Multi-region collaborative logistics transportation path global planning management system

Abstract

The invention relates to the technical field of path planning, in particular to a multi-region collaborative logistics transportation path global planning management system, which comprises a path node characteristic identification module, a path section passing load analysis module, a path scheduling buffer construction module, a path sequence stability grading module and a path execution sequence optimization module. According to the invention, through collecting entry and exit cargo circulation volumes of each node in a logistics network in unit time and calculating a difference value of the entry and exit cargo circulation volumes, a scheduling key point with a load consistent with a circulation direction is screened out in combination with node task density, a pressure intensity abnormal section in a transportation path is identified, a scheduling collision point is extracted, and scheduling elasticity between tasks is improved. By analyzing the stability of the task sequence of the path section in the buffer time, the stability level division is implemented, and the path execution sequence is optimized, so that the quick response to the dynamic change of the path and the preposed avoidance of task conflicts are realized, and the flexibility of path planning and the coordination of global scheduling are effectively improved.

Description

Technical Field

[0001] This invention relates to the field of route planning technology, and in particular to a global planning and management system for multi-regional collaborative logistics transportation routes. Background Technology

[0002] The field of path planning technology involves technical methods for planning optimal or suboptimal paths for mobile units within a given geographic space or topology. Core aspects include geospatial modeling, path search algorithms, traffic element modeling, constraint setting, and dynamic environmental adaptation strategies. This field typically combines graph theory modeling, heuristic search algorithms, traffic rule modeling, and multi-objective optimization to generate, evaluate, and update paths. Path planning can be applied to various scenarios such as autonomous driving navigation, robot motion control, and intelligent logistics scheduling. It possesses the ability to perform static path calculations and dynamic path adjustments, encompassing elements such as multi-source traffic information integration, real-time road condition perception, and travel time assessment. With the increasing complexity of urban traffic and the diversification of logistics demands, path planning technology is gradually expanding to global path management problems involving multi-regional and multi-task collaboration, emphasizing multi-regional information fusion and the coordination and unification of path solutions.

[0003] Traditional multi-regional collaborative logistics transportation route global planning and management systems refer to systems that centrally plan and manage routes involving multi-regional transportation tasks under the condition of multiple logistics service areas, aiming to achieve route rationality and resource allocation coordination in cross-regional transportation processes. These systems typically generate global route schemes by setting static delivery routes and transportation node locations for multiple regions, combined with preset time windows and regional priorities, using static rules. Traditional systems generally employ fixed route library matching, static inter-regional route mapping tables, and time-series scheduling methods for route planning. During route generation, they rely on prior regional distribution models and transportation cost matrices between regions. Route selection is based on sequential splicing according to preset rules, lacking dynamic adjustment and cross-regional interactive task modeling capabilities, making it difficult to adapt to frequently changing transportation demands and route optimization problems under complex regional intersection conditions. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a global planning and management system for multi-regional collaborative logistics transportation routes.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a multi-regional collaborative logistics transportation route global planning and management system, the system comprising: The path node characteristic identification module acquires logistics data from a multi-regional logistics transportation network, filters nodes whose flow direction is consistent with the regional task load as key scheduling points, and generates a list of key scheduling nodes. The path segment traffic load analysis module calls the list of key scheduling nodes, calculates the path segment traffic fluctuation value, combines it with the transportation task density of the area where the node is located, filters out path segments whose traffic fluctuation value exceeds the average transportation task density, and generates a path segment load anomaly list. The path scheduling buffer construction module extracts the task overlap time points as scheduling collision points based on the path segment load anomaly list, identifies the densely changing task segments, constructs the scheduling buffer segment boundary, inserts the corresponding time period into the path scheduling plan to implement time separation between tasks, and generates the path buffer scheduling segment structure result. The path sequence stability classification module calls the path buffer scheduling segment structure result, calculates the frequency of scheduling sequence variation of the path segment task passage order, divides the stability level interval of the path segment, prioritizes the selection of stable paths for logistics planning, and generates the path segment stability level zoning result.

[0006] The present invention is improved in that the list of key scheduling nodes includes the difference between inbound and outbound tasks of nodes, the load of regional transportation resources, and the consistency status of node flow direction; the list of abnormal path segment loads includes the fluctuation range of path segment traffic, the density ratio of path segment transportation tasks, and a list of abnormal path segment numbers; the structure result of the path buffer scheduling segment includes task time overlap segments, changes in the density of scheduling collision points, and time boundary values ​​of buffer segments; and the zoning result of the path segment stability level includes the path segment passage time deviation value, the frequency of scheduling order fluctuations, and the path stability level label.

[0007] The present invention is improved in that the path node characteristic identification module includes: The circulation volume calculation submodule acquires logistics data from the multi-regional logistics transportation network, calculates the inbound and outbound cargo circulation volumes of all task nodes within a unit of time, calculates the difference between the inbound and outbound cargo volumes of the nodes, and determines whether the logistics direction of the node is net inflow or net outflow based on the sign of the difference, and generates a node circulation direction value set. The regional load gradient calculation submodule calls the regional number of each node in the node flow direction value set, collects the amount of transportation resources used and the number of transportation tasks to be processed in the corresponding region, calculates the difference between the two, obtains the regional load increase / decrease gradient value, and maps and associates the node with the corresponding load gradient value according to the regional number to generate a node regional load gradient mapping set. The node direction consistency screening submodule judges whether the sign of the logistics flow direction of each node in the node area load gradient mapping set is the same as that of the regional load increase / decrease gradient value, filters the node numbers with consistent directions, and clusters them according to the area number to generate a list of key scheduling nodes.

[0008] The present invention is improved in that the path segment traffic load analysis module includes: The path traffic volume extraction submodule calls the path segment number involved in the scheduling key node list to obtain the number of tasks passing through each path segment in multiple consecutive time slices, organizes the passing numbers by time slice, establishes the time series traffic data structure for each path segment, and generates a path segment time series traffic value set. The traffic flow fluctuation calculation submodule calculates the difference between the maximum and minimum throughput between adjacent time slots based on the traffic task quantity data of each path segment in the path segment time series traffic value set, and uses it as the unit time traffic fluctuation value of the path segment to generate the path segment traffic flow fluctuation value set. The path segment pressure screening submodule compares the fluctuation value of the traffic flow fluctuation value set of the path segment with the average transportation task density of the area where the path segment is located within the same time period, determines whether the fluctuation value exceeds the average transportation task density of the area, organizes the path segment numbers that meet the conditions, and generates a path segment pressure anomaly list.

[0009] The present invention is improved in that the path scheduling buffer construction module includes: The abnormal path extraction submodule obtains the abnormal path segment number in the abnormal path segment pressure list, collects the start and end times and path segment distribution range of the corresponding transportation task within the current scheduling cycle of the abnormal path segment, and extracts the task path length and expected arrival time interval to generate an abnormal path task interval value set. The scheduling conflict identification submodule identifies the task arrival time intersection area based on the abnormal path task interval value set, and collects the change in the number of tasks within an equal time window before and after each intersection segment. It extracts the time point with the largest increase gradient of the number of tasks as the scheduling collision point, calculates and obtains the task density change value, marks the time slice with the task density change value greater than the density screening threshold, and generates a scheduling collision segment identifier set. The buffer segment boundary construction submodule constructs a buffer boundary on the path scheduling plan time axis based on the marked time slice in the scheduling collision segment identifier set as the center point, and inserts time intervals according to the execution order of task path segments to generate the path buffer scheduling segment structure result.

[0010] The present invention is improved in that the path sequence stability hierarchical module includes: The task travel deviation calculation submodule obtains the number of the path segment involved in the path buffer scheduling segment structure result, collects the average task transit time and the corresponding minimum transit time value of the path segment within the scheduling buffer time range, calculates the difference between the two as the travel time deviation value of each path segment, and generates a path segment travel time deviation value set. The scheduling variation frequency extraction submodule detects the number of times the task number changes in the execution order within the scheduling period based on the path segment number in the path segment travel time deviation value set, extracts the number of task number changes, and calculates the path segment task sequence variation frequency index in combination with the task path span to generate a variation frequency index set. The path level determination submodule compares the variation frequency index set with the stability level boundary value to determine the level range of the path segment, establishes the correspondence between path number and stability level, prioritizes the selection of stable paths for logistics planning, and generates the stability level zoning result of the path segment.

[0011] The present invention is improved in that the formula for calculating the mutation frequency index of the path segment task sequence is specifically as follows: ; in, Indicator representing the frequency of mutations in the task sequence of a path segment. Indicates the first The range of task number changes for each task within the path segment scheduling period. Indicates the first Normalized value of the task path span. This indicates the number of transport tasks carried by the current path segment within the scheduling period. This represents a normalized value indicating the length of the path segment scheduling buffer period. This indicates the total number of tasks within the path segment.

[0012] The present invention has an improvement, wherein the system further includes: The path execution order optimization module calls the path segment number of the unstable level in the path segment stability level division result, obtains the task start and end time and task path segment number involved in the path buffer scheduling segment structure result, determines whether there is a phenomenon of overlapping path segments and time overlap between tasks, and if so, rearranges the index position of the task in the global path execution order according to the number of task path segments in ascending order, and generates the path task execution order adjustment result. The results of the path task execution order adjustment include the task path segment overlap, task time overlap strength, and task execution order index table.

[0013] The present invention is improved in that the path execution order optimization module includes: The path task filtering submodule calls the path segment number marked as unstable in the stability level zoning result of the path segment, obtains the task path segment number, task start time and task end time associated with the path segment in the path buffer scheduling segment structure result, and integrates the task path set by the path segment number to generate the unstable segment task path set. The path overlap detection submodule determines whether there is any intersection of path segment numbers in the path segment task sequence and the task start and end time based on the path segment number sequence of the unstable segment task path set and the task start and end time, and determines whether there is an overlapping interval in the task time range. It then filters out task pairs that have both path segment overlap and time intersection relationships and generates a set of path task conflict relationship pairs. The execution order reordering submodule obtains the corresponding task path segment quantity value for the task number in the set according to the path task conflict relationship, sorts the task path segment quantity value in ascending order, extracts the original task execution index position, updates the task index position to the new index in the global path task sequence after sorting, and generates the path task execution order adjustment result.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are as follows: In this invention, the volume of inbound and outbound goods at each node in the logistics network within a unit time is collected and the difference is calculated. Combined with the node task density, key scheduling points with load and flow direction are selected. Based on the historical task passage data of the path segment, pressure abnormality sections in the transportation path are identified. Combined with the time distribution of tasks and the path overlap, scheduling collision points are extracted. A buffer segment insertion time separation mechanism is constructed to improve the scheduling flexibility between tasks. By analyzing the stability of the task sequence within the buffer time of the path segment, stability level classification is implemented and the path execution order is optimized to achieve rapid response to dynamic changes in the path and advance avoidance of task conflicts, effectively improving the flexibility of path planning and the coordination of global scheduling. Attached Figure Description

[0015] Figure 1 This is a system flowchart of the present invention; Figure 2 This is a flowchart of the path node characteristic identification module of the present invention; Figure 3 This is a flowchart of the path segment traffic load analysis module of the present invention; Figure 4 This is a flowchart of the path scheduling buffer construction module of the present invention; Figure 5 This is a flowchart of the path sequence stability hierarchical module of the present invention; Figure 6 This is a flowchart of the path execution order optimization module of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0017] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0018] Please see Figure 1 The present invention provides a technical solution, a global planning and management system for multi-regional collaborative logistics transportation routes, the system including a route node characteristic identification module, a route segment traffic load analysis module, a route scheduling buffer construction module, a route sequence stability classification module, and a route execution order optimization module; The path node characteristic identification module acquires logistics data in the multi-regional logistics transportation network, counts the inbound and outbound cargo circulation volumes of all task nodes within a unit of time, calculates the difference between inbound and outbound volumes, identifies the circulation direction, and combines the amount of transportation resources used in the region where the node is located with the number of tasks to be executed to select nodes whose circulation direction is consistent with the regional task load as scheduling key points and generate a list of scheduling key nodes. The path segment traffic load analysis module calls the path segment number involved in the list of key scheduling nodes, collects the historical task throughput of continuous time slices on the path segment, calculates the difference between the maximum throughput per unit time and the minimum throughput per unit time as the path segment throughput fluctuation value, and combines it with the transportation task density of the area where the node is located to filter path segments whose throughput fluctuation value exceeds the average transportation task density, and generates a path segment load anomaly list. The path scheduling buffer construction module extracts the transportation task distribution of abnormal path segments in the current scheduling cycle based on the abnormal path segment load list, collects the path length range corresponding to the task and the expected task arrival time offset range, extracts the task overlap time points as scheduling collision points, calculates the change in the number of tasks within a certain period before and after the scheduling collision point, identifies the densely changing task segments, constructs the scheduling buffer segment boundary with the segment as the center, inserts the corresponding time period into the path scheduling plan to implement time separation between tasks, and generates the path buffer scheduling segment structure result. The path sequence stability classification module calls the number of the path segment involved in the path buffer scheduling segment structure result, obtains the deviation between the average and minimum passing time of the corresponding path segment within the scheduling buffer segment time, calculates the frequency of scheduling sequence variation of the task passing order of the path segment, compares it with the path segment scheduling reference task scheduling stability index, divides the stability level interval of the path segment, prioritizes the selection of stable paths for logistics planning, and generates the path segment stability level zoning result. The path execution order optimization module calls the path segment number of the unstable level in the path segment stability level division result, obtains the task start and end time and task path segment number involved in the path buffer scheduling segment structure result, and determines whether there is a phenomenon of overlapping path segments and time overlap between tasks. If so, the index position of the task in the global path execution order is rearranged in ascending order according to the number of task path segments, and the path task execution order adjustment result is generated. The list of key scheduling nodes includes the difference between inbound and outbound tasks, the load of regional transportation resources, and the consistency status of node flow direction. The list of abnormal path segment loads includes the fluctuation range of path segment traffic, the ratio of path segment transportation task density, and a list of abnormal path segment numbers. The results of the path buffer scheduling segment structure include task time overlap segments, changes in scheduling collision point density, and buffer segment time boundary values. The results of the path segment stability level zoning include the path segment passage time deviation value, the frequency of scheduling order fluctuations, and the path stability level label. The results of the path task execution order adjustment include the task path segment overlap, the intensity of task time intersection, and the task execution order index table.

[0019] Please see Figure 2 The path node feature recognition module includes: The circulation volume calculation submodule acquires logistics data from the multi-regional logistics transportation network, calculates the inbound and outbound cargo circulation volumes of all task nodes within a unit of time, calculates the difference between the inbound and outbound cargo volumes of the nodes, and determines whether the logistics direction of the node is net inflow or net outflow based on the sign of the difference, and generates a node circulation direction value set. Lock a specified time window (e.g., 08:00 to 12:00 on November 1, 2024) using SQL query commands, and assign each task node number (marked as...) Iterate through all its freight records, summing the products of all dimensions of goods destined for that node within the time window to obtain the total volume of inbound cargo, and summing the products of all dimensions of goods originating from that node to obtain the total volume of outbound cargo. Then, subtract the total volume of outbound cargo from the total volume of inbound cargo to obtain the volume difference. ,like Then determine the node. The net inflow status is marked with a direction value. ,like If so, it is determined to be a net outflow state and the direction value is marked. ,like Then mark as After traversing all nodes in the network, the node number is matched with its corresponding direction marker value. , or These are used to form key-value pairs, generating a set of node flow direction values.

[0020] The regional load gradient calculation submodule calls the regional number of each node in the node flow direction value set, collects the amount of transportation resources used and the number of transportation tasks to be processed in the corresponding region, calculates the difference between the two, obtains the regional load increase / decrease gradient value, and maps and associates the node with the corresponding load gradient value according to the regional number to generate a node regional load gradient mapping set. Find the region number to which each node belongs based on the preset node-region affiliation table (e.g., node). Belongs to the region Then, it connects to the regional resource monitoring interface to collect the region's ID number in real time. The number of currently idle transportation resource units (including the sum of available trucks and warehouse throughput units, denoted as [missing information]) is as follows: At the same time, the number of pending transport tasks generated within this region but not yet loaded and dispatched in the current time slice is counted (set as...). ), perform subtraction operation Obtain the regional load increase / decrease gradient values, for example , ,but A positive result indicates abundant resources, while a negative result indicates overload. Finally, the nodes are numbered. The gradient value calculated for this region Establish a one-to-one mapping relationship and perform this operation on all nodes to generate a node region load gradient mapping set.

[0021] The node direction consistency screening submodule judges whether the sign of the logistics flow direction of each node in the node area load gradient mapping set is the same as that of the regional load increase / decrease gradient value, filters the node numbers with consistent directions, and clusters them according to the area number to generate a list of key scheduling nodes. Extract the current logistics flow direction value of the node ( or The sign comparison logic is executed between the node direction and the corresponding load increase / decrease gradient value (positive or negative). (Net inflow) and the regional load gradient value is positive (resource surplus), or the node direction is If a node has a net outflow and a negative regional load gradient (resource shortage), it is considered to have the same sign and direction, and is included in the screening list. Otherwise, it is removed. After screening, all retained node numbers are grouped and clustered according to their respective regional numbers. For example, nodes belonging to... All consistent nodes in a region are grouped together to form a list of key scheduling nodes with scheduling priority.

[0022] Please see Figure 3 The path segment traffic load analysis module includes: The path traffic volume extraction submodule calls the path segment number involved in the scheduling key node list, obtains the number of tasks passing through each path segment in multiple consecutive time slices, organizes the passing numbers by time slice, establishes the time series traffic data structure for each path segment, and generates the path segment time series traffic value set. Retrieve the topology network database to obtain all path segment numbers connecting nodes (e.g., , For each path segment (in...) For example, set a continuous monitoring time slice sequence ( The number of transport tasks that actually entered and completed the journey through the route segment within each time slice is counted. The results are then arranged in chronological order to construct a two-dimensional array structure in the form of "time slice - throughput". For example, the route segment... Data records are As shown in Table 1, this method is used to complete the data processing of all involved path segments and generate a time series access value set for the path segments.

[0023] Table 1 Path Segments Continuous Time Segment Traffic Volume Monitoring Table As shown in Table 1, the path segments are recorded. The number of traffic tasks within four consecutive time slices serves as the direct basis for subsequent calculations of traffic fluctuations.

[0024] The traffic flow fluctuation calculation submodule calculates the difference between the maximum and minimum throughput between adjacent time slots based on the number of traffic tasks for each path segment in the path segment time series traffic value set, and uses this difference as the unit time traffic fluctuation value of the path segment to generate the path segment traffic flow fluctuation value set. For the path segments in Table 1 The data is used to calculate the difference in the number of passes between adjacent time slices. The maximum and minimum values ​​of the number of passes between two adjacent time slices are extracted, and the absolute value of the difference between the two is calculated as the fluctuation amplitude at that time boundary. For example, in... (12) and Between (18), the maximum value is 18, the minimum value is 12, and the difference is 6; (14) and (25) The difference is 11. The system selects the maximum value (11 in this example) among all adjacent differences calculated within the statistical period and marks it as the unit time traffic fluctuation value of this path segment. This operation is performed on all path segments to generate a set of path segment traffic flow fluctuation values.

[0025] The path segment pressure screening submodule compares the fluctuation value of the traffic flow fluctuation value set of the path segment with the average transportation task density of the area where the path segment is located within the same time period to determine whether the fluctuation value exceeds the average transportation task density of the area, organizes the path segment numbers that meet the conditions, and generates a path segment pressure anomaly list. Read the fluctuation values ​​of the traffic flow fluctuation value set of the path segment (e.g., path) The fluctuation value is 11). At the same time, the region's transportation records within the same time period are retrieved to calculate the average regional transportation task density. The calculation method is to divide the total number of transportation tasks in the region by the total number of route segments in the region. Let's assume the total number of tasks in the region is 300 and the total number of route segments is 50. Then, the path fluctuation value is compared with the regional density mean. If (Right now If the path segment is subjected to abnormal pulse pressure, it is determined that the path segment is subjected to abnormal pulse pressure and is numbered accordingly. Add the abnormal items to the list, traverse all paths, compile all path segment numbers that meet the conditions, and generate a list of abnormal path segment pressures.

[0026] Please see Figure 4 The path scheduling buffer building module includes: The abnormal path extraction submodule obtains the abnormal path segment number from the abnormal path segment pressure list, collects the start and end times and path segment distribution range of the corresponding transportation task within the current scheduling cycle for the abnormal path segment, and extracts the task path length and expected arrival time interval to generate an abnormal path task interval value set. Locking the abnormal path segment number Query the task assignment database within the current scheduling period and filter out all tasks that are planned to pass through or have already passed through this path segment. Transportation tasks (such as tasks) , For each task, collect its path segment Expected entry time Compared to the expected departure time Calculate the time span as the estimated arrival time interval, and simultaneously read the total path length of the task in the planned route. (e.g., 50km) and the route segment In the distribution range of the entire task path (such as the 3rd to 5th nodes), these spatiotemporal parameters are packaged and stored to generate an abnormal path task interval value set.

[0027] The scheduling conflict identification submodule identifies overlapping regions of task arrival times based on the abnormal path task interval value set, and collects the change in the number of tasks within an equal time window before and after each overlapping segment. It extracts the time point with the largest increase gradient in the number of tasks as the scheduling collision point, using the formula: ; The algorithm calculates the task density change value, marks the time slices whose task density change value is greater than the density filtering threshold, and generates a scheduling collision segment identifier set. in, This represents the normalized value of the estimated arrival time offset of a task, obtained by dividing the offset time difference of each task by the maximum value among all task offset time differences. This represents the normalized value of the path length, obtained by dividing the task's path length by the maximum value of all path lengths within the current scheduling period. This indicates the number of transportation tasks within the time slice where the scheduling collision point is located. The data is collected by counting the start and end times of tasks within that time slice. Indicates the first The number of path segments that overlap with abnormal path segments in this time slice is obtained by extracting the task path segment sequence and performing a path segment intersection count with the abnormal path segment set. This represents the total number of task paths that overlap with the abnormal path segment within the given time slice. The data type is a positive integer, and it is obtained by counting the number of task paths that satisfy the condition that the intersection of path segments is not empty. This represents the degree of task density variation. The density screening threshold is a dimensionless constant used to determine whether a collision period should be marked as a buffer processing area. It is obtained by determining the 75th percentile value of the peak number of tasks within the scheduling cycle. That is, the number of tasks in all time slices is collected, sorted, and the task load value in the upper quartile is determined and used as the basis for threshold determination. Multiple tasks were identified in the path segment using the time-axis projection method. Within the overlapping time region, select 10 minutes before and after the point of highest overlap as the observation window, and extract the parameters within this window and substitute them into the formula. The calculation is performed. The formula logic is as follows: the numerator is normalized by time offset. Normalized value of path length The product of these values ​​quantifies the urgency and scale of the current conflict task in the spatiotemporal dimensions. A larger value indicates a higher time sensitivity and a longer path for the conflict task, resulting in greater scheduling difficulty; the denominator... This reflects the environmental load capacity of the current time slice. Based on the basic workload, This reflects the degree of entanglement between related tasks and other abnormal paths. A larger denominator indicates a more crowded environment and a weaker ability to absorb single conflicts (leading to...). The value decreases, which needs to be understood in reverse, in conjunction with the threshold, or here. The value is designed to reflect "conflict salience" rather than "buffer demand," and according to the formula structure, the denominator is large. A smaller value means that in an extremely crowded environment, the relative impact of changes in a single task is diluted. Let's set up a practical example: Assume that within the observed time slice, the time offset difference for a certain task is 5 minutes, and the maximum offset difference for all tasks is 20 minutes. The task path is 50km long, and the maximum path length within the cycle is 100km. There are a total of [number] time slices. The task has a start and end date. It involves... There are 10 overlapping task paths, of which the number of segments in the first overlapping path is 1. Article 2 ,but , Substitute the values ​​into the calculation: molecular ; denominator ; Calculation results ; Dense filtering threshold set to The method for obtaining it is: calculating the time slices of all time slices within the statistical scheduling period. The value distribution is determined by taking the 75th percentile value. This is based on historical data statistics, with the 75th percentile being the most significant value. Value Comparison logic: Because This indicates that the task density changes in this time slice exceed the upper quartile limit of normal fluctuations, belonging to a high-frequency conflict period. This result shows that the temporal and spatial coupling of the task flow within the current time slice generates significant scheduling pressure. The system marks this time slice as a collision point requiring intervention and generates a set of scheduling collision segment identifiers.

[0028] The buffer segment boundary construction submodule constructs the buffer boundary on the path scheduling plan time axis based on the marked time slice in the scheduling collision segment identifier set as the center point, and inserts time intervals according to the execution order of the task path segments to generate the path buffer scheduling segment structure result. Read the collision time slices (e.g., 10:00-10:15) marked in the scheduling collision segment identifier set, and take the midpoint of this time slice (10:07:30) as the axis, along the path Extend the scheduled timeline forward and backward in both directions to construct a symmetrical time buffer boundary. Set the extension step size to 5 minutes to form a buffer protection zone from 10:02:30 to 10:12:30. Insert a time interval of 2 minutes in this zone according to the original task execution order to forcibly postpone the entry time of subsequent tasks and regenerate the path buffer scheduling segment structure result containing physical gaps.

[0029] Please see Figure 5 The path sequence stability classification module includes: The task travel deviation calculation submodule obtains the number of the path segment involved in the path buffer scheduling segment structure result, collects the average passage time of the task within the scheduling buffer time range of the path segment and the corresponding minimum passage time value, calculates the difference between the two as the travel time deviation value of each path segment, and generates a path segment travel time deviation value set. Lock path segment The system collects real-time data on the time taken by all vehicles to pass through the buffer zone during actual operation and calculates the average passage time. (e.g., 25 minutes), and simultaneously retrieve the minimum transit time value of this path segment under the historical best conditions. (For example, 20 minutes), perform subtraction. Each 5-minute interval is used as the travel time deviation value for that route segment. If the deviation value is negative, it is set to zero. This calculation is performed on all involved route segments to generate a set of route segment travel time deviation values.

[0030] The scheduling variation frequency extraction submodule, based on the path segment numbers in the path segment travel time deviation value set, detects the number of times the task number changes in execution order within the scheduling period, extracts the task number change count, and combines it with the task path span, using the following formula: ; The mutation frequency index of the task sequence of the path segment is obtained by calculation, and a mutation frequency index set is generated. in, This index represents the frequency of task sequence variation within a path segment, used to measure the degree of dynamic change in the execution order of tasks within that path segment. Indicates the first The change in task number for each task within a path segment scheduling period is obtained by measuring the change in the task's index within the actual scheduling path sequence. Indicates the first The normalized value of the task path span is calculated by dividing the number of task path segments by the maximum number of all task path segments within the scheduling cycle. This indicates the number of transportation tasks carried by the current path segment within the scheduling period. It is obtained by counting the number of times the path segment number appears in the scheduling task path table. This represents a normalized value indicating the length of the path segment scheduling buffer segment. It is obtained by dividing the number of buffer segment time slices for that path segment by the maximum number of buffer segment time slices among all path segments. This represents the total number of tasks within a path segment. The stability level boundary values ​​are a set of segmented thresholds used to classify the stability level of the path segment. The method for setting these values ​​is as follows: first, the distribution range of the frequency variation index values ​​of all path segment task sequences within the scheduling cycle is statistically analyzed; then, the index is divided into quartiles, corresponding to four stability levels (high, medium, low, and unstable). Each boundary value is the quantile value corresponding to this distribution, and the data type is a set of real numbers. For path segments It detects changes in the task execution order within the scheduling cycle, combined with the formula. Calculate the frequency of variation. In the formula, Partially, by summing the product of the ranking change magnitude of each task and the path span weight, the degree of scrambling of the task sequence is directly reflected; the greater the change and the wider the task span involved, the larger the numerator; the denominator... This introduces the total task volume. With buffer resources As a normalization factor, the larger the task or the more sufficient the buffer space, the higher the theoretically tolerable degree of variation, thus serving a smoothing function as the denominator. Example parameter settings: Set the path segment to have... The task has changed. Task 1 was originally ranked 3rd, now it is ranked 5th, the change is significant. Its path spans 5 segments, with a maximum span of 10 segments, normalized. Task 2, originally ranked 4th, is now ranked 3rd. Spanning 4 segments, normalized .

[0031] Molecular calculations: ; Total number of tasks carried by the path segment The number of buffer time slices is 2, with a maximum of 10. .

[0032] Denominator calculation: ; Calculation results: ; Stability level boundary value setting: Statistical analysis of all network path segments Values, divided according to quartiles. Let... , , . determination: It falls into the fourth interval (unstable region). This result indicates that the path segment The task execution order still exhibits extremely high dynamic uncertainty after buffering and adjustment, and belongs to the "unstable" objects that need to be rearranged in a key way. Based on this, a set of mutation frequency indicators is generated.

[0033] The route level determination submodule compares the variation frequency index set with the stability level boundary value to determine the level range of the route segment, establishes the correspondence between the route number and the stability level, prioritizes the selection of stable routes for logistics planning, and generates the stability level zoning results of the route segment. Based on the calculated frequency of variation index Compare with the preset set of stability level boundary values ​​( For high stability, For the sake of stability, For low stability, (For unstable conditions), determine the path segment. The path is classified as "unstable" and will be numbered accordingly. Marked as an unstable state, while for Path segments with lower values ​​(such as The network is marked as highly stable and its existing plans are locked first. Finally, a stability classification view of the entire network path is established, and the stability level zoning results of the path segment are generated.

[0034] Please see Figure 6 The path execution order optimization module includes: The path task filtering submodule calls the path segment number marked as unstable in the path segment stability level zoning result, obtains the task path segment number, task start time and task end time associated with the path segment in the path buffer scheduling segment structure result, and integrates the task path set by path segment number to generate the unstable segment task path set. Extract the path segment numbers marked as "unstable" (e.g.) Based on the path buffer scheduling segment structure results, trace all associated task information on that path segment and obtain the task number ( ), the associated task path segment number sequence, and the task's start time after buffering. With end time This scattered information is grouped and packaged according to the path segment number to form a data set containing all tasks to be adjusted and their spatiotemporal attributes, thus generating an unstable segment task path set.

[0035] The path overlap detection submodule determines whether there is any intersection of path segment numbers in the path segment task sequence and the start and end times of the task based on the path segment number sequence of the unstable segment task path set and the task start and end times. It also determines whether there is an overlapping interval in the task time range, filters out task pairs that have both path segment overlap and time intersection relationship, and generates a set of path task conflict relationship pairs. Traverse task pairs in the unstable segment task path set (by and (For example), first compare the path segment number sequences of the two, and find that both contain and There is path overlap; then, the time ranges of the two are compared. It is from 10:00 to 10:30. The time interval is 10:15-10:45, and there is a time overlap interval of 10:15-10:30. It is confirmed that both spatial overlap and temporal intersection conditions are met, and therefore it is determined to be a substantial conflict. Record it as a conflict pair to generate a set of path task conflict relationship pairs.

[0036] The execution order reordering submodule obtains the corresponding task path segment quantity value based on the task number in the set according to the path task conflict relationship, sorts the task path segment quantity value in ascending order, extracts the original task execution index position, updates the task index position to the new index in the global path task sequence after sorting, and generates the path task execution order adjustment result. Read the path task conflict relationship for a set of conflicting tasks. and Query the task details database to obtain the total number of task path segments covered by each task, and set... Covering 5 path segments, Covering 8 path segments, the tasks are sorted according to a preset strategy of "shortest path first" or "longest path first" (this embodiment uses ascending order, i.e., shortest path first). ,Sure Priority over Execute, extract the index positions of both in the original global sequence (set) For index 5, For index 3), The index is updated to 3. Update to 5 (or sequentially), update the global task list through swapping or re-insertion operations, and generate the result of path task execution order adjustment.

[0037] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A global planning and management system for multi-regional collaborative logistics transportation routes, characterized in that, The system includes: The path node characteristic identification module acquires logistics data from a multi-regional logistics transportation network, filters nodes whose flow direction is consistent with the regional task load as key scheduling points, and generates a list of key scheduling nodes. The path segment traffic load analysis module calls the list of key scheduling nodes, calculates the path segment traffic fluctuation value, combines it with the transportation task density of the area where the node is located, filters out path segments whose traffic fluctuation value exceeds the average transportation task density, and generates a path segment load anomaly list. The path scheduling buffer construction module extracts the task overlap time points as scheduling collision points based on the path segment load anomaly list, identifies the densely changing task segments, constructs the scheduling buffer segment boundary, inserts the corresponding time period into the path scheduling plan to implement time separation between tasks, and generates the path buffer scheduling segment structure result. The path sequence stability classification module calls the path buffer scheduling segment structure result, calculates the frequency of scheduling sequence variation of the path segment task passage order, divides the stability level interval of the path segment, prioritizes the selection of stable paths for logistics planning, and generates the path segment stability level zoning result.

2. The multi-regional collaborative logistics transportation route global planning and management system according to claim 1, characterized in that, The list of key scheduling nodes includes the difference between inbound and outbound tasks at nodes, the load of regional transportation resources, and the consistency status of node flow direction. The list of abnormal path segment loads includes the fluctuation range of path segment traffic, the ratio of transportation task density in path segment, and a list of abnormal path segment numbers. The structure result of the path buffer scheduling segment includes task time overlap segments, changes in the density of scheduling collision points, and time boundary values ​​of the buffer segment. The stability level zoning result of the path segment includes the path segment passage time deviation value, the frequency of scheduling order fluctuations, and the path stability level label.

3. The multi-regional collaborative logistics transportation route global planning and management system according to claim 1, characterized in that, The path node characteristic identification module includes: The circulation volume calculation submodule acquires logistics data from the multi-regional logistics transportation network, calculates the inbound and outbound cargo circulation volumes of all task nodes within a unit of time, calculates the difference between the inbound and outbound cargo volumes of the nodes, and determines whether the logistics direction of the node is net inflow or net outflow based on the sign of the difference, and generates a node circulation direction value set. The regional load gradient calculation submodule calls the regional number of each node in the node flow direction value set, collects the amount of transportation resources used and the number of transportation tasks to be processed in the corresponding region, calculates the difference between the two, obtains the regional load increase / decrease gradient value, and maps and associates the node with the corresponding load gradient value according to the regional number to generate a node regional load gradient mapping set. The node direction consistency screening submodule judges whether the sign of the logistics flow direction of each node in the node area load gradient mapping set is the same as that of the regional load increase / decrease gradient value, filters the node numbers with consistent directions, and clusters them according to the area number to generate a list of key scheduling nodes.

4. The multi-regional collaborative logistics transportation route global planning and management system according to claim 3, characterized in that, The path segment traffic load analysis module includes: The path traffic volume extraction submodule calls the path segment number involved in the scheduling key node list to obtain the number of tasks passing through each path segment in multiple consecutive time slices, organizes the passing numbers by time slice, establishes the time series traffic data structure for each path segment, and generates a path segment time series traffic value set. The traffic flow fluctuation calculation submodule calculates the difference between the maximum and minimum throughput between adjacent time slots based on the traffic task quantity data of each path segment in the path segment time series traffic value set, and uses it as the unit time traffic fluctuation value of the path segment to generate the path segment traffic flow fluctuation value set. The path segment pressure screening submodule compares the fluctuation value of the traffic flow fluctuation value set of the path segment with the average transportation task density of the area where the path segment is located within the same time period, determines whether the fluctuation value exceeds the average transportation task density of the area, organizes the path segment numbers that meet the conditions, and generates a path segment pressure anomaly list.

5. The multi-regional collaborative logistics transportation route global planning and management system according to claim 4, characterized in that, The path scheduling buffer construction module includes: The abnormal path extraction submodule obtains the abnormal path segment number in the abnormal path segment pressure list, collects the start and end times and path segment distribution range of the corresponding transportation task within the current scheduling cycle of the abnormal path segment, and extracts the task path length and expected arrival time interval to generate an abnormal path task interval value set. The scheduling conflict identification submodule identifies the task arrival time intersection area based on the abnormal path task interval value set, and collects the change in the number of tasks within an equal time window before and after each intersection segment. It extracts the time point with the largest increase gradient of the number of tasks as the scheduling collision point, calculates and obtains the task density change value, marks the time slice with the task density change value greater than the density screening threshold, and generates a scheduling collision segment identifier set. The buffer segment boundary construction submodule constructs a buffer boundary on the path scheduling plan time axis based on the marked time slice in the scheduling collision segment identifier set as the center point, and inserts time intervals according to the execution order of task path segments to generate the path buffer scheduling segment structure result.

6. The multi-regional collaborative logistics transportation route global planning and management system according to claim 5, characterized in that, The path sequence stability hierarchical module includes: The task travel deviation calculation submodule obtains the number of the path segment involved in the path buffer scheduling segment structure result, collects the average task transit time and the corresponding minimum transit time value of the path segment within the scheduling buffer time range, calculates the difference between the two as the travel time deviation value of each path segment, and generates a path segment travel time deviation value set. The scheduling variation frequency extraction submodule detects the number of times the task number changes in the execution order within the scheduling period based on the path segment number in the path segment travel time deviation value set, extracts the number of task number changes, and calculates the path segment task sequence variation frequency index in combination with the task path span to generate a variation frequency index set. The path level determination submodule compares the variation frequency index set with the stability level boundary value to determine the level range of the path segment, establishes the correspondence between path number and stability level, prioritizes the selection of stable paths for logistics planning, and generates the stability level zoning result of the path segment.

7. The multi-regional collaborative logistics transportation route global planning and management system according to claim 6, characterized in that, The specific formula for obtaining the mutation frequency index of the path segment task sequence is as follows: ； in, Indicator representing the frequency of mutations in the task sequence of a path segment. Indicates the first The range of task number changes for each task within the path segment scheduling period. Indicates the first Normalized value of the task path span. This indicates the number of transport tasks carried by the current path segment within the scheduling period. This represents a normalized value indicating the length of the path segment scheduling buffer period. This indicates the total number of tasks within the path segment.

8. The multi-regional collaborative logistics transportation route global planning and management system according to claim 7, characterized in that, The system also includes: The path execution order optimization module calls the path segment number of the unstable level in the path segment stability level division result, obtains the task start and end time and task path segment number involved in the path buffer scheduling segment structure result, determines whether there is a phenomenon of overlapping path segments and time overlap between tasks, and if so, rearranges the index position of the task in the global path execution order according to the number of task path segments in ascending order, and generates the path task execution order adjustment result. The results of the path task execution order adjustment include the task path segment overlap degree, task time intersection strength, and task execution order index table.

9. The multi-regional collaborative logistics transportation route global planning and management system according to claim 8, characterized in that, The path execution order optimization module includes: The path task filtering submodule calls the path segment number marked as unstable in the stability level zoning result of the path segment, obtains the task path segment number, task start time and task end time associated with the path segment in the path buffer scheduling segment structure result, and integrates the task path set by the path segment number to generate the unstable segment task path set. The path overlap detection submodule determines whether there is any intersection of path segment numbers in the path segment task sequence and the task start and end time based on the path segment number sequence of the unstable segment task path set and the task start and end time, and determines whether there is an overlapping interval in the task time range. It then filters out task pairs that have both path segment overlap and time intersection relationships and generates a set of path task conflict relationship pairs. The execution order reordering submodule obtains the corresponding task path segment quantity value for the task number in the set according to the path task conflict relationship, sorts the task path segment quantity value in ascending order, extracts the original task execution index position, updates the task index position to the new index in the global path task sequence after sorting, and generates the path task execution order adjustment result.

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