An Internet of Things-based agricultural product supply chain management system and method

Abstract

The present invention relates to the technical field of agricultural product supply chain management, and specifically to an Internet of Things-based agricultural product supply chain management system and method. In the system, a supply route management module generates a response bias value for each transportation route planning scheme based on the road condition feature information and the load fault coefficient of the cold chain equipment of each transportation route planning scheme; screens the best freight supply route for the agricultural products to be transported, and feeds it back to the drivers of the vehicles to be transported and the receiving persons in charge of the corresponding agricultural products at the destination. The present invention evaluates the transportation environment of each transportation route planning scheme from multiple factors such as road condition feature information, types of agricultural products, weather warning information in the areas passed by the transportation route planning scheme, and status monitoring data of the built-in cold chain equipment of historical freight vehicles, 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, so as to assist the drivers of the vehicles to be transported in performing transportation tasks.

Description

Technical Field

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

[0002] With the development of the economy, people's demand for the variety and quality of agricultural products is getting higher and higher, and thus the management pressure on the agricultural product supply chain is also increasing; in recent years, with the rapid development of the Internet of Things technology, the Internet of Things is introduced into the field of agricultural product supply chain management, and agricultural product information is collected in real time through sensors, combined with the Internet and cloud computing, to realize the intelligent management of the agricultural product supply chain.

[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 influence of different transportation environments during the transportation process of agricultural products. Therefore, it is impossible to dynamically plan the transportation route by combining the agricultural product status collection data and transportation environment information, and it is impossible to plan the best freight supply route for vehicle drivers to refer to, so as to reduce the loss risk of agricultural products 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 technique.

[0005] To solve the above technical problems, the present invention provides the following technical solution: an agricultural product supply chain management method based on the Internet of Things, the method includes the following steps:

[0006] S1. Extract the types of agricultural products to be transported and the transportation destinations according to the freight list of the agricultural products to be transported; and plan various transportation route planning schemes from the position of the vehicle to be transported to the transportation destination according to the Beidou navigation software and historical freight transportation routes, and construct a transportation route planning set. 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;

[0007] S2. Generate the 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; and combine the types of agricultural products, the weather warning information in the areas passed by the transportation route planning scheme, and the status monitoring data of the built-in cold chain equipment of historical freight vehicles to analyze the cold chain equipment load failure coefficient of each transportation route planning scheme in the transportation route planning set.

[0008] S3. Generate the response deviation value for each transportation route planning scheme based on the road condition feature information and the cold chain equipment load failure coefficient of each transportation route planning scheme; screen the best freight supply route for the agricultural products to be transported and feedback it to the driver of the vehicle to be transported and the receiving person in charge of the corresponding agricultural products at the destination.

[0009] Further, in S1, the transportation route planning set is equal to the union of the first transportation route planning scheme set and the second transportation route planning scheme set; the first transportation route planning scheme set represents the set composed of each transportation route planning scheme from the position of the vehicle to be transported to the transportation destination generated according to the Beidou navigation software; the second transportation route planning scheme set represents the set composed of each transportation route planning scheme from the position of the vehicle to be transported to the transportation destination in the historical freight transportation route.

[0010] In the present invention, during the process of real-time obtaining the position of the vehicle to be transported through the Beidou positioning system, the real-time obtaining of the position of the vehicle to be transported is to ensure the whole-process monitoring of the vehicle position information and ensure that problems can be found and targeted processing can be carried out in time when the signal transmission is abnormal; however, the transportation route planning set is set to be updated every preset unit time interval instead of being updated in real time. On the one hand, it is to reduce the data processing volume and relieve 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 caused by the frequent change of the feedback freight supply route.

[0011] Further, the road condition feature information of each transportation route planning scheme includes the path length of the corresponding transportation route planning scheme, the road surface flatness coefficient, the section length and the road congestion coefficient in each section of the corresponding transportation route planning scheme. Each intersection passed by in the transportation route planning scheme is used as a section division point;

[0012] The road surface flatness coefficient of each section in the corresponding transportation route planning scheme is equal to Tc / Tz·Amax. Tz represents the average value of the transportation time of each vehicle to be transported passing through the corresponding section in the historical data; Amax represents the average value of the maximum amplitude monitored by the built-in vibration sensor during the process of each vehicle to be transported passing through the corresponding section in the historical data; Tc represents the average value of the transportation time corresponding to the interval from the average amplitude to the maximum amplitude monitored by the built-in vibration sensor of each vehicle to be transported during the process of passing through the corresponding section in the historical data;

[0013] The road congestion coefficient within the described section path is expressed as N / N1 + b·V / V1, where N represents the total number of vehicles monitored by each sensor set within the corresponding section path at the current time; N1 represents the total number of vehicles that can be accommodated within the corresponding section path preset in the database; V represents the average vehicle speed during the process of the vehicles to be transported passing through the corresponding section in the historical data; V1 represents the maximum speed limit of vehicles of the same type as the vehicles to be transported within the corresponding section path preset in the database; and b represents the preset weight coefficient.

[0014] Further, during 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 involved calculation formula is as follows:

[0015] ;

[0016] G k represents the cold chain equipment load failure coefficient of the k-th transportation route planning scheme in the transportation route planning set; NY k represents the number of preset plates passed by the k-th transportation route planning scheme in the transportation route planning set, and the corresponding ranges of each preset regional plate are different; TY （k，i） represents the duration of the k-th transportation route planning scheme in passing through the corresponding i-th preset plate; TY （k，i） The value of is equal to the sum of the quotients obtained by dividing the length of each section path included in the actual road section of the k-th transportation route planning scheme in passing through the corresponding i-th preset plate by the passing speed of the corresponding section path; the passing speed of the corresponding section path is the average vehicle speed of each vehicle passing through when the road congestion coefficient within the corresponding section path in the historical data is N / N1 + b·V / V1; the absolute value of the difference between the average weather warning temperature of the i-th preset plate passed by the k-th transportation route planning scheme within the subsequent TYZ k-1 to TYZ k time period based on the current time and the preset control temperature of the cold chain equipment is denoted as W （k，i） ; R （k，i） represents the cold chain equipment load coefficient corresponding to W （k，i） in the database preset form; TYZ k-1 represents the total duration of the k-th transportation route planning scheme in passing through the corresponding previous i - 1 preset plates; TYZ k represents the total duration of the k-th transportation route planning scheme in passing through the corresponding previous i preset plates; when k = 1, then TYZ k-1 = 0; P{} represents the function of calculating probability; represents the cold chain equipment load evaluation value of the k-th transportation route planning scheme in the transportation route planning set; It represents the difference obtained by subtracting the ratio of the number of failures where the cold chain equipment load evaluation value of the vehicle waiting for shipment with cold chain equipment failures in the historical data is greater than in the h shipments before the corresponding failure from the total number of failures.

[0017] In the present invention, the value of TY （k，i） is equal to the sum of the quotients obtained by dividing the length of each section path included in the actual section of the k-th transportation route planning scheme passing through the corresponding i-th preset section by the passing speed of the corresponding section path; that is, TY （k，i） is the predicted value of the time required for the vehicle waiting for shipment to pass through the actual section of the corresponding i-th preset section when executing the k-th transportation route planning scheme. Here, the average value Tz of the transportation time of each vehicle waiting for shipment passing through the corresponding section in the historical data is not directly called because Tz corresponds to the average time required for a complete section (and is obtained under the premise of not considering traffic congestion). However, the actual section of the k-th transportation route planning scheme passing through the corresponding i-th preset section may involve one or more sections in the traffic condition feature information, and the part involved in the corresponding actual section may not be the corresponding complete section in the traffic condition feature information; therefore, directly calling Tz may cause a large deviation in the settlement result.

[0018] Further, the calculation formula for generating the response deviation value of each transportation route planning scheme in S3 is as follows:

[0019] ;

[0020] where BS k represents the response deviation value of the k-th transportation route planning scheme; falt k represents the sum of the road surface flatness coefficients of each section in the k-th transportation route planning scheme; BUM k represents the transportation road condition bump tolerance coefficient corresponding to the type of agricultural product to be transported in the freight list of agricultural products to be transported in the database preset form; r1, r2, and r3 all represent preset weight factors, and the values of r1, r2, and r3 are all different.

[0021] Further, the best freight supply route for the agricultural product to be transported in S3 is the transportation route planning scheme with the smallest corresponding response deviation value.

[0022] An agricultural product supply chain management system based on the Internet of Things, the system includes the following modules:

[0023] Transport route planning set construction module, which extracts the types of agricultural products to be transported and the transportation destinations 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 for each transport route planning scheme between the position of the vehicle to be transported and 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;

[0024] Planning scheme equipment load analysis module, which generates the road condition feature 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, the weather warning information in the areas passed by the transport route planning scheme and the status monitoring data of the built-in cold chain equipment of historical freight vehicles;

[0025] Supply route management module, which generates a response bias value for each transport route planning scheme based on the road condition feature information and the cold chain equipment load failure coefficient of each transport route planning scheme; screens the best freight supply route for the agricultural products to be transported and feeds it back to the driver of the vehicle to be transported and the receiving person in charge of the corresponding agricultural products at the destination.

[0026] Furthermore, the transport route planning set construction module includes a to-be-transported product information extraction unit and a transport route dynamic planning unit,

[0027] The to-be-transported product information extraction unit extracts the types of agricultural products to be transported and the transportation destinations according to the freight list of the agricultural products to be transported;

[0028] The transport route dynamic planning unit constructs a transport route planning set according to the Beidou navigation software and the historical freight transport route planning for each transport route planning scheme between the position of the vehicle to be transported and the transportation destination.

[0029] Furthermore, the planning scheme equipment load analysis module includes a road condition feature information acquisition unit and an equipment load failure analysis unit,

[0030] The road condition feature information acquisition unit generates the road condition feature 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;

[0031] 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 in combination with the types of agricultural products, the weather warning information in the areas passed by the transport route planning scheme and the status monitoring data of the built-in cold chain equipment of historical freight vehicles.

[0032] Further, the supply route management module includes a planning scheme response status analysis unit and a freight supply route update management unit.

[0033] The planning scheme response status analysis unit generates a response bias value for each transportation route planning scheme based on the road condition feature information of each transportation route planning scheme and the load failure coefficient of the cold chain equipment.

[0034] The freight supply route update management unit screens the best freight supply route for the agricultural products to be transported and feeds it back to the driver of the vehicle to be transported and the person in charge of receiving the corresponding agricultural products at the destination.

[0035] 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 aspects such as road condition feature information, types of agricultural products, weather warning information in the areas passed by the transportation route planning scheme, and status monitoring data of the built-in cold chain equipment of historical freight vehicles. And through the response bias value of the transportation route planning scheme, the best freight supply route for the agricultural products to be transported is screened. While assisting the driver of the vehicle to be transported to execute the transportation task, it also provides detailed freight information of agricultural products for the person in charge of receiving agricultural products, facilitating the person in charge of receiving agricultural products to reasonably arrange the time for receiving agricultural products and realizing the effective management of the agricultural product supply chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0037] Figure 1 is a schematic structural diagram of an agricultural product supply chain management system based on the Internet of Things according to the present invention;

[0038] Figure 2 is a schematic flow diagram of an agricultural product supply chain management method based on the Internet of Things according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0040] Please refer to Figure 1 , the present invention provides a technical solution: an agricultural product supply chain management system based on the Internet of Things, and the system includes the following modules:

[0041] A transportation route planning set construction module, which includes a to-be-transported product information extraction unit and a transportation route dynamic planning unit.

[0042] The to-be-transported product information extraction unit extracts the types of to-be-transported agricultural products and the transportation destinations according to the freight list of the to-be-transported agricultural products.

[0043] The transportation route dynamic planning unit constructs a transportation route planning set according to the Beidou navigation software and the historical freight transportation routes to plan various transportation route planning schemes from the position of the to-be-transported vehicle to the transportation destination.

[0044] A planning scheme equipment load analysis module, which includes a road condition feature information acquisition unit and an equipment load fault analysis unit.

[0045] The road condition feature information acquisition unit generates the road condition feature 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.

[0046] The equipment load fault analysis unit combines the types of agricultural products, the weather warning information in the areas passed by the transportation route planning scheme, and the status monitoring data of the built-in cold chain equipment of the historical freight vehicles to analyze the cold chain equipment load fault coefficients of each transportation route planning scheme in the transportation route planning set.

[0047] A supply route management module, which includes a planning scheme response status analysis unit and a freight supply route update management unit.

[0048] The planning scheme response status analysis unit generates a response bias value for each transportation route planning scheme based on the road condition feature information and the cold chain equipment load fault coefficient of each transportation route planning scheme.

[0049] The freight supply route update management unit screens the best freight supply route for the to-be-transported agricultural products and feeds it back to the driver of the to-be-transported vehicle and the receiving person in charge of the corresponding agricultural products at the destination.

[0050] As Figure 2 shown, an agricultural product supply chain management method based on the Internet of Things, the method includes the following steps:

[0051] S1. Extract the types of to-be-transported agricultural products and the transportation destinations according to the freight list of the to-be-transported agricultural products; and construct a transportation route planning set according to the Beidou navigation software and the historical freight transportation routes to plan various transportation route planning schemes from the position of the to-be-transported vehicle to the transportation destination, the position of the to-be-transported vehicle is obtained in real time through Beidou positioning, and the transportation route planning set is updated every preset unit time.

[0052] The transportation route planning set in S1 is equal to the union of the first transportation route planning scheme set and the second transportation route planning scheme set; the first transportation route planning scheme set represents a set composed of each transportation route planning scheme from the position of the vehicle to be transported to the transportation destination generated according to the Beidou navigation software; the second transportation route planning scheme set represents a set composed of each transportation route planning scheme from the position of the vehicle to be transported to the transportation destination in the historical freight transportation route.

[0053] In this embodiment, after S3 generates the best 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, there may be differences between the actual transportation route of the driver of the vehicle to be transported and 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 subsequent transportation route planning.

[0054] S2. Generate the 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; and analyze the cold chain equipment load failure coefficient of each transportation route planning scheme in the transportation route planning set in combination with the type of agricultural products, the weather warning information in the area passed by the transportation route planning scheme, and the status monitoring data of the built-in cold chain equipment of the historical freight vehicles.

[0055] The road condition characteristic information of each transportation route planning scheme includes the path length of the corresponding transportation route planning scheme, the road surface flatness coefficient, the section length, and the road congestion coefficient in each section of the corresponding transportation route planning scheme. Each intersection passed by the transportation route planning scheme is used as a section division point.

[0056] The road surface flatness coefficient of each section in the corresponding transportation route planning scheme is equal to Tc / Tz·Amax. Tz represents the average value of the transportation duration of each vehicle to be transported passing through the corresponding section in the historical data; Amax represents the average value of the maximum amplitude monitored by the built-in vibration sensor during the process of each vehicle to be transported passing through the corresponding section in the historical data; Tc represents the average value of the transportation duration corresponding to the interval from the average amplitude to the maximum amplitude monitored by the built-in vibration sensor of the vehicle during the process of each vehicle to be transported passing through the corresponding section in the historical data.

[0057] The road congestion coefficient within the described section path is expressed as N / N1 + b·V / V1, where N represents the total number of vehicles monitored by each sensor set within the corresponding section path at the current time; N1 represents the total number of vehicles that can be accommodated within the corresponding section path preset in the database; V represents the average vehicle speed during the process of the vehicles to be transported passing through the corresponding section in the historical data; V1 represents the maximum speed limit of the vehicles of the same type as the vehicles to be transported within the corresponding section path preset in the database; and b represents the preset weight coefficient.

[0058] During 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 involved calculation formula is as follows:

[0059] ;

[0060] G k represents the cold chain equipment load failure coefficient of the k-th transportation route planning scheme in the transportation route planning set; NY k represents the number of preset plates passed by the k-th transportation route planning scheme in the transportation route planning set, and the corresponding ranges of each preset regional plate are different; TY （k，i） represents the duration of the k-th transportation route planning scheme in passing through the corresponding i-th preset plate; TY （k，i） The value of is equal to the sum of the quotients obtained by dividing the length of each section path included in the actual road section of the k-th transportation route planning scheme in passing through the corresponding i-th preset plate by the passing speed of the corresponding section path; the passing speed of the corresponding section path is the average vehicle speed of each vehicle passing through when the road congestion coefficient within the corresponding section path in the historical data is N / N1 + b·V / V1; the absolute value of the difference between the average weather warning temperature within the subsequent TYZ k-1 to TYZ k time period based on the current time and the preset control temperature of the cold chain equipment is denoted as W （k，i） ; R （k，i） represents the cold chain equipment load coefficient corresponding to W （k，i） in the database preset form; TYZ k-1 represents the total duration of the k-th transportation route planning scheme in passing through the previous i - 1 preset plates; TYZ k represents the total duration of the k-th transportation route planning scheme in passing through the previous i preset plates; 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 k-th transportation route planning scheme in the transportation route planning set; It represents the difference obtained by subtracting the ratio of the number of failures where the cold chain equipment load evaluation value of the vehicle waiting for shipment with cold chain equipment failures in the historical data is greater than from the total number of failures during the h deliveries before the corresponding failure.

[0061] In this embodiment, if there are three cold chain equipment failures, denoted as A, B, and C respectively, and if the cold chain equipment load evaluation value of the k-th transportation route planning scheme in the transportation route planning set is ξ; if h = 3;

[0062] If there is one case where the cold chain equipment load evaluation value of the vehicle waiting for shipment is greater than ξ during the h deliveries before failure A;

[0063] If there is no case where the cold chain equipment load evaluation value of the vehicle waiting for shipment is greater than ξ during the h deliveries before failure B;

[0064] If there are three cases where the cold chain equipment load evaluation value of the vehicle waiting for shipment is greater than ξ during the h deliveries before failure C;

[0065] Then the cold chain equipment load evaluation value of the k-th transportation route planning scheme in the transportation route planning set is:

[0066] P{ξ}=1 - 2 / 3 = 1 / 3.

[0067] S3. Generate the response deviation value for each transportation route planning scheme based on the road condition feature information and the cold chain equipment load failure coefficient of each transportation route planning scheme; screen the best freight supply route for the agricultural products waiting for shipment and feedback it to the driver of the vehicle waiting for shipment and the receiving person in charge of the corresponding agricultural products at the destination;

[0068] The calculation formula for generating the response deviation value for each transportation route planning scheme in the said S3 is as follows:

[0069] ;

[0070] where BS k represents the response deviation value of the k-th transportation route planning scheme; falt k represents the sum of the road surface flatness coefficients of each section in the k-th transportation route planning scheme; BUM k represents the transportation road condition bump tolerance coefficient corresponding to the type of agricultural products waiting for shipment in the freight list of agricultural products waiting for shipment in the database preset form; r1, r2, and r3 all represent preset weight factors, and the values of r1, r2, and r3 are all different.

[0071] The best freight supply route for the agricultural products waiting for shipment in the said S3 is the transportation route planning scheme with the smallest corresponding response deviation value.

[0072] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device.

[0073] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within 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; 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 in the segment path is expressed as N / N1+b·V / V1, where N represents the total number of vehicles monitored by the sensors set in 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 the vehicles of the same type as the vehicles to be transported in the corresponding segment path preset in the database; b represents the preset weight coefficient; 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: 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.

4. The agricultural product supply chain management method based on the Internet of Things according to claim 3 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.

5. 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.

6. 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 5, 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.

7. The agricultural product supply chain management system based on the Internet of Things according to claim 6 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.

8. The agricultural product supply chain management system based on the Internet of Things according to claim 6 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.

9. The agricultural product supply chain management system based on the Internet of Things according to claim 6 is characterized by: 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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