Intelligent logistics management method and system

Abstract

The application discloses a kind of intelligent logistics management method and system, the method includes: the influence of analyzing environmental data to the temperature change in refrigerated vehicle, the influence of analyzing the temperature change in refrigerated vehicle to goods form obtains the vulnerable degree rating of goods, analyze the path characteristic between refrigerated vehicle and transfer station, based on the path characteristic analysis the predicted transport time of the transport path, based on the time threshold value set to screening transport path, analysis the influence of the jolt characteristic of transport path and the vulnerable degree of goods to transport path score obtains the comprehensive score of transport path, according to the comprehensive score selects target transport path, real-time analysis refrigerated vehicle in target transport path driving time, based on the driving time and predicted time are collated, analysis the influence of the jolt degree of transport path and the vulnerable degree of goods to cost loss obtains loss cost, according to the loss cost to transport path is adjusted, the present application has the characteristics of reducing cost and improving accuracy.

Description

Technical Field

[0001] This invention relates to the field of logistics management technology, specifically to a smart logistics management method and system. Background Technology

[0002] With the rapid development of logistics, the emergence of cold chain logistics has ensured the quality and safety of transported goods. Cold chain logistics includes multiple aspects such as frozen processing, cold storage, and cold chain transportation. It uses various equipment such as refrigerated trucks and freezers to transport goods under cold conditions, and its transportation costs are generally high.

[0003] Currently, cold chain logistics management primarily focuses on the entire logistics process, addressing unforeseen circumstances at each stage only through emergency plans. For instance, if a refrigeration unit in a refrigerated truck malfunctions during transport, preventing it from continuing to refrigerate, existing technologies often rely on the fastest route to the nearest transfer station for replacement. However, temperature fluctuations within the refrigerated truck can cause slight thawing of frozen goods. For example, the ice layer on the surface of frozen goods may slowly melt over time, creating gaps between the frozen items in the insulated container. This makes them susceptible to crushing and collisions due to bumps, altering their fragility. Existing technologies cannot accurately plan transport routes based on real-time fragility, potentially increasing losses and transportation costs. Furthermore, existing technologies, considering only the fastest route, often ignore the impact of actual road conditions on transport time, leading to excessively long transit times and complete damage to the goods. The inability to comprehensively consider losses and transport time for real-time route adjustments results in poor route planning flexibility. Therefore, designing a smart logistics management method and system that reduces costs and improves accuracy is essential. Summary of the Invention

[0004] The purpose of this invention is to provide a smart logistics management method and system to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a smart logistics management method, comprising:

[0006] Collect environmental data and the temperature inside the refrigerated truck, analyze the impact of environmental data on the temperature changes inside the refrigerated truck, and further analyze the impact of temperature changes inside the refrigerated truck on the shape of the goods to obtain a vulnerability rating of the goods.

[0007] The path characteristics between refrigerated trucks and transfer stations are analyzed, and the predicted transportation time of the transportation path is analyzed based on the path characteristics. The transportation path is filtered based on the set time threshold. The impact of the bump characteristics of the transportation path and the fragility of the goods on the transportation path score is analyzed to obtain a comprehensive transportation path score. The target transportation path is selected based on the comprehensive score.

[0008] The system analyzes the travel time of refrigerated trucks on the target transportation route in real time, compares the travel time with the predicted time, analyzes the impact of the bumpiness of the transportation route and the fragility of the goods on cost losses to obtain the loss cost, and adjusts the transportation route according to the loss cost.

[0009] According to the above technical solution, the process of collecting environmental data and the temperature inside the refrigerated truck, analyzing the impact of environmental data on temperature changes inside the refrigerated truck, and further analyzing the impact of temperature changes inside the refrigerated truck on the shape of goods to obtain a fragility rating of the goods includes the following steps:

[0010] The insulation performance of refrigerated trucks is obtained based on the influence of ambient temperature on the trucks, the melting of the ice layer on the outside of the goods is identified, and the fragility of the goods is assessed based on the insulation performance and the melting of the ice layer.

[0011] According to the above technical solution, the method of obtaining the insulation performance of a refrigerated truck based on the influence of ambient temperature, identifying the melting status of the ice layer on the outside of the goods, and assessing the fragility of the goods based on the insulation performance and the melting status of the ice layer includes the following steps:

[0012] For example, the temperature change of the refrigerated truck in an empty state and the corresponding ambient temperature are obtained, a temperature change map is constructed under the ambient temperature, the corresponding temperature change map in the database is retrieved according to different ambient temperatures, the temperature change maps are overlaid and compared, the temperature difference between the temperature change maps at the same time stamp is identified, the difference feature is identified, the influence coefficient ε of ambient temperature on the temperature change of the refrigerated truck is given according to the difference feature, the cargo status change feature with temperature is obtained, the cargo status at different temperatures is analyzed, and the corresponding temperature pair in the database is retrieved according to the cargo status.

[0013] The humidity of the insulated box is obtained. When the humidity of the insulated box is greater than a set threshold, a visual image of the insulated box is obtained, and the edge nodes of the goods in the visual image are identified. The edge nodes of the goods are connected in pairs to construct a node connection line. The length L1 of the node connection line is identified and compared with the length L2 set in the database. If L1-L2 is greater than the set threshold, it means that the ice layer on the surface of the goods has not melted. The influence coefficient θ1 of the goods fragility score set in the database is retrieved. Otherwise, it means that the ice layer on the surface of the goods has melted. The influence coefficient θ2 of the goods fragility score set in the database is retrieved. The weight η of the corresponding influence coefficient θ2 in the database is retrieved according to the humidity of the insulated box.

[0014] Obtain the basic fragility rating P0 of the goods, and calculate the actual fragility rating P1 of the goods using the formula: P1 = (ε + η × θ) j P0, where j = 1, 2, is obtained by matching the actual vulnerability score in the database.

[0015] According to the above technical solution, the steps of analyzing the route characteristics between refrigerated trucks and transfer stations, analyzing the predicted transportation time of the transportation route based on the route characteristics, filtering transportation routes based on a set time threshold, analyzing the impact of the transportation route's bump characteristics and the fragility of the goods on the transportation route score to obtain a comprehensive transportation route score, and selecting a target transportation route based on the comprehensive score include the following steps:

[0016] The theoretical average speed of refrigerated trucks is obtained by analyzing the impact of the route characteristics on the average driving speed of refrigerated trucks. The predicted transportation time of the transportation route is calculated based on the theoretical average driving speed. The transportation routes are then screened based on the impact to obtain a list of alternative transportation routes.

[0017] The three-dimensional data of the candidate transportation routes are obtained, a transportation route model is constructed, the features of the transportation route model are identified, the impact of the features on the driving stability of refrigerated trucks is analyzed to obtain the bumpiness of the transportation route, and the comprehensive score of the transportation route is evaluated based on the bumpiness and the vulnerability, and the highest score is selected as the target transportation route.

[0018] According to the above technical solution, the process of analyzing the impact of the route characteristics on the average driving speed of refrigerated trucks to obtain the theoretical average driving speed of refrigerated trucks, calculating the predicted transportation time of the transportation route based on the theoretical average driving speed, and filtering the transportation routes based on the impact to obtain a list of alternative transportation routes includes the following steps:

[0019] Obtain the location of the refrigerated truck and the location of the transfer station, identify the transportation path between the location of the refrigerated truck and the location of the transfer station, identify the number of inflection points of the transportation path, retrieve the database based on the number of inflection points, and retrieve the corresponding impact coefficient α on the predicted transportation time from the database;

[0020] Identify the length L' of each segment that makes up the transportation route, and retrieve the influence coefficient β set in the database on the average driving speed of refrigerated trucks on the segment based on the length L';

[0021] The predicted transport time for the road segment is calculated using a formula. Where V' represents the set average driving speed of the refrigerated truck, and the predicted transportation time for the transportation route is further calculated. Where i = 1, 2, 3... n, the set transportation time threshold T0 is obtained. If T0 > T1, the transportation path is marked as a candidate path and a candidate path set is constructed. Otherwise, the transportation path is deleted.

[0022] According to the above technical solution, the steps of acquiring the three-dimensional data of the candidate transportation routes, constructing a transportation route model, identifying the features of the transportation route model, analyzing the impact of the features on the driving stability of refrigerated trucks to obtain the bumpiness of the transportation route, evaluating the comprehensive score of the transportation route based on the bumpiness and the vulnerability, and selecting the highest score as the target transportation route include the following steps:

[0023] The three-dimensional data is identified, and a transportation route model is constructed based on the three-dimensional data. The historical driving records of refrigerated trucks are obtained, and the driving speed V1 and pressure data of refrigerated trucks in the historical driving records are identified. The timestamps corresponding to the pressure data are identified, and the pressure data difference K between adjacent timestamps is calculated. If the difference K is greater than a first threshold, the timestamp is marked as the first timestamp. If the difference K is less than the first threshold and greater than a second threshold, the timestamp is marked as the second timestamp. Otherwise, the timestamp is marked as the third timestamp. The location of the refrigerated truck is retrieved based on the first, second, and third timestamps. The location of the refrigerated truck is marked as the first, second, and third bumpy road sections in the transportation route model based on the location of the refrigerated truck. The corresponding influence coefficients φ1, φ2, and φ3 on the driving stability score of the refrigerated truck are retrieved based on the marking of the bumpy road sections.

[0024] The system identifies the difference between timestamps in adjacent bumpy road sections. When the difference is less than a minimum threshold, it identifies the markers in the adjacent bumpy road sections. If the markers are the first bumpy road section and the third bumpy road section or the second bumpy road section and the third bumpy road section, it indicates that the third bumpy road section is a pressure change caused by buffering, and the third bumpy road section is deleted. Otherwise, the markers of the adjacent bumpy road sections are retained. When the difference is greater than the minimum threshold, the markers of the adjacent bumpy road sections are retained.

[0025] Identify the number of the first, second, and third bumpy road sections in the alternative transportation routes, and retrieve the corresponding influence coefficients φ1, φ2, and φ3 influence weights μ1, μ2, and μ3 from the database according to the number;

[0026] The stability score F1 of the refrigerated truck traveling on the alternative transport route was calculated using the formula: F1 = μ k ×φ k ×F0, where k=1,2,3,F0 represents the basic stability score of the refrigerated truck. The bumpiness level of the alternative transportation route is obtained by matching the stability score with the corresponding level according to the set rating conditions.

[0027] Obtain the measured transport time T1 of the candidate transport route. Retrieve the database based on the measured transport time T1 and retrieve the corresponding transport route priority score Q0 from the database. Calculate the comprehensive score Q1 of the candidate transport route using the formula Q1 = (γ × ω + λ) × Q0, where ω represents the influence coefficient of the vulnerability level on the comprehensive score, γ represents the correction coefficient of the vulnerability level influence coefficient ω based on the bumpiness rating of the candidate transport route, and λ represents the influence coefficient of the bumpiness level on the comprehensive score. Sort the candidate transport routes in descending order according to the comprehensive score, select the first-ranked candidate transport route as the target transport route, and navigate according to the target transport route.

[0028] According to the above technical solution, the real-time analysis of the refrigerated truck's travel time on the target transportation route, the comparison between the travel time and the predicted time, the analysis of the impact of the transportation route's bumpiness and the cargo's fragility on cost losses to obtain the loss cost, and the adjustment of the transportation route based on the loss cost, includes the following steps:

[0029] The time T” for the refrigerated truck to pass through the current road segment is obtained and compared with the predicted transportation time T’ of the same road segment. When T”>T’, if T”-T’>T0-T1, then the timeout time T2 = T”-T’-(T0-T1) is calculated. Based on the timeout time, the influence weight κ of the temperature influence coefficient ε set in the database is retrieved, and the second vulnerability score P2 = (κ×ε+η×θ) is calculated using the formula. jP0, based on the second vulnerability score P2, retrieve the database for matching to obtain the second vulnerability level. According to the above method, use the second vulnerability level and the bump level to recalculate the comprehensive score of the transportation routes in the candidate transportation route set, sort the calculation results in descending order, select the first priority transportation route as the switching transportation route, and navigate according to the switching transportation route.

[0030] According to the above technical solution, the intelligent logistics management system includes a data acquisition module and a vulnerability analysis module.

[0031] The data acquisition module is used to establish a logistics database, collect sensor data, and input the sensor data into the logistics database, input comprehensive road condition information into the logistics database, and input comprehensive cargo information into the logistics database. The sensor data may include temperature data, vibration data, etc. The comprehensive road condition information may include three-dimensional path data, historical path data, etc. The comprehensive cargo information may include the characteristics of cargo status changes over time at various temperatures, cargo transportation loss rate, etc.

[0032] The vulnerability analysis module is used to collect environmental data and the temperature inside the refrigerated truck, analyze the impact of environmental data on the temperature changes inside the refrigerated truck, and further analyze the impact of the temperature changes inside the refrigerated truck on the shape of the goods to obtain a vulnerability rating of the goods.

[0033] According to the above technical solution, the system also includes a path planning module;

[0034] The route planning module is used to analyze the route characteristics between refrigerated trucks and transfer stations, analyze the predicted transportation time of the transportation route based on the route characteristics, filter the transportation routes based on the set time threshold, analyze the impact of the transportation route's bump characteristics and the fragility of the goods on the transportation route score to obtain a comprehensive transportation route score, and select the target transportation route based on the comprehensive score.

[0035] According to the above technical solution, the system also includes a path optimization module;

[0036] The route optimization module is used to analyze the travel time of refrigerated trucks on the target transportation route in real time, compare the travel time with the predicted time, analyze the impact of the bumpiness of the transportation route and the fragility of the goods on cost losses to obtain the loss cost, and adjust the transportation route according to the loss cost.

[0037] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention analyzes the characteristics of each segment of the transportation route to determine the impact of these characteristics on the average speed of the refrigerated truck, thereby determining the theoretical average speed of the refrigerated truck on each segment. This allows for accurate prediction of the transportation time required for the route, determination of whether the transportation time meets the criteria, and the generation of alternative transportation routes. This reduces the number of transportation routes requiring subsequent evaluation, thereby reducing data processing volume, increasing system processing speed, reducing wasted time, and improving efficiency. Furthermore, by analyzing the changes in pressure sensor values ​​during the refrigerated truck's operation, it is possible to determine whether the refrigerated truck meets the required standards. When driving on bumpy roads, the system can further determine whether the bumpy section is a buffer zone after a significant bump, accurately identifying the location of the bumpy section and avoiding system misjudgments that could lead to inaccurate route selection. This greatly improves the system's accuracy. Furthermore, by analyzing the degree of bumpiness along the route and the fragility of the goods, the system can minimize damage to the transported goods by selecting the appropriate transportation route, thereby reducing losses during transportation and significantly lowering transportation costs. By calibrating the transportation time, the system can further switch transportation routes during the process, ensuring that losses to the goods are minimized, thus reducing transportation losses and significantly lowering transportation costs. Attached Figure Description

[0038] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0039] Figure 1 This is a flowchart of the method steps of the present invention.

[0040] Figure 2 This is a schematic diagram of the system module composition of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figure 1 The present invention provides a technical solution: a smart logistics management method, comprising:

[0043] Step S1: Establish a logistics database, collect sensor data, and input the sensor data into the logistics database, input the comprehensive road condition information into the logistics database, and input the comprehensive cargo information into the logistics database. Among them, the sensor data may include temperature data, vibration data, etc., the comprehensive road condition information may include three-dimensional route data, historical route data, etc., and the comprehensive cargo information may include the characteristics of cargo status changes over time at various temperatures, cargo transportation loss rate, etc.

[0044] Step S2: Collect environmental data and the temperature inside the refrigerated truck, analyze the impact of environmental data on the temperature changes inside the refrigerated truck, and further analyze the impact of temperature changes inside the refrigerated truck on the shape of the goods to obtain a vulnerability rating of the goods.

[0045] Step S3: Analyze the route characteristics between refrigerated trucks and transfer stations, analyze the predicted transportation time of the transportation route based on the route characteristics, filter the transportation routes based on the set time threshold, analyze the impact of the bump characteristics of the transportation route and the fragility of the goods on the transportation route score to obtain a comprehensive transportation route score, and select the target transportation route based on the comprehensive score.

[0046] Step S4: Analyze the travel time of refrigerated trucks on the target transportation route in real time, compare the travel time with the predicted time, analyze the impact of the bumpiness of the transportation route and the fragility of the goods on cost losses to obtain the loss cost, and adjust the transportation route according to the loss cost.

[0047] In this invention, by analyzing the changes in the pressure sensor values ​​during the refrigerated truck's journey, it is possible to determine whether the refrigerated truck is traveling on a bumpy road. Furthermore, it can be determined whether the bumpy road is a buffer zone after experiencing significant bumps. This allows for accurate identification of the location of bumpy road sections, preventing system misjudgments that could lead to inaccurate route selection and greatly improving system accuracy. Moreover, by analyzing the degree of bumpiness along the route and the fragility of the goods, the selected transportation route can minimize damage to the transported goods, thereby reducing losses during transportation and significantly lowering transportation costs.

[0048] In some preferred embodiments, step S2 further includes the following steps:

[0049] Step S21: Obtain the insulation performance of the refrigerated truck based on the influence of ambient temperature on the refrigerated truck, identify the melting status of the ice layer on the outside of the goods, and assess the fragility of the goods based on the insulation performance and the melting status of the ice layer.

[0050] For example, the temperature change of the refrigerated truck in an empty state and the corresponding ambient temperature are obtained, and a temperature change map is constructed under the ambient temperature. The corresponding temperature change map in the database is retrieved according to different ambient temperatures. The temperature change maps are overlaid and compared to identify the temperature difference between the temperature change maps at the same time stamp. The difference feature is identified, and the influence coefficient ε of ambient temperature on the temperature change of the refrigerated truck is given according to the difference feature. The characteristics of cargo status change with temperature are obtained, and the cargo status at different temperatures is analyzed. The corresponding temperature pair in the database is retrieved according to the cargo status, that is, the heat preservation effect of the refrigerated truck under non-refrigeration conditions is analyzed.

[0051] The humidity of the insulated box is obtained. When the humidity of the insulated box is greater than a set threshold, a visual image of the insulated box is obtained, and the edge nodes of the goods in the visual image are identified. The edge nodes of the goods are connected in pairs to construct a node connection line. The length L1 of the node connection line is identified and compared with the length L2 set in the database. If L1-L2 is greater than the set threshold, it means that the ice layer on the surface of the goods has not melted. The influence coefficient θ1 of the goods fragility score set in the database is retrieved. Otherwise, it means that the ice layer on the surface of the goods has melted. The influence coefficient θ2 of the goods fragility score set in the database is retrieved. The weight η of the corresponding influence coefficient θ2 in the database is retrieved according to the humidity of the insulated box.

[0052] Obtain the basic fragility rating P0 of the goods, and calculate the actual fragility rating P1 of the goods using the formula: P1 = (ε + η × θ) j P0, where j = 1, 2, is obtained by matching the actual vulnerability score in the database.

[0053] In some preferred embodiments, step S3 further includes the following steps:

[0054] Step S31: Analyze the impact of the route characteristics on the average driving speed of the refrigerated truck to obtain the theoretical average driving speed of the refrigerated truck, calculate the predicted transportation time of the transportation route based on the theoretical average driving speed, and filter the transportation routes based on the impact to obtain a list of alternative transportation routes.

[0055] For example, the location of the refrigerated truck and the location of the transfer station are obtained, the transportation path between the location of the refrigerated truck and the location of the transfer station is identified, the number of inflection points of the transportation path is identified, the database is searched based on the number of inflection points, and the corresponding influence coefficient α on the predicted transportation time is retrieved from the database.

[0056] Identify the length L' of each segment that makes up the transportation route, and retrieve the influence coefficient β set in the database on the average driving speed of refrigerated trucks on the segment based on the length L';

[0057] The predicted transport time for the road segment is calculated using a formula. Where V' represents the set average driving speed of the refrigerated truck, and the predicted transportation time for the transportation route is further calculated. Where i = 1, 2, 3...n, a set transportation time threshold T0 is obtained. If T0 > T1, the transportation route is marked as a candidate route, and a candidate route set is constructed; otherwise, the transportation route is deleted. By analyzing the characteristics of each segment in the transportation route, the influence of the characteristics on the average driving speed of the refrigerated truck is determined, thereby determining the theoretical average driving speed of the refrigerated truck on each segment. This enables accurate prediction of the transportation time required for the transportation route, determination of whether the transportation time meets the judgment conditions, and obtaining candidate transportation routes. This reduces the number of transportation routes that need to be evaluated subsequently, thereby reducing the amount of data processing, improving the system's processing speed, reducing time waste, and improving efficiency.

[0058] Step S32: Obtain the three-dimensional data of the candidate transportation routes, construct a transportation route model, identify the features of the transportation route model, analyze the impact of the features on the driving stability of refrigerated trucks to obtain the bumpiness of the transportation route, evaluate the comprehensive score of the transportation route based on the bumpiness and the vulnerability, and select the highest score as the target transportation route.

[0059] For example, the three-dimensional data is identified, a transportation route model is constructed based on the three-dimensional data, historical driving records of refrigerated trucks are obtained, the driving speed V1 and pressure data of refrigerated trucks in the historical driving records are identified, the timestamps corresponding to the pressure data are identified, and the pressure data difference K between adjacent timestamps is calculated. If the difference K is greater than a first threshold, it indicates that the vehicle has vibrated, and the timestamp is marked as the first timestamp. If the difference K is less than the first threshold but greater than a second threshold, it indicates that the vehicle has vibrated again, and the timestamp is marked as the second timestamp. Otherwise, the timestamp is marked as the third timestamp. The location of the refrigerated truck is retrieved based on the first, second, and third timestamps, and the location of the refrigerated truck is marked as the first, second, and third bumpy road sections in the transportation route model based on the location of the refrigerated truck. The corresponding influence coefficients φ1, φ2, and φ3 on the driving stability score of the refrigerated truck are retrieved based on the marking of the bumpy road sections. Here, bumpy road sections and road sections are two different concepts. Bumpy road sections refer to the bumpy part of a road section, while road sections refer to the route segments that make up the transportation route.

[0060] The system identifies the difference between timestamps in adjacent bumpy road sections. When the difference is less than a minimum threshold, it identifies the markers in the adjacent bumpy road sections. If the markers are the first bumpy road section and the third bumpy road section or the second bumpy road section and the third bumpy road section, it indicates that the third bumpy road section is a pressure change caused by buffering, and the third bumpy road section is deleted. Otherwise, the markers of the adjacent bumpy road sections are retained. When the difference is greater than the minimum threshold, the markers of the adjacent bumpy road sections are retained.

[0061] Identify the number of the first, second, and third bumpy road sections in the alternative transportation routes, and retrieve the corresponding influence coefficients φ1, φ2, and φ3 influence weights μ1, μ2, and μ3 from the database according to the number;

[0062] The stability score F1 of the refrigerated truck traveling on the alternative transport route was calculated using the formula: F1 = μ k ×φ k ×F0, where k=1,2,3,F0 represents the basic stability score of the refrigerated truck. The bumpiness level of the alternative transportation route is obtained by matching the stability score with the corresponding level according to the set rating conditions.

[0063] Obtain the measured transport time T1 of the candidate transport routes. Based on the measured transport time T1, retrieve the database and obtain the corresponding transport route priority score Q0. Calculate the comprehensive score Q1 of the candidate transport routes using the formula: Q1 = (γ × ω + λ) × Q0, where ω represents the influence coefficient of the vulnerability level on the comprehensive score, γ represents the correction coefficient of the vulnerability level influence coefficient ω based on the bumpiness rating of the candidate transport routes, and λ represents the influence coefficient of the bumpiness level on the comprehensive score. Sort the candidate transport routes in descending order according to the comprehensive score and select the first-ranked route. The system selects a target transportation route and navigates based on it. By analyzing the changes in pressure sensor values ​​during the refrigerated truck's journey, it can determine whether the truck is traveling on a bumpy road. Furthermore, it can determine whether the bumpy road is a buffer zone after a significant bump, accurately identifying the location of bumpy road sections and avoiding system misjudgments that could lead to inaccurate route selection. This significantly improves the system's accuracy. Moreover, by analyzing the degree of bumpiness along the route and the fragility of the goods, the system can minimize the damage to the transported goods by selecting the appropriate transportation route, thereby reducing losses during transportation and significantly lowering transportation costs.

[0064] In some preferred embodiments, step S4 further includes the following steps:

[0065] The time T” for the refrigerated truck to pass through the current road segment is obtained and compared with the predicted transportation time T’ of the same road segment. If T” > T’, and T” - T’ > T0 - T1, it means that the transportation route of the route cannot complete the transportation task within the set transportation time threshold T0. The timeout time T2 = T” - T’ - (T0 - T1) is calculated. Based on the timeout time, the influence weight κ of the temperature influence coefficient ε set in the database is retrieved, and the second vulnerability score P2 = (κ × ε + η × θ) is calculated using the formula. j P0, based on the second vulnerability score P2, retrieves and matches the database to obtain the second vulnerability level. Using the second vulnerability level and the bump level, the comprehensive score of the transportation routes in the candidate transportation route set is recalculated according to the above method. The calculation results are sorted in descending order, and the first-ranked transportation route is selected as the switching transportation route. Navigation is performed according to the switching transportation route. By checking the transportation time, the transportation route is switched during the transportation process, thereby ensuring that the loss of goods is minimized, reducing transportation losses and greatly reducing transportation costs.

[0066] Using the same inventive concept as the above embodiments, this application also provides an intelligent logistics management system, including: a data acquisition module, a fragility analysis module, a route planning module, and a route optimization module;

[0067] The data acquisition module is used to establish a logistics database, collect sensor data, and input the sensor data into the logistics database, input comprehensive road condition information into the logistics database, and input comprehensive cargo information into the logistics database. The sensor data may include temperature data, vibration data, etc. The comprehensive road condition information may include three-dimensional path data, historical path data, etc. The comprehensive cargo information may include the characteristics of cargo status changes over time at various temperatures, cargo transportation loss rate, etc.

[0068] The vulnerability analysis module is used to collect environmental data and the temperature inside the refrigerated truck, analyze the impact of environmental data on the temperature changes inside the refrigerated truck, and further analyze the impact of the temperature changes inside the refrigerated truck on the shape of the goods to obtain a vulnerability rating of the goods.

[0069] The route planning module is used to analyze the route characteristics between refrigerated trucks and transfer stations, analyze the predicted transportation time of the transportation route based on the route characteristics, filter the transportation routes based on the set time threshold, analyze the impact of the transportation route's bump characteristics and the fragility of the goods on the transportation route score to obtain a comprehensive transportation route score, and select the target transportation route based on the comprehensive score.

[0070] The route optimization module is used to analyze the travel time of refrigerated trucks on the target transportation route in real time, compare the travel time with the predicted time, analyze the impact of the bumpiness of the transportation route and the fragility of the goods on cost losses to obtain the loss cost, and adjust the transportation route according to the loss cost.

[0071] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0072] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended 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 make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart logistics management method, characterized in that: The method includes: Collect environmental data and the temperature inside the refrigerated truck, analyze the impact of environmental data on the temperature changes inside the refrigerated truck, analyze the impact of the temperature changes inside the refrigerated truck on the shape of the goods, and obtain the vulnerability level of the goods. The path characteristics between refrigerated trucks and transfer stations are analyzed. Based on the path characteristics, the predicted transportation time of the transportation path is analyzed. The transportation path is screened based on the set time threshold. The impact of the bump characteristics of the transportation path and the fragility of the goods on the transportation path score is analyzed to obtain a comprehensive transportation path score. The target transportation path is selected based on the comprehensive score. The system analyzes the travel time of refrigerated trucks on the target transportation route in real time, compares the travel time with the predicted time, analyzes the impact of the bumpiness of the transportation route and the fragility of the goods on cost losses to obtain the loss cost, and adjusts the transportation route according to the loss cost. The process involves analyzing the route characteristics between refrigerated trucks and transfer stations, predicting the transport time based on these characteristics, filtering transport routes based on set time thresholds, analyzing the impact of route bump characteristics and cargo fragility on the transport route score to obtain a comprehensive transport route score, and selecting target transport routes based on the comprehensive score. This includes the following steps: The theoretical average speed of refrigerated trucks is obtained by analyzing the impact of the route characteristics on the average driving speed of refrigerated trucks. The predicted transportation time of the transportation route is calculated based on the theoretical average driving speed. The transportation routes are then screened based on the impact to obtain a list of alternative transportation routes. The three-dimensional data of the candidate transportation routes are obtained, a transportation route model is constructed, the characteristics of the transportation route model are identified, the impact of the transportation route model characteristics on the driving stability of refrigerated trucks is analyzed to obtain the bumpiness of the transportation route, and the comprehensive score of the transportation route is evaluated based on the bumpiness and the vulnerability, and the highest score is selected as the target transportation route. The process of acquiring three-dimensional data of the candidate transportation routes, constructing a transportation route model, identifying the characteristics of the transportation route model, analyzing the impact of the transportation route model characteristics on the driving stability of refrigerated trucks to obtain the bumpiness of the transportation route, evaluating the comprehensive score of the transportation route based on the bumpiness and the vulnerability, and selecting the highest score as the target transportation route includes the following steps: The three-dimensional data is identified, a transportation route model is constructed based on the three-dimensional data, historical driving records of refrigerated trucks are obtained, and the driving speed of refrigerated trucks in the historical driving records is identified. Based on the pressure data, identify the timestamps corresponding to the pressure data, and calculate the pressure data difference between adjacent timestamps. If the difference If the difference is greater than the first threshold, then the timestamp is marked as the first timestamp; if the difference is greater than the first threshold, then the timestamp is marked as the first timestamp. If the value is less than a first threshold and greater than a second threshold, the timestamp is marked as the second timestamp; otherwise, it is marked as the third timestamp. The refrigerated truck's location is retrieved based on the first, second, and third timestamps. Based on these locations, the refrigerated truck is marked as a first, second, and third bumpy road segment in the transportation route model. The corresponding influence coefficients on the refrigerated truck's driving stability score are retrieved based on these bumpy road segment markings. ; The system identifies the difference between timestamps in adjacent bumpy road sections. When the difference is less than a minimum threshold, it identifies the markers in the adjacent bumpy road sections. If the markers are the first bumpy road section and the third bumpy road section or the second bumpy road section and the third bumpy road section, it indicates that the third bumpy road section is a pressure change caused by buffering, and the third bumpy road section is deleted. Otherwise, the markers of the adjacent bumpy road sections are retained. When the difference is greater than the minimum threshold, the markers of the adjacent bumpy road sections are retained. Identify the number of the first, second, and third bumpy road sections among the alternative transportation routes, and retrieve the corresponding influence coefficients from the database based on these numbers. Influence weight ; The stability score of the refrigerated truck on the alternative transport routes was calculated using a formula. ,in, , This represents the basic stability score of the refrigerated truck. Based on the set rating criteria, the stability score is matched with the corresponding level to obtain the bumpiness level of the alternative transportation route. Obtain the predicted transit time for the alternative transportation routes. According to the predicted transportation time Retrieve the database and retrieve the corresponding transportation route priority score. The comprehensive score of the alternative transportation routes is calculated using a formula. ,in, The coefficient indicating the influence of the vulnerability level on the overall score. The coefficient indicating the influence of the bumpiness level of the alternative transport route on the vulnerability level on the overall score is identified. Correction factor, The coefficient representing the impact of the bumpiness level on the overall score is used to sort the candidate transportation routes in descending order according to the overall score, select the first candidate transportation route as the target transportation route, and navigate according to the target transportation route.

2. The intelligent logistics management method according to claim 1, characterized in that: The process of collecting environmental data and the temperature inside the refrigerated truck, analyzing the impact of environmental data on temperature changes inside the refrigerated truck, and analyzing the impact of temperature changes inside the refrigerated truck on the shape of goods to obtain the fragility level of the goods includes the following steps: The insulation performance of refrigerated trucks is obtained based on the influence of ambient temperature on the trucks, the melting of the ice layer on the outside of the goods is identified, and the fragility of the goods is assessed based on the insulation performance and the melting of the ice layer.

3. The intelligent logistics management method according to claim 2, characterized in that: The method for obtaining the insulation performance of refrigerated trucks based on the influence of ambient temperature, identifying the melting status of the ice layer on the outside of the goods, and assessing the fragility of the goods based on the insulation performance and the melting status of the ice layer includes the following steps: The temperature changes of an empty refrigerated truck and the corresponding ambient temperature are obtained. A temperature change map is constructed under the ambient temperature. For different ambient temperatures, corresponding temperature change maps are retrieved from the database. These temperature change maps are overlaid and compared to identify the temperature difference between maps at the same time stamp. The characteristics of these temperature differences are identified, and based on these characteristics, an influence coefficient of ambient temperature on the temperature change of the refrigerated truck is given. The system acquires the characteristics of cargo status changes with temperature, analyzes cargo status at different temperatures, and retrieves the corresponding temperature pairs from the database based on the cargo status. The humidity of the insulated box is obtained. When the humidity of the insulated box is greater than a set threshold, a visual image of the inside of the insulated box is acquired. The edge nodes of the goods in the visual image are identified, and each pair of edge nodes is connected to construct a node connection line. The length of the node connection line is then identified. Compare with the length set in the database. ,like If the value exceeds the set threshold, it indicates that the ice layer on the cargo surface has not melted, and the database is retrieved to determine the influence coefficient on the cargo fragility score. Conversely, if the ice layer on the cargo surface melts, the database will be retrieved to determine the impact coefficient on the cargo fragility rating. The corresponding influence coefficient is retrieved from the database based on the humidity of the insulation box. weight ; Obtain the basic vulnerability rating of the goods The actual vulnerability score of the goods is calculated using a formula. ,in, The vulnerability level is obtained by matching the actual vulnerability score with the database.

4. The intelligent logistics management method according to claim 1, characterized in that: The process of analyzing the impact of the route characteristics on the average speed of refrigerated trucks to obtain the theoretical average speed of refrigerated trucks, calculating the predicted transportation time of the transportation route based on the theoretical average speed, and filtering the transportation routes based on the impact to obtain a list of candidate transportation routes includes the following steps: The system obtains the locations of refrigerated trucks and transfer stations, identifies the transportation route between these locations, identifies the number of inflection points on the transportation route, and retrieves the corresponding impact coefficients on the predicted transportation time from the database based on these inflection point numbers. ; Identify the length of each segment that makes up the transportation route. According to the length Retrieve the influence coefficient set in the database on the average driving speed of refrigerated trucks on the road section. ; The predicted transport time for the road segment is calculated using a formula. ,in, This represents the average driving speed set by the refrigerated truck, and the predicted transportation time for the transportation route is calculated. ,in, Get the set transportation time threshold ,like If the condition is met, the transportation route is marked as a candidate route, and a candidate route set is constructed; otherwise, the transportation route is deleted.

5. The intelligent logistics management method according to claim 1, characterized in that: The real-time analysis of the refrigerated truck's travel time on the target transportation route, the comparison between the travel time and the predicted time, the analysis of the impact of the transportation route's bumpiness and the cargo's fragility on cost losses to obtain the loss cost, and the adjustment of the transportation route based on the loss cost, includes the following steps: Get the time it takes for a refrigerated truck to pass through the current road segment. Compare the predicted transport time of the aforementioned road segment. ,when At that time, if Then calculate the timeout period. Based on the timeout period, retrieve the temperature influence coefficient set in the database. Influence weight The second vulnerability score is calculated using a formula. , The temperature influence coefficient is retrieved based on the timeout period; based on the second vulnerability score. The database is searched for matching to obtain a second vulnerability level. Based on the above method, the comprehensive score of the transportation routes in the set of candidate transportation routes is recalculated using the second vulnerability level and the bump level. The calculation results are sorted in descending order, and the first-ranked transportation route is selected as the switching transportation route. Navigation is then performed based on the switching transportation route.

6. A smart logistics management system for implementing the smart logistics management method according to any one of claims 1-5, characterized in that: The system includes a data acquisition module and a vulnerability analysis module; The data acquisition module is used to establish a logistics database, collect sensor data, and input the sensor data into the logistics database, input comprehensive road condition information into the logistics database, and input comprehensive cargo information into the logistics database. The sensor data includes temperature data and vibration data, the comprehensive road condition information includes three-dimensional path data and historical path data, and the comprehensive cargo information includes the characteristics of cargo status changes over time at various temperatures and the cargo transportation loss rate. The vulnerability analysis module is used to collect environmental data and the temperature inside the refrigerated truck, analyze the impact of environmental data on the temperature changes inside the refrigerated truck, analyze the impact of the temperature changes inside the refrigerated truck on the shape of the goods, and obtain the vulnerability level of the goods.

7. The intelligent logistics management system according to claim 6, characterized in that: The system also includes a path planning module; The route planning module is used to analyze the route characteristics between refrigerated trucks and transfer stations, predict the transportation time of the transportation route based on the route characteristics, filter the transportation routes based on the set time threshold, analyze the impact of the transportation route's bump characteristics and the fragility of the goods on the transportation route score to obtain a comprehensive transportation route score, and select the target transportation route based on the comprehensive score.

8. The intelligent logistics management system according to claim 7, characterized in that: The system also includes a path optimization module; The route optimization module is used to analyze the travel time of refrigerated trucks on the target transportation route in real time, compare the travel time with the predicted time, analyze the impact of the bumpiness of the transportation route and the fragility of the goods on cost losses to obtain the loss cost, and adjust the transportation route according to the loss cost.

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