Intelligent logistics management method and system

Abstract

The invention discloses an intelligent logistics management method and system, and the method comprises the steps: analyzing the influence of environment data on the temperature change in a refrigerator car, analyzing the influence of the temperature change in the refrigerator car on the form of goods, obtaining the vulnerability degree grade of the goods, analyzing the path characteristics between the refrigerator car and a transfer station, and obtaining the vulnerability degree grade of the goods. Analyzing the predicted transportation time of the transportation paths based on the path characteristics, screening the transportation paths based on a set time threshold value, analyzing the jolting characteristics of the transportation paths and the influence of the cargo vulnerability degree on the score of the transportation paths to obtain a comprehensive score of the transportation paths, and selecting a target transportation path according to the comprehensive score, the method comprises the steps that the running time of a refrigerator car in a target transportation path is analyzed in real time, proofreading is conducted on the basis of the running time and predicted time, the influence of the bumping degree of the transportation path and the goods vulnerability degree on cost loss is analyzed to obtain loss cost, and the transportation path is adjusted according to the loss cost. The method has the characteristics of reducing cost and improving accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to an intelligent logistics management method and system. Background Technique

[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 refrigerated processing, cold storage, and cold chain transportation. Refrigerated trucks, freezers and other equipment are used to transport goods in a refrigerated manner, and its transportation cost is generally high.

[0003] Currently, the management methods of cold chain logistics mainly manage the entire logistics process. For emergencies that occur in each detailed link, they are only handled according to emergency plans. For example, when the refrigeration equipment of a refrigerated truck fails during transportation, resulting in the refrigerated truck being unable to continue refrigerating. Existing technologies often go to the nearest transfer station along the fastest transportation route to replace the refrigerated truck. However, since the temperature change of the refrigerated truck will cause the frozen goods to thaw slightly, for example, the ice layer on the surface of the frozen goods will slowly melt over time, resulting in gaps between the frozen goods in the insulated box, and it is easy for the frozen goods to be squeezed and collided with each other due to bumps, resulting in changes in the vulnerability of the goods. Existing technologies cannot accurately plan the transportation route according to the real-time vulnerability of the goods, which may lead to an increase in goods losses and an increase in transportation costs. Moreover, existing technologies only consider the fastest transportation route and often ignore the impact of the actual road conditions on the transportation time, resulting in the possibility that the transportation time may be too long according to the established transportation route, leading to all the goods being damaged. Existing technologies cannot comprehensively consider losses and transportation time to accurately adjust the transportation route in real time, resulting in poor flexibility of route planning. Therefore, it is necessary to design an intelligent logistics management method and system that can reduce costs and improve accuracy. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent logistics management method and system to solve the problems raised in the above background technique.

[0005] To solve the above technical problems, the present invention provides the following technical solutions: An intelligent logistics management method, including:

[0006] Collect environmental data and the temperature inside the refrigerated truck, analyze the influence of environmental data on the temperature change inside the refrigerated truck, and further analyze the influence of the temperature change inside the refrigerated truck on the form of the goods to obtain the vulnerability rating of the goods;

[0007] Analyze the path characteristics between the refrigerated truck and the transfer station, analyze the predicted transportation time of the transportation path based on the path characteristics, screen the transportation path based on the set time threshold, analyze the impact of the bumpy characteristics of the transportation path and the vulnerability of the goods on the transportation path score to obtain the comprehensive score of the transportation path, and select the target transportation path according to the comprehensive score;

[0008] Analyze the driving time of the refrigerated truck in the target transportation path in real time, proofread based on the driving time and the predicted time, analyze the impact of the bumpiness of the transportation path and the vulnerability of the goods on the cost loss to obtain the loss cost, and adjust the transportation path according to the loss cost.

[0009] According to the above technical solution, collect the environmental data and the temperature inside the refrigerated truck, analyze the impact of the environmental data on the temperature change inside the refrigerated truck, and further analyze the impact of the temperature change inside the refrigerated truck on the form of the goods to obtain the vulnerability rating of the goods, including the following steps:

[0010] Obtain the heat preservation performance of the refrigerated truck based on the impact of the environmental temperature on the refrigerated truck, identify the melting situation of the ice layer outside the goods, and evaluate the vulnerability of the goods according to the heat preservation performance and the melting situation of the ice layer.

[0011] According to the above technical solution, obtain the heat preservation performance of the refrigerated truck based on the impact of the environmental temperature on the refrigerated truck, identify the melting situation of the ice layer outside the goods, and evaluate the vulnerability of the goods according to the heat preservation performance and the melting situation of the ice layer, including the following steps:

[0012] Exemplarily, obtain the temperature change of the refrigerated truck in the empty box state and the corresponding environmental temperature, construct the temperature change graph at the environmental temperature, retrieve the corresponding temperature change graph in the database according to different environmental temperatures, overlap and compare the temperature change graphs, identify the temperature difference between the temperature change graphs at the same time stamp, identify the difference characteristics, give the influence coefficient ε of the environmental temperature on the temperature change of the refrigerated truck according to the difference characteristics, obtain the characteristics of the goods state changing with temperature, analyze the goods state at different temperatures, and retrieve the corresponding temperature pairs in the database according to the goods state;

[0013] Obtain the humidity of the incubator. When the humidity of the incubator is greater than the set threshold, obtain the visual image inside the incubator, identify the edge nodes of the goods in the visual image, connect the edge nodes of the goods in pairs to construct node connections, identify the length L1 of the node connections, compare it with the set length L2 in the database. If L1 - L2 is greater than the set threshold, it indicates that the ice layer on the surface of the goods has not melted, and retrieve the influence coefficient θ1 of the set impact on the vulnerable score of the goods from the database. Otherwise, it indicates that the ice layer on the surface of the goods has melted, and retrieve the influence coefficient θ2 of the set impact on the vulnerable score of the goods from the database. Retrieve the weight η of the corresponding influence coefficient θ2 in the database according to the humidity of the incubator;

[0014] Obtain the basic vulnerable score P0 of the goods, and calculate the actual vulnerable score P1 of the goods through the formula P1=(ε + η×θ j )P0, where j = 1, 2. Retrieve the vulnerability level through matching in the database according to the actual vulnerable score.

[0015] According to the above technical solution, analyze the path characteristics between the refrigerated truck and the transfer station, analyze the predicted transportation time of the transportation path based on the path characteristics, screen the transportation path based on the set time threshold, analyze the impact of the bump characteristics of the transportation path and the vulnerability degree of the goods on the transportation path score to obtain the comprehensive transportation path score, and select the target transportation path according to the comprehensive score, including the following steps:

[0016] Analyze the impact of the path 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 path according to the theoretical average driving speed, and screen the transportation path according to the impact to obtain an alternative transportation path list. Screen based on the transportation path to obtain an alternative transportation path list;

[0017] Obtain the three-dimensional data of the alternative transportation path, construct a transportation path model, identify the characteristics of the transportation path model, analyze the impact of the characteristics on the driving stability of the refrigerated truck to obtain the bumpiness degree of the transportation path, and evaluate the comprehensive transportation path score according to the bumpiness degree and the vulnerability degree, and select the one with the highest score as the target transportation path.

[0018] According to the above technical solution, analyze the impact of the path 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 path according to the theoretical average driving speed, and screen the transportation path according to the impact to obtain an alternative transportation path list. Screen based on the transportation path to obtain an alternative transportation path list, including the following steps:

[0019] Obtain the positions of the refrigerated truck and the transfer station, identify the transportation route between the position of the refrigerated truck and the position of the transfer station, identify the number of turning points of the transportation route, retrieve the database according to the number of turning points, and retrieve the influence coefficient α on the predicted transportation time corresponding in the database;

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

[0021] Calculate the predicted transportation time of the section through the formula where V' represents the set average driving speed of the refrigerated truck, and further calculate the predicted transportation time of the transportation route where i = 1, 2, 3... n, obtain the set transportation time threshold T0. If T0 > T1, mark the transportation route as an alternative route and construct a set of alternative routes. Otherwise, delete the transportation route.

[0022] According to the above technical solution, obtaining the three-dimensional data of the alternative transportation route, constructing a transportation route model, identifying the characteristics of the transportation route model, analyzing the influence of the characteristics on the driving stability of the refrigerated truck to obtain the bumpiness of the transportation route, and evaluating the comprehensive score of the transportation route according to the bumpiness and the vulnerability degree, and selecting the one with the highest score as the target transportation route, including the following steps:

[0023] Identify the three-dimensional data, construct a transportation route model according to the three-dimensional data, obtain the historical driving records of the refrigerated truck, identify the driving speed V1 and pressure data in the historical driving records, identify the time stamps corresponding to the pressure data, calculate the pressure data difference K between adjacent time stamps. If the difference K is greater than the first threshold, mark the time stamp as the first time stamp. If the difference K is less than the first threshold and greater than the second threshold, mark the time stamp as the second time stamp. Otherwise, mark the time stamp as the third time stamp. Retrieve the position of the refrigerated truck according to the first, second, and third time stamps, and mark the first, second, and third bumpy sections in the transportation route model according to the position of the refrigerated truck. Retrieve the influence coefficients φ1, φ2, φ3 on the driving stability score of the refrigerated truck corresponding to the marks of the bumpy sections respectively;

[0024] Identify the difference between timestamps in adjacent bumpy sections. When the difference is less than the minimum threshold, identify the marks in the adjacent bumpy sections. If the marks are for the first bumpy section and the third bumpy section or the second bumpy section and the third bumpy section, it indicates that the third bumpy section is a pressure change caused by buffering. Delete the third bumpy section. Otherwise, retain the marks of the adjacent bumpy sections. When the difference is greater than the minimum threshold, retain the marks of the adjacent bumpy sections;

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

[0026] Calculate the stability score F1 of the refrigerated truck driving on the alternative transportation route through the formula F1 = μ k ×φ k ×F0, where k = 1, 2, 3, F0 represents the basic stability score of the refrigerated truck, and match the corresponding level according to the set rating conditions for the stability score to obtain the bumpiness level of the alternative transportation route;

[0027] Obtain the measured transportation time T1 of the alternative transportation route. Retrieve the corresponding transportation route priority score Q0 in the database according to the measured transportation time T1. Calculate the comprehensive score Q1 of the alternative transportation route through 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 influence coefficient ω of the vulnerability level based on the bumpiness rating of the alternative transportation route, λ represents the influence coefficient of the bumpiness level on the comprehensive score. Sort the alternative transportation routes in descending order according to the comprehensive score, select the first-ranked alternative transportation route as the target transportation route, and perform navigation based on the target transportation route.

[0028] According to the above technical solution, analyze the driving time of the refrigerated truck on the target transportation route in real time, calibrate based on the driving time and the predicted time, analyze the impact of the bumpiness of the transportation route and the vulnerability of the goods on the cost loss to obtain the loss cost, and adjust the transportation route according to the loss cost, including the following steps:

[0029] Obtain the time T” when the refrigerated truck passes through the current section, and compare it with the predicted transportation time T' of the section. When T” > T', if T” - T' > T0 - T1, calculate the overtime time T2 = T” - T' - (T0 - T1). Retrieve the influence weight κ of the influence coefficient ε of the temperature set in the database according to the overtime time. Calculate the second vulnerability score P2 = (κ×ε + η×θ j)P0, retrieve the database according to the second vulnerability score P2 for matching to obtain the second vulnerability level. According to the above method, recalculate the comprehensive score of the transportation routes in the set of alternative transportation routes by using the second vulnerability level and the bumpiness level, and sort the calculation results in descending order. Select the transportation route in the first place as the switched transportation route, and perform navigation based on the switched 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 enter the sensor data into the logistics database, enter the comprehensive road condition information into the logistics database, and enter 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 path data, historical path data, etc., and the comprehensive cargo information may include the characteristics of the cargo state changing with time at various temperatures, the cargo transportation loss rate, etc.;

[0032] The vulnerability analysis module is used to collect environmental data and the temperature in the refrigerated truck, analyze the influence of the environmental data on the temperature change in the refrigerated truck, and further analyze the influence of the temperature change in the refrigerated truck on the cargo form to obtain the vulnerability level rating of the cargo.

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

[0034] The path planning module is used to analyze the path characteristics between the refrigerated truck and the transfer station, predict the transportation time of the transportation route based on the path characteristics, screen the transportation route based on a set time threshold, analyze the influence of the bumpiness characteristics and cargo vulnerability level of the transportation route on the transportation route score to obtain the comprehensive transportation route score, and select the target transportation route according to the comprehensive score.

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

[0036] The path optimization module is used to analyze the driving time of the refrigerated truck on the target transportation route in real time, proofread based on the driving time and the predicted time, analyze the influence of the bumpiness degree and cargo vulnerability level of the transportation route on the cost loss to obtain the loss cost, and adjust the transportation route according to the loss cost.

[0037] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By analyzing the characteristics of each section of the transportation route, the present invention determines the impact of these characteristics on the average driving speed of the refrigerated truck, and then determines the theoretical average driving speed of the refrigerated truck on each section. As a result, it can accurately predict the transportation time required for the transportation route, judge whether the transportation time meets the judgment conditions, obtain alternative transportation routes, reduce the number of transportation routes that need to be evaluated subsequently, thereby reducing the amount of data processing, improving the processing speed of the system, reducing the waste of time and improving efficiency. By analyzing the numerical changes of the pressure sensor during the driving process of the refrigerated truck, it can be determined whether the refrigerated truck is driving on a bumpy section, and further judge whether the bumpy section is a buffer after a large bump, accurately determine the position of the bumpy section, avoid misjudgment of the system, which may lead to inaccurate route selection, greatly improving the accuracy of the system. And by analyzing the degree of bumpiness in the route and the vulnerability of the goods, the selected transportation route can minimize the damage to the transported goods, thereby reducing the loss of goods during transportation and greatly reducing the transportation cost. By calibrating the transportation time and further switching the transportation route during transportation, the loss of goods can be minimized, reducing transportation losses and greatly reducing the transportation cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention, and do not constitute a limitation to the present invention. In the drawings:

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

[0040] Figure 2 is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0042] Please refer to Figure 1 , the present invention provides a technical solution: A smart logistics management method, including:

[0043] Step S1: Establish a logistics database, collect sensor data, and enter the sensor data into the logistics database, enter the comprehensive road condition information into the logistics database, and enter 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 path data, historical path data, etc., and the comprehensive cargo information may include the characteristics of the cargo state changing with time at various temperatures, the cargo transportation loss rate, etc.;

[0044] Step S2: Collect the environmental data and the temperature inside the refrigerated truck, analyze the influence of the environmental data on the temperature change inside the refrigerated truck, and further analyze the influence of the temperature change inside the refrigerated truck on the cargo form to obtain the vulnerability rating of the cargo;

[0045] Step S3: Analyze the path characteristics between the refrigerated truck and the transfer station, analyze the predicted transportation time of the transportation path based on the path characteristics, screen the transportation path based on the set time threshold, analyze the influence of the bump characteristics of the transportation path and the vulnerability of the cargo on the transportation path score to obtain the comprehensive transportation path score, and select the target transportation path according to the comprehensive score;

[0046] Step S4: Analyze the driving time of the refrigerated truck on the target transportation path in real time, calibrate based on the driving time and the predicted time, analyze the influence of the bump degree of the transportation path and the vulnerability of the cargo on the cost loss to obtain the loss cost, and adjust the transportation path according to the loss cost.

[0047] In the present invention, by analyzing the numerical change of the pressure sensor during the driving of the refrigerated truck, it is possible to determine whether the refrigerated truck is driving on a bumpy section, and further determine whether the bumpy section is a buffer after a large bump, which can accurately judge the position of the bumpy section, avoid misjudgment of the system, resulting in inaccurate path selection, greatly improve the accuracy of the system, and by analyzing the bump degree in the path and the vulnerability of the cargo, it is possible to minimize the damage to the transported cargo of the selected transportation path, thereby reducing the loss of the cargo during transportation and greatly reducing the transportation cost.

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

[0049] Step S21: Obtain the heat preservation performance of the refrigerated truck based on the influence of the environmental temperature, identify the melting situation of the ice layer outside the cargo, and evaluate the vulnerability of the cargo according to the heat preservation performance and the melting situation of the ice layer;

[0050] Exemplarily, obtain the temperature change of the refrigerated truck in the empty box state and the corresponding ambient temperature, construct the temperature change graph at the ambient temperature, retrieve the corresponding temperature change graph in the database according to different ambient temperatures, overlap and compare the temperature change graphs, identify the temperature difference between the temperature change graphs at the same timestamp, identify the difference characteristics, and give the influence coefficient ε of the ambient temperature on the temperature change of the refrigerated truck according to the difference characteristics. Obtain the characteristics of the goods state changing with temperature, analyze the goods state at different temperatures, and retrieve the corresponding temperature pairs in the database according to the goods state, that is, analyze the heat preservation effect of the refrigerated truck without refrigeration;

[0051] Obtain the humidity of the insulated box. When the humidity of the insulated box is greater than the set threshold, obtain the visual image inside the insulated box, identify the cargo edge nodes in the visual image, connect the cargo edge nodes in pairs to construct node connection lines, identify the length L1 of the node connection lines, compare with the set length L2 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, retrieve the influence coefficient θ1 of the set impact on the vulnerable score of the goods in the database. Otherwise, it means that the ice layer on the surface of the goods has melted, retrieve the influence coefficient θ2 of the set impact on the vulnerable score of the goods in the database, and retrieve the weight η of the corresponding influence coefficient θ2 in the database according to the humidity of the insulated box;

[0052] Obtain the basic vulnerable score P0 of the goods, and calculate the actual vulnerable score P1 of the goods through the formula P1=(ε + η×θ j )P0, where j = 1, 2. Retrieve and match in the database according to the actual vulnerable score to obtain the vulnerable degree level.

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

[0054] Step S31: Analyze the influence of the path 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 path according to the theoretical average driving speed, and screen the transportation path according to the influence to obtain an alternative transportation path list. Screen based on the transportation path to obtain an alternative transportation path list;

[0055] Exemplarily, obtain the position of the refrigerated truck and the position of the transfer station, identify the transportation path between the position of the refrigerated truck and the position of the transfer station, identify the number of turning points of the transportation path, retrieve the database according to the number of turning points, and retrieve the corresponding influence coefficient α on the predicted transportation time in the database;

[0056] Identify the length L' of each section constituting the transportation path, and retrieve the influence coefficient β set in the database on the average driving speed of the refrigerated truck in the section according to the length L';

[0057] Calculate the predicted transportation time of the section through a formula where V' represents the set average driving speed of the refrigerated truck, and further calculate the predicted transportation time of the transportation route where i = 1, 2, 3... n, obtain the set transportation time threshold T0. If T0 > T1, mark the transportation route as an alternative route and construct a set of alternative routes; otherwise, delete the transportation route. By analyzing the characteristics of each section in the transportation route, determine the impact of the characteristics on the average driving speed of the refrigerated truck, and then determine the theoretical average driving speed of the refrigerated truck on each section, so as to accurately predict the transportation time required for the transportation route, determine whether the transportation time meets the judgment conditions, obtain alternative transportation routes, reduce the number of transportation routes that need to be evaluated subsequently, thereby reducing the amount of data processing, improving the processing speed of the system, reducing the waste of time, and improving efficiency.

[0058] Step S32: Obtain the three-dimensional data of the alternative transportation route, construct a transportation route model, identify the characteristics of the transportation route model, analyze the impact of the characteristics on the driving stability of the refrigerated truck to obtain the bumpiness of the transportation route, and evaluate the comprehensive score of the transportation route according to the bumpiness and the vulnerability degree, and select the one with the highest score as the target transportation route.

[0059] Exemplarily, identify the three-dimensional data, construct a transportation route model according to the three-dimensional data, obtain the historical driving records of the refrigerated truck, identify the driving speed V1 and pressure data in the historical driving records of the refrigerated truck, identify the time stamps corresponding to the pressure data, calculate the pressure data difference K between adjacent time stamps. If the difference K is greater than the first threshold, it means that the vehicle vibrates, and mark the time stamp as the first time stamp; if the difference K is less than the first threshold and greater than the second threshold, it means that the vehicle vibrates again, and mark the time stamp as the second time stamp; otherwise, mark the time stamp as the third time stamp. According to the first, second, and third time stamps, retrieve the position of the refrigerated truck, and mark the first, second, and third bumpy sections in the transportation route model according to the position of the refrigerated truck. According to the markings of the bumpy sections, retrieve the corresponding influence coefficients φ1, φ2, φ3 on the driving stability score of the refrigerated truck respectively; where the bumpy section and the section belong to two concepts. The bumpy section refers to the bumpy part in the section, and the section refers to the route segment that makes up the transportation route

[0060] Identify the difference between timestamps in adjacent bumpy road sections. When the difference is less than the minimum threshold, identify the markers in the adjacent bumpy road sections. If the markers are for the first and third bumpy road sections or the second and third bumpy road sections, it indicates that the third bumpy road section is a pressure change caused by buffering, and delete the third bumpy road section. Otherwise, retain the markers of the adjacent bumpy road sections. When the difference is greater than the minimum threshold, retain the markers of the adjacent bumpy road sections;

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

[0062] Calculate the stability score F1 of the refrigerated truck traveling in the alternative transportation route through the formula F1 = μ k ×φ k ×F0, where k = 1, 2, 3, F0 represents the basic stability score of the refrigerated truck, and obtain the bumpiness level of the alternative transportation route by matching the corresponding level to the stability score according to the set rating conditions;

[0063] Obtain the measured transportation time T1 of the alternative transportation route. Retrieve the database according to the measured transportation time T1 and retrieve the corresponding transportation route priority score Q0 in the database. Calculate the comprehensive score Q1 of the alternative transportation route through 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 influence coefficient ω of the vulnerability level based on the bumpiness rating of the alternative transportation route, and λ represents the influence coefficient of the bumpiness level on the comprehensive score. Sort the alternative transportation routes in descending order according to the comprehensive score, select the first-ranked alternative transportation route as the target transportation route, and navigate according to the target transportation route. By analyzing the numerical changes of the pressure sensor during the driving of the refrigerated truck, it is possible to determine whether the refrigerated truck is driving on a bumpy road section, and further judge whether the bumpy road section is a buffer after a large bump, which can accurately judge the position of the bumpy road section, avoid misjudgment by the system, resulting in inaccurate path selection, greatly improve the accuracy of the system, and by analyzing the bumpiness level in the route and the vulnerability of the goods, it is possible to minimize the damage to the transported goods by the selected transportation route, thereby reducing the loss of goods during transportation and greatly reducing the transportation cost.

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

[0065] Obtain the time T" when the refrigerated truck passes through the current section, and compare it with the predicted transportation time T' of the section. When T">T', if T"-T'>T0-T1, it means that the transportation path where the path is located cannot complete the transportation task within the set transportation time threshold T0. Calculate the overtime time T2 = T"-T'-(T0-T1). According to the overtime time, retrieve the influence weight κ of the influence coefficient ε of the temperature set in the database, and calculate the second vulnerability score P2=(κ×ε + η×θ j )P0. Retrieve the database according to the second vulnerability score P2 for matching to obtain the second vulnerability level. According to the above method, use the second vulnerability level and the bumpiness level to recalculate the comprehensive score of the transportation paths in the set of alternative transportation paths, and sort the calculation results in descending order. Select the first-ranked transportation path as the switched transportation path, and navigate according to the switched transportation path. By calibrating the transportation time, further switch the transportation path during the transportation process, thereby ensuring that the loss of the goods is reduced to the minimum, reducing the transportation loss, and greatly reducing the transportation cost.

[0066] With the same inventive concept as the above embodiment, the present application also provides an intelligent logistics management system, including: a data acquisition module, a vulnerability analysis module, a path planning module, and a path optimization module;

[0067] The data acquisition module is used to establish a logistics database, collect sensor data, and enter the sensor data into the logistics database, enter the comprehensive road condition information into the logistics database, and enter the comprehensive cargo information into the logistics database. Among them, the sensor data can include temperature data, vibration data, etc., the comprehensive road condition information can include three-dimensional path data, historical path data, etc., and the comprehensive cargo information can include the characteristics of the cargo state changing with time at various temperatures, the cargo transportation loss rate, etc.;

[0068] The vulnerability analysis module is used to collect environmental data and the temperature in the refrigerated truck, analyze the influence of the environmental data on the temperature change in the refrigerated truck, and further analyze the influence of the temperature change in the refrigerated truck on the cargo form to obtain the vulnerability level rating of the cargo;

[0069] The path planning module is used to analyze the path characteristics between the refrigerated truck and the transfer station, analyze the predicted transportation time of the transportation path based on the path characteristics, screen the transportation path based on the set time threshold, analyze the influence of the bumpiness characteristics and cargo vulnerability level of the transportation path on the transportation path score to obtain the comprehensive transportation path score, and select the target transportation path according to the comprehensive score;

[0070] The path optimization module is used to analyze the driving time of the refrigerated truck in the target transportation path in real time, proofread based on the driving time and the predicted time, analyze the impact of the bumpiness of the transportation path and the vulnerability of the goods on the cost loss to obtain the loss cost, and adjust the transportation path according to the loss cost.

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

[0072] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A smart logistics management method, characterized by: The method comprises: Collect environmental data and the temperature inside the refrigerated truck, analyze the impact of environmental data on the temperature change inside the refrigerated truck, and further analyze the impact of the temperature change inside the refrigerated truck on the shape of the goods to obtain the vulnerability rating of the goods; Analyze the path characteristics between the refrigerated truck and the transfer station, analyze the predicted transportation time of the transportation path based on the path characteristics, screen the transportation path based on the set time threshold, analyze the impact of the bumpy characteristics of the transportation path and the vulnerability of the goods on the transportation path score to obtain a comprehensive score of the transportation path, and select the target transportation path according to the comprehensive score; Analyze the driving time of the refrigerated truck in the target transportation route in real time, compare the driving time with the predicted time, analyze the impact of the bumpiness of the transportation route and the fragility of the goods on the cost loss to obtain the loss cost, and adjust the transportation route according to the loss cost.

2. According to claim 1, a smart logistics management method is characterized by: The collecting of environmental data and the temperature in the refrigerated truck, analyzing the influence of the environmental data on the temperature change in the refrigerated truck, and further analyzing the influence of the temperature change in the refrigerated truck on the shape of the goods to obtain the vulnerability rating of the goods includes the following steps: The thermal insulation performance of the refrigerated truck is obtained based on the influence of the ambient temperature on the refrigerated truck, the melting of the ice layer outside the cargo is identified, and the vulnerability of the cargo is evaluated according to the thermal insulation performance and the melting of the ice layer.

3. According to claim 2, a smart logistics management method is characterized by: The method of obtaining the heat preservation performance of the refrigerated truck based on the influence of the ambient temperature on the refrigerated truck, identifying the melting of the ice layer outside the cargo, and assessing the vulnerability of the cargo based on the heat preservation performance and the melting of the ice layer, comprises the following steps: Exemplarily, the temperature change of the refrigerated truck in an empty box state and the corresponding ambient temperature are obtained, the temperature change graph under the ambient temperature is constructed, the corresponding temperature change graph in the database is retrieved according to different ambient temperatures, the temperature change graphs are overlapped and compared, the temperature difference between the temperature change graphs at the same timestamp is identified, the difference feature is identified, the influence coefficient ε of the ambient temperature on the temperature change of the refrigerated truck is given according to the difference feature, the characteristics of the cargo state changing with temperature are obtained, the cargo state under different temperatures is analyzed, and the corresponding temperature pair in the database is retrieved according to the cargo state; The humidity of the insulated box is obtained. When the humidity of the insulated box is greater than a set threshold, a visual image in the insulated box is obtained, and the cargo edge nodes in the visual image are identified. The cargo edge nodes are connected in pairs to construct node lines, and the length L1 of the node line is identified. The length L2 set in the database is compared. If L1-L2 is greater than the set threshold, it means that the ice layer on the surface of the cargo has not melted, and the influence coefficient θ1 set in the database for the cargo vulnerability score is retrieved. Otherwise, it means that the ice layer on the surface of the cargo has melted, and the influence coefficient θ2 set in the database for the cargo vulnerability score is retrieved. According to the humidity of the insulated box, the weight η of the corresponding influence coefficient θ2 in the database is retrieved; Obtain the basic vulnerability score P0 of the goods, and calculate the actual vulnerability score P1 of the goods by the formula = (ε+η×θ j )P0, wherein j=1,2, a database is searched for matching according to the actual vulnerability score to obtain a vulnerability level.

4. The intelligent logistics management method according to claim 1, characterized in that: The method of analyzing the path characteristics between the refrigerated truck and the transfer station, analyzing the predicted transportation time of the transportation path based on the path characteristics, screening the transportation path based on a set time threshold, analyzing the impact of the bumpy characteristics of the transportation path and the vulnerability of the cargo on the transportation path score to obtain a comprehensive score of the transportation path, and selecting a target transportation path according to the comprehensive score includes the following steps: Analyze the influence of the path 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 path according to the theoretical average driving speed, screen the transportation path according to the influence to obtain a table of candidate transportation paths; screen the transportation path based on the influence to obtain a table of candidate transportation paths; Acquire three-dimensional data of the alternative transport path, construct a transport path model, identify the characteristics of the transport path model, analyze the impact of the characteristics on the driving stability of the refrigerated truck to obtain the bumpiness of the transport path, evaluate the comprehensive score of the transport path according to the bumpiness and the vulnerability, and select the one with the highest score as the target transport path.

5. The intelligent logistics management method according to claim 4 is characterized in that: The analyzing the influence of the path characteristics on the average driving speed of the refrigerated truck to obtain the theoretical average driving speed of the refrigerated truck, calculating the predicted transportation time of the transportation path according to the theoretical average driving speed, screening the transportation path according to the influence to obtain a table of candidate transportation paths, and screening the transportation path to obtain a table of candidate transportation paths, including the following steps: Obtaining the location of the refrigerated truck and the location of the transfer station, identifying the transportation path between the location of the refrigerated truck and the location of the transfer station, identifying the number of inflection points of the transportation path, searching a database according to the number of inflection points, and retrieving the corresponding influence coefficient α on the predicted transportation time in the database; Identify the length L' of each road section constituting the transport path, and retrieve the influence coefficient β on the average driving speed of the refrigerated truck in the road section set in the database according to the length L'; The predicted transportation time of the route is calculated by the formula Where V' represents the average speed of the refrigerated truck, and the predicted transportation time of the transportation route is further calculated. Wherein, i=1, 2, 3...n, a set transport time threshold T0 is obtained, if T0>T1, the transport path is marked as an alternative path, and an alternative path set is constructed, otherwise the transport path is deleted.

6. The intelligent logistics management method according to claim 4 is characterized by: The step of acquiring the three-dimensional data of the alternative transport path, constructing a transport path model, identifying the features of the transport path model, analyzing the influence of the features on the driving stability of the refrigerated truck to obtain the bumpiness of the transport path, evaluating the comprehensive score of the transport path according to the bumpiness and the vulnerability, and selecting the highest score as the target transport path includes the following steps: Identify the three-dimensional data, build a transportation path model according to the three-dimensional data, obtain historical driving records of the refrigerated truck, identify the driving speed V1 and pressure data of the refrigerated truck in the historical driving records, identify the timestamp corresponding to the pressure data, calculate the pressure data difference K between adjacent timestamps, if the difference K is greater than a first threshold, mark the timestamp as a first timestamp, if the difference K is less than the first threshold and greater than a second threshold, mark the timestamp as a second timestamp, otherwise mark the timestamp as a third timestamp, retrieve the position of the refrigerated truck according to the first, second and third timestamps, mark the first, second and third bumpy sections in the transportation path model according to the position of the refrigerated truck, and retrieve the corresponding influence coefficients φ1, φ2, φ3 on the driving stability score of the refrigerated truck according to the marking of the bumpy sections; Identify the difference between the timestamps in the adjacent bumpy sections, and when the difference is less than a minimum threshold, identify the marks in the adjacent bumpy sections, and if the marks are the first bumpy section and the third bumpy section or the second bumpy section and the third bumpy section, it means that the third bumpy section is a pressure change caused by buffering, and the third bumpy section is deleted, otherwise the marks of the adjacent bumpy sections are retained, and when the difference is greater than the minimum threshold, the marks of the adjacent bumpy sections are retained; Identify the number of the first, second and third bumpy sections in the alternative transport route, and retrieve the influence weights μ1, μ2, μ3 of the corresponding influence coefficients φ1, φ2, φ3 in the database according to the number; The stability score of the refrigerated truck traveling on the alternative transport route is calculated by the formula F1=μ k ×φ k ×F0, where k=1, 2, 3, F0 represents the basic stability score of the refrigerated truck, and the stability score is matched to the corresponding level according to the set rating conditions to obtain the bumpiness level of the alternative transport route; Obtain the measured transportation time T1 of the alternative transportation route, search the database according to the measured transportation time T1, retrieve the corresponding transportation route priority score Q0 in the database, and calculate the comprehensive score of the alternative transportation route by the formula Q1=(γ×ω+λ)×Q0, wherein ω identifies the influence coefficient of the vulnerability level on the comprehensive score, γ identifies the correction coefficient of the vulnerability level influence coefficient ω based on the bumpiness rating of the alternative transportation route, and λ represents the influence coefficient of the bumpiness level on the comprehensive score. Sort the alternative transportation routes in descending order according to the comprehensive scores, select the first-ranked alternative transportation route as the target transportation route, and navigate according to the target transportation route.

7. The intelligent logistics management method according to claim 1, characterized in that: The real-time analysis of the driving time of the refrigerated truck in the target transportation path, the comparison based on the driving time and the predicted time, the analysis of the impact of the bumpiness of the transportation path and the vulnerability of the goods on the cost loss to obtain the loss cost, and the adjustment of the transportation path according to the loss cost include the following steps: Obtain the time T” for the refrigerated truck to pass the current section, and compare it with the predicted transportation time T' of the section. When T”>T', if T”-T'>T0-T1, calculate the timeout time T2=T”-T'-(T0-T1), and retrieve the influence weight κ of the influence coefficient ε of the temperature set in the database according to the timeout time, and calculate the second vulnerability score P2=(κ×ε+η×θ j )P0, searching the database for matching according to the second vulnerability score P2 to obtain a second vulnerability level, recalculating the comprehensive scores of the transport paths in the set of alternative transport paths using the second vulnerability level and the bumpiness level according to the above method, and sorting the calculation results in descending order, selecting the first-ranked transport path as the switching transport path, and navigating according to the switching transport path.

8. A smart logistics management system, characterized by: 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 enter the sensor data into the logistics database, enter the comprehensive road condition information into the logistics database, and enter the comprehensive cargo information into the logistics database, wherein 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., and the comprehensive cargo information may include the characteristics of cargo status changes over time at various temperatures, cargo transportation loss rate, etc.; The vulnerability analysis module is used to collect environmental data and the temperature inside the refrigerated truck, analyze the impact of the environmental data on the temperature change inside the refrigerated truck, and further analyze the impact of the temperature change inside the refrigerated truck on the shape of the goods to obtain a vulnerability rating of the goods.

9. The intelligent logistics management system according to claim 8, characterized in that: The system also includes a path planning module; The path planning module is used to analyze the path characteristics between the refrigerated truck and the transfer station, analyze the predicted transportation time of the transportation path based on the path characteristics, screen the transportation path based on a set time threshold, analyze the impact of the bumpy characteristics of the transportation path and the fragility of the cargo on the transportation path score to obtain a comprehensive score of the transportation path, and select the target transportation path based on the comprehensive score.

10. The intelligent logistics management system according to claim 9, characterized in that: The system also includes a path optimization module; The path optimization module is used to analyze the driving time of the refrigerated truck in the target transportation path in real time, calibrate the driving time with the predicted time, analyze the impact of the bumpiness of the transportation path and the fragility of the goods on the cost loss to obtain the loss cost, and adjust the transportation path according to the loss cost.

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