Intelligent logistics scheduling system based on AI algorithm

Abstract

The invention discloses an intelligent logistics scheduling system based on an AI algorithm, and relates to the technical field of electric digital data processing. The intelligent logistics scheduling system based on the AI algorithm comprises an intelligent order distribution module, a vehicle path planning module, a warehouse management module and a data analysis module. According to the invention, the to-be-allocated order is intelligently allocated through the to-be-allocated order information to obtain the order allocation accuracy index and determine whether intelligent allocation is completed, and then the transport path of the to-be-transported vehicle is intelligently planned to obtain the path planning accuracy index and determine whether order distribution is performed. And finally, carrying out intelligent warehousing on the to-be-warehoused goods to obtain a warehousing validity evaluation index and judge whether intelligent warehousing is completed or not, and meanwhile, obtaining logistics scheduling data and generating a logistics scheduling report, thereby realizing improvement of the analysis reliability of the intelligent scheduling data of the logistics center. The problem that in the prior art, logistics center intelligent dispatching data analysis is not high in reliability is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic digital data processing, and in particular to an intelligent logistics scheduling system based on AI algorithm. Background Art

[0002] With the booming development of e-commerce, online shopping has become an important part of people's daily life, which has greatly promoted the rapid development of the logistics industry. At the same time, as a key node in the logistics network, the operation efficiency and service quality of the logistics center directly affect the efficiency of the entire logistics chain. Secondly, as an advanced computing technology, AI algorithm has powerful data processing and pattern recognition capabilities, and can automatically learn and optimize scheduling strategies to adapt to the ever-changing logistics needs. However, traditional logistics center scheduling mainly relies on manual experience and simple rule judgments, which is difficult to cope with complex and changing logistics environments and massive data processing requirements. Therefore, it is particularly important to develop an efficient and intelligent logistics center scheduling system that applies AI (Artificial Intelligence) algorithms to the logistics center scheduling system.

[0003] Existing technology obtains scheduling information in real time by analyzing historical data, and then dynamically adjusts the scheduling strategy based on the scheduling information. Finally, based on the results of the dynamic adjustment, the logistics data is deeply analyzed to identify failure risks, and relevant personnel are notified in advance for processing, thereby achieving a more accurate prediction of failure risks during the scheduling process.

[0004] For example, the invention patent announcement with announcement number: CN116521344B announces an AI algorithm scheduling method and system based on a resource bus, including: obtaining data requests and data transmission status of the execution ends of multiple AI algorithm models through the resource bus of the algorithm management platform; calculating the algorithm execution parameters corresponding to each AI algorithm model and the device execution parameters corresponding to each execution end according to the data requests and data transmission status; obtaining the algorithm application scenarios and application requirements input by the user; according to the objective function and constraints, based on the dynamic programming algorithm, calculating the optimal algorithm execution strategy corresponding to multiple adaptive AI algorithm models and multiple adaptive execution ends; and recommending the optimal algorithm execution strategy to the user.

[0005] For example, the invention patent announcement with announcement number: CN117472587B announces a resource scheduling system for an AI intelligent computing center, including: identifying and classifying tasks based on computing task data to generate a task identification report; generating a resource demand forecast report based on the task identification report; dynamically optimizing and allocating resources based on the resource demand forecast report to generate a resource allocation plan; generating a cloud-edge collaborative scheduling plan based on the resource allocation plan; generating multi-objective optimization results based on the cloud-edge collaborative scheduling plan; adaptively adjusting the scheduling strategy based on the multi-objective optimization results to generate an adaptive scheduling strategy; generating a performance analysis report based on the adaptive scheduling strategy; generating a scheduling effect feedback report based on the performance analysis report.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: In the prior art, the reliability of fault risk prediction in the scheduling process is affected due to incomplete acquisition of scheduling information. In addition, the diversity of logistics and transportation links and the influence of environmental factors in the cargo transportation process result in the scheduling system being unable to obtain complete logistics information in a timely manner, resulting in the problem of low reliability of intelligent scheduling data analysis in the logistics center. Summary of the invention

[0007] The embodiment of the present application solves the problem of low reliability of intelligent scheduling data analysis of logistics centers in the prior art by providing an intelligent logistics scheduling system based on an AI algorithm, thereby improving the reliability of intelligent scheduling data analysis of logistics centers.

[0008] The embodiment of the present application provides an intelligent logistics scheduling system based on an AI algorithm, including: an intelligent order allocation module, a vehicle path planning module, a warehouse management module and a data analysis module; wherein the intelligent order allocation module is used to intelligently allocate orders to be allocated according to order information to be allocated to obtain an order allocation accuracy index, and judge whether the intelligent allocation is completed based on the obtained order allocation accuracy index, and the order allocation accuracy index is used to evaluate the accuracy of order allocation; the vehicle path planning module is used to, after the intelligent allocation is completed, intelligently plan the transportation path of the transportation vehicle to be transported according to the order information to be allocated to obtain a path planning accuracy index, and judge whether to perform order delivery based on the obtained path planning accuracy index, and the path planning accuracy index is used to evaluate the rationality of the transportation path planning; the warehouse management module is used to, after the order delivery is completed, perform intelligent warehousing on the warehoused goods according to the status of the goods to be scheduled to obtain a warehousing effectiveness evaluation index, and judge whether the intelligent warehousing is completed based on the obtained warehousing effectiveness evaluation index, and the warehousing effectiveness evaluation index is used to evaluate the timeliness and accuracy of warehousing management; the data analysis module is used to obtain logistics scheduling data and generate a logistics scheduling report, and the logistics scheduling report is used to visualize the changes of key performance indicators of logistics center scheduling over time.

[0009] Furthermore, the order allocation accuracy index is obtained by the following method: comparing the actual allocation time of the order to be allocated with the reference allocation time in the database to obtain the order allocation priority, the order allocation priority including the first order allocation, the second order allocation and the third order allocation; obtaining the order allocation priority weight factor from the preset database, and at the same time counting the actual allocation time of the order to be allocated and obtaining the allocation time score in combination with the reference allocation time; counting the number of orders to be allocated that have been successfully allocated and obtaining the order completion rate in combination with the total number of orders to be allocated, and at the same time combining the obtained allocation time score and the order allocation priority weight factor to obtain the order allocation accuracy index, the order completion rate is the ratio of the number of orders to be allocated that have been successfully allocated to the total number of orders to be allocated.

[0010] Furthermore, the path planning accuracy index is obtained by the following method: counting the actual driving distance, actual driving time and traffic waiting time of the vehicles to be transported within the preset transportation period, judging whether the traffic waiting time is not greater than the maximum allowed traffic waiting time, and if so, obtaining the driving distance evaluation score and driving time evaluation score in combination with the reference shortest driving distance and the reference shortest driving time, otherwise recording the corresponding preset transportation period as a traffic congestion period and changing the transportation route; real-time monitoring of the average fog visibility and environmental impact factors of the transportation route of the vehicles to be transported within the preset transportation period, and obtaining the path planning accuracy index in combination with the obtained order allocation accuracy index, driving distance evaluation score and driving time evaluation score; the driving distance evaluation score is the ratio of the difference between the actual driving distance and the reference shortest driving distance to the reference shortest driving distance; the driving time evaluation score is the ratio of the difference between the total driving time and the reference shortest driving time to the reference shortest driving time; the total driving time is the sum of the actual driving time and the traffic waiting time.

[0011] Furthermore, the specific steps for obtaining the warehouse effectiveness evaluation index are as follows: E1, real-time monitoring of the inventory warning time of the order to be stored in the intelligent warehousing process, and judging whether the inventory warning time is less than the preset inventory warning time. If so, execute E2, otherwise adjust the warning parameters of the controller and continue the intelligent warehousing; E2, obtain the inventory warning time score, and at the same time monitor the inventory scheduling speed in the intelligent warehousing process in real time and obtain the inventory scheduling speed score in combination with the reference inventory scheduling speed. The inventory warning time score is the complement of the difference between the preset inventory warning time and the inventory warning time and the ratio of the preset inventory warning time, and the inventory scheduling speed score is the ratio of the inventory scheduling speed to the reference inventory scheduling speed; E3, obtain the total warehousing time and judge whether it is within the allowable range of the warehousing time. If so, the warehousing effectiveness evaluation index is obtained by combining the acquired path planning accuracy index, inventory warning time score and inventory scheduling speed score.

[0012] Furthermore, the specific restricted expression of the storage effectiveness evaluation index is: ; Where i is the number of the order batch to be allocated, , Y is the total number of order batches to be allocated, e is a natural constant, It represents the evaluation index of storage effectiveness of the goods to be stored corresponding to the i-th batch of orders to be allocated in the intelligent warehousing process, It represents the accuracy index of the path planning of the transportation vehicles corresponding to the i-th batch of orders to be allocated within the preset transportation period. Represents the preset path planning accuracy index, It represents the inventory warning duration score of the goods to be stored corresponding to the i-th batch of orders to be allocated during the intelligent warehousing process. Indicates the preset inventory warning duration. It indicates the inventory warning duration of the goods to be stored corresponding to the i-th batch of orders to be allocated during the intelligent warehousing process. It represents the inventory scheduling speed score of the goods to be stored corresponding to the i-th batch of orders to be allocated in the intelligent warehousing process, It represents the total storage time of the goods to be stored corresponding to the i-th batch of orders to be allocated in the intelligent warehousing process. Indicates the allowed range of storage time.

[0013] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Intelligently allocate the orders to be allocated through the information of the orders to be allocated to obtain the order allocation accuracy index and judge whether the intelligent allocation is completed. Then, intelligently plan the transportation path of the transport vehicle to obtain the path planning accuracy index and judge whether to deliver the order. Finally, intelligently store the stored goods to obtain the storage effectiveness evaluation index and judge whether the intelligent warehousing is completed. At the same time, obtain the logistics scheduling data and generate the logistics scheduling report, thereby realizing the improvement of the accuracy and reliability of logistics scheduling data acquisition, and then realizing the improvement of the reliability of the intelligent scheduling data analysis of the logistics center, which effectively solves the problem of low reliability of the intelligent scheduling data analysis of the logistics center in the prior art.

[0014] 2. The order allocation priority is obtained by comparing the actual allocation time of the orders to be allocated with the reference allocation time in the database, and then the actual allocation time of the orders to be allocated is counted and combined with the reference allocation time to obtain the allocation time score. Finally, the number of orders to be allocated that have been successfully allocated is counted and combined with the total number of orders to be allocated to obtain the order completion rate. At the same time, the order allocation accuracy index is obtained by combining the obtained allocation time score and the order allocation priority weight factor, thereby achieving an improvement in the accuracy of obtaining the order allocation accuracy index, and then achieving a more accurate evaluation of the accuracy of intelligent allocation of the logistics center.

[0015] 3. By obtaining the inventory warning duration score when the inventory warning duration is less than the preset inventory warning duration, the inventory scheduling speed in the intelligent warehousing process is monitored in real time and combined with the reference inventory scheduling speed to obtain the inventory scheduling speed score, and combined with the obtained path planning accuracy index to obtain the warehousing effectiveness evaluation index, the accuracy of obtaining the warehousing effectiveness evaluation index is improved, thereby achieving a more accurate evaluation of the real-time and accuracy of the intelligent warehousing of the logistics center. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A schematic diagram of the structure of an intelligent logistics scheduling system based on an AI algorithm provided in an embodiment of the present application; Figure 2 A specific flow chart of intelligent logistics scheduling provided in the embodiment of the present application; Figure 3 A two-dimensional coordinate diagram of the inventory warning duration score-warehousing effectiveness evaluation index provided in the embodiment of the present application; Figure 4 A two-dimensional coordinate diagram of the path planning accuracy index-warehousing effectiveness evaluation index provided in the embodiment of the present application; Figure 5 A two-dimensional coordinate diagram of the inventory scheduling speed score-warehousing effectiveness evaluation index provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiment of the present application solves the problem of low reliability of intelligent scheduling data analysis of logistics centers in the prior art by providing an intelligent logistics scheduling system based on an AI algorithm. An intelligent order allocation module is used to intelligently allocate orders to be allocated according to order information to be allocated to obtain order allocation accuracy indicators, and whether the intelligent allocation is completed is determined based on the obtained order allocation accuracy indicators. Then, after the intelligent allocation is completed, a vehicle path planning module is used to intelligently plan the transportation path of the transportation vehicle to be allocated according to the order information to be allocated to obtain a path planning accuracy indicator, and whether the order is delivered is determined based on the obtained path planning accuracy indicators. Finally, after the order delivery is completed, a warehousing management module is used to intelligently store the stored goods according to the status of the goods to be scheduled to obtain a warehousing effectiveness evaluation indicator, and whether the intelligent warehousing is completed is determined based on the obtained warehousing effectiveness evaluation indicator. Then, a data analysis module is used to obtain logistics scheduling data and generate a logistics scheduling report, thereby improving the reliability of intelligent scheduling data analysis of the logistics center.

[0018] The technical solution in the embodiment of the present application is to solve the problem of low reliability of intelligent scheduling data analysis in the above-mentioned logistics center. The overall idea is as follows: Through intelligent allocation, the order allocation accuracy index is obtained and it is judged whether the intelligent allocation is completed. Then, the path planning accuracy index obtained by intelligent planning is used to judge whether to deliver the order. Finally, intelligent warehousing is carried out on the stored goods to obtain the warehousing effectiveness evaluation index and judge whether the intelligent warehousing is completed. At the same time, the logistics scheduling data is obtained and the logistics scheduling report is generated, thereby achieving the effect of improving the reliability of the intelligent scheduling data analysis of the logistics center.

[0019] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0020] like Figure 1 As shown, it is a structural schematic diagram of an intelligent logistics scheduling system based on AI algorithm provided in an embodiment of the present application. An intelligent logistics scheduling system based on AI algorithm provided in an embodiment of the present application includes: an intelligent order allocation module, a vehicle path planning module, a warehouse management module and a data analysis module; wherein the intelligent order allocation module is used to intelligently allocate orders to be allocated according to the order information to be allocated to obtain an order allocation accuracy index, and judge whether the intelligent allocation is completed based on the obtained order allocation accuracy index, and the order allocation accuracy index is used to evaluate the accuracy of order allocation; the vehicle path planning module is used to intelligently plan the transportation path of the transportation vehicle to be transported according to the order information to be allocated after the intelligent allocation is completed to obtain the path planning accuracy The warehousing management module is used to perform intelligent warehousing of the goods to be dispatched according to the status of the goods to be dispatched after the order delivery is completed, and to obtain the warehousing effectiveness evaluation index. The warehousing effectiveness evaluation index is used to evaluate the timeliness and accuracy of warehousing management. The data analysis module is used to obtain logistics scheduling data and generate logistics scheduling reports. The logistics scheduling reports are used to visualize the changes in the key performance indicators of the logistics center scheduling over time. The key performance indicators include order allocation accuracy indicators, path planning accuracy indicators and warehousing effectiveness evaluation indicators.

[0021] In this embodiment, the order information to be allocated includes order attributes and vehicle attributes; order attributes include priority, cargo type, volume weight, destination and delivery time; vehicle attributes include the number of empty vehicles, empty vehicle storage space and empty vehicle location; the status of cargo to be dispatched includes waiting for warehousing, waiting for storage, waiting for outbound delivery and inventory status; logistics scheduling data includes order allocation data (in the intelligent order allocation module: 1 indicates that intelligent allocation is completed, and 0 indicates that intelligent allocation is in progress), path planning data (in the vehicle path planning module: 1 indicates that intelligent planning is completed, and 0 indicates that intelligent planning is in progress) and warehouse management data (in the warehouse management module: 1 indicates that intelligent warehousing is completed, and 0 indicates that intelligent warehousing is in progress).

[0022] like Figure 2As shown, it is a specific flow chart of intelligent logistics scheduling provided by the embodiment of the present application. The intelligent distribution process usually uses the decision tree in the machine learning algorithm to intelligently analyze the order attributes of the order to be distributed and the vehicle attributes of the vehicle to be transported; usually uses the Dijkstra algorithm in the path optimization algorithm to intelligently plan the transportation path; usually uses the warehouse management system (WMS, Warehouse Management System) combined with the deep learning algorithm to monitor the status of the goods in real time to realize the intelligent warehousing of the orders to be stored. Among them, the machine learning algorithm, the path optimization algorithm and the deep learning algorithm are all AI algorithms.

[0023] In a specific application scenario: A large e-commerce company has a logistics center that processes more than 100,000 orders per day. In order to improve logistics efficiency, reduce costs and increase customer satisfaction, the company decided to introduce an intelligent logistics scheduling system based on AI algorithms. Through intelligent order allocation, route planning, warehouse management and data analysis, it significantly improved logistics efficiency and the reliability of the intelligent scheduling data analysis of the logistics center, thereby ensuring the timely processing and efficient delivery of massive orders, providing strong support for the rapid development of e-commerce companies.

[0024] Furthermore, the order allocation accuracy index is obtained by the following method: comparing the actual allocation time of the order to be allocated with the reference allocation time in the database to obtain the order allocation priority, the order allocation priority includes the first order allocation, the second order allocation and the third order allocation, the first order allocation (i.e., the expedited order) indicates that the actual allocation time of the corresponding order to be allocated is less than the reference allocation time, the second order allocation (i.e., the normal delivery order) indicates that the actual allocation time of the corresponding order to be allocated is equal to the reference allocation time, and the third order allocation (i.e., the pre-order) indicates that the actual allocation time of the corresponding order to be allocated is greater than the reference allocation time; obtaining the order allocation priority weight factor (i.e., the order allocation accuracy index in the restriction expression) from the preset database ), and at the same time, the actual allocation time of the orders to be allocated is counted and combined with the reference allocation time to obtain the allocation time score (that is, the order allocation accuracy index in the restriction expression ); Count the number of pending orders that have been successfully allocated and combine it with the total number of pending orders to obtain the order completion rate (i.e. the order allocation accuracy index in the restriction expression). ), and the order allocation accuracy index is obtained by combining the obtained allocation time score and the order allocation priority weight factor. The order completion rate is the ratio of the number of orders to be allocated to the total number of orders to be allocated.

[0025] In this embodiment, the specific restriction expression of the order allocation accuracy index is: ; ; ; Where i is the number of the order batch to be allocated, , Y is the total number of order batches to be allocated, e is a natural constant, It represents the order allocation accuracy index of the i-th batch of orders to be allocated in the intelligent allocation process. It represents the order allocation priority weight factor of the i-th batch of orders to be allocated in the intelligent allocation process. It represents the allocation time score of the i-th batch of orders to be allocated in the intelligent allocation process. It represents the actual allocation time of the i-th batch of orders to be allocated in the intelligent allocation process. Indicates the reference allocation time of the i-th batch of orders to be allocated, represents the order completion rate of the i-th batch of orders to be allocated during the intelligent allocation process, It indicates the number of pending orders in the i-th batch that are completed within the actual allocation time. It indicates the total number of orders to be allocated in the i-th batch before intelligent allocation.

[0026] Among them, the reference allocation time is represented by the sum and average of the historical order allocation times in the preset logistics center database; the order allocation priority weight factor is a numerical value in the preset database that corresponds to the degree of influence of the order allocation priority on the correlation compliance index. When used, the weight factor corresponding to the order allocation priority can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the order allocation priority corresponding to the order allocation priority in the process of obtaining the order allocation accuracy index and the weight factor corresponding to the order allocation priority in the preset database form a mapping set, and the real-time order allocation priority is input into the mapping set to obtain the corresponding order allocation priority weight factor, where the mapping relationship can be one-to-one or many-to-one. The value range of the order allocation priority weight factor in this example is .

[0027] It should be understood that the order allocation priority affects the allocation time score and the order completion rate, which in turn affects the value of the order allocation accuracy index. The order allocation priority weight factors corresponding to the first order, the second order, and the third order decrease step by step; the order allocation accuracy index (the larger the better) increases with the increase of the allocation time score (because the allocation time is negatively correlated with the order allocation accuracy index), and increases with the increase of the order completion rate (because the number of orders to be allocated is positively correlated with the order allocation accuracy index). It should be noted that the allocation time score also indirectly affects the value of the order completion rate.

[0028] For example, in case one, if the logistics center gives priority to the orders assigned to the first order (i.e., expedited orders), resulting in a decrease in the allocation time scores of these orders (i.e., faster allocation), then these orders will be quickly marked as “completed”, thereby improving the overall order completion rate.

[0029] Case 2: If the logistics center does not give priority to the first-priority orders, but instead allocates resources equally to all orders, the allocation time scores corresponding to the first-priority orders will increase (that is, the allocation speed will decrease), causing these orders to remain uncompleted for a long time, reducing the overall order completion rate.

[0030] In this example, by setting the order allocation priority weight factor and monitoring the allocation time score and order completion rate in real time, the logistics center can optimize the order allocation strategy to ensure that high-priority orders are processed quickly while trying to increase the overall order completion rate. In this way, the order allocation accuracy index will be improved, thereby reflecting the actual effect of the logistics center's intelligent scheduling, and further realizing the improvement of the reliability of the logistics center's intelligent scheduling data analysis, effectively solving the problem of low reliability of the logistics center's intelligent scheduling data analysis in the prior art.

[0031] Furthermore, the specific process of judging whether the intelligent allocation is completed based on the obtained order allocation accuracy index is as follows: judging whether the obtained order allocation accuracy index is greater than the preset order allocation accuracy index: if the obtained order allocation accuracy index is greater than the preset order allocation accuracy index, the intelligent allocation is completed and the orders to be allocated that have completed the intelligent allocation are recorded as orders to be delivered; if the obtained order allocation accuracy index is not greater than the preset order allocation accuracy index, it indicates that the accuracy of the intelligent allocation does not meet the expected requirements and the intelligent allocation is performed again.

[0032] In this embodiment, the preset order allocation accuracy index (set by the preset personnel) is represented by the sum and average of the historical order allocation accuracy indexes of historical orders in the preset logistics center database; after each intelligent allocation is completed, the system will automatically collect feedback data of the corresponding batch of orders to be allocated, such as: the obtained order allocation accuracy index is not greater than the preset order allocation accuracy index, then the system will automatically trigger the reallocation mechanism and reallocate the corresponding batch of orders to be allocated intelligently until the expected order allocation accuracy requirements are met, thereby improving the accuracy and efficiency of processing orders to be allocated, and helping the logistics center to use intelligent allocation technology more efficiently to adapt to the ever-changing market environment and business needs.

[0033] Furthermore, the path planning accuracy index is obtained by the following method: when the obtained order allocation accuracy index is greater than the preset order allocation accuracy index, the actual driving distance, actual driving time and traffic waiting time of the vehicles to be transported within the preset transportation period are counted, and it is determined whether the traffic waiting time is not greater than the maximum allowed traffic waiting time. If so, the driving distance evaluation score is obtained by combining the reference shortest driving distance and the reference shortest driving time (i.e., the value in the restriction expression of the path planning accuracy index). ) and the travel time evaluation score (i.e., the constraint expression of the path planning accuracy indicator ), otherwise the corresponding preset transportation period is recorded as a traffic congestion period and the transportation route is changed; real-time monitoring of the average fog visibility of the transportation route of the transported vehicle within the preset transportation period (i.e., the limit expression of the path planning accuracy index ) and environmental factors (i.e., the limiting expression of the path planning accuracy index ), combined with the obtained order allocation accuracy index (i.e., the restriction expression of the path planning accuracy index ), driving distance evaluation score and driving time evaluation score to obtain the path planning accuracy index.

[0034] Among them, the driving distance evaluation score is the ratio of the difference between the actual driving distance and the reference shortest driving distance to the reference shortest driving distance; the driving time evaluation score is the ratio of the difference between the total driving time and the reference shortest driving time to the reference shortest driving time; the total driving time is the sum of the actual driving time and the traffic waiting time; the reference shortest driving distance and the reference shortest driving time are the minimum values ​​obtained after multiple intelligent planning are completed; the maximum operating traffic waiting time is the maximum value of the historical traffic congestion time of historical transport vehicles on the transport route in the preset database.

[0035] In this embodiment, the restriction expression of the path planning accuracy index is: ; ; ; ; Where i is the number of the order batch to be allocated, , Y is the total number of order batches to be allocated, e is a natural constant, It represents the accuracy index of the path planning of the transportation vehicles corresponding to the i-th batch of orders to be allocated within the preset transportation period. It represents the order allocation accuracy index of the i-th batch of orders to be allocated within the preset transportation period. Indicates the preset order allocation accuracy indicator, represents the environmental impact factor, It represents the evaluation score of the travel distance of the transportation vehicles corresponding to the i-th batch of orders to be allocated within the preset transportation period, represents the actual travel distance of the transportation vehicles corresponding to the i-th batch of orders to be allocated within the preset transportation period, It represents the reference shortest driving distance of the transportation vehicles corresponding to the i-th batch of orders to be allocated after the intelligent planning is completed. It represents the evaluation score of the driving time of the transportation vehicles corresponding to the i-th batch of orders to be allocated within the preset transportation period, It represents the total driving time of the transportation vehicles corresponding to the i-th batch of orders to be allocated within the preset transportation period. It represents the reference shortest driving time of the transportation vehicles corresponding to the i-th batch of orders to be allocated after the intelligent planning is completed. represents the actual driving time of the transportation vehicles corresponding to the i-th batch of orders to be allocated within the preset transportation period, It represents the waiting time of the transportation vehicles corresponding to the i-th batch of orders to be allocated within the preset transportation period. Indicates the maximum allowed traffic waiting time, It represents the average fog visibility of the vehicles to be transported corresponding to the i-th batch of orders to be allocated during the preset transportation period.

[0036] Specifically, assuming that the preset order allocation accuracy index is 1.25, and within the preset transportation period (such as 6:00 to 8:00 in the morning), the maximum allowed traffic waiting time is 30 minutes, the statistical table of changes in the path planning accuracy index is shown in Table 1: Table 1 Statistics of changes in path planning accuracy indicators

[0037] It should be understood that, as can be seen from Table 1, the path planning accuracy index (the larger the better) decreases with the increase of the environmental impact factor, the driving distance evaluation score and the driving time score, and increases with the increase of the order allocation accuracy index and the average fog visibility.

[0038] Among them, the environmental impact factor is the impact factor corresponding to the process of obtaining the path planning accuracy index in the preset database, which represents the numerical value corresponding to the degree of influence of the environmental factors (such as environmental haze) of the transportation path on the path planning accuracy index during the intelligent planning process. When used, the weight factor corresponding to the environmental factor can be directly obtained from the preset database. The corresponding relationship can be a pre-set mapping relationship. For example, the environmental factors corresponding to the environmental factors in the process of obtaining the path planning accuracy index and the impact factors corresponding to the environmental factors in the preset database form a mapping set, and the real-time environmental factors are input into the mapping set to obtain the corresponding environmental impact factors. The mapping relationship can be a one-to-one correspondence or a many-to-one relationship. The value range of the environmental impact factor in this example is .

[0039] It should be noted that the driving distance evaluation score and the driving time evaluation score also indirectly affect the value of the order accuracy index. Suppose a logistics center needs to deliver a batch of goods to multiple destinations. During the path planning stage, the system plans the optimal transportation route for each destination based on real-time environmental factors (such as traffic conditions, weather) and environmental influencing factors in the preset database.

[0040] During the delivery process, due to accurate and efficient path planning, the goods can be delivered to the destination accurately according to the planned time and route, which reduces the risk of delivery delays and wrong delivery and improves the accuracy and efficiency of order delivery. Therefore, although the driving distance evaluation score and the driving time evaluation score do not directly determine the order accuracy index, they indirectly improve the order accuracy index by optimizing path planning and time management. This indirect impact reflects the importance of path planning accuracy indicators in intelligent logistics scheduling.

[0041] This example can more accurately reflect the actual situation of the vehicle during the transportation process by counting the actual driving distance, actual driving time and traffic waiting time, and can more accurately evaluate the rationality of the driving distance and driving time. Secondly, by real-time monitoring of the average fog visibility (indicating the average value of fog visibility, fog visibility is obtained by scanning laser visibility radar) and environmental influencing factors (including but not limited to slippery road surface and obstructed driver's vision) of the transportation route during the preset transportation period (such as from 6 am to 8 pm), the transportation route planning strategy can be adjusted in time to ensure the safety and efficiency of transportation, which reflects the full consideration of environmental factors, thereby forming a comprehensive route planning accuracy indicator, thereby realizing the improvement of the reliability of intelligent scheduling data analysis of the logistics center, and effectively solving the problem of low reliability of intelligent scheduling data analysis of the logistics center in the prior art.

[0042] Furthermore, the specific process of judging whether to carry out order delivery based on the obtained path planning accuracy index is as follows: judging whether the obtained path planning accuracy index is greater than the preset path planning accuracy index: if the obtained path planning accuracy index is greater than the preset path planning accuracy index, the order delivery is completed and the orders to be delivered that have completed the order delivery are recorded as orders to be stored; if the obtained path planning accuracy index is not greater than the preset path planning accuracy index, it indicates that the transportation route planning is unreasonable and the transportation route is replanned within the next transportation period.

[0043] Among them, the preset path planning accuracy index (set by the preset personnel) is represented by the sum and average of the historical path planning accuracy indicators of historical logistics vehicles in the historical transportation period in the preset logistics center database.

[0044] Furthermore, the warehouse management module includes an order entry unit, an inventory warning unit and a warehouse effectiveness evaluation index acquisition unit; the order entry unit is used to allocate the warehouse orders to the corresponding warehouse workshops and enter the inventory status time, the inventory status time includes the entry time, storage time and outbound time; the inventory warning unit is used to monitor the inventory of the warehouse workshops in real time, and when the inventory is lower than the preset inventory (set by the preset personnel), an early warning is issued and the preset personnel are prompted to replenish the stock in time; the warehouse effectiveness evaluation index acquisition unit is used to obtain the corresponding warehouse effectiveness evaluation index according to the real-time situation in the intelligent warehousing process.

[0045] In this embodiment, the inventory warning is not only to remind the inventory to decrease, but more importantly to prompt the preset personnel (such as warehouse managers, purchasing personnel) to take timely replenishment measures to avoid production interruptions caused by insufficient inventory. Through continuous inventory monitoring and warning, the company can grasp the inventory dynamics more accurately, thereby improving the operational efficiency of the warehouse, while reducing the extra workload and time waste caused by inventory problems.

[0046] Furthermore, the specific steps for obtaining the warehouse effectiveness evaluation index are as follows: E1, when the obtained path planning accuracy index is greater than the preset path planning accuracy index, the inventory warning time of the warehouse order in the intelligent warehousing process is monitored in real time to determine whether the inventory warning time is less than the preset inventory warning time. If so, execute E2, otherwise adjust the warning parameters of the controller and continue intelligent warehousing; E2, obtain the inventory warning time score (that is, the inventory warning time score in the restriction expression of the warehouse effectiveness evaluation index). ), and at the same time monitor the inventory scheduling speed in the intelligent warehousing process in real time and combine it with the reference inventory scheduling speed to obtain the inventory scheduling speed score (that is, the inventory scheduling speed score in the restricted expression of the warehousing effectiveness evaluation index). ), the inventory warning time score is the complement of the difference between the preset inventory warning time and the inventory warning time and the ratio of the preset inventory warning time, and the inventory scheduling speed score is the ratio of the inventory scheduling speed to the reference inventory scheduling speed; E3, obtain the total storage time and determine whether it is within the allowable range of the storage time (including the maximum and minimum values ​​of the historical storage time). If so, combine the obtained path planning accuracy index (that is, the restriction expression of the storage effectiveness evaluation index) ), inventory warning duration score and inventory scheduling speed score to obtain the warehouse effectiveness evaluation index, otherwise it will prompt that the corresponding warehouse order is timed out.

[0047] In this embodiment, the preset inventory warning duration is represented by the sum and average of the historical inventory warning durations of the waiting warehouse workshops in the preset logistics center database. Adjusting the warning parameter of the controller usually means increasing the warning response speed parameter of the controller to shorten the inventory warning duration (indicating the execution duration of the inventory warning in the intelligent warehousing process). The reference inventory scheduling speed is represented by the sum and average of the historical inventory scheduling speeds of the waiting warehouse workshops in the preset logistics center database. The allowable range of storage time represents the range corresponding to the maximum and minimum values ​​of the historical storage time of the waiting warehouse workshops in the preset logistics center database, thereby improving the accuracy and reliability of obtaining storage effectiveness evaluation indicators.

[0048] The specific restricted expression of the storage effectiveness evaluation index is: ; ; ; Where i is the number of the order batch to be allocated, , Y is the total number of order batches to be allocated, e is a natural constant, It represents the evaluation index of storage effectiveness of the goods to be stored corresponding to the i-th batch of orders to be allocated in the intelligent warehousing process, It represents the accuracy index of the path planning of the transportation vehicles corresponding to the i-th batch of orders to be allocated within the preset transportation period. Represents the preset path planning accuracy index, It represents the inventory warning duration score of the goods to be stored corresponding to the i-th batch of orders to be allocated during the intelligent warehousing process. Indicates the preset inventory warning duration. It indicates the inventory warning duration of the goods to be stored corresponding to the i-th batch of orders to be allocated during the intelligent warehousing process. It represents the inventory scheduling speed score of the goods to be stored corresponding to the i-th batch of orders to be allocated in the intelligent warehousing process, It represents the inventory scheduling speed of the goods to be stored corresponding to the i-th batch of orders to be allocated in the intelligent warehousing process, represents the reference inventory dispatch speed, It represents the total storage time of the goods to be stored corresponding to the i-th batch of orders to be allocated in the intelligent warehousing process. Indicates the allowed range of storage time.

[0049] like Figure 3 As shown, it is a two-dimensional coordinate diagram of the inventory warning duration score-warehousing effectiveness evaluation index provided in the embodiment of the present application (the path planning accuracy index is a fixed value of 1.3, and the inventory scheduling speed score is a fixed value of 0.7). Figure 3 It can be seen that the warehouse effectiveness evaluation index decreases as the inventory warning duration score increases.

[0050] like Figure 4 As shown, it is a two-dimensional coordinate diagram of the path planning accuracy index-warehousing effectiveness evaluation index provided in the embodiment of the present application (in this case, the inventory warning duration score is a fixed value of 0.7, and the inventory scheduling speed score is a fixed value of 0.5). Figure 4 It can be seen that the warehouse effectiveness evaluation index increases with the increase of the path planning accuracy index.

[0051] like Figure 5 As shown, it is a two-dimensional coordinate diagram of the inventory scheduling speed score-warehousing effectiveness evaluation index provided in the embodiment of the present application (in this case, the inventory warning duration score is a fixed value of 0.6, and the path planning accuracy index is a fixed value of 1.6). Figure 5 It can be seen that the warehouse effectiveness evaluation index increases with the increase of inventory scheduling speed score.

[0052] Among them, the inventory warning duration score increases with the increase of inventory warning duration, and the inventory scheduling speed score increases with the increase of inventory scheduling speed.

[0053] It should be noted that the inventory warning duration score also indirectly affects the value of the inventory scheduling speed score. When the inventory warning duration is reduced, the system can more quickly identify inventory shortages or surpluses and prompt the preset personnel to replenish, thereby increasing the inventory scheduling speed score, which directly improves customer satisfaction and operational efficiency. The inventory warning duration score indirectly affects the inventory scheduling speed score by affecting the timeliness and accuracy of scheduling decisions, thereby improving the reliability of the intelligent scheduling data analysis of the logistics center, and effectively solving the problem of low reliability of the intelligent scheduling data analysis of the logistics center in the prior art.

[0054] Furthermore, the specific process of judging whether intelligent warehousing is completed based on the obtained warehousing effectiveness evaluation indicators is as follows: judging whether the obtained warehousing effectiveness evaluation indicators are within the warehousing real-time threshold range: if the obtained warehousing effectiveness evaluation indicators are within the warehousing real-time threshold range (including being equal to the maximum and minimum values ​​of the historical warehousing effectiveness evaluation indicators), intelligent warehousing is completed and the pending warehousing orders that have completed intelligent warehousing are recorded as pending outbound orders; if the obtained warehousing effectiveness evaluation indicators are not within the warehousing real-time threshold range, it indicates that the real-time of warehousing management of the waiting warehousing workshop does not meet the expected requirements. At this time, the preset personnel need to be prompted to adjust the intelligent warehousing process and re-perform intelligent warehousing.

[0055] In this embodiment, the warehousing real-time threshold range represents the range corresponding to the maximum and minimum values ​​of the historical warehousing effectiveness evaluation indicators in the historical intelligent warehousing process in the preset logistics center database. Adjusting the intelligent warehousing process usually includes increasing the frequency of intelligent warehousing monitoring and increasing the number of preset personnel in the warehousing workshop. When the warehousing effectiveness evaluation indicators do not meet the requirements, the system can automatically prompt the preset personnel to adjust the intelligent warehousing process, thereby improving the intelligence level of decision-making, realizing automatic judgment of intelligent warehousing, and avoiding the subjectivity and inaccuracy of manual evaluation.

[0056] To summarize, the embodiment of the present application intelligently allocates the orders to be allocated through the order information to be allocated to obtain the order allocation accuracy index and determine whether the intelligent allocation is completed, and then intelligently plans the transportation path of the transportation vehicle to obtain the path planning accuracy index and determine whether to perform order delivery, and finally performs intelligent warehousing on the stored goods to obtain the warehousing effectiveness evaluation index and determine whether the intelligent warehousing is completed, and at the same time obtains logistics scheduling data and generates a logistics scheduling report, thereby achieving an improvement in the accuracy and reliability of logistics scheduling data acquisition, and then achieving an improvement in the reliability of intelligent scheduling data analysis of the logistics center, effectively solving the problem of low reliability of intelligent scheduling data analysis of the logistics center in the prior art.

[0057] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0058] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0059] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0060] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0061] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0062] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An intelligent logistics scheduling system based on AI algorithm, characterized in that: include: Intelligent order distribution module, vehicle path planning module, warehouse management module and data analysis module; The intelligent order allocation module is used to intelligently allocate the orders to be allocated according to the order information to be allocated to obtain the order allocation accuracy index, and judge whether the intelligent allocation is completed based on the obtained order allocation accuracy index, and the order allocation accuracy index is used to evaluate the accuracy of order allocation; The vehicle path planning module is used to intelligently plan the transportation path of the transportation vehicle to be transported according to the information of the order to be allocated after the intelligent allocation is completed to obtain the path planning accuracy index, and judge whether to carry out order delivery based on the obtained path planning accuracy index, and the path planning accuracy index is used to evaluate the rationality of the transportation path planning; The warehousing management module is used to perform intelligent warehousing of the goods to be stored according to the status of the goods to be dispatched after the order delivery is completed to obtain warehousing effectiveness evaluation indicators, and judge whether the intelligent warehousing is completed based on the obtained warehousing effectiveness evaluation indicators. The warehousing effectiveness evaluation indicators are used to evaluate the timeliness and accuracy of warehousing management; The data analysis module is used to obtain logistics scheduling data and generate a logistics scheduling report, and the logistics scheduling report is used to visualize the changes in key performance indicators of logistics center scheduling over time.

2. The intelligent logistics scheduling system based on AI algorithm as claimed in claim 1, characterized in that: The order information to be allocated includes order attributes and vehicle attributes; The order attributes include priority, cargo type, volume weight, destination and delivery time; The vehicle attributes include the number of empty vehicles, storage space for empty vehicles, and locations of empty vehicles; The status of the goods to be dispatched includes the status of waiting to enter the warehouse, the status of waiting to be stored, the status of waiting to be shipped out and the inventory status; The logistics scheduling data includes order allocation data, route planning data and warehouse management data.

3. The intelligent logistics scheduling system based on AI algorithm as claimed in claim 1, characterized in that: The order allocation accuracy index is obtained by the following method: Compare the actual allocation duration of the order to be allocated with the reference allocation duration in the database to obtain an order allocation priority, wherein the order allocation priority includes a first order allocation, a second order allocation, and a third order allocation; Obtain the order allocation priority weight factor from the preset database, and at the same time count the actual allocation time of the order to be allocated and obtain the allocation time score in combination with the reference allocation time; The number of orders to be allocated that have been successfully allocated is counted and combined with the total number of orders to be allocated to obtain the order completion rate. At the same time, the obtained allocation time score and the order allocation priority weight factor are combined to obtain the order allocation accuracy index. The order completion rate is the ratio of the number of orders to be allocated that have been successfully allocated to the total number of orders to be allocated.

4. The intelligent logistics scheduling system based on AI algorithm as claimed in claim 1, characterized in that: The specific process of judging whether intelligent allocation is completed based on the acquired order allocation accuracy index is as follows: Determine whether the obtained order allocation accuracy index is greater than the preset order allocation accuracy index: If the acquired order allocation accuracy index is greater than the preset order allocation accuracy index, the intelligent allocation is completed and the orders to be allocated that have completed the intelligent allocation are recorded as orders to be delivered; If the obtained order allocation accuracy index is not greater than the preset order allocation accuracy index, intelligent allocation will be performed again.

5. The intelligent logistics scheduling system based on AI algorithm as claimed in claim 1, characterized in that: The path planning accuracy index is obtained by the following method: The actual driving distance, actual driving time and traffic waiting time of the vehicles to be transported within the preset transportation period are counted to determine whether the traffic waiting time is not greater than the maximum allowed traffic waiting time. If so, the driving distance evaluation score and driving time evaluation score are obtained by combining the reference shortest driving distance and the reference shortest driving time. Otherwise, the corresponding preset transportation period is recorded as a traffic congestion period and the transportation route is changed; Real-time monitoring of the average fog visibility and environmental impact factors of the transport routes of the transported vehicles during the preset transport period, and combining the acquired order allocation accuracy index, driving distance evaluation score and driving time evaluation score to obtain the path planning accuracy index; The driving distance evaluation score is the ratio of the difference between the actual driving distance and the reference shortest driving distance to the reference shortest driving distance; The driving time evaluation score is the ratio of the difference between the total driving time and the reference shortest driving time to the reference shortest driving time; The total driving time is the sum of the actual driving time and the traffic waiting time.

6. The intelligent logistics scheduling system based on AI algorithm as claimed in claim 1, characterized in that: The specific process of determining whether to perform order delivery based on the acquired path planning accuracy index is as follows: Determine whether the obtained path planning accuracy index is greater than the preset path planning accuracy index: If the obtained path planning accuracy index is greater than the preset path planning accuracy index, the order delivery is completed and the to-be-delivered order that has completed the order delivery is recorded as a to-be-stored order; If the obtained path planning accuracy index is not greater than the preset path planning accuracy index, the transportation path will be replanned within the next transportation period.

7. The intelligent logistics scheduling system based on AI algorithm as claimed in claim 1, characterized in that: The warehouse management module includes an order entry unit, an inventory warning unit and a warehouse effectiveness evaluation index acquisition unit; The order entry unit is used to allocate the storage order to the corresponding storage workshop and enter the inventory status time, which includes the storage time, storage time and outbound time; The inventory warning unit is used to monitor the inventory in the warehouse in real time, and issue an early warning when the inventory is lower than the preset inventory and prompt the preset personnel to replenish the inventory in time; The storage effectiveness evaluation index acquisition unit is used to acquire the corresponding storage effectiveness evaluation index according to the real-time situation in the intelligent warehousing process.

8. The intelligent logistics scheduling system based on AI algorithm as claimed in claim 1, characterized in that: The specific steps for obtaining the storage effectiveness evaluation index are as follows: E1, real-time monitoring of the inventory warning duration of the order to be stored in the intelligent warehousing process, and judging whether the inventory warning duration is less than the preset inventory warning duration. If so, E2 is executed, otherwise, the warning parameters of the controller are adjusted and the intelligent warehousing is continued; E2, obtain the inventory warning duration score, monitor the inventory dispatching speed in the intelligent warehousing process in real time, and obtain the inventory dispatching speed score in combination with the reference inventory dispatching speed. The inventory warning duration score is the complement of the difference between the preset inventory warning duration and the inventory warning duration and the ratio of the preset inventory warning duration. The inventory dispatching speed score is the ratio of the inventory dispatching speed to the reference inventory dispatching speed. E3, obtain the total storage time and determine whether it is within the allowed range of storage time. If so, combine the obtained path planning accuracy index, inventory warning time score and inventory scheduling speed score to obtain the storage effectiveness evaluation index.

9. The intelligent logistics scheduling system based on AI algorithm as claimed in claim 8, characterized in that: The specific limiting expression of the storage effectiveness evaluation index is: ； Where i is the number of the order batch to be allocated, , Y is the total number of order batches to be allocated, e is a natural constant, It represents the evaluation index of storage effectiveness of the goods to be stored corresponding to the i-th batch of orders to be allocated in the intelligent warehousing process, It represents the accuracy index of the path planning of the transportation vehicles corresponding to the i-th batch of orders to be allocated within the preset transportation period. Represents the preset path planning accuracy index, It represents the inventory warning duration score of the goods to be stored corresponding to the i-th batch of orders to be allocated during the intelligent warehousing process. Indicates the preset inventory warning duration. It indicates the inventory warning duration of the goods to be stored corresponding to the i-th batch of orders to be allocated during the intelligent warehousing process. It represents the inventory scheduling speed score of the goods to be stored corresponding to the i-th batch of orders to be allocated in the intelligent warehousing process, It represents the total storage time of the goods to be stored corresponding to the i-th batch of orders to be allocated in the intelligent warehousing process. Indicates the allowed range of storage time.

10. The intelligent logistics scheduling system based on AI algorithm as claimed in claim 1, characterized in that: The specific process of judging whether intelligent warehousing is completed based on the obtained warehousing effectiveness evaluation index is as follows: Determine whether the obtained storage effectiveness evaluation index is within the storage real-time threshold range: If the obtained warehousing effectiveness evaluation index is within the warehousing real-time threshold range, the intelligent warehousing is completed and the warehousing orders that have completed the intelligent warehousing are recorded as orders to be shipped; If the obtained warehousing effectiveness evaluation index is not within the warehousing real-time threshold range, the preset personnel will be prompted to adjust the intelligent warehousing process and re-perform intelligent warehousing.

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