Intelligent logistics supply chain wisdom management method and system

Abstract

The application discloses an intelligent logistics supply chain wisdom management method and system, which comprises the following steps: collecting historical transportation time data of each path, calculating average transportation time and path historical fluctuation rate according to the obtained historical data; obtaining real-time data, calculating buffer duration according to the average transportation time and the path historical fluctuation rate, and dynamically adjusting the buffer duration according to the current road condition and time period; obtaining a predicted supply time and an actual time limit, calculating a fault tolerance time difference value according to the predicted supply time and the actual time limit, and dividing a risk level according to the fault tolerance time difference value; taking the calculated buffer duration as a correction factor to correct the fault tolerance time difference value, adjusting the priority according to the corrected fault tolerance time, and through the buffer duration mechanism, short-term fluctuations are absorbed, resource allocation confusion is avoided, long-term trends are still captured, the frequent change of priority caused by temporary road condition change is effectively reduced, and the stability of the whole distribution system is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics transportation, and in particular to an intelligent logistics supply chain wisdom management method and system. BACKGROUND

[0002] In the modern business environment, logistics supply chain management faces many complex and dynamic challenges. With the continuous growth of global trade and the rapid development of e-commerce, logistics demand is showing explosive growth, and the traditional logistics supply chain management mode has been difficult to meet the current efficient, accurate and flexible distribution requirements.

[0003] For example, Chinese patent application No. 202210294506.2 discloses a smart supply chain logistics intelligent optimization management method and system. To obtain logistics product supply chain information, the supply chain information includes product basic data, supply chain node information, and supply device information; the product basic data is read through a dynamic supply time early warning model, and the early warning analysis result corresponding to the time limit information of the logistics product supply is obtained by analysis; the logistics supply level is divided according to the early warning analysis result, and the logistics product supply scheme is optimized. Through early warning analysis of each logistics product, the corresponding supply scheme can be planned for products that may have supply risks to improve the supervision of products with supply risks and ensure that various products can be efficiently, quickly and timely supplied within the safety warning time limit. The logistics product supply scheme is adjusted and optimized in a timely manner for different logistics supply levels, facilitating intelligent supply chain logistics intelligent optimization management.

[0004] In existing patent technologies, for multi-task collaborative distribution scenarios, the demand of tasks for various resources (such as vehicle resources, loading and unloading equipment, and human resources) cannot be fully considered, and the coupling conflicts between tasks cannot be accurately identified. This makes the resource allocation unreasonable, and the task scheduling frequently changes, which not only increases the scheduling cost, but also reduces the distribution efficiency, resulting in a decrease in customer satisfaction. At the same time, due to the lack of a scientific priority sorting mechanism, urgent tasks and regular tasks cannot be effectively distinguished, further exacerbating the risk of resource competition and task delay. SUMMARY

[0005] The present application provides an intelligent logistics supply chain wisdom management method and system, which absorbs short-term fluctuations through a buffer duration mechanism, avoids resource allocation chaos, still captures long-term trends, effectively reduces the frequent changes in priority caused by temporary road conditions, and improves the stability of the entire distribution system.

[0006] The present application provides an intelligent logistics supply chain wisdom management method, which includes:

[0007] S101, collect historical path transportation time data, calculate average transportation time and path historical volatility rate according to the obtained historical data;

[0008] S102, obtain real-time data, calculate buffer duration according to the average transportation time and path historical volatility rate, dynamically adjust the buffer duration according to the current road condition and time period;

[0009] S103, obtain predicted supply time and actual time limit, calculate fault tolerance time difference value according to the predicted supply time and the actual time limit, and divide the risk level according to the fault tolerance time difference value;

[0010] S104, modify the fault tolerance time difference value by taking the calculated buffer duration as a correction factor, and adjust the priority and resource allocation according to the modified fault tolerance time.

[0011] Preferably, the formula of the path historical volatility rate is: wherein, n is the number of transportation time records of the path, is the transportation time of the i-th transportation, σ is the standard deviation of transportation time, and μ is the average transportation time, , is the transportation time of the i-th transportation, and the path historical volatility rate reflects the relative variability of transportation time, that is, the fluctuation degree of transportation time relative to the average value. The greater the fluctuation rate, the greater the variability of transportation time of the path, and the more unstable the transportation time.

[0012] Preferably, the formula for calculating the buffer duration according to the average transportation time and the path historical volatility rate is: wherein, is the buffer duration, is the time average value, is the path historical volatility rate, and 0.2 is the percentage coefficient which is adjusted according to the actual situation.

[0013] Preferably, the buffer duration is adjusted by setting the time range of the peak period. If the current time is within the peak period, it is determined that it is the peak period, and the congestion index CI is set. When 4≤CI<6, the road is in a light congestion state; when 6≤CI<8, the road enters a moderate congestion state; when 8≤CI<10, the road is in a severe congestion state. The peak period shortening ratio and the non-peak period extension ratio are set. If it is in the peak period or the congestion state, the adjusted buffer duration is calculated by the formula =B If it is in the non-peak period and the road condition is good, the adjusted buffer duration is calculated by the formula =B The adjusted buffer duration is calculated, wherein, The dynamic adjusted buffer duration is represented.

[0014] Preferably, the formula for correcting the fault tolerance time difference value with the buffer duration is: ΔT- wherein, is the corrected fault tolerance time difference value, is the dynamic adjusted buffer duration, ΔT is the fault tolerance time difference value, and if <12 hours, the priority is adjusted to the highest; if 12 hours≤ <24 hours, the priority is maintained as medium; and if ≥ 24 hours, the priority is adjusted to the lowest.

[0015] Preferably, the method for calculating the buffer duration further comprises:

[0016] S201, calculating the coupling conflict coefficient according to the demand of each type of resource by the task, correcting the buffer duration with the coupling conflict coefficient, and performing static grouping with the resource type;

[0017] S202, inputting the calculated coupling conflict coefficient into the algorithm to generate a scheduling scheme;

[0018] S203, dynamically adjusting the corrected buffer duration, and expanding the dynamic buffer duration from a single task to a multi-task to form a buffer mechanism.

[0019] Preferably, the formula for calculating the coupling conflict coefficient is: wherein, respectively represent the resource demand vectors of task i and task j, , are respectively the demands of task i and task j for the nth resource, reflects the similarity of the two vectors in the direction, and in the logistics transportation, the size of the dot product reflects the similarity or conflict of the tasks in the resource demand, represents the length or size of the vector, and in the logistics transportation, the modulus of the vector can represent the overall demand intensity of the task for the resource.

[0020] Preferably, the step of static grouping comprises:

[0021] S301, performing static grouping according to the type of goods and the type of resource;

[0022] S302, according to the result of static grouping, collecting information in each group, according to the preset rule, priority of the task in the group is sorted, the distribution plan is adjusted by real-time monitoring of the distribution process, and dynamic grouping is carried out on the basis of static grouping.

[0023] S303, according to the real-time state of each group task, a rule is formulated, and the task state is automatically transferred according to the formulated rule.

[0024] Preferably, the method of dynamic grouping is: according to the calculated coupling conflict coefficient, dynamic grouping is carried out through business type and area, the coupling conflict coefficient threshold is set, the calculated coupling conflict coefficient is compared with the coupling conflict coefficient threshold, when the coupling conflict coefficients of two business types exceed the coupling conflict coefficient threshold, the two business types are distributed to different groups, and when the coupling conflict coefficient is lower than the threshold, the two business types are distributed to the same group.

[0025] The application also provides an intelligent logistics supply chain wisdom management system, comprising: a data collection module, a buffer duration calculation module, a multi-objective scheduling module and a grouping module; the data collection module is used for extracting the transportation time record of each path; the buffer duration calculation module is used for calculating the initial buffer duration according to the average transportation time and the path historical fluctuation rate; the multi-objective scheduling module is used for generating an optimal scheduling scheme by using a multi-objective optimization scheduling algorithm; and the grouping module is used for static grouping and dynamic grouping.

[0026] One or more technical solutions provided in the application have at least the following technical effects or advantages: through the buffer duration mechanism, short-term fluctuations are absorbed, resource allocation chaos is avoided, long-term trends are still captured, the stability of the entire distribution system is improved, the identification of high-risk tasks is more accurate, resource waste and distribution delay caused by misjudgment are reduced, resource utilization and distribution capacity are improved, the efficiency and benefit of the entire supply chain logistics system are directly improved, the buffer duration and the fault tolerance time difference are dynamically adjusted according to real-time road conditions, vehicle position, transportation time prediction and other information, and the accuracy and timeliness of risk assessment and priority adjustment are ensured.

[0027] According to the path history volatility, real-time traffic and other factors, the coupling conflict coefficient, multi-objective optimization scheduling algorithm and hierarchical buffer mechanism are introduced, the influence of task priority, resource occupation, path coupling and other factors on the buffer time is considered more comprehensively, the coupling conflict coefficient is used to modify the buffer time, the dynamic buffer time is adjusted, the dynamic buffer time is extended from single task to multi-task, the buffer mechanism is formed, the potential delay caused by resource competition between tasks can be more accurately coped with, the multi-objective optimization scheduling algorithm can generate the optimal scheduling scheme, the emergency task priority and global resource balanced allocation are realized, the scheduling change frequency is reduced, the transportation efficiency is improved, the emergency task priority and global resource balanced allocation are realized, the chain delay risk caused by resource competition between tasks is reduced, the transportation efficiency is improved, the delay cost is reduced, and the customer satisfaction is improved;

[0028] Through the grouping autonomy and priority allocation mechanism, the need for real-time adjustment is reduced, through the preset priority rules, the VIP customer order and the fresh order can be executed preferentially, the adjustment caused by the change of customer level or goods type is reduced, through multi-task cooperative optimization and buffer time setting, the resource utilization efficiency is improved, and the task delay is reduced. For example, the fresh distribution task ensures the freshness through the short buffer time and the priority execution mechanism, the home appliance distribution task improves the distribution efficiency through the long buffer time and reasonable resource allocation, the grouping autonomy mechanism reduces the manual intervention, improves the response speed, and reduces the adjustment frequency;

[0029] By combining the coupling conflict coefficient, the business type and the regional factors, a more reasonable and efficient dynamic grouping strategy is realized, the distribution efficiency and the service quality are improved, by considering the business type and the regional factors, the resources are more accurately allocated, the resource waste is reduced, the distribution tasks in the same region have higher cooperativity in path planning, which helps to reduce the driving distance and reduce the transportation cost, the dynamic grouping strategy can adapt to the business changes and the demand fluctuations in real time, the flexibility and the adaptability of grouping are improved, through the more efficient distribution service, the customer satisfaction and the loyalty are improved. BRIEF DESCRIPTION OF DRAWINGS

[0030] Figure 1 It is a flowchart of the intelligent logistics supply chain intelligent management method of the application;

[0031] Figure 2 It is a flowchart of the buffer time calculation of the embodiment of the application;

[0032] Figure 3 It is a flowchart of the static grouping of the embodiment of the application;

[0033] Figure 4 It is a block diagram of the intelligent logistics supply chain intelligent management system of the application. DETAILED DESCRIPTION

[0034] For the purpose of promoting the understanding of the present application, the present application will be more fully described by referring to the attached drawings, in which the preferred embodiments of the present application are shown. It should be noted, however, that the present application can be carried out in many different forms and should not be considered limited to the embodiments set forth in the description and / or drawings. Rather, these embodiments are provided as examples of the disclosure, with the understanding that the application is to be considered limited only by the claims.

[0035] It should be noted that the terms "vertical", "horizontal", "upper", "lower", "left", "right", and similar terms used herein are for the purpose of illustration only and are not intended to limit the present application to only the described embodiments.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0037] Embodiment one: Figure 1 is a flowchart of an intelligent logistics supply chain intelligent management method and system according to an embodiment of the present application, which comprises:

[0038] S101, collect historical transportation time data of each path, and calculate the path historical volatility rate according to the obtained historical data;

[0039] Specifically, the time range of collecting historical data is determined, such as the past week, month or longer, the transportation time records of each path are extracted from the logistics management system, order system or related database according to the determined time range, SQL query statement is used to extract the historical data, the extracted data is sorted to ensure the integrity and accuracy of the data, and the transportation time records are classified according to the path; the path historical volatility rate is obtained by calculating the standard deviation and average value of the historical transportation time of each path, the path historical volatility rate is used to measure the variability or volatility of the transportation time of the path, and the calculation formula of the average value of the transportation time is: , wherein, is the average value of time, n is the number of transportation time records of the path, is the transportation time of the i-th time, and the formula for calculating the standard deviation of the transportation time is: , wherein, n is the number of transportation time records of the path, is the time of the ith transportation, μ is the average transportation time, σ is the standard deviation of transportation time, which measures the dispersion of transportation time relative to the average value, reflects the volatility of transportation time, the average value and the standard deviation are calculated by calculation, the path historical volatility is calculated, the formula of the path historical volatility is: wherein σ is the standard deviation of transportation time, is the time average, the path historical volatility reflects the relative variability of transportation time, that is, the fluctuation degree of transportation time relative to the average value, the greater the volatility, the greater the variability of transportation time of the path, the more unstable the transportation time.

[0040] S102, acquiring real-time data, calculating the buffer duration according to the average transportation time and the path historical volatility, dynamically adjusting the buffer duration according to the current traffic condition and the time period;

[0041] Further, the buffer duration is a time threshold determined based on historical data (such as the volatility of transportation time, the frequency of path change, etc.), which is used to control the frequency and amplitude of priority adjustment, real-time information such as current traffic condition, vehicle position, transportation time prediction is acquired, the congestion index, average speed, accident report and other traffic information of the current road are acquired through the traffic management system, the position information of the vehicle is acquired in real time by using the GPS positioning technology, the formula for calculating the buffer duration according to the average transportation time and the path historical volatility is: wherein, is the buffer duration, is the time average, is the path historical volatility, 0.2 is the percentage coefficient, which is adjusted according to the actual situation, the above formula for calculating the buffer duration sets the initial buffer duration as 20% of the volatility of the average transportation time, by multiplying the path historical volatility V, the influence of the variability of transportation time on the buffer duration is considered, according to the real-time traffic condition and the vehicle position information, it is judged whether the current is in the peak period or the congestion will occur, the time range of the peak period (early peak 7:00-9:00, late peak 17:00-19:00) is set, if the current time is in the peak period, it is determined that it is in the peak period, the congestion index (CI) range is set, when 4≤CI<6, the road is in a light congestion state, at this time, the vehicle speed begins to be affected, but the overall traffic flow can still maintain a certain fluidity; when 6≤CI<8, the road enters a moderate congestion state, the vehicle speed decreases obviously, the traffic flow begins to slow down, and queuing phenomenon appears; when 8≤CI<10, the road is in a severe congestion state, the vehicle speed is very low, the traffic flow is almost stagnant, and serious traffic congestion occurs, the peak period shortening ratio and the non-peak period extension ratio if it is in the peak period or the congestion is expected, the formula =B The adjusted buffer duration is calculated. If it is a non-peak period and the road condition is good, the formula is =B The adjusted buffer duration is calculated, wherein represents the dynamically adjusted buffer duration.

[0042] S103, obtain the predicted supply time and the actual time limit, calculate the fault tolerance time difference value according to the predicted supply time and the actual time limit, and divide the risk level according to the fault tolerance time difference value;

[0043] Specifically, the transportation time data is obtained from the logistics management system, a prediction model is constructed using a machine learning model, historical transportation data and real-time traffic information are input into the prediction model, the prediction model outputs the predicted supply time, the actual time limit is obtained according to the logistics contract and customer demand, and the formula for calculating the fault tolerance time difference value according to the predicted supply time and the actual time limit is: , wherein represents the actual time limit, represents the predicted supply time, represents the fault tolerance time difference value, is used to predict the buffer margin between the supply time and the actual time limit. If the fault tolerance time difference value ΔT < 12 hours, it is classified as a first-level risk; if 12 hours ≤ ΔT < 24 hours, it is classified as a second-level risk; if 24 hours ≤ ΔT < 48 hours, it is classified as a medium risk; and if ΔT ≥ 48 hours, it is classified as a low risk, wherein the first-level risk and the second-level risk are high risks.

[0044] S104, the calculated buffer duration is used as a correction factor to correct the fault tolerance time difference value, and the priority and resource allocation are adjusted according to the corrected fault tolerance time factor;

[0045] Further, the formula for correcting the fault tolerance time difference value using the buffer duration is: ΔT- , wherein is the corrected fault tolerance time difference value, is the dynamically adjusted buffer duration, ΔT is the fault tolerance time difference value, and if < 12 hours, the priority is adjusted to the highest; if 12 hours ≤ < 24 hours, the medium priority is maintained; and if ≥ 24 hours, the priority is adjusted to the lowest, for the task adjusted to high risk, the transport vehicle is preferentially arranged, the monitoring frequency is increased, the experienced driver and the order follower are provided, for the task adjusted to medium risk, the transport progress is focused on, for the task adjusted to low risk, the resource investment is reduced, and the combined transport and the more economical transport mode are considered.

[0046] The technical solutions in the embodiments of the application have at least the following technical effects or advantages: through the buffer duration mechanism, short-term fluctuations are absorbed, resource allocation chaos is avoided, long-term trends are still captured, frequent priority changes caused by temporary road conditions are effectively reduced, the stability of the entire distribution system is improved, the identification of high-risk tasks is more accurate, resource waste and distribution delays caused by misjudgment are reduced, resource utilization and distribution capacity are improved, the efficiency and benefit of the entire supply chain logistics system are directly improved, the buffer duration and the fault tolerance time difference are dynamically adjusted according to real-time road conditions, vehicle positions, and transportation time prediction information, and the accuracy and timeliness of risk assessment and priority adjustment are ensured.

[0047] Embodiment two: according to the calculation of the buffer duration for a certain goods or a batch of goods in embodiment one, this embodiment is how to calculate the dynamic buffer duration to adjust the possible conflicts between different tasks when multiple tasks are coordinated, for example, for a certain warehouse, when multiple tasks are coordinated for distribution, to avoid excessive frequent adjustment while ensuring the accuracy of the arrival time limit.

[0048] As shown in Figure 2 , the calculation method of the buffer duration further includes:

[0049] S201, the coupling conflict coefficient is calculated according to the demand of the task for various resources, and the coupling conflict coefficient is used to correct the buffer duration;

[0050] Further, the resources include vehicle resources, loading and unloading equipment, and human resources, the basic information of the task is collected, the demand of the task for resources is estimated according to the basic information of the task, combined with historical data and experience, the elements of the resource demand vector are determined according to the resource demand classification, the vehicle resources, the loading and unloading equipment, and the human resources are taken as three elements of the vector, task i needs 2 trucks, 3 forklifts, and 5 workers, and the resource demand vector is represented as =(2, 3, 5), the formula for calculating the coupling conflict coefficient based on the above resource demand is: , wherein, and , are the demands of the task i and the task j for the nth resource, respectively. The dot product reflects the similarity of two vectors in the direction, in the logistics transportation, the size of the dot product reflects the similarity or conflict of the resource demand between tasks, The modulus of the vector represents the length or size of the vector, in the logistics transportation, the modulus of the vector can represent the overall demand intensity of the task for the resource.

[0051] The calculated coupling conflict coefficient is substituted into the formula of the buffer duration to modify the buffer duration, and the modified formula is: B'=B+k× ×B, wherein B' is the modified buffer duration, Cij is the coupling conflict coefficient, k is the modification coefficient, which is a parameter for adjusting the influence degree of the coupling conflict coefficient on the buffer duration, and reflects the sensitivity to the resource competition intensity between tasks. The value of the modification coefficient k is set according to the actual situation.

[0052] A specific example is: task i needs to transport a batch of goods from warehouse A to warehouse B, the goods type is ordinary goods, and the expected transportation volume is 100 tons; task j needs to transport another batch of goods from warehouse C to warehouse D, the goods type is refrigerated goods, and the expected transportation volume is 80 tons, task i is expected to need 2 trucks with a load capacity of 50 tons due to the goods type being ordinary goods, task j is expected to need 1 refrigerated truck with a load capacity of 80 tons due to the goods type being refrigerated goods, task i is expected to need 3 forklifts for loading and unloading, task j is expected to need 2 specially designed refrigerated forklifts due to the goods needing refrigeration, and an additional 1 ordinary forklift for other loading and unloading work, a total of 3, task i is expected to need 5 workers, including drivers and loaders, task j is expected to need 5 workers, including refrigerated truck drivers, loaders and possible temperature monitoring personnel, according to the resource demand classification (vehicle resources, loading and unloading equipment, human resources), the elements of the resource demand vector, and the resource demand of task i and task j is represented as a vector: the resource demand vector of task i: =(2,3,5), the resource demand vector of task j (after adjusting the human resource demand): =(1,3,5), =2×1+3×3+5×5=2+9+25=36, , , = .98, the original buffer duration B is 10 hours, which is calculated based on historical data, path fluctuation rate and other factors, according to the actual situation, we set the modification coefficient k as 0.5, which means that when the coupling conflict coefficient increases, the buffer duration will increase by a certain percentage, B'=B+k×Cij×B=10+0.5×0.98×10=10+4.9=14.9 (hours).

[0053] S202, the calculated coupling conflict coefficient is input into the algorithm, and a scheduling scheme is generated by using the algorithm;

[0054] Specifically, the multi-objective optimization scheduling algorithm is a genetic algorithm, and an objective function in the genetic algorithm is used: minZ=w1 x delay time+w2 x scheduling change number, wherein Z represents a total objective function value, that is, a weighted sum that needs to be minimized; w1 and w2 are weights of the delay time and the scheduling change number respectively, and are set according to a specific strategy of an enterprise. The coupling conflict coefficient is used to dynamically adjust the relative importance of the weights in different task pairs or scenes. When the coupling conflict coefficient between two tasks is high, the weight related to the delay time is increased. The delay time refers to a difference between an actual completion time and a planned completion time of a task, and reflects the level of transportation efficiency. The scheduling change number refers to a scheduling plan adjustment number caused by various reasons (such as resource conflict, path change, etc.) in a scheduling process, and reflects the stability of scheduling. The task priority, resource occupation and path coupling are input into the genetic algorithm, the genetic algorithm outputs a scheduling scheme, and the scheduling scheme includes task execution order, resource allocation, predicted completion time and the like.

[0055] S203, the corrected buffer duration is dynamically adjusted, and the dynamic buffer duration is extended from a single task to a multi-task to form a buffer mechanism;

[0056] Further, real-time monitoring of task execution progress is performed, and when the task execution progress falls behind or resource occupation exceeds the expectation, it indicates that the task faces greater risks or challenges, and the buffer duration is appropriately increased to provide more execution time margin for the task to cope with potential delays, and the magnitude of the increased buffer duration should be determined according to the risk level and potential delay possibility to avoid excessive increase leading to resource waste; when the task execution is smooth and the resource occupation is lower than expected, it indicates that the task execution efficiency is higher and the resource utilization is sufficient, and the buffer duration is reduced to optimize resource utilization and improve the overall efficiency of the project, the relationship of the task in the execution process is identified, and if the relationship is a relationship of competing for the same resources, it is a competitive relationship, and if it is not a competitive relationship, it is a dependent relationship, if the task is identified as a dependent relationship, the order of the dependent relationship is further identified, the identified task relationship is marked on the network diagram using a network diagram, the name, expected execution time, required resources and association relationship with other tasks of the task are all marked on the network diagram to form a task association relationship diagram, when it is found that the buffer duration of a task needs to be adjusted, the task is compared with the task association relationship diagram, the task is located in the task association relationship diagram, if the task has a competitive relationship with other tasks, the influence of increasing or decreasing its buffer duration on the resource acquisition and execution progress of other tasks is analyzed, if the task has a dependent relationship with other tasks, the influence of adjusting its buffer duration on the start time of subsequent tasks and the overall project path is analyzed, according to the analysis results obtained, the buffer duration is dynamically adjusted according to the task association relationship diagram, for the tasks facing risks or challenges, the buffer duration is increased to provide execution time margin; for the tasks with higher execution efficiency, the buffer duration is reduced to optimize resource utilization, according to the adjustment scheme, the buffer duration of the task is modified, the project management plan and related documents are updated, the task execution progress and resource occupation are continuously monitored, and the adjustment effect is evaluated, if it is found that the adjustment leads to new problems or risks, timely corrective measures are taken and the buffer mechanism is updated, the experience and lessons learned in the adjustment process are incorporated into the iterative optimization of the buffer mechanism, the task association relationship diagram and the buffer duration adjustment strategy are continuously improved, the flexibility and efficiency of project management are improved, through the cyclic execution and continuous optimization of the above steps, the buffer mechanism is formed.

[0057] The technical solutions in the embodiments of the present application have at least the following technical effects or advantages: according to the path historical fluctuation rate, real-time road conditions and other factors, a coupling conflict coefficient, a multi-target optimization scheduling algorithm and a hierarchical buffer mechanism are introduced, factors such as task priority, resource occupation and path coupling are more comprehensively considered to affect the buffer duration, the coupling conflict coefficient is used to correct the buffer duration, the corrected buffer duration is dynamically adjusted, the dynamic buffer duration is extended from a single task to a multi-task buffer mechanism, potential delays caused by resource competition between tasks can be more accurately coped with, the multi-target optimization scheduling algorithm can generate an optimal scheduling scheme, emergency tasks are prioritized and global resource allocation is balanced, the scheduling change frequency is reduced, the transportation efficiency is improved, emergency tasks are prioritized and global resource allocation is balanced, the risk of chain delays caused by resource competition between tasks is reduced, the transportation efficiency is improved, the delay cost is reduced, and the customer satisfaction is improved.

[0058] Embodiment three: according to embodiments one and two, the warehouse, vehicle and personnel are statically grouped, task coordination is performed within the group, and the groups are in a parallel relationship, the adjustment frequency is reduced, and the distribution efficiency is improved.

[0059] As shown in Figure 3 the steps of static grouping are as follows:

[0060] S301, static grouping according to the type of goods and the type of resources;

[0061] Specifically, all goods in the warehouse are classified, such as fresh food, home appliances, daily necessities, etc. According to the type of goods and its storage requirements, the warehouse is divided into different storage areas. Fresh goods are stored in areas with refrigeration or freezing functions to ensure freshness. Home appliance goods are stored in dry and well-ventilated areas for easy handling and installation. Daily necessities are stored in normal temperature areas and are reasonably stacked according to their size and weight. Clear signboards are set up in each storage area to indicate the type of goods, storage requirements, etc. According to the type of goods and delivery needs, a dedicated delivery vehicle is provided for each type of goods. The fresh delivery team is equipped with cold chain vehicles to ensure that the goods maintain an appropriate temperature during transportation. The home appliance delivery team is equipped with ordinary trucks according to the size and weight of the appliances. The daily necessities delivery team is equipped with van trucks. Each delivery unit is equipped with drivers and delivery personnel who are familiar with the delivery of corresponding goods. The drivers and delivery personnel of the fresh delivery team should be familiar with cold chain operation specifications and have the ability to handle fresh goods. The delivery personnel of the home appliance delivery team should have skills such as appliance handling and installation to ensure the safe delivery of goods to customers. The configured vehicles and personnel are combined into fixed delivery teams, such as: Delivery Team A: Warehouse X + 5 cold chain vehicles + 10 delivery personnel → dedicated for fresh food; Delivery Team B: Warehouse Y + 8 ordinary trucks + 15 delivery personnel → dedicated for home appliances; Delivery Team C: Warehouse Z + 6 van trucks + 12 delivery personnel → dedicated for daily necessities. The logistics management system is selected to record the resource allocation information.

[0062] S302, according to the result of static grouping, collect information within each group, prioritize tasks within the group according to preset rules, and adjust the delivery plan by real-time monitoring of the delivery process.

[0063] Further, within each group, the logistics management system collects information on all delivery tasks, including customer name, type of goods, quantity, delivery address, and expected delivery time. According to the preset rules, the priority is assigned based on the customer's level and the type of goods, such as VIP customer orders, fresh food delivery, and urgent orders. According to the assigned priority, the priority is sorted, the tasks with time requirements are identified, and for each task with time requirements, the specific time window is determined, including the start time and end time, to ensure that the delivery task is completed within this time period. If the customer does not specify a specific time window, but the type of goods or business requirements has implicit time requirements (such as fresh food delivery usually needs to be completed within a specific time period in the morning or afternoon), a reasonable time window is determined according to the business rules. The determined time window information is recorded in the logistics management system and associated with the corresponding task.

[0064] By using GPS positioning technology, combined with the logistics management system, the position information of each delivery vehicle is obtained in real time. When the delivery personnel complete the task (such as successfully delivering goods) or encounter problems (such as traffic congestion, customers not at home, etc.), they need to timely report to the logistics management system. When the customer temporarily changes the delivery time, address, etc., the change information is recorded in the logistics management system in a timely manner. According to the changes in traffic conditions, customer demand changes, etc., the delivery path of the vehicle is adjusted in real time. By using the path planning algorithm in the logistics management system, the optimal path is recalculated to avoid congested road sections or meet new delivery requirements. The adjusted path information is sent to the delivery personnel to ensure that they drive according to the new path. When the vehicle or delivery personnel fails, delays, etc., the task reassignment mechanism is triggered. According to the location, state and task demand of available resources (such as other vehicles, delivery personnel), a new task allocation scheme is developed. The new task allocation information is sent to the relevant vehicles and delivery personnel to ensure that they receive and understand the new plan. The delivery time window is adjusted according to the actual situation (such as traffic congestion, task reassignment, etc.). The new time window is confirmed with the customer to ensure that they agree and accept the adjustment. The logistics management system sends the adjusted delivery plan to the relevant vehicles and delivery personnel, including new path, task allocation and time window information. The vehicle and delivery personnel confirm receipt and understand the new plan content through the confirmation button or reply message in the logistics management system. The vehicle and delivery personnel perform the delivery task according to the adjusted plan to ensure efficient use of resources and timely completion of the task. The time window information of the delivery plan is updated.

[0065] S303, according to the real-time state of each task in the group, a rule is formulated, and the task state is automatically transferred according to the formulated rule;

[0066] Specifically, within each group, the limited state of the task is determined, and common task states include "to be allocated", "in execution", and "completed". Other states such as "abnormal" and "pause" can be added according to actual conditions. According to the task state, a rule is formulated to realize automatic transfer of the task state. The formulated rule is: when the vehicle is idle and the task queue is not empty, the system automatically allocates the next highest priority task, and the task state is converted from "to be allocated" to "in execution". When the task delay exceeds the buffer duration, the system automatically upgrades the risk level and enables emergency resources such as other vehicles or delivery personnel to assist in completing the task, and the task state can be marked as "abnormal" or other appropriate measures can be taken.

[0067] The technical solutions in the embodiments of the present application have at least the following technical effects or advantages:

[0068] Through the grouping autonomy and priority allocation mechanism, the need for real-time adjustment is reduced, through the preset priority rules, the VIP customer orders and fresh orders can be executed preferentially, reducing the adjustment caused by the change of customer level or goods type, through multi-task collaborative optimization and buffer time length setting, the resource utilization efficiency is improved, and the task delay is reduced. For example, fresh delivery tasks ensure freshness through shorter buffer time length and priority execution mechanism; home appliance delivery tasks improve delivery efficiency through longer buffer time length and reasonable resource allocation; the grouping autonomy mechanism reduces manual intervention, improves response speed, and reduces adjustment frequency.

[0069] In this embodiment, a coupling conflict coefficient is used to guide grouping based on the static grouping of embodiment three.

[0070] The method of dynamic grouping is: according to the calculated coupling conflict coefficient, dynamic grouping is carried out through business type and region; specifically, according to the coupling conflict coefficient calculated in step S201, according to the actual performance data of different business type combinations in grouping distribution and the experience of logistics management personnel and delivery personnel, the coupling conflict coefficient threshold is set, the coupling conflict coefficients of all business type combinations are calculated, the calculated coupling conflict coefficients are compared with the coupling conflict coefficient threshold, when the coupling conflict coefficients of two business types exceed the coupling conflict coefficient threshold, they are separated in grouping, and the two business types are allocated to different groups in initial grouping, so as to reduce resource competition and path conflict; when the coupling conflict coefficient is lower than the threshold, they are divided into the same group, with the change of business and the update of data, the coupling conflict coefficient is recalculated regularly, and whether the grouping scheme needs to be adjusted is judged according to the new threshold.

[0071] According to the characteristics of business type, business types with similar resource demand and path planning characteristics are divided into the same group, for example, fresh business which needs refrigerated vehicles and fast delivery is divided into a group with medicine delivery business which is also sensitive to time, so as to better coordinate resources and plan paths, according to the demand characteristics of different business types in each region, the business types with concentrated demand in the same region are integrated into the same grouping, for example, if a region mainly needs fresh delivery, and the order quantity of the region is large, a special fresh delivery grouping is set in the region, and enough vehicles and personnel are equipped to improve the delivery efficiency; for business across multiple regions, the cooperativity between regions is considered, for example, when a large home appliance order needs to be delivered from the warehouse to multiple adjacent regions, a cross-regional home appliance delivery grouping can be planned, the vehicle driving route is reasonably arranged, and the driving distance and time are reduced.

[0072] Set a fixed adjustment cycle (e.g., hourly, daily). At the end of each cycle, re-collect and analyze order data, vehicle status data, and traffic condition data. Based on the updated data, recalculate the grouping scheme. Consider changes in business type demand, vehicle status, and traffic conditions to adjust the initial grouping. When the order volume in a certain area suddenly increases or decreases, the grouping adjustment is triggered immediately. For example, if the fresh food order volume in a certain area increases by 30% within a certain period, the system immediately detects this change and initiates the grouping adjustment process, increasing the number of fresh food delivery vehicles and personnel in that area. When vehicles break down, require repair, or are scrapped, the grouping scheme is adjusted promptly. For example, if a vehicle in a fresh food delivery group breaks down and cannot continue to perform its tasks, vehicles are transferred from other groups, or the tasks of that group are reassigned to other groups. When traffic conditions change significantly, such as severe congestion or accidents on a certain road segment, the grouping scheme is adjusted promptly. For example, if traffic congestion in a certain area causes a significant increase in the travel time of fresh food delivery vehicles, the delivery routes in that area are replanned, or the grouping is adjusted to allocate some tasks to areas with better traffic conditions.

[0073] The technical solutions described in the above embodiments of this application have at least the following technical effects or advantages: by combining coupling conflict coefficients, business types, and regional factors, a more reasonable and efficient dynamic grouping strategy is achieved, improving delivery efficiency and service quality; by considering business types and regional factors, resources are allocated more accurately, reducing resource waste; delivery tasks in the same region have higher synergy in route planning, which helps to reduce driving distance and transportation costs; the dynamic grouping strategy can adapt to business changes and demand fluctuations in real time, improving the flexibility and adaptability of grouping; and through more efficient delivery services, customer satisfaction and loyalty are improved.

[0074] Example 5: Figure 4 As shown, an intelligent logistics supply chain management system includes: a data collection module, a buffer duration calculation module, a multi-objective scheduling module, and a grouping module. The data collection module is used to extract transportation time records for each path from the logistics management system, order system, or related databases, including data from the past week, month, or longer. The buffer duration calculation module is used to calculate the initial buffer duration based on the average transportation time and the historical volatility of the path, and dynamically adjust the buffer duration based on real-time road conditions and vehicle location information. The multi-objective scheduling module is used to generate the optimal scheduling scheme by using a multi-objective optimization scheduling algorithm (such as a genetic algorithm) that considers factors such as task priority, resource consumption, and path coupling. The grouping module is used for static grouping and dynamic grouping.

[0075] The above merely describes the preferred embodiments of the present application and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent logistics supply chain management method, characterized in that, include: S101, collect historical transportation time data for each route, and calculate the average transportation time and historical volatility of the route based on the acquired historical data; S102: Obtain real-time data, calculate buffer duration based on average transportation time and historical route volatility, and dynamically adjust buffer duration according to current road conditions and time period; S103, obtain the predicted supply time and actual time limit, calculate the fault tolerance time difference based on the predicted supply time and actual time limit, and classify the risk level based on the fault tolerance time difference; S104, use the calculated buffer duration as a correction factor to correct the fault tolerance time difference, and adjust the priority and resource allocation according to the corrected fault tolerance time; The formula for correcting the fault tolerance time difference using the buffer duration is: ΔT- ,in, This is the corrected fault tolerance time difference. This is the dynamically adjusted buffer duration, where ΔT is the fault tolerance time difference. If the timeframe is less than 12 hours, prioritize it to the highest level; if the timeframe is 12 hours or less... < 24 hours, maintain medium priority; if ≥ 24 hours, priority set to lowest; The methods for calculating buffer duration also include: S201, calculate the coupling conflict coefficient based on the task's demand for various resources, use the coupling conflict coefficient to correct the buffer duration, and use resource types for static grouping. S202, The calculated coupling conflict coefficient is input into the algorithm, and the algorithm is used to generate a scheduling scheme; S203, dynamically adjusts the corrected buffer duration, and extends the dynamic buffer duration from a single task to multiple tasks to form a buffer mechanism; The steps of static grouping: S301, static grouping based on the type of goods and the type of resources; S302: Based on the results of static grouping, information is collected within each group, and tasks within the group are prioritized according to preset rules. The delivery plan is adjusted by real-time monitoring of the delivery process. Dynamic grouping is performed based on static grouping. S303: Develop rules based on the real-time status of tasks within each group, and automatically transfer task status according to these rules.

2. The intelligent logistics supply chain management method as described in claim 1, characterized in that, The formula for historical volatility along a path is: ,in, , where n is the number of transport time records for this route. Let σ be the time of the i-th transport, σ be the standard deviation of transport time, and μ be the average transport time. , Let be the transport time for the i-th time. The historical volatility of the route reflects the relative variability of the transport time, that is, the degree of fluctuation of the transport time relative to the average value. The greater the volatility, the greater the variability of the transport time of the route, and the more unstable the transport time.

3. The intelligent logistics supply chain management method as described in claim 1, characterized in that, The formula for calculating the buffer duration based on average transit time and historical route volatility is as follows: ,in, For buffer duration, It is a time average. This represents the historical volatility of the path, and 0.2 is a percentage coefficient, which is adjusted according to the actual situation.

4. The intelligent logistics supply chain management method as described in claim 1, characterized in that, The buffer time is dynamically adjusted by setting a peak time range. If the current time falls within the peak time range, it is considered a peak time, and a congestion index (CI) is set. When 4 ≤ CI < 6, the road is in a state of light congestion; when 6 ≤ CI < 8, the road is in a state of moderate congestion; and when 8 ≤ CI < 10, the road is in a state of heavy congestion. A percentage reduction in peak time is then set. Off-peak hours extension ratio If it is during peak hours or in congested conditions, use the formula. =B Calculate the adjusted buffer time. If it is during off-peak hours and road conditions are good, use the formula. =B Calculate the adjusted buffer duration, where, This indicates the dynamically adjusted buffer duration.

5. The intelligent logistics supply chain management method as described in claim 1, characterized in that, The formula for calculating the coupling conflict coefficient is: ,in, Let represent the resource requirement vectors for task i and task j, respectively. , These represent the resource requirements of task i and task j, respectively. The dot product reflects the degree of similarity between two vectors in direction. In logistics and transportation, the magnitude of the dot product reflects the similarity or conflict between tasks in terms of resource requirements. It represents the length or size of a vector. In logistics and transportation, the magnitude of a vector can represent the overall intensity of a task's demand for resources.

6. The intelligent logistics supply chain management method as described in claim 1, characterized in that, The dynamic grouping method is as follows: Based on the calculated coupling conflict coefficient, dynamic grouping is performed by business type and region. A coupling conflict coefficient threshold is set, and the calculated coupling conflict coefficient is compared with the coupling conflict coefficient threshold. When the coupling conflict coefficient of two business types exceeds the coupling conflict coefficient threshold, the two business types are assigned to different groups. When the coupling conflict coefficient is lower than the threshold, the two business types are assigned to the same group.

7. An intelligent logistics supply chain management system, applied to the intelligent logistics supply chain management method as described in any one of claims 1-6, characterized in that, include: Data collection module, buffer duration calculation module, multi-objective scheduling module, and grouping module; The data collection module is used to extract transportation time records for each route; The buffer duration calculation module is used to calculate the initial buffer duration based on the average transportation time and the historical volatility of the route; the multi-objective scheduling module is used to generate the optimal scheduling scheme using a multi-objective optimization scheduling algorithm; and the grouping module is used to perform static grouping and dynamic grouping.

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