preparation and preprocessing subunit, an application and prediction subunit; The data preparation and preprocessing subunit is used to preprocess the pre-acquired farmland environmental data and farmland freeze-thaw disaster damage data to form modeling data, and divide the modeling data into training set data and validation set data; The application and prediction subunit is used to use the trained prediction model to predict the damage caused by freeze-thaw disasters in the target area, so as to obtain the freeze-thaw damage prediction results.

7. The system according to claim 1, wherein: The knowledge base of freeze-thaw risk prevention measures for black soil high-standard farmland construction is constructed based on knowledge graph technology.

8. A method for identifying and assessing freeze-thaw risks in the construction of high-standard black soil farmland, characterized in that: include: Based on meteorological data, the freeze-thaw risk areas are identified through the fusion index method, and then the freeze-thaw spatiotemporal distribution map is drawn; The freeze-thaw spatiotemporal distribution map, regional black soil distribution map, and high-standard farmland plot distribution map were superimposed and analyzed to obtain the freeze-thaw risk areas for black soil high-standard farmland construction; Constructing a freeze-thaw hazard prediction model, and performing freeze-thaw hazard prediction based on the freeze-thaw risk area of the black soil high-standard farmland construction, to obtain a freeze-thaw hazard prediction result; The freeze-thaw hazard prediction unit includes a model construction and verification subunit; the model construction and verification subunit is used to construct a freeze-thaw hazard prediction model that maps the relationship between farmland environmental variables and farmland freeze-thaw disaster damage using plots in the freeze-thaw risk area of black soil high-standard farmland construction as statistical units, and use the validation set data to evaluate the accuracy of the prediction model to obtain a trained prediction model; the farmland freeze-thaw disaster damage refers to damage to farmland facilities; Based on the freeze-thaw hazard prediction results and combined with the knowledge base of freeze-thaw risk prevention measures for black soil high-standard farmland construction, freeze-thaw prevention measures are given; On the basis of the traditional high-standard farmland construction evaluation index system, the anti-freeze-thaw risk index is supplemented to conduct a comprehensive assessment of the anti-freeze-thaw risk capacity of high-standard farmland constructed in freeze-thaw black soil areas.

9. An electronic device, characterized in that: include: a memory for storing instructions to be executed by one or more processors of the electronic device; The processor, when the processor executes the instructions in the memory, can enable the electronic device to implement the steps of the freeze-thaw risk identification and assessment method for the construction of black soil high-standard farmland as described in claim 8.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, which, when executed on a computer, implement the steps of the freeze-thaw risk identification and assessment method for the construction of high-standard black soil farmland as described in claim 8.

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