Intelligent logistics distribution method and system for providing full-link visualization and electronic receipts

Abstract

The invention discloses an intelligent logistics distribution method and system for providing full-link visualization and electronic receipts, and the method comprises the steps: collecting vehicle positioning information, a cargo monitoring video and compartment temperature and humidity information, generating full-link distribution visualization information of each logistics vehicle, and transmitting the full-link distribution visualization information to a user side, thereby achieving the full-link distribution visualization information of each logistics vehicle. Therefore, the user and the logistics company can quickly know detailed information in the logistics distribution process, and the whole-process monitoring of the logistics distribution is realized. In addition, an electronic receipt containing information such as signing time, place, personnel and cargo information and signing photos is generated for each logistics distribution route; on the basis, compared with paper receipts, electronic receipts are easier to store, recorded information is more comprehensive, and therefore complete goods receiving information can be provided for follow-up tracing; therefore, the invention provides an intelligent logistics distribution mode which can simultaneously realize whole-process monitoring of distribution and has traceability, and the intelligent logistics distribution method is very suitable for large-scale application and popularization in the field of logistics distribution.

Description

Technical Field

[0001] This invention belongs to the field of intelligent logistics and distribution technology, specifically relating to an intelligent logistics and distribution method and system that provides end-to-end visualization and electronic receipts. Background Technology

[0002] With the development of e-commerce and the upgrading of people's consumption patterns, the volume of express delivery business has grown rapidly, and the demand for logistics and distribution has also increased year by year. Among them, the logistics and distribution process is greatly affected by external factors. Various situations that occur during the distribution process and different service customers make the entire logistics and distribution process more complex and changeable. Therefore, grasping the real-time dynamic situation of logistics and distribution, so as to take reasonable measures to deal with abnormal situations in a timely manner, is an important part of the logistics and distribution process.

[0003] Currently, during the logistics delivery process, when the shipper or buyer wants to know the transportation status of the goods, they usually confirm with the logistics company or relevant personnel. This often requires the logistics company to spend a significant amount of time and effort on cargo information retrieval, making it impossible for both users and the logistics company to quickly obtain detailed information about the delivery process, thus hindering real-time monitoring. Furthermore, after goods are received, they are typically delivered via paper receipts, which are inconvenient to store and difficult to trace (due to limited information). Therefore, given these shortcomings, providing an intelligent logistics delivery method that simultaneously achieves full-process monitoring and traceability has become an urgent problem to solve. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent logistics delivery method and system that provides end-to-end visualization and electronic receipts, in order to solve the problems of existing technologies that cannot achieve real-time monitoring during the delivery process and are inconvenient for receiving and tracing goods.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: Firstly, a smart logistics delivery method providing end-to-end visualization and electronic receipts is provided, applicable to logistics platforms, logistics vehicles, and electronic signature terminals. The logistics vehicles are equipped with positioning and monitoring devices, and the method includes: The logistics platform generates delivery routes for each logistics vehicle and sends each delivery route to the corresponding logistics vehicle. Each logistics vehicle receives its own logistics delivery route and, while delivering goods according to its own logistics delivery route, obtains real-time location monitoring information collected by positioning and monitoring equipment. The location monitoring information includes vehicle location information, cargo monitoring video, and temperature and humidity information of the vehicle compartment. Each logistics vehicle sends its acquired location monitoring information to the logistics platform in real time; Based on the received location monitoring information, the logistics platform generates visualized end-to-end delivery information for each logistics vehicle and synchronizes it to the user's end. The logistics platform receives logistics receipt information sent by electronic receipt terminals on each logistics delivery route. The logistics receipt information includes receipt time, location, personnel, goods information and receipt photos. The logistics platform generates and stores electronic delivery receipts for each logistics delivery route based on the delivery confirmation information.

[0006] Based on the above disclosure, this invention installs positioning and monitoring equipment on each logistics vehicle, and also equips them with electronic signature terminals. Thus, the positioning and monitoring equipment and electronic signature terminals can be used to achieve visualization and traceability of logistics. Specifically, the logistics platform first generates logistics delivery routes for each logistics vehicle, enabling each vehicle to deliver goods according to its corresponding route. While each logistics vehicle is delivering goods according to its designated route, it uses the positioning and monitoring equipment installed on its vehicle to collect real-time vehicle location information, cargo monitoring video, and temperature and humidity information of the cargo compartment, and sends this information to the logistics platform in real time. The logistics platform... Based on the aforementioned information, visualized end-to-end delivery information for each logistics vehicle can be generated and sent to the user terminal. This allows users and logistics companies to quickly understand detailed dynamic information during the logistics delivery process, thereby achieving full-process monitoring of logistics delivery. In addition, this invention also utilizes an electronic signature terminal to obtain logistics signature information, including signature time, location, personnel, goods information, and signature photos, when goods are received. Based on this, electronic delivery receipts are generated for each logistics delivery route. Thus, compared to paper receipts, electronic receipts are easier to store and contain more comprehensive information, providing complete receipt information for subsequent traceability, thereby improving the traceability of logistics delivery.

[0007] In one possible design, the logistics platform generates delivery routes for each logistics vehicle, including: The logistics platform constructs a path planning function with the optimization objective of minimizing the total logistics and distribution cost; The logistics platform uses an improved genetic algorithm to solve the path planning function to obtain the logistics path scheme that minimizes the total logistics and distribution cost. Based on the derived logistics route plan, the logistics platform determines the delivery routes for each logistics vehicle.

[0008] In one possible design, the logistics platform uses an improved genetic algorithm to solve the path planning function to derive the logistics path scheme that minimizes the total logistics and delivery cost, including: Obtain the chromosome population at the nth iteration, where when n is 1, the chromosome population at the nth iteration is the initial population, and any initial chromosome in the initial population is used to characterize a delivery route for each logistics vehicle. The reciprocal of the path planning function is used as the fitness function, and the fitness of each chromosome in the chromosome population at the nth iteration is calculated based on the fitness function. Based on the fitness of each chromosome, the global optimal fitness at the nth iteration is determined; Based on the globally optimal fitness, determine whether the iteration stopping condition is met; If not, then perform a crossover operation on the chromosome population at the nth iteration to obtain the crossover population; The crossover population is divided into a first subpopulation and a second subpopulation with the same individual size; The positions of the first and second subpopulations are updated to obtain the first updated population and the second updated population. Perform individual mating operations on the first and second updated populations to obtain a mating population, and use the mating population as the chromosome population in the (n+1)th iteration; Increment n by 1 and reacquire the chromosome population at the nth iteration until the iteration stopping condition is met. Then, determine the logistics path scheme based on the chromosome corresponding to the global optimal fitness at the time the iteration stopping condition is met.

[0009] In one possible design, a crossover operation is performed on the chromosome population at the nth iteration to obtain a crossover population, including: Generate the first crossover random number and determine whether the first crossover random number is less than the crossover threshold; If so, perform a single-individual crossover operation on the chromosome population at the nth iteration to obtain the crossover population; otherwise, perform a double-individual crossover operation on the chromosome population at the nth iteration to obtain the crossover population.

[0010] In one possible design, a two-individual crossover operation is performed on the chromosome population at the nth iteration, including: The chromosome population at the nth iteration is divided into pairs to obtain several crossover chromosome groups; For any set of crossed chromosomes, generate a second crossover random number and determine whether the second crossover random number is less than the crossover threshold; If so, a closed-loop gene selection algorithm is used to select genes from the first individual and the second individual, and the selected genes are used as fixed genes. The first individual is one chromosome in any of the crossover chromosome sets, and the second individual is another chromosome in any of the crossover chromosome sets. Genes in the first and second individuals other than the fixed genes are used as crossover genes. Crossover operations are performed on the crossover genes in the first and second individuals to obtain the first and second crossover individuals. After all crossover chromosome sets have been queried, the crossover population is obtained.

[0011] In one possible design, a closed-loop gene selection algorithm is used to select genes from the first and second individuals in any of the crossover chromosome sets, including: Obtain the initial gene position, wherein the initial gene position is a gene position randomly selected from the first individual; Obtain the target position at the k-th gene selection, where when k is 1, the target position is the initial gene position; The gene at the target location in the first individual is selected as the first gene, and the gene at the target location in the second individual is selected as the second gene. Determine the location of the second gene in the first individual, and use it as the target location for the (k+1)th gene selection. Increment k by 1 and re-obtain the target position at the time of the kth gene selection until the obtained target position returns to the initial gene position. Then, the selected first and second genes are used as the fixed genes.

[0012] In one possible design, if the first crossover random number is greater than or equal to the crossover threshold, the method further includes: Several genes are randomly selected from the first individual to serve as the third gene; The gene positions corresponding to each third gene in the second body are determined as designated positions; The gene at the specified position is selected from the second individual to serve as the fourth gene; Each third gene and each fourth gene are used as crossover genes, and crossover operations are performed on the crossover genes according to the order in which they appear, so that the first crossover individual and the second crossover individual are obtained after the crossover operation.

[0013] In one possible design, each logistics vehicle sends its acquired location monitoring information to the logistics platform in real time, including: Each logistics vehicle encrypts its acquired location monitoring information to obtain encrypted information; Each logistics vehicle sends encrypted information to the edge node, so that the edge node forwards the encrypted information to the logistics platform after receiving it. Correspondingly, based on the received location monitoring information, the logistics platform generates end-to-end delivery visualization information for each logistics vehicle, including: The logistics platform decrypts each encrypted message to obtain the location monitoring information sent by each logistics vehicle. The logistics platform generates the actual delivery trajectory of each logistics vehicle based on the vehicle location information in various location monitoring information. The logistics platform establishes a delivery GIS map and renders the actual delivery trajectory of each logistics vehicle onto the delivery GIS map to obtain a real-time logistics delivery trajectory map of each logistics vehicle. The real-time logistics delivery trajectory map of each logistics vehicle is linked with the temperature and humidity information of the vehicle compartment and the cargo monitoring video in the location monitoring information of each logistics vehicle, so as to generate full-link delivery visualization information of each logistics vehicle after the information is linked.

[0014] Secondly, an intelligent logistics and distribution system that provides full-chain visualization and electronic receipts is provided, including: a logistics platform, logistics vehicles and electronic signature terminals, wherein the logistics vehicles are equipped with positioning and monitoring equipment. The logistics platform is used to generate logistics delivery routes for each logistics vehicle and send each logistics delivery route to the corresponding logistics vehicle. Each logistics vehicle is used to receive its own logistics delivery route and, while carrying out logistics delivery according to its own logistics delivery route, to obtain real-time location monitoring information collected by positioning and monitoring equipment. The location monitoring information includes vehicle location information, cargo monitoring video, and temperature and humidity information of the vehicle compartment. Each logistics vehicle is also used to send its acquired location monitoring information to the logistics platform in real time; The logistics platform is used to generate visualized end-to-end delivery information for each logistics vehicle based on the received location monitoring information, and synchronize it to the user terminal. The logistics platform is used to receive logistics receipt information sent by electronic receipt terminals on various logistics delivery routes. The logistics receipt information includes receipt time, location, personnel, goods information and receipt photos. The logistics platform is also used to generate and store electronic delivery receipts for each logistics delivery route based on the various logistics receipt information.

[0015] Thirdly, an intelligent logistics delivery device that provides end-to-end visualization and electronic receipts is provided. Taking the device as an electronic device as an example, it includes a memory, a processor, and a transceiver that are connected in sequence. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the intelligent logistics delivery method that provides end-to-end visualization and electronic receipts as described in the first aspect or any possible design of the first aspect.

[0016] Fourthly, a storage medium is provided, on which instructions are stored, which, when executed on a computer, perform the intelligent logistics delivery method described in the first aspect or any possible design of the first aspect, providing end-to-end visualization and electronic receipts.

[0017] Fifthly, a computer program product containing instructions is provided, which, when executed on a computer, causes the computer to perform the intelligent logistics distribution method that provides end-to-end visualization and electronic receipts, as described in the first aspect or any possible design of the first aspect.

[0018] Beneficial effects: (1) This invention generates full-chain delivery visualization information for each logistics vehicle by collecting vehicle positioning information, cargo monitoring video, and temperature and humidity information of the cargo compartment, and sends it to the user terminal. In this way, users and logistics companies can quickly understand the detailed information of the logistics delivery process, thereby realizing full-process monitoring of logistics delivery. In addition, this invention generates electronic receipts for each logistics delivery route, including information such as the signing time, location, personnel, cargo information, and signing photos. Based on this, compared with paper receipts, electronic receipts are easier to store and contain more comprehensive information, thus providing complete receiving information for subsequent traceability. Therefore, this invention provides an intelligent logistics delivery method that can simultaneously realize full-process monitoring of delivery and has traceability, which is very suitable for large-scale application and promotion in the field of logistics delivery. Attached Figure Description

[0019] Figure 1 A flowchart illustrating the steps of an intelligent logistics delivery method providing end-to-end visualization and electronic delivery receipts, as provided in an embodiment of the present invention. Figure 2 This is a structural diagram of an intelligent logistics and distribution system that provides end-to-end visualization and electronic delivery receipts, as provided in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0021] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0022] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0023] Example: Before describing the intelligent logistics delivery method provided in this embodiment, a corresponding intelligent logistics delivery system is disclosed. The system includes a logistics platform, logistics vehicles, and an electronic signature terminal. Specifically, the logistics vehicles are equipped with positioning and monitoring devices, which may include, but are not limited to, positioning devices (such as GPS devices), cargo monitoring devices (cameras), and temperature and humidity monitoring devices (temperature and humidity sensors). At the same time, multiple temperature and humidity sensors can be installed in the vehicle compartment to realize the detection of temperature and humidity in various local areas within the vehicle compartment.

[0024] The logistics platform is used for route planning and information processing. Logistics vehicles deliver goods according to the routes planned by the platform, and during delivery, onboard positioning and monitoring equipment collects vehicle location monitoring information and sends it to the logistics platform in real time. The logistics platform processes the location monitoring information sent by each vehicle to generate a visualized end-to-end delivery information for each vehicle on its respective delivery route, which is then sent to the user. This allows users and logistics companies to quickly understand detailed information about the delivery process, enabling full-process monitoring of logistics delivery. In this embodiment, an electronic signature terminal records the receipt information (including but not limited to the time, location, personnel, goods information, and receipt photos) after the goods arrive at the delivery station and sends the recorded information to the logistics platform. Finally, the logistics platform generates and stores electronic receipts for each delivery route, thus ensuring traceability of logistics delivery receipts.

[0025] Through the above design, this embodiment provides an intelligent logistics delivery method that can simultaneously achieve full-process delivery monitoring and traceability, and is therefore very suitable for large-scale application and promotion.

[0026] See Figure 1 As shown, the intelligent logistics delivery method that provides full-link visualization and electronic receipts disclosed in this embodiment can be operated on, but is not limited to, logistics platforms, logistics vehicles, and electronic signature terminals. It is understood that the aforementioned execution entities do not constitute a limitation on the embodiments of this application. Accordingly, the operation steps of this method can be, but are not limited to, the steps S1 to S6 below.

[0027] S1. The logistics platform generates delivery routes for each logistics vehicle and sends each delivery route to the corresponding logistics vehicle. In specific implementation, for example, the logistics platform plans the delivery routes for each logistics vehicle with the optimization objective of minimizing the total logistics delivery cost. In this way, the cost of the generated delivery routes can be minimized, thereby improving the delivery efficiency of the logistics company. The delivery route of any logistics vehicle includes a starting point, delivery stations along the way, and a destination. Of course, the starting point and the destination are both distribution centers. Specifically, the aforementioned logistics planning process is described in detail in the second aspect of the following embodiment.

[0028] After generating the logistics delivery routes for each logistics vehicle, the logistics platform can distribute the routes so that each logistics vehicle can carry out logistics delivery according to its own logistics delivery route. The process is shown in step S2 below.

[0029] S2. Each logistics vehicle receives its own logistics delivery route and, while carrying out logistics delivery according to its own logistics delivery route, obtains real-time positioning and monitoring information collected by positioning and monitoring equipment, wherein the positioning and monitoring information includes vehicle positioning information, cargo monitoring video, and temperature and humidity information of the vehicle compartment.

[0030] In this embodiment, each logistics vehicle is equipped with a navigation terminal. The logistics platform distributes the logistics delivery routes of each logistics vehicle to the navigation terminals on each logistics vehicle. At the same time, the navigation terminal of any logistics vehicle will display the corresponding logistics delivery route and provide navigation prompts. In this way, any logistics vehicle will deliver the goods according to the navigation prompts.

[0031] Furthermore, when each logistics vehicle travels along the route on the navigation terminal, the positioning and monitoring equipment on each logistics vehicle is automatically activated. At this time, the GPS device, camera, and temperature and humidity sensor will start working to locate the vehicle in real time, obtain the vehicle positioning information, capture real-time video of the goods inside the vehicle compartment to obtain the goods monitoring video, and simultaneously collect the temperature and humidity information inside the vehicle compartment to obtain the vehicle compartment temperature and humidity information. Based on this, each logistics vehicle will obtain its own positioning, goods monitoring video, and vehicle temperature and humidity information, and then the aforementioned information can be uploaded in real time, as shown in step S3 below.

[0032] S3. Each logistics vehicle sends its acquired location monitoring information to the logistics platform in real time. In practice, each logistics vehicle first encrypts its acquired location monitoring information to obtain encrypted information. Then, it sends the encrypted information to the edge node, so that the edge node forwards the encrypted information to the logistics platform after receiving it. In this way, by encrypting the location monitoring information, the security of data transmission can be guaranteed. At the same time, setting up edge nodes as data relay stations between vehicles and the logistics platform can reduce data transmission latency and improve real-time performance.

[0033] Furthermore, examples, including but not limited to, symmetric encryption algorithms, asymmetric encryption algorithms, or hash algorithms, can be used to encrypt location monitoring information; among them, due to the large amount of data transmission, this embodiment prioritizes the use of symmetric encryption algorithms for data encryption, and the key can be distributed to each logistics vehicle by the logistics platform.

[0034] Thus, after the location monitoring information is uploaded, the logistics platform can generate full-chain delivery visualization information for each logistics vehicle based on this information, as shown in step S4 below.

[0035] S4. Based on the received location monitoring information, the logistics platform generates full-chain delivery visualization information for each logistics vehicle and synchronizes it to the user terminal. In specific implementation, for example, but not limited to, the following steps S41 to S44 can be used to generate full-chain delivery visualization information.

[0036] S41. The logistics platform decrypts each encrypted message to obtain the location monitoring information sent by each logistics vehicle. In this embodiment, since symmetric encryption is used to encrypt the location monitoring information, the same key is used for encryption and decryption. Based on this, the logistics platform can use the generated key to decrypt each encrypted message, thereby obtaining the location monitoring information corresponding to each logistics vehicle. Then, a delivery trajectory can be generated, as shown in step S42 below.

[0037] S42. The logistics platform generates the actual delivery trajectory of each logistics vehicle based on the vehicle positioning information in each positioning monitoring information. In this embodiment, the logistics platform generates the actual delivery trajectory of each logistics vehicle based on the vehicle positioning information in each positioning monitoring information and according to the delivery order. Then, it can combine the GIS map to generate the actual logistics delivery trajectory map. The process is shown in step S43 below.

[0038] S43. The logistics platform establishes a delivery GIS map and renders the actual delivery trajectories of each logistics vehicle onto the delivery GIS map to obtain a real-time logistics delivery trajectory map for each logistics vehicle. In specific applications, examples, but not limited to, using a GIS platform (such as Baidu Map API, MapGIS, ArcGIS, etc.) as the spatial data carrier base map; then, basic geographic information such as delivery stations, delivery centers, road networks, and regions are overlaid to form a delivery GIS map; finally, the actual delivery trajectories are dynamically rendered on the delivery GIS map, i.e., overlaid on the delivery GIS map, to form a real-time logistics delivery trajectory map for each logistics vehicle; at the same time, the logistics delivery routes can also be overlaid on the delivery GIS map to achieve a comparison between the actual delivery routes and the planned routes, thereby realizing the path tracking comparison function.

[0039] After obtaining the real-time logistics delivery trajectory map of each logistics vehicle, it can be associated with the monitoring information of the goods to form full-link delivery visualization information with path visualization and goods visualization; wherein, the aforementioned information association process can be, but is not limited to, as shown in step S44 below.

[0040] S44. Link the real-time logistics delivery trajectory map of each logistics vehicle with the temperature and humidity information of the vehicle compartment and the cargo monitoring video in the location monitoring information of each logistics vehicle, so as to generate full-link delivery visualization information of each logistics vehicle after information linking.

[0041] In this embodiment, for example, but not limited to, generating a temperature and humidity thermal distribution map of the compartment based on temperature and humidity information sent by various temperature and humidity sensors inside the compartment, thereby intuitively reflecting the temperature and humidity of the goods; thus, by associating the real-time logistics delivery trajectory map of each logistics vehicle with the corresponding temperature and humidity thermal distribution map and cargo monitoring video, the full-chain delivery visualization information of each logistics vehicle can be obtained; at the same time, the logistics platform will display the aforementioned full-chain delivery visualization information in the logistics dispatch center, and for example, but not limited to, setting temperature and video display icons on the real-time logistics delivery trajectory map, when the display icon is clicked or the cursor is moved to the display icon, the cargo monitoring video and temperature and humidity thermal distribution map can be displayed.

[0042] Furthermore, when the temperature and / or humidity exceed the corresponding thresholds, an alarm can be triggered to alert logistics dispatch center staff of any abnormalities in the delivery process, prompting timely handling. Simultaneously, an alarm will also be triggered when the actual delivery trajectory deviates from the planned delivery route, allowing staff to monitor the vehicle's path. Even further, for example, the logistics platform may be equipped with an image recognition model that can analyze cargo monitoring videos to determine if the packaging has been damaged during transport; if so, an alarm will be triggered and the video will be saved. Thus, this comprehensive end-to-end delivery visualization information allows logistics companies to monitor the detailed dynamics of the delivery process in real time, achieving full-process monitoring of logistics delivery.

[0043] In addition, for example, logistics platforms will generate electronic links to visualize the entire delivery process and send these links to users. Buyers can then use these links to view the delivery route, temperature and humidity, and monitoring videos in real time.

[0044] Therefore, through the aforementioned design, users and logistics companies can quickly understand detailed information about the logistics and distribution process, thereby achieving full monitoring of the logistics and distribution process.

[0045] After completing the full visualization of logistics and distribution, this embodiment also uses an electronic signature terminal to realize the electronic signing of goods, thereby facilitating the formation of complete receipt information for information traceability during logistics after-sales service; specifically, the process of generating electronic receipts may be, but is not limited to, as shown in steps S5 and S6 below.

[0046] S5. The logistics platform receives logistics receipt information sent by electronic signature terminals on each logistics delivery route. This logistics receipt information includes the receipt time, location, personnel, goods information, and a receipt photo. In practice, the electronic signature terminal is equipped with GPS, a camera, and a barcode scanner. When a logistics vehicle arrives at a delivery station, the delivery personnel on board can use the barcode scanner on the electronic signature terminal to scan the logistics barcode on the goods, thereby generating receipt information (i.e., scanning the barcode to obtain goods information) and recording the receipt time. Simultaneously, the GPS on the electronic signature terminal is used to automatically obtain the receipt location.

[0047] Furthermore, upon receipt, the terminal's camera will be used to take a photo of the goods and the person signing for them. Finally, the aforementioned receipt time, location, personnel, goods information, and receipt photo can be used to form logistics receipt information. Then, it can be sent to the logistics platform to generate an electronic receipt, as shown in step S6 below.

[0048] S6. The logistics platform generates and stores electronic receipts for each logistics delivery route based on the various logistics receipt information. In this embodiment, each delivery station on each logistics delivery route corresponds to an electronic receipt. Thus, electronic receipts for each delivery item at each delivery station on each logistics delivery route can be generated on a per-route basis. Finally, the receipts can be retained to fully record the receipt time, location, personnel, and goods information for each item, thereby providing complete delivery data chain information for subsequent logistics traceability.

[0049] Therefore, through the intelligent logistics delivery method described in detail in steps S1 to S6 above, which provides end-to-end visualization and electronic receipts, this invention generates end-to-end delivery visualization information for each logistics vehicle by collecting vehicle location information, cargo monitoring videos, and vehicle temperature and humidity information, and sends it to the user terminal. In this way, users and logistics companies can quickly understand detailed information in the logistics delivery process, thereby realizing full-process monitoring of logistics delivery. In addition, this invention generates electronic receipts for each logistics delivery route, including information such as the signing time, location, personnel, cargo information, and signing photos. Based on this, compared with paper receipts, electronic receipts are easier to store and record more comprehensive information, thus providing complete receipt information for subsequent traceability. Therefore, this invention provides an intelligent logistics delivery method that can simultaneously realize end-to-end delivery monitoring and traceability, which is very suitable for large-scale application and promotion in the field of logistics delivery.

[0050] In one possible design, the second aspect of this embodiment provides one implementation of the logistics platform in the first aspect of the embodiment, which optimizes the logistics delivery route with the goal of minimizing the total logistics delivery cost. The process may be, but is not limited to, the steps S11 to S13 below.

[0051] S11. The logistics platform constructs a path planning function with the optimization objective of minimizing the total logistics and distribution cost; in this embodiment, the logistics vehicle is an electric vehicle, and the path planning function can be constructed using, but is not limited to, the following formula.

[0052] ; In the formula, Represents the path planning function. This represents the sum of vehicle wear and tear costs incurred per unit distance traveled by the logistics vehicle (i.e., the maintenance costs of the battery as mileage increases, measured in yuan / km) and electricity consumption costs (i.e., the charging costs for the electricity used to travel 1km). Indicates the first The delivery station and the first The distance between delivery stations As decision variables, It is 1 or 0, where when When it is 1, it means the first... The logistics vehicle from the first The delivery station was reached by the first... Each delivery station, when When it is 0, it means the first... The logistics vehicles did not depart from the first The delivery station was reached by the first... One delivery station For the collection of logistics vehicles, This refers to a collection of delivery sites (including delivery stations and distribution centers). This indicates a penalty for delivery time. Indicates the first The delivery time penalty cost for each delivery station and its corresponding logistics vehicle.

[0053] in, ; In the formula, The early arrival penalty cost coefficient, The penalty cost coefficient for being late. For the first Each delivery station corresponds to the arrival time of the delivery vehicle. They represent the first Expected delivery start time and expected delivery end time for each delivery station.

[0054] In this embodiment, the first term in the aforementioned path planning function is the transportation cost, and the second term is the delivery time penalty cost. The sum of the two gives the total logistics delivery cost. The second term represents the total cost incurred when the logistics vehicle arrives at each delivery station earlier than the expected delivery start time of the corresponding delivery station, or later than the expected delivery end time of the corresponding delivery station. That is, arriving early or late will incur corresponding penalty costs, thereby increasing the total cost of the entire path. Of course, the aforementioned early arrival penalty cost coefficient and late arrival penalty cost coefficient can be specifically set according to actual use, and are not specifically limited here.

[0055] Based on this, this embodiment, when performing route planning, introduces the delivery time penalty cost incurred by logistics vehicles arriving early or late at various delivery stations, in addition to the traditional method of minimizing transportation costs. Thus, this embodiment takes into account the additional cost impact of early or delayed delivery, thereby enabling the generated logistics delivery routes of each logistics vehicle to minimize transportation costs while keeping delivery times as close as possible to the user's expected delivery time, thereby improving user satisfaction and reducing additional idle costs.

[0056] Furthermore, the constraints of the aforementioned path planning function are as follows: (1) All vehicles start from the distribution center and return to the distribution center after completing the delivery task; (2) Distribution point flow balance constraint, that is, the number of vehicles entering and leaving is equal; (3) Distribution station service constraint, each distribution station is only served once; (4) Vehicle driving distance constraint, the delivery distance of each vehicle does not exceed the maximum delivery distance; (5) Logistics vehicle quantity constraint; since the number of logistics vehicles in each logistics center is limited, it must be ensured that the number of vehicles used according to the constraints is not greater than the number of logistics vehicles owned by the logistics center; (6) Logistics vehicle load capacity constraint, that is, the load of logistics vehicles during the delivery process is less than or equal to the rated load capacity; (7) Logistics vehicle range constraint, that is, the remaining power of the vehicle after leaving a distribution station is sufficient to drive to the next distribution station.

[0057] Thus, based on the aforementioned step S11, after constructing the path planning function, the path planning function can be solved to obtain the logistics path scheme that minimizes the total logistics distribution cost, as shown in step S12 below.

[0058] S12. The logistics platform uses an improved genetic algorithm to solve the path planning function to obtain the logistics path scheme that minimizes the total logistics and distribution cost. In specific implementation, for example, but not limited to, the following steps S12a to S12i can be used to obtain the aforementioned logistics path scheme.

[0059] S12a. Obtain the chromosome population at the nth iteration, wherein when n is 1, the chromosome population at the nth iteration is the initial population, and any initial chromosome in the initial population is used to characterize a delivery route for each logistics vehicle.

[0060] In this embodiment, an initial population generation method is provided: all delivery stations and charging stations are randomly arranged; the cargo demand of each delivery station is accumulated; if the load capacity to the a-th delivery station exceeds the rated load capacity Q of the logistics vehicle, a starting point (i.e., a distribution center) is inserted before the a-th delivery station; then, the load capacity is recalculated from here until the last delivery station is reached, and then the distribution center is inserted, indicating that the logistics vehicle has completed its task and returned; in this way, a chromosome is generated; thus, based on the above method, this process is repeated until a specified number of chromosomes are generated, which can form the initial population.

[0061] Specifically, assuming there are 10 delivery stations (represented by 1-10) and 2 charging stations (represented by 11 and 12), a chromosome can be represented as: 0,2,3,7,4,11,5,0,1,6,8,10,12,9,0. The first logistics vehicle first passes through delivery stations 2,3,7, and 4; then it goes to charging station 11 for charging; finally, it returns to the distribution center via delivery station 5. Similarly, the second logistics vehicle first passes through delivery stations 1,6,8, and10; then it goes to charging station 12 for charging; finally, it returns to the distribution center via delivery station 9. Of course, the above example is merely illustrative, and this embodiment is not limited to this. Furthermore, each initial chromosome in the example also corresponds to an initial velocity vector, which is used for subsequent local searches based on the initial velocity vector.

[0062] Thus, after obtaining the chromosome population at the nth iteration, chromosome fitness can be calculated, as shown in step S12b below.

[0063] S12b. The reciprocal of the path planning function is used as the fitness function, and the fitness of each chromosome in the chromosome population at the nth iteration is calculated based on the fitness function. In this embodiment, for any chromosome, the delivery routes of each logistics vehicle are first determined based on that chromosome to obtain... The value of the value is obtained, and then the time for each logistics vehicle to arrive at each delivery station along the route is obtained through simulation. Then, the aforementioned data is substituted into the fitness function to obtain the fitness of any chromosome. That is, the greater the fitness of any chromosome, the smaller the total logistics and delivery cost of its corresponding delivery route.

[0064] After obtaining the fitness of each chromosome in the chromosome population at the nth iteration, the global optimal fitness at the current iteration can be determined, as shown in step S12c below.

[0065] S12c. Based on the fitness of each chromosome, determine the global optimal fitness at the nth iteration. In practice, first select the largest fitness among the fitness of each chromosome at the nth iteration; then, determine whether the largest fitness is greater than the global optimal fitness at the (n-1)th iteration. If so, the largest fitness is taken as the global optimal fitness at the nth iteration; otherwise, the global optimal fitness at the (n-1)th iteration is taken as the global optimal fitness at the nth iteration. Of course, when n is 1, the global optimal fitness at the nth iteration is the largest fitness at the nth iteration. After obtaining the global optimal fitness, the iteration stopping condition can be determined, as shown in step S12d below.

[0066] S12d. Based on the global optimal fitness, determine whether the iteration stopping condition is met; in this embodiment, the iteration stopping condition may be, but is not limited to, the global optimal fitness at the nth iteration being greater than or equal to the fitness threshold, or n reaching the maximum number of iterations; if the aforementioned condition is not met, population crossover is required, and the process is as shown in step S12e below.

[0067] S12e. If not, then perform a crossover operation on the chromosome population at the nth iteration to obtain a crossover population. In this embodiment, for example, but not limited to, first generating a first crossover random number and determining whether the first crossover random number is less than the crossover threshold; wherein, if yes, then perform a single-individual crossover operation on the chromosome population at the nth iteration to obtain a crossover population; otherwise, perform a double-individual crossover operation on the chromosome population at the nth iteration to obtain the crossover population; specifically, the value range of the first crossover random number is [0,1], and the crossover threshold is 0.5; thus, when the first crossover random number is less than 0.5, then the single-individual crossover method is used for population crossover; otherwise, the double-individual crossover method is used for population crossover.

[0068] Furthermore, the specific crossover process of the single-person crossover operation is as follows: for any chromosome in the chromosome population at the nth iteration, two genes are randomly selected from the chromosome, and then the positions of the two selected genes are swapped to obtain the crossover chromosome; in this way, based on the above method, after performing single-person crossover on all chromosomes, a crossover population can be obtained.

[0069] Furthermore, for the crossover operation between two individuals, the process can be, but is not limited to, the steps S12e1 to S12e5 shown below.

[0070] S12e1. Divide the chromosome population at the nth iteration into pairs to obtain several crossover chromosome groups.

[0071] After completing the pairwise grouping of chromosomes, for each pairwise chromosome group, different double-individual crossover methods can be selected to perform individual crossover, as shown in steps S12e2 to S12e5 below.

[0072] S12e2. For any crossover chromosome set, generate a second crossover random number and determine whether the second crossover random number is less than the crossover threshold; in this embodiment, the value range of the second crossover random number is also [0,1]; wherein, when the second crossover random number is less than 0.5, the fixed genes of the two chromosomes in any crossover chromosome set are first determined; then, the remaining genes are used as crossover genes to perform individual crossover, the process of which is shown in the following steps S12e3~S12e5.

[0073] S12e3. If so, a closed-loop gene selection algorithm is used to select genes from the first individual and the second individual, and the selected genes are used as fixed genes, wherein the first individual is one chromosome in any of the cross chromosome sets, and the second individual is another chromosome in any of the cross chromosome sets.

[0074] In this embodiment, the specific process of the closed-loop gene selection algorithm is as follows: Step 1: Obtain the initial gene position, wherein the initial gene position is a gene position randomly selected from the first individual; in this embodiment, it is equivalent to randomly selecting a gene position from the first individual as the initial gene position; then, starting from the initial gene position, gene selection is performed until finally returning to the initial gene position, the process is as shown in Step 2 to Step 5 below.

[0075] Step 2: Obtain the target position for the kth gene selection, where when k is 1, the target position is the initial gene position; after obtaining the target position for the kth gene selection, the first gene selection can be performed from two individuals in any chromosome set, as shown in Step 3 below.

[0076] Step 3: Select the gene at the target position in the first body as the first gene, and select the gene at the target position in the second body as the second gene; In this embodiment, assuming the target position is the second gene position, then the second gene in the first body is used as the first gene, and the second gene in the second body is used as the second gene; After completing one gene selection, the target position can be updated, and the process is shown in Step 4 below.

[0077] Step 4: Determine the location of the second gene in the first individual as the target location for the (k+1)th gene selection. In practice, assuming the second gene in the second individual is 9, then the location corresponding to gene 9 in the first individual needs to be determined, let's say it's 5. In this case, the target location for the (k+1)th gene selection is the 5th gene location. After updating the target location, the second gene selection can be performed, selecting the gene at the target location again from the first and second individuals, that is, selecting the 5th gene from the first and second individuals respectively. This process is repeated until the target location returns to the initial gene location. Then, the selected first and second genes can be used as fixed genes, as shown in Step 5 below.

[0078] Step 5: Increment k by 1 and re-obtain the target position at the time of the kth gene selection until the obtained target position returns to the initial gene position. Then, use the selected first and second genes as the fixed genes.

[0079] The following example illustrates the aforementioned gene selection process: Assume the first individual has the following sequence: 5, 3, 6, 8, 9, 4, 2, 1, 7; and the second individual has the following sequence: 4, 9, 7, 3, 1, 5, 8, 2, 6. Assume the initial gene position is 2. The target position for the first gene selection is the second gene position. Therefore, the first selected gene is 3, and the second gene is 9. Next, the position of the second gene in the first individual needs to be determined, which is the position of gene 9, which is 5. The target position for the second gene selection is the fifth gene position. Therefore, the first selected gene in the second selection is 9, and the second gene is 1.

[0080] Similarly, the position of gene 1 in the first individual needs to be determined, which is the 8th gene position; therefore, the target position for the third selection is 8; based on this, the first gene for the third selection is 1, and the second gene is 2; then, the target position for the fourth selection is 7, and the first gene for the fourth selection is 2, and the second gene is 8; further, the target position for the fifth selection is 4, that is, the first gene for the fifth selection is 8, and the second gene is 3; thus, the target position for the sixth selection is the 2nd gene position. At this point, the target position returns to the initial gene position, and the selection is complete.

[0081] Based on this, the first gene selected in the first individual is: 3, 9, 1, 2, 8; the second gene selected in the second individual is: 9, 1, 2, 8, 3; thus, the aforementioned selected genes can be used as fixed genes.

[0082] After obtaining the fixed gene, the genes of the first and second individuals, excluding the fixed gene, can be used as crossover genes for subsequent crossover operations, as shown in step S12e4 below.

[0083] S12e4. The genes in the first and second individuals, excluding the fixed genes, are taken as crossover genes. In this embodiment, the crossover genes in the first individual are: 5, 6, 4, 7; and the crossover genes in the second individual are: 4, 7, 5, 6. After obtaining the crossover genes, the crossover operation between the first and second individuals can be performed, as shown in step S12e5 below.

[0084] S12e5. Perform a crossover operation on the crossover genes in the first and second individuals to obtain the first crossover individual and the second crossover individual after the crossover operation, and obtain the crossover population after polling all crossover chromosome sets; in this embodiment, the crossover genes in the first individual and the crossover genes in the second individual are swapped to obtain the first crossover individual and the second crossover individual.

[0085] The first number is: 5,3,6,8,9,4,2,1,7; The second individual is: 4,9,7,3,1,5,8,2,6; then the first individual after crossing is: 4,3,7,8,9,5,2,1,6; the second individual after crossing is: 5,9,6,3,1,4,8,2,7; of course, the above examples are just examples, and this embodiment is not limited to them.

[0086] In this way, the crossover of chromosome sets is continuously performed in the aforementioned manner until all crossover chromosome sets have been queried, and then a crossover population can be obtained.

[0087] Therefore, through the aforementioned steps S12e1 to S12e5, the crossover gene can be determined by the closed-loop gene selection method, thereby realizing the two-individual crossover operation of the chromosome population.

[0088] Similarly, when the second crossover random number is greater than or equal to the crossover threshold, another two-individual crossover method is required, and the process is shown in steps S12e6 to S12e9 below.

[0089] S12e6. Randomly select several genes from the first individual to serve as the third gene; in this embodiment, assume that the genes selected from the first individual are: 5, 6, 4, 1, 7; then, it is necessary to determine the location of genes 5, 6, 4, 1, 7 from the second individual, as shown in step S12e7 below.

[0090] S12e7. Determine the gene positions corresponding to each third gene in the second body as designated positions; in this embodiment, the second body is: 4, 9, 7, 3, 1, 5, 8, 2, 6. Then, the positions of genes 5, 6, 4, 1, 7 are respectively: the sixth gene position, the ninth gene position, the first gene position, the fifth gene position, and the third gene position. At this time, the aforementioned gene positions can be used as designated positions to select genes in the second body. The process is as shown in step S12e8 below.

[0091] S12e8. Select the gene at the specified position from the second body as the fourth gene; in this embodiment, the genes at the sixth, ninth, first, fifth and third gene positions in the second body are selected as the fourth gene, that is, the fourth gene is extracted according to the gene position order as: 4,7,1,5,6.

[0092] In this embodiment, the values ​​of the third gene and the fourth gene are the same, but their orders in their respective chromosomes are different. Therefore, crossover can be performed, as shown in step S12e9 below.

[0093] S12e9. Take each third gene and each fourth gene as the crossover gene, and perform the crossover operation on the crossover gene according to the order of gene appearance, so as to obtain the first crossover individual and the second crossover individual after the crossover operation.

[0094] In this embodiment, the first body is 5,3,6,8,9,4,2,1,7; The second individual is: 4,9,7,3,1,5,8,2,6; therefore, the third and fourth genes are crossed according to their order of appearance, and the first individual after the crossover is: 4,3,7,8,9,1,2,5,6; the second individual after the crossover is: 5,9,6,3,4,1,8,2,7.

[0095] Thus, through the aforementioned steps S12e and their sub-steps, the chromosome population crossover operation at the nth iteration can be completed. In this embodiment, based on the generated crossover random number, the single-individual crossover or double-individual crossover method is selected. In the double-individual crossover, two different crossover methods are set to perform population crossover. In this way, using multiple different crossover methods can produce different offspring, thereby increasing the diversity of the population and avoiding the algorithm from getting trapped in local optima. At the same time, different crossover methods can explore different solution spaces, thereby improving the search efficiency of the algorithm and finding the optimal solution faster.

[0096] After the crossover operation is completed, this embodiment divides the population into two subpopulations. Then, different position update methods are used to update the individual positions and the individuals are mated to improve the local search capability of the population. The process is shown in steps S12f to S12h below.

[0097] S12f. Divide the crossover population into a first subpopulation and a second subpopulation of equal size.

[0098] S12g. The positions of the first and second subpopulations are updated to obtain the first and second updated subpopulations. In specific applications, S12g1 to S12g4 can be used, but are not limited to, to update the first subpopulation.

[0099] S12g1. For any first chromosome in the first subpopulation, obtain the globally optimal individual and the historically optimal individual corresponding to the first chromosome. The globally optimal individual is the chromosome corresponding to the globally optimal fitness at the nth iteration. In this embodiment, the historically optimal individual is the individual corresponding to the first chromosome with the highest fitness during the 1st to nth iterations. Thus, if the first chromosome A has the highest fitness at the 4th iteration during the first to fifth iterations, then the historically optimal individual of the first chromosome A is the first chromosome A at the 4th iteration.

[0100] Thus, after obtaining the globally optimal individual and the historically optimal individual, the individual distance can be calculated, as shown in step S12g2 below.

[0101] S12g2. Calculate the first distance between any first chromosome and the globally optimal individual, and the second distance between any first chromosome and the historically optimal individual.

[0102] After calculating the distance between any first chromosome and the global best individual and the historical best individual, the velocity can be updated, as shown in step S12g3 below.

[0103] S12g3. Based on the global optimal individual, the historical optimal individual, the first distance, and the second distance, update the velocity vector of any first chromosome to obtain the updated velocity vector.

[0104] In this embodiment, for example, but not limited to, the velocity vector of any first chromosome can be updated according to the following formula.

[0105] ; In the formula, This represents the updated velocity vector corresponding to any of the first chromosomes. This represents the velocity vector corresponding to any first chromosome (i.e., the velocity vector at the nth iteration; when n is 1, it is the initial velocity vector). This represents the update factor for the first position (with values ​​of 1 and 0.5 respectively). This represents the update factor for the second position (with a value of 2). Indicates the second distance. Indicates the first distance. This represents the historical best individual. This represents the globally optimal individual. This refers to any of the first chromosomes.

[0106] Meanwhile, when any of the first chromosomes is the individual with the highest fitness in the first subpopulation, its velocity vector is updated as follows: = In the formula, R is the update factor for the third position (with a value of 0.1), and R is a random number for the first position between [-1, 1].

[0107] Thus, based on the aforementioned formula, after updating the velocity vector of any first chromosome, the position of any first chromosome can be updated, as shown in step S12g4 below.

[0108] S12g4. Using the updated velocity vector, the position of any first chromosome is updated to obtain the updated first chromosome. After all chromosomes in the first subpopulation have been polled, the first updated population is obtained. In this embodiment, for example, the updated velocity vector is added to the position vector of any first chromosome in the nth iteration (i.e., the chromosome itself) to obtain the updated first chromosome.

[0109] After updating the position of the first subpopulation through the aforementioned steps S12g1 to S12g4, the position update of the second subpopulation can be performed, as shown in the following steps S12g5 to S12g9.

[0110] S12g5. For any second chromosome in the second subpopulation, determine a first chromosome from the first subpopulation as the matching chromosome corresponding to any second chromosome.

[0111] In this embodiment, assuming that any of the aforementioned second chromosomes is the 5th second chromosome in the second subpopulation, then its corresponding matching chromosome is the 5th first chromosome in the first subpopulation; thus, after obtaining the matching chromosome, fitness comparison can be performed, as shown in step S12g6 below.

[0112] S12g6. Determine whether the fitness of any second chromosome is greater than the fitness of the matching chromosome.

[0113] S12g7. If not, then calculate the third distance between any second chromosome and the matching chromosome; in this embodiment, after calculating the third distance between any second chromosome and the matching chromosome, the velocity vector can be updated, and the process is as shown in step S12g8 below.

[0114] S12g8. Based on the matched chromosome and the third distance, update the velocity vector of any second chromosome to obtain the updated velocity vector.

[0115] In specific implementation, for example, but not limited to, updating the velocity vector of any second chromosome according to the following formula.

[0116] ; In the formula, This represents the updated velocity vector corresponding to any of the second chromosomes. This represents the velocity vector of any of the second chromosomes. Indicates the third distance. These respectively represent either the second chromosome and the matching chromosome.

[0117] Furthermore, when the fitness of any second chromosome is greater than the fitness of the matching chromosome, a second position random number (within the range of -1, 1) is generated. Then, the second position random number is multiplied by the random walk coefficient (with a value of 0.1), and the product is added to the velocity vector of the second chromosome to obtain the updated velocity vector.

[0118] Thus, after obtaining the updated velocity vector, the position can be updated, as shown in step S12g9 below.

[0119] S12g9. Using the updated velocity vector, the position of any second chromosome is updated to obtain the updated second chromosome. After polling all chromosomes in the second subpopulation, the second updated population is obtained. In this embodiment, the position update method of any second chromosome is the same as the position update method of any first chromosome, and will not be described again here.

[0120] Thus, through the aforementioned steps S12g1 to S12g9, the positional update of chromosomes in the first and second subpopulations can be completed using different positional update methods; then, individual mating can be carried out, as shown in step S12h below.

[0121] S12h. Perform individual mating operations on the first and second updated populations to obtain a mating population, and use the mating population as the chromosome population in the (n+1)th iteration; In this embodiment, since the number of individuals in the two updated populations is the same, the updated first chromosome corresponds one-to-one with the updated second chromosome. Therefore, a mating random number (taking values ​​between (0,1)) can be generated, and then individual mating is performed based on the mating random number.

[0122] Specifically, for any updated first chromosome and its corresponding updated second chromosome in the first updated population, the interbreeding process between the two is as follows: The first intermediate individual is obtained by multiplying the mating random number by the updated second chromosome corresponding to any updated first chromosome; then, the random number is subtracted from 1 to obtain the random number difference; next, the random number difference is multiplied by the updated first chromosome to obtain the second intermediate individual; finally, the first intermediate individual and the second intermediate individual are summed to obtain the mated first chromosome.

[0123] Similarly, the third intermediate individual is obtained by multiplying the mating random number by any updated first chromosome; then, the fourth intermediate individual is obtained by multiplying the random number difference by the updated second chromosome corresponding to any updated first chromosome; finally, the third and fourth intermediate individuals are summed to obtain the mated second chromosome.

[0124] Therefore, by using the aforementioned method, the mating of individuals in the two updated populations can be completed, and after the mating is completed, the chromosome population at the (n+1)th iteration is obtained.

[0125] Through the above design, this invention uses different position update methods to update the positions of subpopulations in different ways, which can increase population diversity, thereby exploring different solution space regions and avoiding getting trapped in local optima. At the same time, subpopulations exchange information through individual mating, which can combine excellent genes from different subpopulations to form better individuals. In this way, the local search capability of the algorithm can be increased.

[0126] After obtaining the mating population, the aforementioned steps can be repeated until the iteration stopping condition is met, thus obtaining the logistics route scheme that minimizes the total logistics and distribution cost. The process is shown in step S12i below.

[0127] S12i. Increment n by 1 and reacquire the chromosome population at the nth iteration until the iteration stopping condition is met. Determine the logistics path scheme based on the chromosome corresponding to the global optimal fitness at the time the iteration stopping condition is met.

[0128] In this embodiment, when the population genetic operation meets the iteration stopping condition, the logistics path scheme can be obtained by decoding the chromosome corresponding to the global optimal fitness at this time, that is, the logistics delivery route of each logistics vehicle.

[0129] Therefore, through the aforementioned steps S12a to S12i, this embodiment, through the improved genetic algorithm, can increase population diversity, avoid the population getting trapped in local optima, and thus improve the algorithm's search capability.

[0130] After obtaining the logistics route plan, the logistics delivery routes of each logistics vehicle can be determined, as shown in step S13 below.

[0131] S13. Based on the derived logistics route plan, the logistics platform determines the logistics delivery routes for each logistics vehicle.

[0132] Once the delivery routes of each logistics vehicle are obtained, they can be sent to the navigation terminals of the corresponding vehicles, enabling them to deliver goods according to the planned routes. This, combined with the onboard positioning and monitoring equipment and electronic signature terminals, allows for full monitoring of the delivery process and generates electronic receipts for each delivery route, thus providing complete delivery data chain information for subsequent logistics traceability.

[0133] like Figure 2 As shown, the third aspect of this embodiment provides a hardware system for implementing the intelligent logistics delivery method with end-to-end visualization and electronic receipts described in the first aspect of the embodiment, including: a logistics platform, logistics vehicles and an electronic signature terminal, wherein the logistics vehicles are equipped with positioning and monitoring devices.

[0134] The logistics platform is used to generate delivery routes for each logistics vehicle and send each delivery route to the corresponding logistics vehicle.

[0135] Each logistics vehicle is used to receive its own logistics delivery route and, while carrying out logistics delivery according to its own logistics delivery route, obtains real-time location monitoring information collected by positioning and monitoring equipment. The location monitoring information includes vehicle location information, cargo monitoring video, and temperature and humidity information of the vehicle compartment.

[0136] Each logistics vehicle is also used to send its acquired location monitoring information to the logistics platform in real time.

[0137] The logistics platform is used to generate visualized end-to-end delivery information for each logistics vehicle based on the received location monitoring information, and synchronize it to the user terminal.

[0138] The logistics platform is used to receive logistics receipt information sent by electronic receipt terminals on various logistics delivery routes. The logistics receipt information includes receipt time, location, personnel, goods information and receipt photos.

[0139] The logistics platform is also used to generate and store electronic delivery receipts for each logistics delivery route based on the various logistics receipt information.

[0140] The working process, working details and technical effects of the system provided in this embodiment can be found in the first and second aspects of the embodiment, and will not be repeated here.

[0141] like Figure 3 As shown, the fourth aspect of this embodiment provides an intelligent logistics delivery device that provides end-to-end visualization and electronic receipts. Taking the device as an electronic device as an example, it includes: a memory, a processor, and a transceiver that are connected in sequence. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the intelligent logistics delivery method that provides end-to-end visualization and electronic receipts as described in the first and second aspects of the embodiments.

[0142] For specific examples, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0143] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard) transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0144] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first and second aspects of the embodiment, and will not be repeated here.

[0145] The fifth aspect of this embodiment provides a storage medium for storing instructions containing the intelligent logistics delivery method for providing end-to-end visualization and electronic receipts as described in the first and second aspects of the embodiments. That is, the storage medium stores instructions, and when the instructions are run on a computer, the intelligent logistics delivery method for providing end-to-end visualization and electronic receipts as described in the first and second aspects of the embodiments is executed.

[0146] The storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0147] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0148] The sixth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the intelligent logistics distribution method providing end-to-end visualization and electronic receipts as described in the first and second aspects of the embodiments, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0149] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A smart logistics delivery method that provides end-to-end visualization and electronic delivery receipts, characterized in that, The method is applied to logistics platforms, logistics vehicles, and electronic signature terminals, wherein the logistics vehicles are equipped with positioning and monitoring devices, and the method includes: The logistics platform generates delivery routes for each logistics vehicle and sends each delivery route to the corresponding logistics vehicle. Each logistics vehicle receives its own logistics delivery route and, while delivering goods according to its own logistics delivery route, obtains real-time location monitoring information collected by positioning and monitoring equipment. The location monitoring information includes vehicle location information, cargo monitoring video, and temperature and humidity information of the vehicle compartment. Each logistics vehicle sends its acquired location monitoring information to the logistics platform in real time; Based on the received location monitoring information, the logistics platform generates visualized end-to-end delivery information for each logistics vehicle and synchronizes it to the user's end. The logistics platform receives logistics receipt information sent by electronic receipt terminals on each logistics delivery route. The logistics receipt information includes receipt time, location, personnel, goods information and receipt photos. The logistics platform generates and stores electronic delivery receipts for each logistics delivery route based on the delivery confirmation information.

2. The method according to claim 1, characterized in that, The logistics platform generates delivery routes for each logistics vehicle, including: The logistics platform constructs a path planning function with the optimization objective of minimizing the total logistics and distribution cost; The logistics platform uses an improved genetic algorithm to solve the path planning function to obtain the logistics path scheme that minimizes the total logistics and distribution cost. Based on the derived logistics route plan, the logistics platform determines the delivery routes for each logistics vehicle.

3. The method according to claim 2, characterized in that, The logistics platform uses an improved genetic algorithm to solve the path planning function to derive the logistics route scheme that minimizes the total logistics and delivery cost, including: Obtain the chromosome population at the nth iteration, where when n is 1, the chromosome population at the nth iteration is the initial population, and any initial chromosome in the initial population is used to characterize a delivery route for each logistics vehicle. The reciprocal of the path planning function is used as the fitness function, and the fitness of each chromosome in the chromosome population at the nth iteration is calculated based on the fitness function. Based on the fitness of each chromosome, the global optimal fitness at the nth iteration is determined; Based on the globally optimal fitness, determine whether the iteration stopping condition is met; If not, then perform a crossover operation on the chromosome population at the nth iteration to obtain the crossover population; The crossover population is divided into a first subpopulation and a second subpopulation with the same individual size; The positions of the first and second subpopulations are updated to obtain the first updated population and the second updated population. Perform individual mating operations on the first and second updated populations to obtain a mating population, and use the mating population as the chromosome population in the (n+1)th iteration; Increment n by 1 and reacquire the chromosome population at the nth iteration until the iteration stopping condition is met. Then, determine the logistics path scheme based on the chromosome corresponding to the global optimal fitness at the time the iteration stopping condition is met.

4. The method according to claim 3, characterized in that, Perform a crossover operation on the chromosome population at the nth iteration to obtain the crossover population, which includes: Generate the first crossover random number and determine whether the first crossover random number is less than the crossover threshold; If so, perform a single-individual crossover operation on the chromosome population at the nth iteration to obtain the crossover population; otherwise, perform a double-individual crossover operation on the chromosome population at the nth iteration to obtain the crossover population.

5. The method according to claim 4, characterized in that, Perform a two-individual crossover operation on the chromosome population at the nth iteration, including: The chromosome population at the nth iteration is divided into pairs to obtain several crossover chromosome groups; For any set of crossed chromosomes, generate a second crossover random number and determine whether the second crossover random number is less than the crossover threshold; If so, a closed-loop gene selection algorithm is used to select genes from the first individual and the second individual, and the selected genes are used as fixed genes. The first individual is one chromosome in any of the crossover chromosome sets, and the second individual is another chromosome in any of the crossover chromosome sets. Genes in the first and second individuals other than the fixed genes are used as crossover genes. Crossover operations are performed on the crossover genes in the first and second individuals to obtain the first and second crossover individuals. After all crossover chromosome sets have been queried, the crossover population is obtained.

6. The method according to claim 5, characterized in that, Using a closed-loop gene selection algorithm, genes are selected from the first and second individuals in any of the crossed chromosome sets, including: Obtain the initial gene position, wherein the initial gene position is a gene position randomly selected from the first individual; Obtain the target position at the k-th gene selection, where when k is 1, the target position is the initial gene position; The gene at the target location in the first individual is selected as the first gene, and the gene at the target location in the second individual is selected as the second gene. Determine the location of the second gene in the first individual, and use it as the target location for the (k+1)th gene selection. Increment k by 1 and re-obtain the target position at the time of the kth gene selection until the obtained target position returns to the initial gene position. Then, the selected first and second genes are used as the fixed genes.

7. The method according to claim 5, characterized in that, If the first cross-number is greater than or equal to the cross-threshold, the method further includes: Several genes are randomly selected from the first individual to serve as the third gene; The gene positions corresponding to each third gene in the second body are determined as designated positions; The gene at the specified position is selected from the second individual to serve as the fourth gene; Each third gene and each fourth gene are used as crossover genes, and crossover operations are performed on the crossover genes according to the order in which they appear, so that the first crossover individual and the second crossover individual are obtained after the crossover operation.

8. The method according to claim 1, characterized in that, Each logistics vehicle will send its acquired location monitoring information to the logistics platform in real time, including: Each logistics vehicle encrypts its acquired location monitoring information to obtain encrypted information; Each logistics vehicle sends encrypted information to the edge node, so that the edge node forwards the encrypted information to the logistics platform after receiving it. Correspondingly, based on the received location monitoring information, the logistics platform generates end-to-end delivery visualization information for each logistics vehicle, including: The logistics platform decrypts each encrypted message to obtain the location monitoring information sent by each logistics vehicle. The logistics platform generates the actual delivery trajectory of each logistics vehicle based on the vehicle location information in various location monitoring information. The logistics platform establishes a delivery GIS map and renders the actual delivery trajectory of each logistics vehicle onto the delivery GIS map to obtain a real-time logistics delivery trajectory map of each logistics vehicle. The real-time logistics delivery trajectory map of each logistics vehicle is linked with the temperature and humidity information of the vehicle compartment and the cargo monitoring video in the location monitoring information of each logistics vehicle, so as to generate full-link delivery visualization information of each logistics vehicle after the information is linked.

9. An intelligent logistics and distribution system that provides end-to-end visualization and electronic delivery receipts, characterized in that, include: Logistics platform, logistics vehicles and electronic signature terminal, among which the logistics vehicles are equipped with positioning and monitoring equipment; The logistics platform is used to generate logistics delivery routes for each logistics vehicle and send each logistics delivery route to the corresponding logistics vehicle. Each logistics vehicle is used to receive its own logistics delivery route and, while carrying out logistics delivery according to its own logistics delivery route, to obtain real-time location monitoring information collected by positioning and monitoring equipment. The location monitoring information includes vehicle location information, cargo monitoring video, and temperature and humidity information of the vehicle compartment. Each logistics vehicle is also used to send its acquired location monitoring information to the logistics platform in real time; The logistics platform is used to generate visualized end-to-end delivery information for each logistics vehicle based on the received location monitoring information, and synchronize it to the user terminal. The logistics platform is used to receive logistics receipt information sent by electronic receipt terminals on various logistics delivery routes. The logistics receipt information includes receipt time, location, personnel, goods information and receipt photos. The logistics platform is also used to generate and store electronic delivery receipts for each logistics delivery route based on the various logistics receipt information.

10. An electronic device, characterized in that, include: A memory, a processor, and a transceiver are sequentially connected in communication, wherein the memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the intelligent logistics distribution method that provides end-to-end visualization and electronic receipts as described in any one of claims 1 to 8.

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