Food supply service space flow path simulation method based on supply-demand relationship

Abstract

The invention discloses a food supply service space flow path simulation method based on a supply-demand relationship, and belongs to the technical field of ecological system services. The method comprises the steps of data acquisition and preprocessing; quantizing the food supply quantity and demand quantity of each region to realize spatial expression; evaluating a supply and demand quantity relationship and a space matching condition of the food supply service, and knowing the correlation between the supply quantity and the demand quantity in the whole space and the aggregation and differentiation characteristics in the local space; through traffic accessibility evaluation and cost path analysis, an optimal flow path of food from a food supply area to a food demand area is determined so as to optimize space configuration of food supply service. According to the method, gridding distribution is carried out on the food heat by combining the land utilization data and the normalized vegetation index based on the grid scale, so that the defect that fine scale is difficult to popularize to large scale implementation is overcome, the supply-demand relationship of food supply service can be finely represented, and quantitative representation of the space flow path of the food supply service is realized.

Description

Technical Field

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

[0002] Research by domestic and international scholars on the quantification of regional food supply has primarily focused on the mechanisms of primary production capacity of land ecosystems or the relationship between food production and cultivated land, as well as the multiple cropping index. However, most of these studies have overlooked the differences between different types of food and their nutritional content, resulting in limitations in the quantification of food supply capacity. Quantification of food supply and demand relationships is typically conducted from both qualitative and quantitative perspectives. Qualitative analysis, due to its high subjectivity and lack of precise quantification, leads to significant differences in the scale and speed of supply and demand changes among different researchers, limiting its ability to interpret and predict practical issues. Domestic and international scholars have often used index evaluation methods such as the supply-demand ratio, demand rate, and supply-demand coordination coupling to quantify supply and demand relationships. 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 of food supply services and their spatial flow have become a hot topic of concern for scholars at home and abroad. Due to the spatial dislocation between supply areas and demand areas, the spatial flow of food supply services has become an important mechanism for balancing regional supply and demand. The enhanced two-step moving search method can characterize the spatial flow characteristics of food supply services by simulating the supply and demand balance of food supply services and the transportation accessibility of nearby food supply and demand, providing an important method for studying the spatial optimization of food supply services. Based on the grid scale, the present invention combines land use data and normalized vegetation index to perform grid-based distribution of food calories. It not only makes up for the defect that fine scale is difficult to extend to large-scale implementation, but also can further finely characterize the supply and demand relationship of food supply services and realize the quantitative characterization of the spatial flow path of food supply services. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for simulating the spatial flow path of a food supply service based on the supply and demand relationship, so as to solve the problems of low precision in the supply and demand assessment of food supply services and low accuracy in the spatial flow path simulation in the prior art.

[0005] To achieve the above objectives, the present invention discloses a method for simulating the flow path of a food supply service space based on the supply and demand relationship, comprising the following steps: S1. Obtain comprehensive data and preprocess the comprehensive data; S2. Quantification of supply and demand: Quantify the food supply and demand in each region to achieve spatial expression; S3. Evaluation of the quantitative relationship between supply and demand and spatial matching: Evaluate the quantitative relationship between supply and demand and spatial matching of food supply services, and understand the correlation between supply and demand in the overall space, as well as the aggregation and differentiation characteristics in the local space; S4. Spatial flow path simulation of 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.

[0006] Preferably, step S1 specifically includes the following steps: S11. Obtain comprehensive data, which includes vector data, raster data and other data; The vector data includes administrative division data and traffic road data; The raster data includes land use data, normalized difference vegetation index data and population density data; The other data include food production data of six types of crops, food calorie data, and per capita calorie demand data; S12. Preprocessing of comprehensive data, including caloric conversion and spatial gridding of food production; The calorie conversion is to convert the food production data of six types of crops into calorie supply; Spatial gridding is to create a 1km×1km spatial grid, divide the administrative area data into grids, and combine land use data, normalized vegetation index data and population density data to obtain the area of different land use types in each grid area and their corresponding normalized vegetation index and population density data.

[0007] Preferably, the administrative division data includes county-level and township-level administrative division vector boundary data; the six categories of crops include grain, vegetables, fruits, meat, aquatic products and oil crops.

[0008] Preferably, step S2 specifically includes the following steps: S21. Quantification and grid allocation of food provision service supply; The quantification of supply is to convert the supply of ecosystem services into heat form; The grid allocation method of supply is to realize grid spatial allocation of total food supply of each district and county through spatial grid data; S22. Quantification of demand for food supply services; Based on the per capita daily calorie requirement method, the spatial expression of food demand is achieved according to the per capita daily calorie intake standard and the population density data in the grid area; 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 grid contained in each township is used as the supply and demand data of the township.

[0009] Preferably, the sources of ecosystem service provision are six types of crops; The grid space allocation is as follows: (1) The calories from grains, vegetables, and oilseed crops are attributed to cultivated land, the calories from fruits are attributed to forest land, the calories from aquatic products are attributed to water bodies, and the calories from meat are allocated according to the proportion of cultivated land, forest land, and grassland within the district or county; (2) The normalized difference vegetation index was introduced to measure the spatial differences in land use types and soil fertility among administrative regions at the grid scale.

[0010] Preferably, step S3 specifically includes the following steps: S31. Evaluation of the quantitative relationship between supply and demand: Quantitatively analyze the supply and demand of food provision 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 regional townships were 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 regional food supply services was evaluated. S32. Supply and demand space matching evaluation: Based on grid-scale supply and demand quantitative data, the local bivariate spatial autocorrelation analysis of supply and demand data was conducted to obtain the correlation between supply and demand in the overall space. A local bivariate spatial autocorrelation analysis was conducted on the supply and demand data. The regional grid was subdivided into coordinated areas and uncoordinated areas based on the spatial aggregation and differentiation characteristics of supply and demand, and the spatial matching of regional food supply services and demand was evaluated.

[0011] Preferably, in step S32: the global bivariate spatial autocorrelation analysis formula is: ; Among them, the variable is the food supply, variable is the food demand, is the spatial correlation between food supply and food demand within a region, and Variables and In the Hedi j The value of the spatial unit, is the spatial weight value, and Variables and The average value of is the total number of spatial units; The formula for local bivariate spatial autocorrelation analysis is: ; Among them, the attributes is the food supply, attribute is the food demand, is the food supply and food demand in a spatial unit The local bivariate spatial autocorrelation index of It is a spatial unit Attributes The value of It is a spatial unit Attributes The value of and Attributes and The average value of and Attributes and attributes The variance of is the spatial weight value between spatial units p and q.

[0012] Preferably, step S4 specifically includes the following steps: S41. Evaluation of transportation accessibility in demand areas: Based on township-level supply-demand ratio hotspot analysis results and transportation network data, an enhanced two-step mobile search method is used to measure the service accessibility of food demand areas. S42. Analysis of regional food supply service flow paths: construct a time cost grid based on road traffic data, and perform cost path analysis using the centroid of towns in the food supply area as the starting point and the centroid of towns in the food demand area as the ending point to achieve quantitative representation of the spatial flow paths of food supply services.

[0013] Preferably, the enhanced two-step mobile search method in step S41 measures the service accessibility of the demand area as follows: Step 1: Each supply zone j Centered on the threshold transport time d 0 All service demand areas within the food supply service area and calculate the weighted supply-demand ratio of each food supply service supply area R j , the calculation formula is as follows: ; Where: S j Indicates supply area j service provision capabilities;D k Indicates the demand area within the search range The demand for services; Demand area and supply areas j The transportation time between W r Indicates sub-area r Cost weights; Step 2: For each service demand area location i, search for all service supply areas within the threshold travel time d0 and perform weighted summation to obtain the accessibility of the demand area. , the calculation formula is as follows: ; Where: Indicates the location of the demand area spatial accessibility; R j Weighted supply-demand ratio for each food supply service area R j ; Demand area and supply area j travel time between W r Indicates sub-area r cost weight.

[0014] Therefore, the present invention has the following beneficial effects: (1) The present invention adopts the food nutrient composition method to comprehensively consider the contributions of agriculture, forestry, animal husbandry, sideline production and fishery industries, and can quantify the total caloric supply of regional food according to the food output and caloric content of different industries, thereby improving the accuracy of the quantification of food supply capacity.

[0015] (2) Based on the grid scale, the present invention combines land use data and normalized vegetation index to perform grid distribution of food calories, so as to achieve a refined characterization of the supply and demand relationship of food supply services.

[0016] (3) Based on the supply-demand ratio and bivariate spatial autocorrelation, the present invention can realize the supply and demand status and spatial clustering analysis of food supply services from the perspective of quantity balance and spatial matching.

[0017] (4) This invention measures the accessibility of food supply services based on an enhanced two-step mobile search method, ultimately enabling a quantitative characterization of the spatial flow paths 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.

[0018] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] 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 the distribution map of the towns and villages in the main urban area of the city; Figure 2 Grid distribution map of food supply service supply; Figure 3 Grid allocation map of demand for food supply services; Figure 4 This is a grid-scale supply-demand ratio map for a city's main urban area; Figure 5 This is a supply-demand ratio chart for the main urban and rural areas of a city; Figure 6 It is a heat map of supply and demand ratio at the township scale; Figure 7 It is a grid-scale supply-demand spatial matching diagram; Figure 8 This is a simulation diagram of the spatial flow path of food supply services in a city's main urban area in 2020, where (a) is the traffic distribution map of the city, (b) is the traffic accessibility map of the food supply and demand areas, and (c) is the spatial flow path simulation map. DETAILED DESCRIPTION

[0020] The technical solution of the present invention is further illustrated by the following examples.

[0021] Unless otherwise defined, technical or scientific terms used in the present invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0022] Furthermore, it should be understood that although this specification describes the embodiments, not every embodiment includes only one independent technical solution. This description is for clarity only. Those skilled in the art should consider the specification as a whole. The technical solutions in the various embodiments may also be appropriately combined to form other embodiments that are understandable to those skilled in the art. These other embodiments are also encompassed within the scope of protection of the present invention.

[0023] Example 1. Data acquisition and preprocessing 1.1 Data Acquisition This embodiment selects the main urban area of a city. The administrative divisions of the main urban area of a city are as follows: Figure 1 As shown in (a), the distribution of towns and villages is as follows Figure 1 As shown in (b), the research years are 2000, 2005, 2010, 2015, and 2020. The data used are mainly divided into vector data, raster data, and other data. Detailed descriptions of the data are shown in Table 1.

[0024] Table 1 Sources of data ; 1.2 Data Preprocessing Based on the food production data and food calorie data of each district and county in the main urban area of a city at each time point, Figure 1 The five-year crop yields in the AI region shown in (a) are converted into different food calorie supplies. The conversion results in 2020 are shown in Table 2.

[0025] Table 2 Calorie supply conversion results (kcal×10 9 ) ; 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 in each grid area were obtained by combining raster data.

[0026] 2. Quantifying supply and demand 2.1 Quantification of supply and grid allocation This paper considers six major categories of agricultural products (including grain, vegetables, fruits, meat, aquatic products, and oil crops) as the main sources of ecosystem food supply services. Based on the thermal characteristics and yield data of agricultural products, the ecosystem service supply is converted into heat. The calculation formula is: ; Where: NUTR Provides total calories for food, For the Food production, For the Calories per unit of food, is the number of food types.

[0027] The calorie content of different agricultural products is spatially allocated based on the land use type of each district and county. Specifically, the calorie content of grain, vegetables, and oilseed crops is attributed to cultivated land, the calorie content of fruit to forest land, and the calorie content of aquatic products to water bodies. Meat calorie content is allocated based on the proportion of cultivated land, forest land, and grassland within the district and county. Furthermore, by introducing the Normalized Difference Vegetation Index (NDVI) into the allocation process, a comprehensive allocation of food calorie content based on land use type and soil fertility is achieved. Taking the calorie content of grain as an example, the spatial allocation of grain calorie content at the grid scale is achieved based on the ratio of the total NDVI value of cultivated land within the grid to the total NDVI value of all cultivated land in the district and county. Since water bodies are not suitable for calorie allocation based on the NDVI, the calorie content of aquatic products is directly allocated based on the proportion of the water area within the grid to the total water area in the district and county.

[0028] 2.2 Quantification of demand for food supply services The study is based on the per capita daily calorie requirement method. According to the per capita daily calorie intake standard and population density grid data, the food demand is spatially expressed. The calculation formula is: ; Where: E d Total calorie requirement for food; For the study area grid; is the total number of grids; is the grid population density; The average daily calorie requirement per person is 2300kcal.

[0029] Figure 2 and Figure 3 The grid allocation results of food supply service supply and demand are shown respectively. From the perspective of time change, from 2000 to 2020, the food supply in the main city of a certain city showed a downward trend year by year, and the food supply increased from 5.2×10 12 kcal is reduced to 1.7×10 12 kcal. From the perspective of overall spatial distribution, the food supply in the main urban area of a certain city presents a multi-center supply pattern and regional differences. From 2000 to 2020, the food demand in the main urban area showed an overall increasing trend, from 4.7×10 10 kcal increased to 6.5×10 10 From the perspective of overall spatial distribution, food demand is generally higher in the central and southwestern regions and lower in the northern and southern regions.

[0030] 2.3 Aggregation of supply and demand at the township level Based on the grid-scale supply and demand quantitative data and regional township administrative division data, the average supply and demand of the spatial grid contained in each township is used as the supply and demand data of the township.

[0031] 3. Evaluation of supply-demand relationship and spatial coordination 3.1 Supply and demand relationship evaluation The supply and demand situation of food supply services is quantitatively analyzed based on the supply-demand ratio. The calculation formula is: ; Where: ESS and ESD are the quantity supplied and quantity demanded of food provision services respectively; and is the maximum value of supply and demand. ESDR >0 indicates a food supply service surplus, that is, supply exceeds demand. ESDR =0 indicates supply and demand balance, that is, supply equals demand, ESDR <0 indicates a deficit, meaning supply is less than demand.

[0032] Figure 4 and Figure 5 The supply-demand ratios calculated for a city's main urban area at the grid and township scales are shown. Between 2000 and 2020, the average food supply-demand ratio for the city's main urban area was 0.348, 0.329, 0.310, 0.324, and 0.222, respectively. This ratio showed a fluctuating downward trend, indicating increasing food supply pressure. The spatial pattern of food supply and demand in the city's main urban area has undergone significant changes, with areas of food surplus shrinking and areas of food deficit expanding. At the township scale, the number of townships with food surpluses decreased from 87 to 56 between 2000 and 2020, while the number of townships with food deficits increased from 26 to 57, indicating a significant decline in food supply capacity.

[0033] The spatial clustering characteristics of the food supply and demand ratio are further identified to reveal the spatial distribution pattern of hot spots and cold spots in the food supply and demand ratio. The specific formula for hot spot analysis is: ; Where: It is a spatial unit j The supply-demand ratio, It is a spatial unit and j The spatial weight between is the total number of spatial units. When , the region shows a supply-demand surplus area with high-value cluster distribution, where the supply far exceeds the demand, that is, the hotspot area (food supply area); when When , the region to which it belongs presents a supply and demand deficit zone with a low-value clustered distribution. The demand in this area is far less than the supply, that is, the cold spot area (food demand area).

[0034] Figure 6 This is a heat map of the food service supply and demand ratio at the township level. From 2000 to 2020, the area of sufficient food supply areas and general food supply areas showed an increasing trend, increasing from 285.27 km² to 1815.87 km². At the township level, sufficient food supply areas are mainly distributed in Figure 1 As shown in (b), in the northeastern town of D23 and the southeastern town of I8, the general supply area is mainly distributed around the sufficient area. The area of the supply and demand balance area is decreasing, decreasing from 4579.08 km² to 2732.81 km². The area of the excess demand area and the general area is increasing, increasing from 631.64 km² to 947.31 km². At the township level, the excess demand area is mainly distributed in Figure 1 (b) shows F5 town, G1 urban area, E3 town and surrounding towns; general demand areas are mainly distributed in towns in the central and eastern, southwestern and northwestern regions.

[0035] 3.2 Evaluation of spatial coordination between supply and demand Based on grid-scale supply and demand quantitative data, we first conduct a global bivariate spatial autocorrelation analysis to obtain the overall spatial correlation between supply and demand. Next, we conduct a local bivariate spatial autocorrelation analysis on the supply and demand data to refine the spatial aggregation and differentiation characteristics of supply and demand. The relevant formula is: ;

[0036] Among them, the variable is the food supply, variable y is the food demand, I is the spatial correlation between food supply and food demand within a region, and Variables and In the Hedi j The value of the spatial unit, is the spatial weight value, and Variables and The average value of is the total number of space units.

[0037] The formula for local bivariate spatial autocorrelation analysis is: ;

[0038] Among them, the attributes is the food supply, attribute is the food demand, is the food supply and food demand in a spatial unit p The local bivariate spatial autocorrelation index of It is a spatial unit p Attributes The value of It is a spatial unit q Attributes The value of and Attributes and The average value of and Attributes and attributes The variance of .

[0039] Between 2000 and 2020, the spatial coordination of food supply and demand in a certain city's main urban area showed a significant deterioration, and the spatial imbalance between supply and demand increased significantly. Spatial autocorrelation analysis showed a significant increase in the negative correlation between food supply and demand, indicating a significant increase in the spatial imbalance between supply and demand and a deterioration in coordination. Figure 7 This is a grid-scale map showing the supply and demand of food supply services. The figure shows that the proportion of coordinated regions has shrunk significantly, while uncoordinated regions have expanded significantly. The collapse of coordinated regions and the expansion of uncoordinated regions have led to an increasingly prominent imbalance in food supply and demand.

[0040] 4. Spatial flow analysis of food supply services 4.1 Evaluation of traffic accessibility in demand areas Based on the results of identifying supply-demand ratio hotspots at the township level in 2015 and 2020 and transportation network data, this paper uses an enhanced two-step mobile search method to measure the service accessibility of demand areas. This study selects 30 minutes as the time threshold and implements accessibility calculation through two steps: Step 1: With each supply zone j as the center, search for all service demand zones within the threshold transportation time d0 and calculate the weighted supply-demand ratio R of each food supply service supply zone. j , the calculation formula is as follows: ;

[0041] Where: R j The weighted supply-demand ratio for each food provision service area; S j represents the service supply capacity of supply point j; D k Indicates the demand points within the search range The demand for services; represents the transportation time between k and j; W r Indicates sub-area r cost weight.

[0042] Step 2: For each service demand area location , search for all service supply areas within the threshold travel time d0, and obtain the accessibility of the demand area by weighted summation. The calculation formula is as follows: ;

[0043] Where: Indicates location spatial accessibility; The ratio of service supply to demand; Demand area and supply areaj travel time between Indicates sub-area r cost weight.

[0044] 4.2 Analysis of regional food supply service flow paths The present invention further constructs a time cost grid based on road traffic data, and performs cost path analysis by taking towns in supply and demand areas as path endpoints.

[0045] Figure 8 This is a simulation diagram of the accessibility and spatial flow paths of food supply services in a city's main urban area in 2020. Comparing the analysis results of 2015 and 2020, it can be seen that the transportation accessibility of the main urban food supply demand area has always maintained a high-east-low-west distribution pattern in space from 2015 to 2020. Over the past five years, the number of food supply service flow routes has increased from two to four, such as Figure 8 The increase in supply areas in Area C (b) has improved 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 routes of multiple supply and multiple demand to effectively alleviate the contradiction between supply and demand of regional food supply services.

[0046] Therefore, the present invention distributes food calories in a grid-based manner based on the grid scale, combined with land use data and the normalized vegetation index. This not only makes up for the defect that fine-scale implementation is difficult to extend to large-scale implementation, but also can further refine the characterization of the supply and demand relationship of food supply services and realize the quantitative characterization of the spatial flow path of food supply services.

[0047] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as 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: The following steps are involved: S1. Obtain comprehensive data and preprocess the comprehensive data; S2. Quantification of supply and demand: Quantify the food supply and demand in each region to achieve spatial expression; S3. Evaluation of the quantitative relationship between supply and demand and spatial matching: Evaluate the quantitative relationship between supply and demand and spatial matching of food supply services, and obtain the correlation between supply and demand in the overall space, as well as the aggregation and differentiation characteristics in the local space; S4. Simulation of spatial flow paths of food supply services: Through traffic accessibility evaluation and cost path analysis, the optimal flow path of food from food supply areas to food demand areas is determined to optimize the spatial configuration of food supply services.

2. A food supply service space flow path simulation method based on supply and demand relationship 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 traffic road data; Raster data include land use data, normalized difference vegetation index data, and population density data; Other data include food production data for six categories of crops, food calorie data, and per capita calorie requirements; S12. Preprocessing of comprehensive data, including caloric conversion and spatial gridding of food production; The calorie conversion is to convert the food production data of six types of crops into calorie supply; Spatial gridding is to create a 1km×1km spatial grid, divide the administrative area data into grids, and combine land use data, normalized vegetation index data and population density data to obtain the area of different land use types in each grid area and their corresponding normalized vegetation index and population density data.

3. The method for simulating food supply service space flow paths based on supply and demand relationships according to claim 2, characterized in that: Administrative division data include county-level and township-level administrative division vector boundary data; six categories of agricultural crops include grain, vegetables, fruits, meat, aquatic products and oil crops.

4. The method for simulating food supply service space flow paths based on supply and demand relationships according to claim 1, characterized in that: Step S2 specifically includes the following steps: S21. Quantification and grid allocation of food provision service supply; The quantification of supply is to convert the supply of ecosystem services into heat form; The grid allocation method of supply is to realize grid spatial allocation of total food supply of each district and county through spatial grid data; S22. Quantification of demand for food supply services; Based on the per capita daily calorie requirement method, the spatial expression of food demand is achieved according to the per capita daily calorie intake standard and the population density data in the grid area; 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 grid contained in each township is used as the supply and demand data of the township.

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

6. The method for simulating food supply service space flow paths based on supply and demand relationships according to claim 1, characterized in that: Step S3 specifically includes the following steps: S31. Evaluation of the quantitative relationship between supply and demand: Quantitatively analyze the supply and demand of food provision 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 regional townships were 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 regional food supply services was evaluated. S32. Supply and demand space matching evaluation: Based on grid-scale supply and demand quantitative data, the local bivariate spatial autocorrelation analysis of supply and demand data was conducted to obtain the correlation between supply and demand in the overall space. A local bivariate spatial autocorrelation analysis was conducted on the supply and demand data. The regional grid was subdivided into coordinated areas and uncoordinated areas based on the spatial aggregation and differentiation characteristics of supply and demand, and the spatial matching of regional food supply services and demand was evaluated.

7. The method for simulating food supply service space flow paths based on supply and demand relationships according to claim 6, characterized in that: In step S32: the global bivariate spatial autocorrelation analysis formula is: ； Among them, the variable is the food supply, variable is the food demand, is the spatial correlation between food supply and food demand within a region, and Variables and In the Hedi The value of the spatial unit, is the spatial weight value, and Variables and The average value of is the total number of spatial units; The formula for local bivariate spatial autocorrelation analysis is: ； Among them, the attributes is the food supply, attribute is the food demand, is the food supply and food demand in a spatial unit p The local bivariate spatial autocorrelation index of It is a spatial unit p Attributes The value of It is a spatial unit q Attributes The value of and Attributes and The average value of and Attributes and attributes The variance of It is a spatial unit p and q The spatial weight value between .

8. The method for simulating food supply service space flow paths based on supply and demand relationships according to claim 1, characterized in that: Step S4 The specific steps include: S41. Evaluation of transportation accessibility in demand areas: Based on township-level supply-demand ratio hotspot analysis results and transportation network data, an enhanced two-step mobile search method is used to measure the service accessibility of food demand areas. S42. Analysis of regional food supply service flow paths: construct a time cost grid based on road traffic data, and perform cost path analysis using the centroid of towns in the food supply area as the starting point and the centroid of towns in the food demand area as the ending point to achieve quantitative representation of the spatial flow paths of food supply services.

9. The method for simulating food supply service space flow paths based on supply and demand relationships according to claim 8, characterized in that: The enhanced two-step mobile search method in step S41 measures the service accessibility of the demand area as follows: Step 1: Each supply zone j Centered on the threshold transport time d 0 All service demand areas within the food supply service area and calculate the weighted supply-demand ratio of each food supply service supply area R j , the calculation formula is as follows: ； Where: S j Indicates supply area j service provision capabilities; D k Indicates the demand area within the search range k The demand for services; d kj Demand area k and supply areas j The transportation time between W r Indicates sub-area r Cost weights; Step 2: For each service demand area location , search for the threshold travel time d 0 All service supply areas within the area are weighted and summed to obtain the accessibility of the demand area. , the calculation formula is as follows: ； Where: Indicates the location of the demand area spatial accessibility; R j Weighted supply-demand ratio for each food supply service area R j ; Demand area and supply area j travel time between W r Indicates sub-area r cost weight.

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