A food supply service space flow path simulation method based on supply and demand relationship

Abstract

The application discloses a food supply service space flow path simulation method based on supply-demand relationship and belongs to the technical field of ecosystem services. Through data acquisition and preprocessing, the food supply and demand of each region are quantified to realize spatial expression. The supply-demand quantity relationship and spatial matching of food supply services are evaluated to understand the correlation of supply and demand in the overall space and the aggregation and differentiation characteristics in the local space. Through traffic accessibility evaluation and cost path analysis, the optimal flow path of food from the food supply area to the food demand area is determined to optimize the spatial configuration of food supply services. The application is based on the grid scale, combines land use data and normalized vegetation index to distribute food heat in the grid, not only makes up for the defects that fine scale is difficult to promote to large scale implementation, but also finely characterizes the supply-demand relationship of food supply services and realizes quantitative characterization of food supply service space flow path.

Description

Technical Field

[0001] This invention relates to the field of ecosystem service technology, and in particular to a method for simulating the spatial flow path of food supply services based on supply and demand relationships. Background Technology

[0002] Domestic and international scholars' research on quantifying regional food supply mainly focuses on the mechanisms of primary productivity in land ecosystems or the relationship between food production and arable land and multiple cropping indices. However, most of these studies neglect the differences in different types of food and their nutritional components, leading to limitations in the quantification of food supply capacity. The quantification of food supply and demand is typically conducted from both qualitative and quantitative perspectives. Qualitative analysis, due to its high subjectivity and lack of precise quantification, results in significant differences in the inferences of the scale and speed of supply and demand changes among different researchers, limiting its ability to explain and predict practical problems. Domestic and international scholars often use indices such as supply-demand ratios, demand rates, and the degree of supply-demand coordination to quantify supply and demand relationships. Among these, the ecosystem service supply-demand ratio, due to its high operability and accuracy, has been widely used in quantitative research on supply and demand relationships.

[0003] The spatial mismatch between supply and demand for food services and its spatial flow have become a hot topic of interest for scholars both domestically and internationally. Due to the spatial misalignment between supply and demand areas, the spatial flow of food services has become a crucial mechanism for balancing regional supply and demand. The enhanced two-step movement search method, by simulating the supply-demand balance of food services and the accessibility of food supply and demand proximity, can characterize the spatial flow characteristics of food services, providing an important method for studying the spatial optimization of food services. This invention, based on a grid scale and combining land use data and the normalized difference in vegetation index (NDDE) for grid-based allocation of food calories, not only overcomes the limitation of fine-scale implementation being difficult to extend to large-scale applications, but also further refines the characterization of the supply-demand relationship of food services, achieving a quantitative representation of the spatial flow paths of food services. Summary of the Invention

[0004] The purpose of this invention is to provide a method for simulating the spatial flow path of food supply services based on supply and demand relationships, thereby solving the problems of low accuracy in food supply and demand assessment and low accuracy in spatial flow path simulation in the prior art.

[0005] To achieve the above objectives, this invention discloses a method for simulating the spatial flow path of food supply services based on supply and demand relationships, comprising the following steps:

[0006] S1. Obtain comprehensive data and preprocess the comprehensive data;

[0007] S2. Quantification of Supply and Demand: Quantifying the food supply and demand in each region to achieve spatial representation;

[0008] S3. Evaluation of the quantity relationship between supply and demand and spatial matching: Evaluate the quantity relationship between supply and demand and spatial matching of food supply services, and understand the correlation between supply and demand in the overall space and the clustering and differentiation characteristics in local spaces.

[0009] S4. Simulation of spatial flow path for food supply services: Through traffic accessibility evaluation and cost path analysis, the optimal flow path of food from the food supply area to the food demand area is determined in order to optimize the spatial configuration of food supply services.

[0010] Preferably, step S1 specifically includes the following steps:

[0011] S11. Obtain comprehensive data, which includes vector data, raster data, and other data;

[0012] The vector data includes administrative division data and traffic road data;

[0013] The raster data includes land use data, normalized vegetation index data, and population density data;

[0014] The other data includes food yield data, food calorie data, and per capita calorie requirements data for six types of crops.

[0015] S12. Preprocessing of comprehensive data, including heat conversion and spatial gridding of food production;

[0016] The heat conversion involves converting food yield data for six types of crops into heat supply.

[0017] Spatial gridding involves creating 1km×1km spatial grids, dividing administrative region data into grids, and combining land use data, normalized vegetation index data, and population density data to obtain the area of ​​different land use types and their corresponding normalized vegetation index and population density data within each grid area.

[0018] Preferably, the administrative division data includes vector boundary data of county-level and township-level administrative divisions; the six types of crops include grains, vegetables, fruits, meat, aquatic products, and oil crops.

[0019] Preferably, step S2 specifically includes the following steps:

[0020] S21. Quantification and grid allocation of food supply services;

[0021] Quantifying supply involves converting the supply of ecosystem services into the form of heat.

[0022] The grid-based allocation method for food supply involves spatially allocating the total food supply of each district and county using spatially gridded data.

[0023] S22. Quantification of demand for food supply services;

[0024] Based on the per capita daily calorie requirement method, and according to the per capita daily calorie intake standard and population density data within the grid area, the spatial expression of food demand is realized.

[0025] S23, Aggregation of supply and demand at the township level;

[0026] Based on the quantitative data of supply and demand at the grid scale in S21 and S22, and the regional township administrative division data, the average supply and demand of the spatial grids contained in each township is used as the supply and demand data of that township.

[0027] Preferably, the sources of ecosystem service provision are six types of crops;

[0028] The gridded spatial allocation is specifically as follows:

[0029] (1) The calories of grains, vegetables and oil crops are allocated to cultivated land, the calories of fruits are allocated to forest land, the calories of aquatic products are allocated to water areas, and the calories of meat are allocated according to the proportion of cultivated land, forest land and grassland area within the district / county.

[0030] (2) Introduce the normalized vegetation index to measure the spatial differences in land use types and soil fertility in administrative regions at the grid scale.

[0031] Preferably, step S3 specifically includes the following steps:

[0032] S31. Evaluation of the supply and demand relationship:

[0033] Quantitative analysis of the supply and demand of food supply services based on the supply-demand ratio;

[0034] By conducting hot and cold spot analysis on the supply and demand ratio of food supply services at the township level, the townships in the region are subdivided into food supply areas, food demand areas, and insignificant areas. Based on the hot spot analysis results, the quantitative relationship between the supply and demand of food supply services in the region is evaluated.

[0035] S32. Evaluation of Supply and Demand Spatial Matching:

[0036] Based on grid-scale quantitative supply and demand data, local bivariate spatial autocorrelation analysis of supply and demand data is performed to obtain the correlation between supply and demand in the overall space.

[0037] Local bivariate spatial autocorrelation analysis was performed on supply and demand data. Based on the spatial clustering and differentiation characteristics of supply and demand, the regional grid was subdivided into coordinated and uncoordinated regions to evaluate the spatial matching of food supply services supply and demand in the region.

[0038] Preferably, in step S32: the formula for global bivariate spatial autocorrelation analysis is:

[0039] ;

[0040] Among them, variables For food supply, variables For food demand, The degree of spatial correlation between food supply and food demand within a region. and Variables and In the and the j The value of each spatial unit, For spatial weight values, and Variables and The average value; This represents the total number of spatial units.

[0041] The formula for local bivariate spatial autocorrelation analysis is:

[0042] ;

[0043] Among them, attributes For food supply, attributes For food demand, For food supply and food demand in spatial units Local bivariate spatial autocorrelation index, It is a spatial unit Attributes The value, It is a spatial unit Attributes The value; and These are attributes and The average value; and These are attributes and attributes variance It is the spatial weight value between spatial units p and q.

[0044] Preferably, step S4 specifically includes the following steps:

[0045] S41. Evaluation of accessibility to demand areas: Based on the results of hotspot analysis of supply and demand ratios at the township level and traffic network data, an enhanced two-step mobile search method is used to measure the service accessibility of food demand areas.

[0046] S42. Analysis of the flow path of regional food supply services: Based on road traffic data, a time cost grid is constructed. The centroid of towns in the food supply area is used as the starting point and the centroid of towns in the food demand area is used as the ending point to conduct cost path analysis, so as to realize the quantitative representation of the spatial flow path of food supply services.

[0047] Preferably, the enhanced two-step move search method for measuring service accessibility in the demand area in step S41 specifically involves:

[0048] Step 1, with each supply area j Centered on the threshold transportation time, the search is performed. d 0 All service demand areas within the area are considered, and the weighted supply-demand ratio for each food supply service supply area is calculated. R j The calculation formula is as follows:

[0049] ;

[0050] In the formula: S j Indicates supply area j The service supply capacity; D k Indicates the demand area within the search range. The demand for services; Indicates the demand area and supply area j Transportation time between; W r Subregion r Cost weighting;

[0051] Step 2: For each service demand region location i, search all service supply regions within the threshold travel time d0, and obtain the accessibility of the demand region by weighted summation. The calculation formula is as follows:

[0052] ;

[0053] In the formula: Indicates the location of the demand area Spatial accessibility; R j Weighted supply-demand ratio for each food supply service area R j ; Indicates the demand area With supply area j Travel time between; W r Subregion r Cost weighting.

[0054] Therefore, the present invention has the following beneficial effects:

[0055] (1) The present invention adopts the food nutrient composition method, which comprehensively considers the contributions of agriculture, forestry, animal husbandry, sideline and fishery industries. It can quantify the total regional food calorie supply based on the food output and calories of different industries, thereby improving the accuracy of food supply capacity quantification.

[0056] (2) Based on the grid scale, this invention combines land use data and normalized vegetation index to grid-distribute food calories in order to achieve a refined characterization of the supply and demand relationship of food supply services.

[0057] (3) Based on the supply-demand ratio and bivariate spatial autocorrelation, this invention can realize the analysis of the supply and demand status and spatial clustering of food supply services from the perspective of quantity balance and spatial matching.

[0058] (4) This invention measures the accessibility of food supply services based on an enhanced two-step movement search method, ultimately enabling a quantitative characterization of the spatial flow path of food supply services. The technical results of this invention can provide a scientific basis for the formulation of ecological compensation policies for food supply.

[0059] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0060] Figure 1 This is a map of the main urban area of ​​a city, where (a) is the administrative division of the main urban area of ​​the city, and (b) is a map of the distribution of towns and townships in the main urban area of ​​the city.

[0061] Figure 2 Grid distribution map of food supply service supply;

[0062] Figure 3 Grid distribution map of demand for food supply services;

[0063] Figure 4 A supply and demand ratio chart for the grid grid size of a certain city's main urban area;

[0064] Figure 5 A supply and demand ratio chart for towns and townships in the main urban area of ​​a certain city;

[0065] Figure 6 Hotspot map of supply and demand ratio at the township level;

[0066] Figure 7 A grid-scale supply and demand spatial matching diagram;

[0067] Figure 8 This is a simulation diagram of the spatial flow path of food supply services in the main urban area of ​​a certain city in 2020. Among them, (a) is a traffic distribution map of the city, (b) is a traffic accessibility map of the food supply area and the demand area, and (c) is a simulation diagram of the spatial flow path. Detailed Implementation

[0068] The technical solution of the present invention will be further described below through examples and embodiments.

[0069] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0070] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. These other embodiments are also covered within the scope of protection of this invention.

[0071] Example

[0072] 1. Data Acquisition and Preprocessing

[0073] 1.1 Data Acquisition

[0074] This embodiment selects the main urban area of ​​a city, and the administrative divisions of the main urban area of ​​the city are as follows: Figure 1 As shown in (a), the distribution area of ​​townships is as follows: Figure 1 As shown in (b), the study years are 2000, 2005, 2010, 2015, and 2020. The data used are mainly divided into vector data, raster data, and other data. See Table 1 for a detailed description of the data.

[0075] Table 1. Sources of Data Acquisition

[0076] ;

[0077] 1.2 Data Preprocessing

[0078] Based on food production and calorie data of various districts and counties in the main urban area of ​​a certain city at various time points, the following will be used as follows: Figure 1 (a) shows the conversion of five years of crop yield in the AI ​​region into different food calorie supplies. The conversion results for 2020 are shown in Table 2.

[0079] Table 2. Results of heat supply conversion (kcal×10) 9 )

[0080] ;

[0081] By establishing a 1km×1km grid, the main urban area of ​​a city was divided into grids, and the area of ​​different land use types, the corresponding normalized vegetation index, and population density data of each grid area were obtained by combining the grid data.

[0082] 2. Quantification of supply and demand

[0083] 2.1 Quantification and Grid Allocation of Supply

[0084] This invention uses six major categories of land-based agricultural products (including grains, vegetables, fruits, meat, aquatic products, and oilseeds) as the primary sources of ecosystem food services. Based on the calorific characteristics and yield data of these agricultural products, the supply of ecosystem services is converted into a calorific value. The calculation formula is as follows:

[0085] ;

[0086] In the formula: NUTR Provides total calories from food. For the first Food yield, For the first The calories per unit of a type of food This represents the number of food types.

[0087] Based on the land use types of each district and county, the calories of different agricultural products are spatially allocated. Specifically, the calories of grains, vegetables, and oil crops are allocated to cultivated land; the calories of fruits to forest land; the calories of aquatic products to water areas; and the calories of meat are allocated according to the proportion of cultivated land, forest land, and grassland areas within the district and county. Furthermore, the normalized vegetation index (NVI) is introduced into the allocation process to achieve a comprehensive distribution of food calories based on land use type and soil fertility conditions. Taking grain calorie allocation as an example, the spatial allocation of grain calorie values ​​at the grid scale is achieved based on the proportion of the total NVI value of cultivated land within the grid area to the total NVI value of all cultivated land in the district and county. Since water areas are not suitable for calorie allocation using the NVI, the proportion of the water area within the grid area to the total water area of ​​the district and county is directly used for calorie allocation in the aquatic product calorie distribution.

[0088] 2.2 Quantification of Food Supply Service Demand

[0089] This study, based on the per capita daily calorie requirement method, uses per capita daily calorie intake standards and population density grid data to spatially express food requirements. The calculation formula is as follows:

[0090] ;

[0091] In the formula: E d Total calorie requirement from food; For the study area Individual grid; Total number of grid cells; For grid population density; The average daily calorie requirement is 2300 kcal.

[0092] Figure 2 and Figure 3 These are the grid distribution results for the supply and demand of food services, respectively. Looking at the time change, from 2000 to 2020, the food supply in the main urban area of ​​a certain city showed a downward trend year by year, with the food supply increasing from 5.2 × 10⁻⁶. 12 kcal decreased to 1.7 × 10 12 kcal. From an overall spatial distribution perspective, the food supply in the main urban area of ​​a certain city exhibits a multi-center supply pattern and regional differences. Between 2000 and 2020, the overall food demand in the main urban area showed an increasing trend, from 4.7 × 10 kcal. 10 kcal increased to 6.5 × 10 10 kcal. In terms of overall spatial distribution, food demand generally shows a pattern of high demand in the central and southwestern regions and low demand in the northern and southern regions.

[0093] 2.3 Aggregation of supply and demand at the township level

[0094] Based on grid-scale supply and demand quantification data and regional township administrative division data, the average supply and demand of the spatial grids contained in each township is used as the supply and demand data of that township.

[0095] 3. Evaluation of supply and demand relationship and spatial coordination

[0096] 3.1 Evaluation of Supply and Demand Relationship

[0097] The supply and demand situation of food supply services is quantitatively analyzed based on the supply-demand ratio. The calculation formula is as follows:

[0098] ;

[0099] In the formula: ESS and ESD These refer to the supply and demand of food supply services; and This represents the maximum value of both supply and demand. When... ESDR A value greater than 0 indicates a food supply surplus, meaning that supply exceeds demand. ESDR =0 indicates that supply and demand are in equilibrium, meaning that supply equals demand. ESDR <0 indicates a deficit, meaning supply is less than demand.

[0100] Figure 4 and Figure 5The figures show the calculated supply-demand ratios at the grid scale and township scale for a certain city. From 2000 to 2020, the average food supply-demand ratio in the city's main urban area was 0.348, 0.329, 0.310, 0.324, and 0.222, respectively, showing a fluctuating downward trend, indicating increasing food supply pressure year by year. The spatial pattern of food supply and demand in the city's main urban area has undergone significant changes, with the area of ​​food supply surplus continuously shrinking, while the area of ​​deficit continuously expanding. At the township scale, from 2000 to 2020, the number of townships with food supply surplus decreased from 87 to 56; the number of townships with food supply deficit increased from 26 to 57, indicating a significant decline in food supply capacity.

[0101] Further analysis is conducted to identify the spatial clustering characteristics of the food supply service supply-demand ratio, revealing the spatial distribution patterns of hotspots and colds in this ratio. The specific formula for hotspot analysis is as follows:

[0102] ;

[0103] In the formula: It is a spatial unit j The supply-demand ratio, It is a spatial unit and j Spatial weights between them This represents the total number of spatial units. When... At that time, the region exhibits a high-value clustered distribution of supply and demand surplus, where supply far exceeds demand, i.e., a hotspot (food supply area); when At that time, the region exhibits a supply-demand deficit with low-value clustering, where demand is far less than supply, i.e., a cold spot (food demand area).

[0104] Figure 6 A hotspot map of food service supply and demand ratios at the township level. Between 2000 and 2020, the areas of food-sufficient and general supply zones showed an increasing trend, rising from 285.27 km² to 1815.87 km². At the township level, the supply-sufficient areas are mainly distributed in areas such as... Figure 1 (b) As shown in the diagram, in towns D23 in the northeast and I8 in the southeast, the supply-sufficient areas are mainly distributed around the ample areas. The area of ​​the supply-demand balance area shows a decreasing trend, decreasing from 4579.08 km² to 2732.81 km². The areas of the demand surplus area and the general area show an increasing trend, increasing from 631.64 km² to 947.31 km². At the township level, the demand surplus area is mainly distributed in areas such as... Figure 1 (b) shows F5 town, G1 urban area and E3 town and surrounding towns; the demand area is mainly distributed in towns in the central and eastern, southwestern and northwestern regions.

[0105] 3.2 Evaluation of Spatial Coordination between Supply and Demand

[0106] Based on grid-scale supply and demand quantification data, firstly, a global bivariate spatial autocorrelation analysis is performed to obtain the correlation between supply and demand in the overall space. Next, a local bivariate spatial autocorrelation analysis is conducted on the supply and demand data to refine the spatial clustering and differentiation characteristics of supply and demand. The relevant formula is:

[0107] ;

[0108] Among them, variables For food supply, variables y For food demand, I The degree of spatial correlation between food supply and food demand within a region. and Variables and In the and the j The value of each spatial unit, For spatial weight values, and Variables and The average value; This represents the total number of spatial units.

[0109] The formula for local bivariate spatial autocorrelation analysis is:

[0110] ;

[0111] Among them, attributes For food supply, attributes For food demand, For food supply and food demand in spatial units p Local bivariate spatial autocorrelation index, It is a spatial unit p Attributes The value, It is a spatial unit q Attributes The value; and These are attributes and The average value; and These are attributes and attributes The variance.

[0112] Between 2000 and 2020, the spatial coordination of food supply and demand in the main urban area of ​​a certain city showed a significant deterioration trend, and the degree of spatial imbalance between supply and demand significantly increased. Spatial autocorrelation analysis revealed a significantly enhanced negative correlation between food supply and demand, indicating a significant exacerbation of the spatial imbalance between supply and demand and a deterioration trend in coordination. Figure 7 This is a graph showing the supply and demand relationship of food supply services at the grid scale. The graph reveals a significant decrease in the proportion of coordinated areas; conversely, uncoordinated areas show a marked expansion trend. The disintegration of coordinated areas and the expansion of uncoordinated areas together contribute to the increasingly prominent contradiction between food supply and demand.

[0113] 4. Spatial Flow Analysis of Food Supply Services

[0114] 4.1 Evaluation of Traffic Accessibility in the Demand Area

[0115] This invention, based on township-level supply-demand ratio hotspot identification results and transportation network data from 2015 and 2020, employs an enhanced two-step mobile search method to measure service accessibility in demand areas. This study selects 30 minutes as the time threshold and calculates accessibility in two steps, specifically:

[0116] Step 1: Centered on each supply zone j, search for all service demand zones within the threshold transportation time d0, and calculate the weighted supply-demand ratio R for each food supply service supply zone. j The calculation formula is as follows:

[0117] ;

[0118] In the formula: R j Weighted supply-demand ratio for each food supply service area; S j This indicates the service supply capacity of supply point j; D k Indicates the demand points within the search range. The demand for services; This represents the transportation time between k and j; W r Subregion r Cost weighting.

[0119] Step 2, for each service demand area location Search all service supply regions within the threshold travel time d0, and obtain the accessibility of the demand region by weighted summation. The calculation formula is as follows:

[0120] ;

[0121] In the formula: Indicates position Spatial accessibility; The ratio of service supply to demand; Indicates the demand area With supply area j Travel time between; Subregion r Cost weighting.

[0122] 4.2 Analysis of the flow path of regional food supply services

[0123] This invention further constructs a time cost grid based on road traffic data, and uses townships in the supply and demand areas as path endpoints for cost path analysis.

[0124] Figure 8 This is a simulation map of the accessibility and spatial flow paths of food supply services in the main urban area of ​​a city in 2020. Comparing the analysis results from 2015 and 2020, it can be seen that the spatial accessibility of the food supply demand area in the main urban area consistently maintained a pattern of higher accessibility in the east and lower accessibility in the west from 2015 to 2020. The number of food supply service flow routes increased from two to four over the five years, such as... Figure 8 (b) The increase in the supply area of ​​Zone C as shown in the figure improves the efficiency and accessibility of food supply services. The spatial flow of food supply services within the study area should focus on optimizing the two supply lines with multiple supply and multiple demand to effectively alleviate the supply and demand imbalance of regional food supply services.

[0125] Therefore, this invention, based on grid scale and combining land use data and normalized vegetation index, performs grid-based allocation of food calories. This not only makes up for the shortcomings of fine-scale implementation which is difficult to extend to large-scale implementation, but also further refines the characterization of the supply and demand relationship of food supply services, and realizes the quantitative characterization of the spatial flow path of food supply services.

[0126] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for simulating the spatial flow path of food supply services based on supply and demand relationships, characterized in that, Includes the following steps: S1. Obtain comprehensive data and preprocess the comprehensive data; S2. Quantification of Supply and Demand: Quantifying the food supply and demand in each region to achieve spatial representation; Step S2 specifically includes the following steps: S21. Quantification and grid allocation of food supply services; Quantifying supply involves converting the supply of ecosystem services into the form of heat. The grid-based allocation method for food supply involves spatially allocating the total food supply of each district and county using spatially gridded data. S22. Quantification of demand for food supply services; Based on the per capita daily calorie requirement method, and according to the per capita daily calorie intake standard and population density data within the grid area, the spatial expression of food demand is realized. S23, Aggregation of supply and demand at the township level; Based on the quantitative data of supply and demand at the grid scale in S21 and S22, and the regional township administrative division data, the average supply and demand of the spatial grids contained in each township is used as the supply and demand data of that township. S3. Evaluation of the quantity relationship between supply and demand and spatial matching: Evaluate the quantity relationship between supply and demand and spatial matching of food supply services, and obtain the correlation between supply and demand in the overall space and the clustering and differentiation characteristics in local space. Step S3 specifically includes the following steps: S31. Evaluation of the supply and demand relationship: Quantitative analysis of the supply and demand of food supply services based on the supply-demand ratio; By conducting hot and cold spot analysis on the supply and demand ratio of food supply services at the township level, the townships in the region are subdivided into food supply areas, food demand areas, and insignificant areas. Based on the hot spot analysis results, the quantitative relationship between the supply and demand of food supply services in the region is evaluated. S32. Evaluation of Supply and Demand Spatial Matching: Based on grid-scale quantitative supply and demand data, local bivariate spatial autocorrelation analysis of supply and demand data is performed to obtain the correlation between supply and demand in the overall space. Local bivariate spatial autocorrelation analysis was performed on supply and demand data. Based on the spatial clustering and differentiation characteristics of supply and demand, the regional grid was subdivided into coordinated and uncoordinated regions to evaluate the spatial matching of food supply services supply and demand in the region. S4. Simulation of spatial flow path for food supply services: Through traffic accessibility evaluation and cost path analysis, determine the optimal flow path of food from the food supply area to the food demand area and optimize the spatial configuration of food supply services. Step S4 specifically includes the following steps: S41. Evaluation of accessibility to demand areas: Based on the results of hotspot analysis of supply and demand ratios at the township level and traffic network data, an enhanced two-step mobile search method is used to measure the service accessibility of food demand areas. S42. Analysis of the flow path of regional food supply services: Based on road traffic data, a time cost grid is constructed. The centroid of towns in the food supply area is used as the starting point and the centroid of towns in the food demand area is used as the ending point to conduct cost path analysis, so as to realize the quantitative representation of the spatial flow path of food supply services.

2. The method for simulating the spatial flow path of food supply services based on supply and demand relationships according to claim 1, characterized in that, Step S1 specifically includes the following steps: S11. Obtain comprehensive data, which includes vector data, raster data, and other data; Vector data includes administrative division data and transportation road data; Raster data includes land use data, normalized difference vegetation index data, and population density data; Other data includes food yield data for six types of crops, food calorie data, and per capita calorie requirements data; S12. Preprocess the comprehensive data, including the conversion of food production heat and spatial gridding; The heat conversion involves converting food yield data for six types of crops into heat supply. Spatial gridding involves creating 1km×1km spatial grids, dividing administrative region data into grids, and combining land use data, normalized vegetation index data, and population density data to obtain the area of ​​different land use types and their corresponding normalized vegetation index and population density data within each grid area.

3. The method for simulating the spatial flow path of food supply services based on supply and demand relationships according to claim 2, characterized in that, The administrative division data includes vector boundary data of county-level and township-level administrative divisions; the six types of crops include grains, vegetables, fruits, meat, aquatic products, and oil crops.

4. The method for simulating the spatial flow path of food supply services based on supply and demand relationships according to claim 1, characterized in that, The sources of ecosystem service provision are six types of crops; The gridded spatial allocation is specifically as follows: (1) The calories of grains, vegetables and oil crops are allocated to cultivated land, the calories of fruits are allocated to forest land, the calories of aquatic products are allocated to water areas, and the calories of meat are allocated according to the proportion of cultivated land, forest land and grassland area within the district / county. (2) The normalized vegetation index was introduced into the caloric distribution of grains, vegetables, oil crops, fruits and meats to measure the spatial differences in land use types and soil fertility in administrative regions at the grid scale.

5. The method for simulating the spatial flow path of food supply services based on supply and demand relationships according to claim 1, characterized in that, In step S32: the formula for global bivariate spatial autocorrelation analysis is: ； Among them, variables For food supply, variables For food demand, The degree of spatial correlation between food supply and food demand within a region. and Variables and In the and the The value of each spatial unit, For spatial weight values, and Variables and The average value; This represents the total number of spatial units. The formula for local bivariate spatial autocorrelation analysis is: ； Among them, attributes For food supply, attributes For food demand, For food supply and food demand in spatial units Local bivariate spatial autocorrelation index, It is a spatial unit Attributes The value, It is a spatial unit Attributes The value; and These are attributes and The average value; and These are attributes and attributes variance It is a spatial unit and Spatial weight values ​​between them.

6. The method for simulating the spatial flow path of food supply services based on supply and demand relationships according to claim 1, characterized in that, The enhanced two-step move search method in step S41 measures the service reachability of the demand area as follows: Step 1, with each supply area Centered on the threshold transportation time, the search is performed. All service demand areas within the area are considered, and the weighted supply-demand ratio for each food supply service supply area is calculated. The calculation formula is as follows: ； In the formula: Indicates supply area The service supply capacity; Indicates the demand area within the search range. The demand for services; Indicates the demand area and supply area Transportation time between; Subregion Cost weighting; Step 2, for each service demand area location Search within the threshold travel time The accessibility of the demand area is obtained by weighted summation of all service supply areas within the area. The calculation formula is as follows: ； In the formula: Indicates the location of the demand area Spatial accessibility; Weighted supply-demand ratio for each food supply service area Indicates the demand area With supply area Travel time between; Subregion Cost weighting.

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