Agricultural product supply chain management system and method based on Internet of Things

Abstract

The present invention relates to the technical field of agricultural product supply chain management, specifically to an agricultural product supply chain management system and method based on the Internet of Things, in which a supply route management module generates a response bias value for each transport route planning scheme based on the road condition characteristic information and cold chain equipment load failure coefficient of each transport route planning scheme; selects the best freight supply route for the agricultural products to be transported, and feeds back to the driver of the vehicle to be transported and the person in charge of receiving the corresponding agricultural products at the destination. The present invention evaluates the transportation environment of each transport route planning scheme based on multiple factors such as road condition characteristic information, agricultural product types, weather warning information of the area through which the transport route planning scheme passes, and status monitoring data of the built-in cold chain equipment of historical freight vehicles, and realizes the selection of the best freight supply route for the agricultural products to be transported through the response bias value of the transport route planning scheme, and assists the driver of the vehicle to be transported to perform the transportation task.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product supply chain management, and in particular to an agricultural product supply chain management system and method based on the Internet of Things. Background Art

[0002] With the development of the economy, people's demand for the variety and quality of agricultural products is getting higher and higher, and the management pressure on the agricultural product supply chain is also increasing. In recent years, with the rapid development of Internet of Things technology, the Internet of Things has been introduced into the field of agricultural product supply chain management. Through real-time collection of agricultural product information through sensors, combined with the Internet and cloud computing, intelligent management of the agricultural product supply chain is realized.

[0003] The existing agricultural product supply chain management system based on the Internet of Things only focuses on the real-time collection of agricultural product status data through Internet of Things sensors, without considering the impact of differences in the transportation environment on agricultural products during transportation. As a result, it is impossible to combine the agricultural product status collection data and transportation environment information to realize dynamic planning of transportation routes, and it is impossible to plan the best freight supply route for vehicle drivers to refer to, so as to reduce the risk of agricultural product loss during transportation. Summary of the invention

[0004] The purpose of the present invention is to provide an agricultural product supply chain management system and method based on the Internet of Things to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for managing an agricultural product supply chain based on the Internet of Things, the method comprising the following steps: S1. Extract the types of agricultural products to be transported and the transportation destination according to the freight list of agricultural products to be transported; and construct a transportation route planning set based on Beidou navigation software and historical freight transportation routes to plan various transportation route planning schemes from the position of the vehicle to be transported to the transportation destination. The position of the vehicle to be transported is obtained in real time through Beidou positioning, and the transportation route planning set is updated every preset unit time; S2. Generate road condition characteristic information of each transport route planning scheme according to the road condition information in the transport route planning scheme corresponding to each element in the transport route planning set; and analyze the cold chain equipment load failure coefficient of each transport route planning scheme in the transport route planning set in combination with the types of agricultural products, weather warning information of the areas through which the transport route planning scheme passes, and the status monitoring data of the built-in cold chain equipment of historical freight vehicles; S3. Generate a response bias value for each transport route planning scheme based on the road condition characteristic information and cold chain equipment load failure coefficient of each transport route planning scheme; select the best freight supply route for the agricultural products to be transported, and provide feedback to the drivers of the vehicles to be transported and the persons in charge of receiving the corresponding agricultural products at the destination.

[0006] Furthermore, the transport route planning set in S1 is equal to the union of the first transport route planning scheme set and the second transport route planning scheme set; the first transport route planning scheme set represents a set of transport route planning schemes between the position of the vehicle to be transported and the transport destination generated according to the Beidou navigation software; the second transport route planning scheme set represents a set of transport route planning schemes between the position of the vehicle to be transported and the transport destination in the historical freight transport routes.

[0007] In the present invention, in the process of obtaining the position of the vehicle to be transported in real time through the Beidou positioning system, the real-time acquisition of the position of the vehicle to be transported is to ensure the full monitoring of the vehicle position information, to ensure that the problem is discovered in time and targeted processing is carried out when the signal transmission is abnormal; however, the transportation route planning set is set to be updated every preset unit time, rather than updated in real time. On the one hand, it is to reduce the data processing volume and alleviate the pressure on the data processing center. On the other hand, it is to ensure the stability of the transportation path of the vehicle to be transported to a certain extent, and reduce the impact on the driver of the vehicle to be transported due to frequent changes in the feedback freight supply route.

[0008] Furthermore, the road condition characteristic information of each transport route planning scheme includes the path length of the corresponding transport route planning scheme, the road surface flatness coefficient of each section in the corresponding transport route planning scheme, the section length and the road congestion coefficient within the section path, and each intersection passed through in the transport route planning scheme is regarded as a section dividing point; The road surface smoothness coefficient of each section in the corresponding transport route planning scheme is equal to Tc / Tz·Amax, where Tz represents the average transportation time of each vehicle to be transported passing through the corresponding section in the historical data; Amax represents the average maximum amplitude monitored by the built-in vibration sensor of each vehicle to be transported in the process of passing through the corresponding section in the historical data; Tc represents the average transportation time corresponding to the range from the average amplitude to the maximum amplitude monitored by the built-in vibration sensor of each vehicle to be transported in the process of passing through the corresponding section in the historical data; The road congestion coefficient within the segment path is expressed as N / N1+b·V / V1, where N represents the total number of vehicles monitored by various sensors set within the corresponding segment path at the current time; N1 represents the total number of vehicles accommodated in the corresponding segment path preset in the database; V represents the average speed of the vehicles to be transported passing through the corresponding segment in the historical data; V1 represents the maximum speed limit of vehicles of the same type as the vehicle to be transported within the corresponding segment path preset in the database; and b represents the preset weight coefficient.

[0009] Furthermore, in the process of analyzing the cold chain equipment load failure coefficient of each transport route planning scheme in the transport route planning set in S2, the calculation formula involved is as follows: ; G k represents the cold chain equipment load failure coefficient of the kth transportation route planning scheme in the transportation route planning set; NY k Indicates the number of preset blocks that the kth transport route planning scheme in the transport route planning set passes through. The range corresponding to each preset regional block is different; TY （k，i） It indicates the duration of the kth transport route planning scheme in the transport route planning set passing through the corresponding ith preset block; TY （k，i） The value of is equal to the sum of the length of each segment path contained in the actual road section of the k-th transport route planning scheme passing through the corresponding i-th preset block divided by the travel speed of the corresponding segment path; the travel speed of the corresponding segment path is the average speed of each vehicle passing through the corresponding segment path when the road congestion coefficient is N / N1+b·V / V1 in the historical data; the i-th preset block passed by the k-th transport route planning scheme in the transport route planning set is calculated based on the subsequent TYZ of the current time. k-1 To TYZ k The absolute value of the difference between the average weather warning temperature within the time period and the preset control temperature of the cold chain equipment is recorded as W （k，i） ; R （k，i） Indicates that the database preset form is W （k，i） Corresponding cold chain equipment load factor; TYZ k-1 represents the total time of the k-th transportation route planning scheme passing through the corresponding first i-1 preset blocks; TYZ k represents the total time of the k-th transportation route planning scheme passing through the corresponding first i preset blocks; when k=1, then TYZ k-1 =0; P{} represents the function of finding probability; represents the cold chain equipment load evaluation value of the kth transportation route planning scheme in the transportation route planning set; It means that 1 minus the number of cold chain equipment failure vehicles in the historical data have cold chain equipment load assessment values ​​greater than h in the transportation behaviors before the corresponding failure. The difference between the ratio of the number of failures to the total number of failures.

[0010] In the present invention, TY （k，i） The value of is equal to the sum of the length of each segment path included in the actual road section of the k-th transportation route planning scheme passing through the corresponding i-th preset block divided by the travel speed of the corresponding segment path; that is, TY （k，i）It is the predicted value of the actual time required for the transported vehicle to pass through the corresponding ith preset block when the kth transport route planning scheme is executed. The average value Tz of the transport time of each vehicle to be transported passing through the corresponding section in the historical data is not directly called here, because Tz corresponds to the average time required for a complete section (and is obtained in the early stage without considering the traffic congestion). However, when the kth transport route planning scheme is executed, the actual road section passing through the corresponding ith preset block may involve one or more sections in the road condition feature information, and the part involved in the corresponding actual road section may not be the corresponding complete section in the road condition feature information; therefore, directly calling Tz may cause a large deviation in the settlement result.

[0011] Furthermore, the calculation formula for generating the response bias value of each transportation route planning scheme in S3 is as follows: ; Among them, BS k represents the response bias value of the k-th transportation route planning scheme; k BUM represents the sum of the road surface smoothness coefficients of each section in the k-th transportation route planning scheme; k It represents the transport road bump tolerance coefficient corresponding to the type of agricultural products to be transported in the agricultural products freight list in the preset form of the database; r1, r2 and r3 all represent preset weight factors, and the values ​​of r1, r2 and r3 are all different.

[0012] Furthermore, the optimal freight supply route for the agricultural products to be transported in S3 is a transportation route planning scheme with the smallest corresponding response deviation value.

[0013] An agricultural product supply chain management system based on the Internet of Things, the system includes the following modules: A transport route planning set construction module, wherein the transport route planning set construction module extracts the types of agricultural products to be transported and the transportation destination according to the freight list of the agricultural products to be transported; and constructs a transport route planning set according to the Beidou navigation software and the historical freight transport route planning schemes from the position of the vehicle to be transported to the transportation destination. The position of the vehicle to be transported is obtained in real time through Beidou positioning, and the transport route planning set is updated every preset unit time; A planning scheme equipment load analysis module, which generates road condition characteristic information of each transport route planning scheme according to the road condition information in the transport route planning scheme corresponding to each element in the transport route planning set; and analyzes the cold chain equipment load failure coefficient of each transport route planning scheme in the transport route planning set in combination with the types of agricultural products, weather warning information of the areas through which the transport route planning scheme passes, and status monitoring data of the built-in cold chain equipment of historical freight vehicles; A supply route management module generates a response bias value for each transport route planning scheme based on the road condition characteristic information and the cold chain equipment load failure coefficient of each transport route planning scheme; selects the best freight supply route for the agricultural products to be transported, and feeds back to the driver of the vehicle to be transported and the person in charge of receiving the corresponding agricultural products at the destination.

[0014] Furthermore, the transport route planning set building module includes a product information extraction unit and a transport route dynamic planning unit. The product information extraction unit extracts the types of agricultural products to be transported and the transportation destination according to the freight list of agricultural products to be transported; The transport route dynamic planning unit plans various transport route planning schemes from the location of the vehicle to be transported to the transport destination according to the Beidou navigation software and historical freight transport routes, and constructs a transport route planning set.

[0015] Furthermore, the planning scheme equipment load analysis module includes a road condition characteristic information acquisition unit and an equipment load fault analysis unit. The road condition characteristic information acquisition unit generates road condition characteristic information of each transport route planning scheme according to the road condition information in the transport route planning scheme corresponding to each element in the transport route planning set; The equipment load failure analysis unit analyzes the cold chain equipment load failure coefficient of each transport route planning scheme in the transport route planning set by combining the types of agricultural products, weather warning information of the areas through which the transport route planning scheme passes, and status monitoring data of historical built-in cold chain equipment of freight vehicles.

[0016] Furthermore, the supply route management module includes a planning scheme response status analysis unit and a freight supply route update management unit. The planning scheme response state analysis unit generates a response bias value for each transportation route planning scheme based on the road condition characteristic information of each transportation route planning scheme and the cold chain equipment load failure coefficient; The freight supply route update management unit selects the best freight supply route for the agricultural products to be transported, and feeds back the route to the driver of the vehicle to be transported and the person in charge of receiving the corresponding agricultural products at the destination.

[0017] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: while collecting agricultural product information through Internet of Things sensors, the present invention also evaluates the transportation environment of each transportation route planning scheme from multiple factors such as road condition characteristic information, agricultural product types, weather warning information of the areas through which the transportation route planning scheme passes, and status monitoring data of historical freight vehicles' built-in cold chain equipment, and realizes the screening of the best freight supply route for the agricultural products to be transported through the response bias value of the transportation route planning scheme. While assisting the drivers of the vehicles to be transported to perform the transportation tasks, the present invention also provides detailed agricultural product freight information to the person in charge of receiving the agricultural products, so that the person in charge of receiving the agricultural products can reasonably arrange the time for receiving the agricultural products and realize effective management of the agricultural product supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings: Figure 1 It is a structural schematic diagram of an agricultural product supply chain management system based on the Internet of Things of the present invention; Figure 2 It is a flow chart of an agricultural product supply chain management method based on the Internet of Things of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1 The present invention provides a technical solution: an agricultural product supply chain management system based on the Internet of Things, the system comprising the following modules: A transport route planning set building module, wherein the transport route planning set building module includes a product information extraction unit to be transported and a transport route dynamic planning unit, The product information extraction unit extracts the types of agricultural products to be transported and the transportation destination according to the freight list of agricultural products to be transported; The transport route dynamic planning unit plans each transport route planning scheme from the location of the vehicle to be transported to the transport destination according to the Beidou navigation software and the historical freight transport route, and constructs a transport route planning set; A planning scheme equipment load analysis module, the planning scheme equipment load analysis module includes a road condition feature information acquisition unit and an equipment load fault analysis unit, The road condition characteristic information acquisition unit generates road condition characteristic information of each transport route planning scheme according to the road condition information in the transport route planning scheme corresponding to each element in the transport route planning set; The equipment load failure analysis unit analyzes the cold chain equipment load failure coefficient of each transport route planning scheme in the transport route planning set by combining the types of agricultural products, weather warning information of the area through which the transport route planning scheme passes, and status monitoring data of the built-in cold chain equipment of historical freight vehicles; A supply route management module, the supply route management module includes a planning scheme response status analysis unit and a freight supply route update management unit, The planning scheme response state analysis unit generates a response bias value for each transportation route planning scheme based on the road condition characteristic information of each transportation route planning scheme and the cold chain equipment load failure coefficient; The freight supply route update management unit selects the best freight supply route for the agricultural products to be transported, and feeds back the route to the driver of the vehicle to be transported and the person in charge of receiving the corresponding agricultural products at the destination.

[0021] like Figure 2 As shown, a method for managing an agricultural product supply chain based on the Internet of Things comprises the following steps: S1. Extract the types of agricultural products to be transported and the transportation destination according to the freight list of agricultural products to be transported; and construct a transportation route planning set based on Beidou navigation software and historical freight transportation routes to plan various transportation route planning schemes from the position of the vehicle to be transported to the transportation destination. The position of the vehicle to be transported is obtained in real time through Beidou positioning, and the transportation route planning set is updated every preset unit time; The transport route planning set in S1 is equal to the union of the first transport route planning scheme set and the second transport route planning scheme set; the first transport route planning scheme set represents a set of transport route planning schemes between the position of the vehicle to be transported and the transport destination generated according to the Beidou navigation software; the second transport route planning scheme set represents a set of transport route planning schemes between the position of the vehicle to be transported and the transport destination in the historical freight transport routes.

[0022] In this embodiment, after S3 generates the optimal freight supply route for the agricultural products to be transported and feeds it back to the driver of the vehicle to be transported, the feedback route can only serve as a reference. During the actual transportation process, the driver of the vehicle to be transported can fine-tune the feedback route according to the actual road conditions to ensure smooth transportation. Therefore, the actual transportation route of the driver of the vehicle to be transported may differ from the feedback route, but the actual transportation route of the driver of the vehicle to be transported can provide a certain degree of reference value for the planning of subsequent transportation routes.

[0023] S2. Generate road condition characteristic information of each transport route planning scheme according to the road condition information in the transport route planning scheme corresponding to each element in the transport route planning set; and analyze the cold chain equipment load failure coefficient of each transport route planning scheme in the transport route planning set in combination with the types of agricultural products, weather warning information of the areas through which the transport route planning scheme passes, and the status monitoring data of the built-in cold chain equipment of historical freight vehicles; The road condition characteristic information of each transport route planning scheme includes the path length of the corresponding transport route planning scheme, the road surface flatness coefficient of each section in the corresponding transport route planning scheme, the section length and the road congestion coefficient within the section path, and each intersection passed through in the transport route planning scheme is regarded as a section division point; The road surface smoothness coefficient of each section in the corresponding transport route planning scheme is equal to Tc / Tz·Amax, where Tz represents the average transportation time of each vehicle to be transported passing through the corresponding section in the historical data; Amax represents the average maximum amplitude monitored by the built-in vibration sensor of each vehicle to be transported in the process of passing through the corresponding section in the historical data; Tc represents the average transportation time corresponding to the range from the average amplitude to the maximum amplitude monitored by the built-in vibration sensor of each vehicle to be transported in the process of passing through the corresponding section in the historical data; The road congestion coefficient within the segment path is expressed as N / N1+b·V / V1, where N represents the total number of vehicles monitored by various sensors set within the corresponding segment path at the current time; N1 represents the total number of vehicles accommodated in the corresponding segment path preset in the database; V represents the average speed of the vehicles to be transported passing through the corresponding segment in the historical data; V1 represents the maximum speed limit of vehicles of the same type as the vehicle to be transported within the corresponding segment path preset in the database; and b represents the preset weight coefficient.

[0024] In the process of analyzing the cold chain equipment load failure coefficient of each transportation route planning scheme in the transportation route planning set in S2, the calculation formula involved is as follows: ; G k represents the cold chain equipment load failure coefficient of the kth transportation route planning scheme in the transportation route planning set; NY k Indicates the number of preset blocks that the kth transport route planning scheme in the transport route planning set passes through. The range corresponding to each preset regional block is different; TY （k，i） It indicates the duration of the kth transport route planning scheme in the transport route planning set passing through the corresponding ith preset block; TY （k，i）The value of is equal to the sum of the length of each segment path contained in the actual road section of the k-th transport route planning scheme passing through the corresponding i-th preset block divided by the travel speed of the corresponding segment path; the travel speed of the corresponding segment path is the average speed of each vehicle passing through the corresponding segment path when the road congestion coefficient is N / N1+b·V / V1 in the historical data; the i-th preset block passed by the k-th transport route planning scheme in the transport route planning set is calculated based on the subsequent TYZ of the current time. k-1 To TYZ k The absolute value of the difference between the average weather warning temperature within the time period and the preset control temperature of the cold chain equipment is recorded as W （k，i） ; R （k，i） Indicates that the database preset form is W （k，i） Corresponding cold chain equipment load factor; TYZ k-1 represents the total time of the k-th transportation route planning scheme passing through the corresponding first i-1 preset blocks; TYZ k represents the total time of the k-th transportation route planning scheme passing through the corresponding first i preset blocks; when k=1, then TYZ k-1 =0; P{} represents the function of finding probability; represents the cold chain equipment load evaluation value of the kth transportation route planning scheme in the transportation route planning set; It means that 1 minus the number of cold chain equipment failure vehicles in the historical data have cold chain equipment load assessment values ​​greater than h in the transportation behaviors before the corresponding failure. The difference between the ratio of the number of failures to the total number of failures.

[0025] In this embodiment, if there are three cold chain equipment failures, recorded as A, B, and C respectively, if the cold chain equipment load evaluation value of the kth transportation route planning scheme in the transportation route planning set is ξ; if h=3; If the cold chain equipment load assessment value of the transport vehicle is greater than ξ in the h transportation behaviors before fault A; If the cold chain equipment load assessment value of the vehicle to be transported does not exceed ξ in the h transportation behaviors before fault B; If the cold chain equipment load assessment value of the vehicle to be transported is greater than ξ three times in the h transportation behaviors before the failure; Then the cold chain equipment load evaluation value of the kth transportation route planning scheme in the transportation route planning set is: P{ξ}=1-2 / 3=1 / 3.

[0026] S3. Generate a response bias value for each transport route planning scheme based on the road condition characteristic information and cold chain equipment load failure coefficient of each transport route planning scheme; select the best freight supply route for the agricultural products to be transported, and provide feedback to the driver of the vehicle to be transported and the person in charge of receiving the corresponding agricultural products at the destination; The calculation formula for generating the response bias value of each transportation route planning scheme in S3 is as follows: ; Among them, BS k represents the response bias value of the k-th transportation route planning scheme; k BUM represents the sum of the road surface smoothness coefficients of each section in the k-th transportation route planning scheme; k It represents the transport road bump tolerance coefficient corresponding to the type of agricultural products to be transported in the agricultural products freight list in the preset form of the database; r1, r2 and r3 all represent preset weight factors, and the values ​​of r1, r2 and r3 are all different.

[0027] The optimal freight supply route for the agricultural products to be transported in S3 is the transportation route planning scheme with the smallest corresponding response deviation value.

[0028] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0029] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for managing agricultural product supply chain based on the Internet of Things, characterized in that: The method comprises the following steps: S1. Extract the types of agricultural products to be transported and the transportation destination according to the freight list of agricultural products to be transported; and construct a transportation route planning set based on Beidou navigation software and historical freight transportation routes to plan various transportation route planning schemes from the position of the vehicle to be transported to the transportation destination. The position of the vehicle to be transported is obtained in real time through Beidou positioning, and the transportation route planning set is updated every preset unit time; S2. Generate road condition characteristic information of each transport route planning scheme according to the road condition information in the transport route planning scheme corresponding to each element in the transport route planning set; and analyze the cold chain equipment load failure coefficient of each transport route planning scheme in the transport route planning set in combination with the types of agricultural products, weather warning information of the areas through which the transport route planning scheme passes, and the status monitoring data of the built-in cold chain equipment of historical freight vehicles; S3. Generate a response bias value for each transport route planning scheme based on the road condition characteristic information and cold chain equipment load failure coefficient of each transport route planning scheme; select the best freight supply route for the agricultural products to be transported, and provide feedback to the drivers of the vehicles to be transported and the persons in charge of receiving the corresponding agricultural products at the destination.

2. The agricultural product supply chain management method based on the Internet of Things according to claim 1 is characterized in that: The transport route planning set in S1 is equal to the union of the first transport route planning scheme set and the second transport route planning scheme set; the first transport route planning scheme set represents a set of transport route planning schemes between the position of the vehicle to be transported and the transport destination generated according to the Beidou navigation software; the second transport route planning scheme set represents a set of transport route planning schemes between the position of the vehicle to be transported and the transport destination in the historical freight transport routes.

3. The agricultural product supply chain management method based on the Internet of Things according to claim 1 is characterized in that: The road condition characteristic information of each transport route planning scheme includes the path length of the corresponding transport route planning scheme, the road surface flatness coefficient of each section in the corresponding transport route planning scheme, the section length and the road congestion coefficient within the section path, and each intersection passed through in the transport route planning scheme is regarded as a section division point; The road surface smoothness coefficient of each section in the corresponding transport route planning scheme is equal to Tc / Tz·Amax, where Tz represents the average transportation time of each vehicle to be transported passing through the corresponding section in the historical data; Amax represents the average maximum amplitude monitored by the built-in vibration sensor when each vehicle to be transported passes through the corresponding section in the historical data; Tc represents the average transportation time corresponding to the range from the average amplitude to the maximum amplitude monitored by the built-in vibration sensor of each vehicle to be transported when it passes through the corresponding section in the historical data; The road congestion coefficient within the segment path is expressed as N / N1+b·V / V1, where N represents the total number of vehicles monitored by various sensors set within the corresponding segment path at the current time; N1 represents the total number of vehicles accommodated in the corresponding segment path preset in the database; V represents the average speed of the vehicles to be transported passing through the corresponding segment in the historical data; V1 represents the maximum speed limit of vehicles of the same type as the vehicle to be transported within the corresponding segment path preset in the database; and b represents the preset weight coefficient.

4. The agricultural product supply chain management method based on the Internet of Things according to claim 3 is characterized in that: In the process of analyzing the cold chain equipment load failure coefficient of each transportation route planning scheme in the transportation route planning set in S2, the calculation formula involved is as follows: ； G k represents the cold chain equipment load failure coefficient of the kth transportation route planning scheme in the transportation route planning set; NY k Indicates the number of preset blocks that the kth transport route planning scheme in the transport route planning set passes through. The range corresponding to each preset regional block is different; TY （k，i） It represents the duration of the kth transport route planning scheme in the transport route planning set passing through the corresponding i-th preset block; TY （k，i） The value of is equal to the sum of the length of each segment path contained in the actual road section of the k-th transport route planning scheme passing through the corresponding i-th preset block divided by the travel speed of the corresponding segment path; the travel speed of the corresponding segment path is the average speed of each vehicle passing through the corresponding segment path when the road congestion coefficient is N / N1+b·V / V1 in the historical data; the i-th preset block passed by the k-th transport route planning scheme in the transport route planning set is calculated based on the subsequent TYZ of the current time. k-1 To TYZ k The absolute value of the difference between the average weather warning temperature within the time period and the preset control temperature of the cold chain equipment is recorded as W （k，i） ; R （k，i） Indicates that the database preset form is W （k，i） Corresponding cold chain equipment load factor; TYZ k-1 represents the total time of the k-th transportation route planning scheme passing through the corresponding first i-1 preset blocks; TYZ k represents the total time of the k-th transportation route planning scheme passing through the corresponding first i preset blocks; when k=1, then TYZ k-1 =0; P{} represents the function of finding probability; represents the cold chain equipment load evaluation value of the kth transportation route planning scheme in the transportation route planning set; It means that 1 minus the number of cold chain equipment failure vehicles in the historical data have cold chain equipment load assessment values ​​greater than h in the transportation behaviors before the corresponding failure. The difference between the ratio of the number of failures to the total number of failures.

5. The agricultural product supply chain management method based on the Internet of Things according to claim 4 is characterized in that: The calculation formula for generating the response bias value of each transportation route planning scheme in S3 is as follows: ； Among them, BS k represents the response bias value of the k-th transportation route planning scheme; k BUM represents the sum of the road surface smoothness coefficients of each section in the k-th transportation route planning scheme; k It represents the transport road bump tolerance coefficient corresponding to the type of agricultural products to be transported in the agricultural products freight list in the preset form of the database; r1, r2 and r3 all represent preset weight factors, and the values ​​of r1, r2 and r3 are all different.

6. The agricultural product supply chain management method based on the Internet of Things according to claim 1 is characterized in that: The optimal freight supply route for the agricultural products to be transported in S3 is the transportation route planning scheme with the smallest corresponding response deviation value.

7. An agricultural product supply chain management system based on the Internet of Things, applying an agricultural product supply chain management method based on the Internet of Things as described in any one of claims 1 to 6, characterized in that: The system includes the following modules: A transport route planning set building module, wherein the transport route planning set building module extracts the types of agricultural products to be transported and the transportation destination according to the freight list of the agricultural products to be transported; And according to the Beidou navigation software and historical freight transportation route planning, each transportation route planning scheme from the location of the vehicle to be transported to the transportation destination is constructed to build a transportation route planning set. The location of the vehicle to be transported is obtained in real time through Beidou positioning, and the transportation route planning set is updated every preset unit time; A planning scheme equipment load analysis module, wherein the planning scheme equipment load analysis module generates road condition characteristic information of each transportation route planning scheme according to the road condition information in the transportation route planning scheme corresponding to each element in the transportation route planning set; Combined with the types of agricultural products, weather warning information of the areas through which the transportation route planning scheme passes, and the status monitoring data of the built-in cold chain equipment of historical freight vehicles, the cold chain equipment load failure coefficient of each transportation route planning scheme in the transportation route planning set is analyzed; A supply route management module generates a response bias value for each transport route planning scheme based on the road condition characteristic information and the cold chain equipment load failure coefficient of each transport route planning scheme; selects the best freight supply route for the agricultural products to be transported, and feeds back to the driver of the vehicle to be transported and the person in charge of receiving the corresponding agricultural products at the destination.

8. The agricultural product supply chain management system based on the Internet of Things according to claim 7 is characterized by: The transport route planning set building module includes a product information extraction unit and a transport route dynamic planning unit. The product information extraction unit extracts the types of agricultural products to be transported and the transportation destination according to the freight list of agricultural products to be transported; The transport route dynamic planning unit plans various transport route planning schemes from the location of the vehicle to be transported to the transport destination according to the Beidou navigation software and historical freight transport routes, and constructs a transport route planning set.

9. The agricultural product supply chain management system based on the Internet of Things according to claim 7 is characterized by: The planning scheme equipment load analysis module includes a road condition feature information acquisition unit and an equipment load fault analysis unit. The road condition characteristic information acquisition unit generates road condition characteristic information of each transport route planning scheme according to the road condition information in the transport route planning scheme corresponding to each element in the transport route planning set; The equipment load failure analysis unit analyzes the cold chain equipment load failure coefficient of each transport route planning scheme in the transport route planning set by combining the types of agricultural products, weather warning information of the areas through which the transport route planning scheme passes, and status monitoring data of historical built-in cold chain equipment of freight vehicles.

10. The agricultural product supply chain management system based on the Internet of Things according to claim 7, characterized in that: The supply route management module includes a planning scheme response status analysis unit and a freight supply route update management unit. The planning scheme response state analysis unit generates a response bias value for each transportation route planning scheme based on the road condition characteristic information of each transportation route planning scheme and the cold chain equipment load failure coefficient; The freight supply route update management unit selects the best freight supply route for the agricultural products to be transported, and feeds back the route to the driver of the vehicle to be transported and the person in charge of receiving the corresponding agricultural products at the destination.

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