An intelligent logistics management system for agricultural products based on the Internet of Things

Abstract

The present invention discloses an intelligent logistics management system for agricultural products based on the Internet of Things, and relates to the technical field of logistics management data processing. The intelligent logistics management system for agricultural products based on the Internet of Things includes: a delay monitoring module; a delay analysis module; and a delay processing module. The present invention obtains an estimated arrival time by collecting real-time logistics information of refrigerated trucks and combining it with historical logistics information, and then obtains optional warehouses through delayed agricultural product information and storage information, and obtains the shortest transfer time by combining real-time logistics information. Then, the delayed agricultural product information is combined with the estimated arrival time and the shortest transfer time to obtain a first result and a second result, and a transfer index is obtained accordingly. Finally, it is determined whether to make a transfer request according to the transfer index, thereby achieving a more accurate response to emergency situations in the transportation of short-shelf-life agricultural products, and solving the problem of untimely emergency response in the logistics transportation of short-shelf-life agricultural products in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management data processing, and in particular to an intelligent logistics management system for agricultural products based on the Internet of Things. Background Art

[0002] As the global agricultural supply chain becomes more complex and consumers' demand for fresh agricultural products increases, agricultural product logistics management becomes increasingly important. Traditional agricultural product transportation methods often lead to loss and quality degradation of agricultural products during transportation due to the lack of real-time monitoring and intelligent adjustment. In order to solve these problems, modern scientific and technological means such as the Internet of Things, big data analysis, cloud computing and artificial intelligence are introduced into the smart logistics management system for agricultural products, with the aim of improving efficiency, reducing losses and ensuring the quality and safety of agricultural products during transportation. The smart logistics management system for agricultural products ensures that agricultural products are transported and stored under the most suitable conditions by real-time monitoring of key factors such as temperature, location, and transportation time of cold chain transportation, thereby maximizing their freshness and nutritional value.

[0003] Existing smart logistics management systems for agricultural products mainly use sensor networks and communication technologies to achieve real-time monitoring of the transportation process. Smart logistics management systems for agricultural products use global positioning system positioning technology to track the location of transport vehicles to ensure that goods arrive on time. At the same time, temperature sensors are widely used to monitor the ambient temperature in refrigerated trucks to ensure that agricultural products are always in suitable storage conditions. Some advanced smart logistics management systems for agricultural products also integrate big data analysis functions to optimize transportation routes and schedules by analyzing historical transportation data.

[0004] For example, the cross-order interconnected NAP logistics management system and logistics information sharing method disclosed in the Chinese patent announcement with announcement number: CN108229895B include: an information entry module, a storage module and a cargo list query module connected in sequence; wherein the information entry module is used to enter cargo order information and information of mobile terminals associated with cargo logistics operations; the storage module is communicatively connected to multiple PC terminals, and is used to receive, update and store the logistics information of the cargo; the PC terminal is communicatively connected to multiple mobile terminals, and the mobile terminal receives logistics operation commands from the PC terminal and returns the location information of the associated cargo to the PC terminal in real time; the cargo list query module is used to query information associated with the cargo.

[0005] For example, an agricultural logistics management system disclosed in a Chinese patent publication with publication number CN117952497B includes: a first prediction module, which is used to calculate the first similarity between the extracted first information of the current crop and the first information of the historical crop; a second prediction module, which deletes the first information of the historical crop; and then calculates the second similarity between the processed first information of the historical crop and the first information of the current crop; a third prediction module, which is used to extract the first information of the top N historical crops with the largest third similarity to the first information of the current crop, select the first information of the historical crop with the highest corresponding crop yield, extract its corresponding logistics management information, and generate a logistics order.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the prior art, since the existing smart logistics management system for agricultural products lacks an emergency response plan for short-shelf-life agricultural products, there is a lack of effective real-time detection methods when encountering emergencies that cause transportation delays. Even if delays are detected, effective remedial measures are often not taken in time, which ultimately affects the quality and transportation efficiency of agricultural products. There is a problem of untimely emergency response in the logistics transportation of short-shelf-life agricultural products. Summary of the invention

[0008] The embodiments of the present application provide an intelligent logistics management system for agricultural products based on the Internet of Things, thereby solving the problem of untimely emergency response in the logistics transportation of short-shelf-life agricultural products in the prior art, and achieving a more accurate response to emergency situations in the transportation of short-shelf-life agricultural products.

[0009] The embodiment of the present application provides an intelligent logistics management system for agricultural products based on the Internet of Things, including: a delay monitoring module, a delay analysis module, and a delay processing module: wherein the delay monitoring module is used to collect real-time logistics information of refrigerated trucks, and combine the corresponding historical logistics information to make delay judgments to obtain the estimated arrival time of the refrigerated trucks; the delay analysis module is used to perform transfer sorting on the optional warehouses obtained through delayed agricultural product information and storage information according to the real-time logistics information, to obtain the shortest transfer time and the optimal transfer warehouse, and at the same time, the delayed agricultural product information is combined with the estimated arrival time and the shortest transfer time to obtain a first result and a second result, thereby obtaining a transfer index; the delay processing module is used to make transfer requests and visualized path planning.

[0010] Furthermore, the delay monitoring module includes a real-time status acquisition unit, a historical status acquisition unit and a delay judgment unit; the real-time status acquisition unit is used to transmit the real-time transportation time of the refrigerated truck collected by the clock at the preset time to the historical status acquisition unit and the delay analysis module, and transmit the real-time logistics information of the refrigerated truck collected by the on-board sensor to the delay judgment unit, the real-time transportation time represents the time experienced by the refrigerated truck from departure to the preset time, and the real-time logistics information includes the current position, current distance, current average speed and current energy consumption; the historical status acquisition unit is used to obtain the historical logistics information of the refrigerated truck according to the received real-time transportation time and transmit it to the delay judgment unit, the historical logistics information includes the historical total distance, historical arrival time and historical energy consumption; the delay judgment unit is used to perform delay judgment based on the received real-time logistics information and historical logistics information to obtain the estimated arrival time and delay index of the refrigerated truck at the preset time, and make a judgment in combination with the preset delay threshold. If the delay index is greater than the preset delay threshold, the estimated arrival time is transmitted to the delay analysis module, otherwise no processing is performed.

[0011] Furthermore, the delay analysis module includes a warehouse selection unit, a first result calculation unit, a second result calculation unit and a transfer decision unit; the warehouse selection unit is used to compare the warehouse space and the scale of agricultural products to obtain optional warehouses, and to perform transfer sorting in combination with the warehouse position and the current position of the refrigerated truck to obtain the corresponding shortest transfer time and the optimal transfer warehouse, and transmit the shortest transfer time to the second result calculation unit, and transmit the optimal transfer warehouse to the transfer request unit; the first result calculation unit is used to obtain the first result of the refrigerated truck at a preset time according to the received estimated arrival time, real-time transportation time and agricultural product quality reduction rate, and transmit the first result to the transfer decision unit, and the agricultural product quality reduction rate is used to describe the degree to which the quality of the agricultural products transported by the refrigerated truck decreases over time; the second result calculation unit is used to obtain the second result of the refrigerated truck at a preset time according to the received shortest transfer time, real-time transportation time and agricultural product quality reduction rate, and transmit the second result to the transfer decision unit; the transfer decision unit is used to derive the transfer index of the refrigerated truck at a preset time according to the first result and the second result, and to make a judgment in combination with the preset transfer threshold. If the transfer index is greater than the preset transfer threshold, the function of the delay processing module is executed, otherwise no processing is performed.

[0012] Furthermore, the delay processing module includes a transfer request unit and a visualization path unit; the transfer request unit is used to send a request message to the received optimal transfer warehouse through a network transmission device, and determine whether the corresponding response message is received within a preset time interval. If the response message is received, it indicates that the transfer request is successful, otherwise it is fed back to the delay analysis module to reselect the optimal transfer warehouse; the visualization path unit is used to plan the optimal path according to the current position and the warehouse location of the optimal transfer warehouse, and mark the optimal path on the map.

[0013] Furthermore, the real-time logistics information is acquired through a logistics information collection device, which includes a GPS module, a mileage sensor, a vehicle speed sensor, and an energy sensor. The GPS module is installed on the top of the refrigerated truck, the mileage sensor is installed inside the wheel of the refrigerated truck, the vehicle speed sensor is installed inside the gearbox of the refrigerated truck, and the installation position of the energy sensor is determined by a preset personnel according to the energy type of the refrigerated truck.

[0014] Furthermore, the specific process of the delay judgment is as follows: the current remaining distance is obtained by the current distance of the refrigerated truck and the historical total distance; the estimated arrival time is obtained by the current remaining distance and the current average speed of the refrigerated truck, and the delay time of the refrigerated truck at the preset time is obtained by combining the historical arrival time; the delay index of the refrigerated truck at the preset time is obtained by the obtained delay time, current energy consumption and historical energy consumption. The specific calculation formula of the delay index is as follows:

[0015] ;

[0016] Where n is the number of the preset time. , N is the total number of preset moments, is the delay index of the refrigerated truck at the nth preset time, is the delay time of the refrigerated truck at the nth preset time, is the reference deviation of the delay time of the refrigerated truck, is the current energy consumption of the refrigerated truck at the nth preset moment, is the historical energy consumption of the refrigerated truck at the nth preset moment.

[0017] Furthermore, the specific steps of the transfer sorting are as follows: A1, obtaining the transfer time of the optional warehouse according to the warehouse location and current location of the optional warehouse; A2, sorting the transfer time to obtain a transfer time sequence, and obtaining the shortest transfer time and the optimal transfer warehouse according to the transfer time sequence, the transfer time sequence represents a sequence obtained by sorting the transfer time in ascending order, and the shortest transfer time represents the first transfer time in the transfer time sequence.

[0018] Furthermore, the specific process of obtaining the transfer index is as follows: obtaining a transfer weight from a preset database, wherein the transfer weight includes a first weight and a second weight; obtaining the transfer index of the refrigerated truck at a preset time through the first result, the second result and the transfer weight, and the specific calculation formula of the transfer index is as follows:

[0019] ;

[0020] Where n is the number of the preset time. , N is the total number of preset moments, is the transfer index of the refrigerated truck at the nth preset moment, The first result of delaying agricultural products at the nth preset moment, The second result of delaying agricultural products at the nth preset time, is the first weight, is the second weight.

[0021] Furthermore, the specific process of obtaining the first result is as follows: the first time is obtained by the estimated arrival time of the refrigerated truck and the real-time transportation time, and the first result of the delayed agricultural products in the refrigerated truck at the preset time is obtained in combination with the corresponding agricultural product quality decline rate. The calculation formula of the first result is as follows:

[0022] ;

[0023] In the formula, is the first result of delayed agricultural products in the refrigerated truck at the nth preset time, The first time of the refrigerated truck at the nth preset time, is the agricultural product quality decline rate of the delayed agricultural products in the refrigerated truck; the specific process of obtaining the second result is as follows: the second time is obtained by the shortest transfer time and the real-time transportation time of the refrigerated truck, and the second result of the delayed agricultural products in the refrigerated truck at the preset time is obtained in combination with the corresponding agricultural product quality decline rate. The calculation formula of the second result is as follows:

[0024] ;

[0025] In the formula, The second result of delayed agricultural products in the refrigerated truck at the nth preset time, The second time of the refrigerated truck at the nth preset time.

[0026] Furthermore, the specific steps of planning the optimal path according to the current position and the warehouse location of the optimal transfer warehouse are as follows: B1, converting the map into a path network, and obtaining node and edge weights according to the path network, wherein the path network represents the connection relationship between geographical locations; B2, using the Dijkstra algorithm to obtain the optimal path, wherein the optimal path represents the shortest path from the starting node to the ending node, wherein the starting node is the current position of the refrigerated truck, and the ending node is the warehouse location of the optimal transfer warehouse.

[0027] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0028] 1. Delay judgment is performed through the real-time logistics information and historical logistics information of the refrigerated truck to obtain the estimated arrival time of the refrigerated truck, and then the optional warehouse is obtained through the delayed agricultural product information and storage information. The shortest transfer time and the optimal transfer warehouse are obtained by combining the real-time logistics information. At the same time, the delayed agricultural product information is combined with the estimated arrival time and the shortest transfer time to obtain the first result and the second result, and the transfer index is obtained based on this. Finally, transfer requests and visual path planning are performed according to the transfer index, thereby realizing intelligent monitoring and processing of refrigerated truck delays, and then realizing a more accurate response to emergency situations in the transportation of short-shelf-life agricultural products, effectively solving the problem of untimely emergency response in the logistics transportation of short-shelf-life agricultural products in the prior art.

[0029] 2. The current distance of the refrigerated truck is obtained through the mileage sensor, and the current remaining distance is obtained by combining the historical total distance. The estimated arrival time is obtained by combining the current average speed obtained by the vehicle speed sensor. Then, the delay time of the refrigerated truck at the preset time is obtained by combining the historical arrival time. Finally, the delay index of the refrigerated truck at the preset time is obtained by combining the current energy consumption and the historical energy consumption, thereby realizing the standardized evaluation of the delay index and the rapid identification of delays in refrigerated trucks.

[0030] 3. The first time is obtained through the estimated arrival time and real-time transportation time of the refrigerated truck, and the first result of the delayed agricultural products in the refrigerated truck at the preset time is obtained in combination with the corresponding agricultural product quality decline rate. Then, the second time is obtained through the shortest transfer time and real-time transportation time of the refrigerated truck, and the second result of the delayed agricultural products in the refrigerated truck at the preset time is obtained in combination with the corresponding agricultural product quality decline rate. Finally, the transfer index of the refrigerated truck at the preset time is obtained through the first result, the second result and the transfer weight obtained from the preset database, so as to accurately evaluate the urgency of transferring the delayed agricultural products in the refrigerated truck, thereby minimizing the loss of agricultural products. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1A schematic diagram of the structure of an agricultural product intelligent logistics management system based on the Internet of Things provided in an embodiment of the present application;

[0032] Figure 2 A schematic diagram of the structure of a delay monitoring module in an agricultural product smart logistics management system based on the Internet of Things provided in an embodiment of the present application;

[0033] Figure 3 A schematic diagram of the structure of a delay analysis module in an agricultural product smart logistics management system based on the Internet of Things provided in an embodiment of the present application;

[0034] Figure 4 A schematic diagram of the structure of a delay processing module in an agricultural product smart logistics management system based on the Internet of Things provided in an embodiment of the present application;

[0035] Figure 5 A schematic diagram of the changes in the first result provided in the embodiment of the present application. DETAILED DESCRIPTION

[0036] The embodiment of the present application solves the problem of untimely emergency response in the logistics transportation of short-shelf-life agricultural products in the prior art by providing an intelligent logistics management system for agricultural products based on the Internet of Things. The real-time logistics information of refrigerated trucks is collected, and delay judgment is performed in combination with corresponding historical logistics information to obtain the estimated arrival time of the refrigerated trucks. Then, optional warehouses are obtained through delayed agricultural product information and storage information. Transfer sorting is performed in combination with real-time logistics information to obtain the shortest transfer time and the optimal transfer warehouse. At the same time, the delayed agricultural product information is combined with the estimated arrival time and the shortest transfer time to obtain a first result and a second result, respectively. Based on this, a transfer index is obtained, and whether to make a transfer request and visual path planning is determined according to the transfer index, thereby achieving a more accurate response to emergency situations in the transportation of short-shelf-life agricultural products.

[0037] The technical solution in the embodiment of the present application is to solve the problem of untimely emergency response in the logistics and transportation of the above-mentioned short-shelf-life agricultural products. The overall idea is as follows:

[0038] By collecting real-time logistics information of refrigerated trucks and combining it with historical logistics information, the estimated arrival time is obtained. Then, the optional warehouses are obtained through the delayed agricultural product information and storage information. The shortest transfer time is obtained by combining the real-time logistics information. At the same time, the delayed agricultural product information is combined with the estimated arrival time and the shortest transfer time to obtain the first result and the second result. Based on this, the transfer index is obtained, and whether to make a transfer request is decided according to the transfer index, thereby achieving the effect of more accurately responding to emergency situations in the transportation of short-shelf-life agricultural products.

[0039] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0040] like Figure 1 As shown, it is a structural schematic diagram of an agricultural product smart logistics management system based on the Internet of Things provided by an embodiment of the present application. The agricultural product smart logistics management system based on the Internet of Things provided by an embodiment of the present application includes: a delay monitoring module, a delay analysis module, and a delay processing module: wherein the delay monitoring module is used to collect real-time logistics information of refrigerated trucks, and combine the corresponding historical logistics information to make delay judgments to obtain the estimated arrival time of the refrigerated trucks, the real-time logistics information is used to describe the current driving time, current position, current distance, average speed and energy consumption of the refrigerated trucks, the historical logistics information is used to describe the historical total distance in the historical transportation process of the refrigerated trucks and the historical arrival time and historical energy consumption of the same driving time, and the estimated arrival time indicates the estimated time required for the refrigerated truck to reach the destination from the current position; the delay analysis module is used to transfer and sort the optional warehouses obtained through the delayed agricultural product information and the storage information according to the real-time logistics information, obtain the shortest transfer time and the optimal transfer warehouse, and at the same time combine the delayed agricultural product information with the estimated arrival time and the shortest transfer time to obtain the first result and the second result, thereby obtaining the transfer index, the delay index, and the delay index. Errored agricultural products refer to agricultural products transported by refrigerated trucks, delayed agricultural product information refers to the scale and quality decline information of agricultural products transported by refrigerated trucks, storage information refers to the location and remaining space information of agricultural product warehouses, optional warehouses refer to agricultural product warehouses with sufficient remaining space to store delayed agricultural products, transfer sorting is used to find the most suitable agricultural product warehouse for storing delayed agricultural products, the shortest transfer time refers to the shortest time from the current position of the refrigerated truck to the position of the optional warehouse, the optimal transfer warehouse refers to the agricultural product warehouse closest to the current position of the refrigerated truck, the first result is used to describe the damage of agricultural products that are not transferred, and the second result is used to describe the damage of agricultural products that are transferred, and the transfer index indicates the degree of influence of transfer on the damage of agricultural products; the delay processing module is used to make transfer requests and visualize path planning, the transfer request refers to sending request information to the optimal transfer warehouse through the network transmission equipment, the response information indicates that the optimal transfer warehouse accepts the transfer request, the path planning refers to obtaining the shortest path between the current position of the refrigerated truck and the position of the optimal transfer warehouse based on the real-time logistics information analysis, and the visualization refers to displaying the result of path planning through a map.

[0041] In this embodiment, the delay monitoring module is responsible for determining whether the refrigerated truck is delayed based on the real-time logistics information and historical logistics information of the refrigerated truck. If a delay occurs, the delay analysis module is entered; the delay analysis module is responsible for obtaining the degree of impact of the transfer on the damage of the agricultural products based on the real-time logistics information, delayed agricultural product information and storage information; the delay processing module is responsible for generating request information and sending it to the optimal transfer warehouse through the network transmission device. If the optimal transfer warehouse responds to the request information, the delay processing module is also responsible for planning the shortest transfer path of the refrigerated truck and visually displaying it through a map; through the collaborative work of the three modules, intelligent monitoring and processing of delays in agricultural products is realized, thereby achieving a more accurate response to emergency situations in the transportation of short-shelf-life agricultural products, and solving the problem of untimely emergency response in the logistics transportation of short-shelf-life agricultural products in the prior art.

[0042] Further, such as Figure 2As shown, it is a structural schematic diagram of a delay monitoring module in an agricultural product smart logistics management system based on the Internet of Things provided by an embodiment of the present application, the delay monitoring module includes a real-time status acquisition unit, a historical status acquisition unit and a delay judgment unit; the real-time status acquisition unit: is used to transmit the real-time transportation time of the refrigerated truck collected by the clock at a preset time to the historical status acquisition unit and the delay analysis module, and transmit the real-time logistics information of the refrigerated truck collected by the on-board sensor to the delay judgment unit, the real-time transportation time represents the time experienced by the refrigerated truck from departure to the preset time, the real-time logistics information includes the current position, the current distance, the current average speed and the current energy consumption, the current position represents the geographical location coordinates of the refrigerated truck at the preset time, the current distance represents the distance from the starting point to the current position, the current average speed represents the average driving speed from departure to the preset time, and the current energy consumption represents the energy consumption of the refrigerated truck at the preset time; the historical status acquisition unit: is used to obtain the historical logistics information of the refrigerated truck according to the received real-time transportation time and transmit it to the delay judgment unit, the historical logistics information includes the historical total distance, the historical arrival time and the historical energy consumption, the historical total distance represents the average total transportation distance of the refrigerated truck during the historical transportation process, the historical arrival time table The delay judgment unit is used to judge the delay by receiving the real-time logistics information and the historical logistics information to obtain the estimated arrival time and delay index of the refrigerated truck at the preset time, and judge it in combination with the preset delay threshold. If the delay index is greater than the preset delay threshold, the estimated arrival time is transmitted to the delay analysis module, otherwise no processing is performed. The delay index indicates the degree of delay of the refrigerated truck: the aforementioned database is the agricultural product smart IoT based on the Internet of Things. The database used to store various set data established before the construction of the logistics management system includes but is not limited to preset delay thresholds, preset transfer thresholds, preset times and preset time intervals. The preset delay threshold is determined by the preset personnel based on the actual application scenario of the smart logistics management system for agricultural products before the system is established. For example, assuming that the refrigerated truck transports a certain type of vegetable with a short shelf life, the data of the refrigerated truck transporting the same type of vegetable with the same starting point and end point in the historical time period is substituted into the calculation formula of the delay index to obtain the corresponding delay index. The maximum value of the delay index based on the premise that the sales of the vegetable are not affected is the preset delay threshold. In addition, the various values ​​in the database can be set and fine-tuned by technical personnel based on actual debugging.

[0043] In this embodiment, the preset time represents a monitoring time point set by a preset personnel according to the transportation process of the refrigerated truck. For example, if it takes two days for the refrigerated truck to transport agricultural products to the destination, a preset time can be set every two hours after the refrigerated truck starts transportation, wherein the preset personnel represents an agricultural technician who performs intelligent management of the transportation process of the refrigerated truck; the real-time status acquisition unit is responsible for collecting the real-time transportation time and real-time logistics information of the refrigerated truck; the historical status acquisition unit is responsible for acquiring the historical logistics information of the refrigerated truck based on the real-time transportation time; the delay judgment unit is responsible for obtaining the degree of delay of the refrigerated truck based on the real-time logistics information and the historical logistics information. If the degree of delay of the refrigerated truck is greater than the preset delay threshold, the estimated arrival time is transmitted to the delay analysis module; through the collaborative work of the three units, the agricultural products intelligent logistics management system can monitor and judge the transportation status of the refrigerated truck in real time, and enter the delay analysis module for further analysis when a delay occurs, thereby improving the delay identification efficiency of the agricultural products intelligent logistics management system.

[0044] Further, such as Figure 3As shown, it is a structural schematic diagram of a delay analysis module in an agricultural product intelligent logistics management system based on the Internet of Things provided by an embodiment of the present application, the delay analysis module includes a warehouse selection unit, a first result calculation unit, a second result calculation unit and a transfer decision unit; the warehouse selection unit: is used to compare the warehouse space and the scale of agricultural products to obtain optional warehouses, and to perform transfer sorting in combination with the warehouse position and the current position of the refrigerated truck to obtain the corresponding shortest transfer time and the optimal transfer warehouse, and transmit the shortest transfer time to the second result calculation unit, and transmit the optimal transfer warehouse to the transfer request unit, the warehouse space is used to describe the remaining space situation of the agricultural product warehouse, the agricultural product scale is used to describe the scale of agricultural products transported by the refrigerated truck, and the warehouse position represents the geographical location coordinates of the optional warehouse; the first result calculation unit: is used to obtain the first result of the refrigerated truck at a preset time according to the received estimated arrival time, real-time transportation time and agricultural product quality decline rate, and transmit the first result to the transfer decision unit, the agricultural product The quality decline rate is used to describe the degree to which the quality of agricultural products transported by refrigerated trucks declines over time; the second result calculation unit is used to obtain the second result of the refrigerated truck at a preset time based on the received shortest transfer time, the real-time transportation time and the quality decline rate of the agricultural products, and transmit the second result to the transfer decision unit; the transfer decision unit is used to obtain the transfer index of the refrigerated truck at the preset time based on the first result and the second result and make a judgment in combination with the preset transfer threshold. If the transfer index is greater than the preset transfer threshold, the function of the delay processing module is executed, otherwise no processing is performed. The preset transfer threshold is determined by the preset personnel before the system is established based on the difficulty of transferring the delayed agricultural products. Specifically, assuming that the refrigerated truck transports a certain type of vegetable with a short shelf life, the transfer data of the refrigerated truck transporting the same type of vegetable with the same starting point and end point in the historical time period is substituted into the calculation formula of the transfer index to obtain the corresponding transfer index. The maximum value of the transfer index based on the premise that the quality of the vegetable is not affected is the preset transfer threshold.

[0045] In this embodiment, the warehouse space is the volume of the remaining space in the agricultural product warehouse, and the scale of agricultural products is the volume of delayed agricultural products. It is judged whether the warehouse space is larger than the scale of agricultural products. If it is larger than the scale of agricultural products, the agricultural product warehouse is recorded as an optional warehouse, otherwise it is not marked, and the fastest optional warehouse is obtained through the warehouse position of the optional warehouse and the current position of the refrigerated truck; the first result calculation unit is responsible for obtaining the loss rate of agricultural products that are not transferred according to the estimated arrival time, the real-time transportation time and the quality decline rate of agricultural products; the second result calculation unit is responsible for obtaining the loss rate of agricultural products that are transferred according to the shortest transfer time, the real-time transportation time and the quality decline rate of agricultural products; the transfer decision unit is responsible for obtaining whether the impact of the transfer on the damage to agricultural products reaches the preset transfer threshold based on the first result and the second result, and if the preset transfer threshold is reached, the delay processing module is entered; through the collaborative work of these units, the agricultural product intelligent logistics management system can analyze the delay of the refrigerated truck, thereby deciding whether to transfer, reducing unnecessary transfer operations, and thereby improving the logistics efficiency of the agricultural product intelligent logistics management system.

[0046] Further, such as Figure 4 As shown, it is a structural schematic diagram of a delay processing module in an agricultural product smart logistics management system based on the Internet of Things provided by an embodiment of the present application. The delay processing module includes a transfer request unit and a visualization path unit; the transfer request unit: is used to send a request message to the received optimal transfer warehouse through a network transmission device, and determine whether the corresponding response message is received within a preset time interval. If the response message is received, it indicates that the transfer request is successful, otherwise it is fed back to the delay analysis module to reselect the optimal transfer warehouse. The preset time interval is determined by the preset personnel according to the urgency of the transfer of delayed agricultural products; the visualization path unit: is used to plan the optimal path according to the current position and the warehouse location of the optimal transfer warehouse, and mark the optimal path on the map. The optimal path represents the path with the shortest time from the current position of the refrigerated truck to the position of the optimal transfer warehouse.

[0047] In this embodiment, the preset time interval represents a time period set by the preset personnel according to the historical response situation. For example, if the longest response time within the historical time period without affecting the transfer effect is half an hour, the corresponding preset time interval is set to half an hour; the network transmission equipment refers to the satellite communication equipment. The satellite communication equipment is not restricted by the geographical location. Even remote areas can communicate smoothly with the optimal transfer warehouse, which improves the stability of the smart logistics management system for agricultural products; the transfer request unit is responsible for sending a transfer request to the optimal transfer warehouse, and judging whether a response message is received within the preset time interval. If a response message is received, it enters the visualization path unit. If no response message is received, it feeds back to the delay analysis module to reselect the optimal transfer warehouse; the visualization path unit is responsible for planning the warehouse location of the current position and the optimal transfer warehouse and displaying the optimal path; through the collaborative work of these units, the smart logistics management system for agricultural products can send transfer requests and plan the optimal path in a timely manner, which improves the management efficiency of the smart logistics management system for agricultural products.

[0048] Furthermore, real-time logistics information is obtained through logistics information collection equipment, which includes a GPS module, a mileage sensor, a vehicle speed sensor and an energy sensor. The GPS module is installed on the top of the refrigerated truck, the mileage sensor is installed inside the wheel of the refrigerated truck, the vehicle speed sensor is installed inside the gearbox of the refrigerated truck, and the energy sensor is installed at a location determined by preset personnel based on the energy type of the refrigerated truck.

[0049] In this embodiment, the GPS module receives satellite signals in real time and obtains the current longitude and latitude data, which are recorded as the specific location of the refrigerated truck; the product of the wheel revolutions and the wheel circumference of the refrigerated truck automatically recorded by the mileage sensor is recorded as the current distance of the refrigerated truck; the real-time speed is collected by the speed sensor and the average of the real-time speed in the historical time period is calculated and recorded as the current average speed of the refrigerated truck; the total energy consumption of the refrigerated truck is recorded by the energy sensor and recorded as the current energy consumption of the refrigerated truck; through the comprehensive use of various sensors, the real-time logistics information of the refrigerated truck can be comprehensively collected, which provides data support for subsequent delay judgment, thereby improving the reliability of the delay monitoring module of the smart logistics management system for agricultural products.

[0050] Furthermore, the specific process of delay judgment is as follows: the current remaining distance is obtained by the current distance and the historical total distance of the refrigerated truck, and the current remaining distance is obtained by the difference between the historical total distance and the current distance; the estimated arrival time is obtained by the current remaining distance and the current average speed of the refrigerated truck, and the delay time of the refrigerated truck at the preset time is obtained by combining the historical arrival time, and the delay time is obtained by the difference between the estimated arrival time and the historical arrival time; the delay index of the refrigerated truck at the preset time is obtained by the obtained delay time, current energy consumption and historical energy consumption, and the specific calculation formula of the delay index is as follows:

[0051] ;

[0052] Where n is the number of the preset time. , N is the total number of preset moments, is the delay index of the refrigerated truck at the nth preset time, is the delay time of the refrigerated truck at the nth preset time, is the reference deviation of the delay time of the refrigerated truck, is the current energy consumption of the refrigerated truck at the nth preset moment, is the historical energy consumption of the refrigerated truck at the nth preset moment.

[0053] In this embodiment, when the delay time of the refrigerated truck at the preset time is not greater than 0, it means that there is no delay in the refrigerated truck at the preset time, and the delay index at this time is 0. When the delay time of the refrigerated truck at the preset time is greater than 0, the delay index is obtained through the delay time, current energy consumption and historical energy consumption.

[0054] In order to simplify the description of the independent variables of the delay index, the energy consumption index is defined as , Indicates the energy consumption index of the refrigerated truck at the nth preset time, defining the time index , represents the time index of the refrigerated truck at the nth preset time, The standard deviation of the delay time in the historical time period is represented by the simplified formula: ,The statistical table of the change of the delay index is shown in Table 1:

[0055] From the first set of data in Table 1, we can see that due to Less than 0, so the delay index of the first group is 0; according to the second and third groups of data, it can be seen that the time index of the third group is greater than the time index of the second group, so the delay index of the third group is greater than the delay index of the second group, and the delay index is positively correlated with the time index; according to the third and fourth groups of data, it can be seen that the energy consumption index of the fourth group is greater than the energy consumption index of the third group, so the delay index of the fourth group is greater than the delay index of the third group, and the delay index is positively correlated with the energy consumption index; the numerical evaluation of the delay index of the smart logistics management system for agricultural products is realized.

[0056] Table 1 Statistics of changes in delay index

[0057]

[0058] The algorithm of this embodiment combines the "time index of the refrigerated truck at the nth preset time" and the "energy consumption index of the refrigerated truck at the nth preset time" to obtain the "delay index of the refrigerated truck at the nth preset time" through comprehensive analysis. In addition, the various parameters are related to each other and do not exist independently. They are a whole, and the "delay index of the refrigerated truck at the nth preset time" cannot be obtained by simple combination and addition. For example, the "time index of the refrigerated truck at the nth preset time" in this formula is conducive to increasing the "delay index of the refrigerated truck at the nth preset time", which does not necessarily mean that the "delay index of the refrigerated truck at the nth preset time" is large. At this time, it is also necessary to consider that the "energy consumption index of the refrigerated truck at the nth preset time" can also determine the degree of delay of the refrigerated truck at the preset time. The correlation and mutual influence between various factors are considered in a quantitative manner. The algorithm itself is obtained through comprehensive analysis, which can accurately and quantitatively analyze the degree of delay of the refrigerated truck at the preset time, indicating the effect of comprehensive evaluation of the delay index. Therefore, accurate analysis is performed by establishing a mathematical form.

[0059] Furthermore, the specific steps of transfer sorting are as follows: A1, according to the warehouse location and current location of the optional warehouse, the transfer time of the optional warehouse is obtained, and the transfer time represents the time required for the refrigerated truck to travel from the current location to the warehouse location; A2, the transfer time is sorted to obtain a transfer time sequence, and the shortest transfer time and the optimal transfer warehouse are obtained according to the transfer time sequence. The transfer time sequence represents the sequence obtained by sorting the transfer time in ascending order, the shortest transfer time represents the first transfer time in the transfer time sequence, and the optimal transfer warehouse refers to the optional warehouse corresponding to the shortest transfer time.

[0060] In this embodiment, the transfer time is obtained by calculating the estimated driving time from the current location of the refrigerated truck to the warehouse location through navigation software, thereby enhancing the dynamic adaptability of the smart logistics management system for agricultural products; by calculating and sorting the transfer time of optional warehouses, it is possible to quickly determine the optimal transfer warehouse and the shortest transfer time, thereby improving the efficiency of transfer sorting of the smart logistics management system for agricultural products.

[0061] Furthermore, the specific process of obtaining the transfer index is as follows: obtaining a transfer weight from a preset database, the transfer weight including a first weight and a second weight, the first weight indicating the influence of the first result on the transfer index, and the second weight indicating the influence of the second result on the transfer index; obtaining the transfer index of the refrigerated truck at a preset time through the first result, the second result and the transfer weight, and the specific calculation formula of the transfer index is as follows:

[0062] ;

[0063] Where n is the number of the preset time. , N is the total number of preset moments, is the transfer index of the refrigerated truck at the nth preset time, The first result of delaying agricultural products at the nth preset moment, The second result of delaying agricultural products at the nth preset time, is the first weight, is the second weight.

[0064] In this embodiment, the transfer index is positively correlated with the first result and negatively correlated with the second result. The first weight and the second weight respectively represent the degree of influence of the first result and the second result on the transfer index, and the first result, the second result and the transfer weight are used for calculation, which reduces the influence of subjective judgment and improves the reliability of the smart logistics management system for agricultural products.

[0065] Specifically, the first weight is obtained from a preset database. In a specific embodiment, a fitting curve is obtained by fitting the relationship between the first result and the transfer index by the number of agricultural products that are not transferred in a historical time period and the corresponding number of damaged agricultural products, and the first weight corresponding to the input first result is obtained according to the fitting curve.

[0066] Specifically, the second weight is obtained from a preset database. In a specific embodiment, a fitting curve is obtained by fitting the relationship between the second result and the transfer index by the number of agricultural products transferred in a historical time period and the corresponding number of damaged agricultural products, and the second weight corresponding to the input second result is obtained according to the fitting curve.

[0067] The algorithm of this embodiment combines the "first result of agricultural products delayed at the nth preset time" and the "second result of agricultural products delayed at the nth preset time", and comprehensively analyzes to obtain the "transfer index of the refrigerated truck at the nth preset time", and the various parameters are related and do not exist independently. They are a whole, and the "transfer index of the refrigerated truck at the nth preset time" cannot be obtained by simple combination and addition. For example, in this formula, the "first result of agricultural products delayed at the nth preset time" is conducive to increasing the "transfer index of the refrigerated truck at the nth preset time", which does not necessarily mean that the "transfer index of the refrigerated truck at the nth preset time" is large. At this time, it is also necessary to consider that the "second result of agricultural products delayed at the nth preset time" can also determine the urgency of the transfer. The correlation and mutual influence between various factors are considered in a quantitative way. The algorithm itself is obtained by comprehensive analysis, which can accurately and quantitatively analyze the urgency of the transfer, indicating the effect of comprehensive evaluation of the transfer index. Therefore, accurate analysis is performed by establishing a mathematical form.

[0068] Furthermore, the specific process of obtaining the first result is as follows: the first time is obtained by the estimated arrival time and the real-time transportation time of the refrigerated truck, the first time represents the sum of the estimated arrival time and the real-time transportation time, and the first result of the delayed agricultural products in the refrigerated truck at the preset time is obtained in combination with the corresponding agricultural product quality decline rate. The calculation formula of the first result is as follows:

[0069] ;

[0070] In the formula, is the first result of delayed agricultural products in the refrigerated truck at the nth preset time, The first time of the refrigerated truck at the nth preset time, The rate of decline in agricultural product quality for agricultural products delayed in refrigerated trucks;

[0071] The specific process of obtaining the second result is as follows: the second time is obtained by the shortest transfer time and the real-time transportation time of the refrigerated truck, and the second time represents the sum of the shortest transfer time and the real-time transportation time. The second result of the delayed agricultural products in the refrigerated truck at the preset time is obtained in combination with the corresponding agricultural product quality decline rate. The calculation formula of the second result is as follows:

[0072] ;

[0073] In the formula, The second result of delayed agricultural products in the refrigerated truck at the nth preset time, The second time of the refrigerated truck at the nth preset time.

[0074] In this embodiment, Figure 5 Taking as an example, a schematic diagram of the change of the first result provided in the embodiment of the present application shows that the first result is positively correlated with the first time and the quality decline rate of agricultural products. When the first time is 0 days or the quality decline rate of agricultural products is 0, the first result is 0. The quality decline of agricultural products without transfer and with transfer is calculated respectively through the estimated arrival time, the shortest transfer time and the real-time transportation time, providing comprehensive data support for the transfer index, thereby improving the reliability of the transfer index of the smart logistics management system for agricultural products.

[0075] Furthermore, the specific steps for planning the optimal path according to the current position and the warehouse location of the optimal transfer warehouse are as follows: B1, converting the map into a path network, and obtaining node and edge weights according to the path network. Nodes refer to road entrances and exits, and edge weights refer to the average driving time of roads. The path network represents the connection relationship between geographical locations; B2, using the Dijkstra algorithm to obtain the optimal path. The optimal path represents the shortest path from the starting node to the end node. The shortest path is determined by the minimum value of the sum of the edge weights corresponding to the edges passed through. The starting node is the current position of the refrigerated truck, and the end node is the warehouse location of the optimal transfer warehouse. The starting node represents the starting point of the path planning, and the end node represents the end point of the path planning.

[0076] In this embodiment, the map is provided by a built-in geographic information system. Starting from the starting node, the Dijkstra algorithm is used to traverse each node to update the path estimation value of its adjacent nodes. If the path through the current node to the adjacent node is shorter, the shortest path estimation value and the predecessor node of the adjacent node are updated until the shortest path estimation value of the terminal node is updated. This indicates that the shortest path from the starting node to the terminal node is found, which is recorded as the optimal path between the current position and the warehouse position of the optimal transfer warehouse, thereby improving the path planning efficiency of the smart logistics management system for agricultural products.

[0077] To summarize, the embodiment of the present application collects real-time logistics information of refrigerated trucks, and judges delays in combination with corresponding historical logistics information to obtain the estimated arrival time of the refrigerated trucks. Then, optional warehouses are obtained through delayed agricultural product information and storage information. Transfer sorting is performed in combination with real-time logistics information to obtain the shortest transfer time and the optimal transfer warehouse. At the same time, the delayed agricultural product information is combined with the estimated arrival time and the shortest transfer time to obtain the first result and the second result, and a transfer index is obtained based on this. Finally, according to the transfer index, it is decided whether to make a transfer request and visualize the path planning, thereby realizing intelligent monitoring, analysis and processing of refrigerated truck delays, and further realizing a more accurate response to emergency situations in the transportation of short-shelf-life agricultural products, effectively solving the problem of untimely emergency response in the logistics transportation of short-shelf-life agricultural products in the prior art.

[0078] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0079] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0080] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0081] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0082] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0083] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent logistics management system for agricultural products based on the Internet of Things, characterized in that: Including delay monitoring module, delay analysis module and delay processing module: The delay monitoring module is used to collect real-time logistics information of the refrigerated truck and to determine the delay in combination with the corresponding historical logistics information to obtain the estimated arrival time of the refrigerated truck. The delay analysis module is used to transfer and sort the optional warehouses obtained through delayed agricultural product information and storage information according to real-time logistics information to obtain the shortest transfer time and the optimal transfer warehouse, and at the same time, the delayed agricultural product information is combined with the estimated arrival time and the shortest transfer time to obtain a first result and a second result, thereby obtaining a transfer index; The delay processing module is used to perform transfer requests and visual path planning; The delay processing module includes a transfer request unit and a visualization path unit; The transfer request unit is used to send a request message to the received optimal transfer warehouse through the network transmission device, and determine whether the corresponding response message is received within a preset time interval. If the response message is received, it indicates that the transfer request is successful, otherwise it is fed back to the delay analysis module to reselect the optimal transfer warehouse; The visualization path unit is used to plan the optimal path according to the current position and the warehouse position of the optimal transfer warehouse, and mark the optimal path on the map; The specific process of delay judgment is as follows: The current remaining distance is obtained by the current distance of the refrigerated truck and the total historical distance; The estimated arrival time is obtained by the current remaining distance and current average speed of the refrigerated truck, and the delay time of the refrigerated truck at the preset time is obtained by combining the historical arrival time; The delay index of the refrigerated truck at a preset time is obtained by obtaining the delay time, current energy consumption and historical energy consumption. The specific calculation formula of the delay index is as follows: ； Where n is the number of the preset time. , N is the total number of preset moments, is the delay index of the refrigerated truck at the nth preset time, is the delay time of the refrigerated truck at the nth preset time, is the reference deviation of the delay time of the refrigerated truck, is the current energy consumption of the refrigerated truck at the nth preset moment, is the historical energy consumption of the refrigerated truck at the nth preset moment.

2. The smart logistics management system for agricultural products based on the Internet of Things as claimed in claim 1, characterized in that: The delay monitoring module includes a real-time status acquisition unit, a historical status acquisition unit and a delay judgment unit; The real-time status acquisition unit is used to transmit the real-time transportation time of the refrigerated truck collected by the clock at the preset time to the historical status acquisition unit and the delay analysis module, and transmit the real-time logistics information of the refrigerated truck collected by the on-board sensor to the delay judgment unit, wherein the real-time transportation time represents the time taken by the refrigerated truck from departure to the preset time, and the real-time logistics information includes the current position, current distance, current average speed and current energy consumption; The historical status acquisition unit is used to acquire historical logistics information of the refrigerated truck according to the received real-time transportation time and transmit it to the delay judgment unit, wherein the historical logistics information includes historical total distance, historical arrival time and historical energy consumption; The delay judgment unit is used to make a delay judgment based on the received real-time logistics information and historical logistics information to obtain the estimated arrival time and delay index of the refrigerated truck at a preset time, and make a judgment based on the preset delay threshold obtained from the database. If the delay index is greater than the preset delay threshold obtained from the database, the estimated arrival time is transmitted to the delay analysis module, otherwise no processing is performed.

3. The smart logistics management system for agricultural products based on the Internet of Things as claimed in claim 1, characterized in that: The delay analysis module includes a warehouse selection unit, a first result calculation unit, a second result calculation unit and a transfer decision unit; The warehouse selection unit is used to compare the warehouse space and the scale of agricultural products to obtain optional warehouses, and to perform transfer sorting based on the warehouse location and the current location of the refrigerated truck to obtain the corresponding shortest transfer time and optimal transfer warehouse, transmit the shortest transfer time to the second result calculation unit, and transmit the optimal transfer warehouse to the transfer request unit; The first result calculation unit is used to obtain the first result of the refrigerated truck at a preset time according to the received estimated arrival time, the real-time transportation time and the agricultural product quality decline rate, and transmit the first result to the transfer decision unit, wherein the agricultural product quality decline rate is used to describe the degree to which the quality of the agricultural products transported by the refrigerated truck declines over time; The second result calculation unit is used to obtain the second result of the refrigerated truck at a preset time according to the received shortest transfer time, the real-time transportation time and the agricultural product quality reduction rate, and transmit the second result to the transfer decision unit; The transfer decision unit is used to obtain the transfer index of the refrigerated truck at a preset time based on the first result and the second result and make a judgment in combination with the preset transfer threshold obtained from the database. If the transfer index is greater than the preset transfer threshold obtained from the database, the function of the delay processing module is executed, otherwise no processing is performed.

4. The smart logistics management system for agricultural products based on the Internet of Things as claimed in claim 2, characterized in that: The real-time logistics information is obtained through a logistics information collection device, which includes a GPS module, a mileage sensor, a vehicle speed sensor and an energy sensor. The GPS module is installed on the top of the refrigerated truck, the mileage sensor is installed inside the wheel of the refrigerated truck, the vehicle speed sensor is installed inside the gearbox of the refrigerated truck, and the installation position of the energy sensor is determined by a preset personnel according to the energy type of the refrigerated truck.

5. The smart logistics management system for agricultural products based on the Internet of Things as claimed in claim 3, characterized in that: The specific steps of the transfer sorting are as follows: A1, obtain the transfer time of the optional warehouse according to the warehouse location and current location of the optional warehouse; A2, sort the transfer time to obtain a transfer time sequence, and obtain the shortest transfer time and the optimal transfer bin according to the transfer time sequence. The transfer time sequence represents a sequence obtained by sorting the transfer time in ascending order, and the shortest transfer time represents the first transfer time in the transfer time sequence.

6. The smart logistics management system for agricultural products based on the Internet of Things as claimed in claim 3, characterized in that: The specific process of obtaining the transfer index is as follows: Acquire a transfer weight from a preset database, where the transfer weight includes a first weight and a second weight; The transfer index of the refrigerated truck at a preset time is obtained by the first result, the second result and the transfer weight. The specific calculation formula of the transfer index is as follows: ； Where n is the number of the preset time. , N is the total number of preset moments, is the transfer index of the refrigerated truck at the nth preset moment, The first result of delaying agricultural products at the nth preset moment, The second result of delaying agricultural products at the nth preset time, is the first weight, is the second weight.

7. The smart logistics management system for agricultural products based on the Internet of Things as claimed in claim 6, characterized in that: The specific process of obtaining the first result is as follows: The first time is obtained by the estimated arrival time of the refrigerated truck and the real-time transportation time, and the first result of the delayed agricultural products in the refrigerated truck at the preset time is obtained by combining the corresponding agricultural product quality decline rate. The calculation formula of the first result is as follows: ； In the formula, is the first result of delayed agricultural products in the refrigerated truck at the nth preset time, The first time of the refrigerated truck at the nth preset time, The rate of decline in agricultural product quality for agricultural products delayed in refrigerated trucks; The specific process of obtaining the second result is as follows: The second time is obtained by the shortest transfer time of the refrigerated truck and the real-time transportation time, and the second result of the delayed agricultural products in the refrigerated truck at the preset time is obtained by combining the corresponding agricultural product quality decline rate. The calculation formula of the second result is as follows: ； In the formula, The second result of delayed agricultural products in the refrigerated truck at the nth preset time, The second time of the refrigerated truck at the nth preset time.

8. The smart logistics management system for agricultural products based on the Internet of Things as claimed in claim 1, characterized in that: The specific steps of planning the optimal path according to the current location and the warehouse location of the optimal transfer warehouse are as follows: B1, converting the map into a path network, and obtaining node and edge weights according to the path network, wherein the path network represents the connection relationship between geographical locations; B2, using Dijkstra algorithm to obtain the optimal path, the optimal path represents the shortest path from the starting node to the end node, the starting node is the current position of the refrigerated truck, and the end node is the warehouse position of the optimal transfer warehouse.

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