A dynamic path planning method and system for cold chain distribution

Abstract

This invention relates to the field of cold chain distribution technology, specifically to a dynamic path planning method and system for cold chain distribution. The method includes the following steps: acquiring and recording the three-dimensional spatial coordinates, three-dimensional dimensions, and loading completion timestamp sequence of each cargo unit within the vehicle's cargo compartment; constructing a loading dependency graph of cargo units based on the three-dimensional spatial coordinates and dimensions; receiving and processing dynamic adjustment requests in real time; the dynamic adjustment request at least includes the identifier of the target cargo unit whose delivery order needs to be adjusted and the expected delivery time window after adjustment; according to the received dynamic adjustment request, retrieving the loading dependency graph, identifying all cargo units corresponding to nodes in the loading dependency graph that have directed edges directly or indirectly pointing to the target cargo unit, defining the cargo units as the set of preceding dependent units of the target cargo unit; generating a path adjustment scheme; which can improve the efficiency of cold chain distribution.

Description

Technical Field

[0001] This invention relates to the field of cold chain distribution technology, specifically to a dynamic route planning method and system for cold chain distribution. Background Technology

[0002] In traditional cold chain logistics, route planning is typically optimized statically or quasi-statically based on external factors such as geographical distance, traffic conditions, and time windows. However, in actual delivery processes, situations often arise where changes in customer demand (such as expedited orders, cancellations, or time changes) necessitate dynamic adjustments to the subsequent delivery sequence. Current technologies, when making such dynamic adjustments, mostly only consider route replanning, neglecting the physical loading status of goods within the vehicle's cargo compartment. This makes it difficult to allocate appropriate adjustment permissions based on the urgency of the request, impacting the effectiveness of cold chain delivery. The loading of cold chain delivery vehicles (especially those with multiple temperature zones and stations) is often meticulously planned, with goods requiring a strict physical order of storage and retrieval due to stacking and compression. If a dynamically adjusted delivery order requires prior delivery of goods at the bottom, a time-consuming "turnover" operation must be performed, which cannot be safely carried out while on the roadside. If this is done on the roadside, there are risks to food safety and damage to goods, impacting the efficiency of cold chain delivery.

[0003] To address these issues, we propose a dynamic route planning method and system for cold chain distribution. Summary of the Invention

[0004] The purpose of this invention is to provide a dynamic route planning method and system for cold chain distribution to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a dynamic path planning method and system for cold chain distribution, the method comprising the following steps: acquiring and recording the three-dimensional spatial coordinates, three-dimensional dimensions and loading completion timestamp sequence of each cargo unit within the vehicle cargo compartment; Based on three-dimensional spatial coordinates and three-dimensional dimension data, a loading dependency graph of cargo units is constructed; Receive and process dynamic adjustment requests in real time; a dynamic adjustment request must include at least the target cargo unit identifier whose delivery order needs to be adjusted and the adjusted expected delivery time window. Based on the received dynamic adjustment request, retrieve the loading dependency graph, identify all cargo units corresponding to nodes in the loading dependency graph that have directed edges that directly or indirectly point to the target cargo unit, and define the cargo unit as the set of the target cargo unit's preceding dependent units. Generate a route adjustment plan; the generation of the route adjustment plan must meet the following hard constraint: in the adjusted delivery sequence, the delivery order of each cargo unit in the set of preceding dependent units is earlier than the delivery order of the target cargo unit.

[0006] Preferably, the step of acquiring and recording the three-dimensional spatial coordinates and three-dimensional dimensions of each cargo unit within the vehicle cargo compartment includes: During the loading process, a three-dimensional contour scan is performed on each cargo unit loaded into the cargo compartment by using multiple monitoring points fixed to the top corner or side wall inside the cargo compartment. Assign a unique identifier to each scanned and identified cargo unit, and calculate the minimum bounding cube; the geometric center coordinates of the minimum bounding cube are defined as the three-dimensional spatial coordinates of the cargo unit, and the length, width, and height of the minimum bounding cube are defined as the three-dimensional dimensions of the cargo unit; The unique identifier, three-dimensional spatial coordinates, three-dimensional dimensions, and system timestamp of the completed scan of each cargo unit are associated and stored in the loading database.

[0007] Preferably, the step of constructing the loading dependency graph of cargo units based on three-dimensional spatial coordinates and three-dimensional dimension data includes: Based on the three-dimensional spatial coordinates and three-dimensional dimensions of all cargo units, a model of the stacking and arrangement of cargo units within the cargo compartment is reconstructed in a three-dimensional spatial coordinate system. Using the opening plane of the cargo door as the reference plane, a virtual radial line is generated along the depth direction of the cargo box; for any cargo unit B, the straight-line movement path directly taken out from the cargo door is simulated. Determine whether there are other cargo unit A entities occupying the path space on the virtual radial path; If it exists, then it is determined that cargo unit A occludes cargo unit B; for all cargo unit pairs with occlusion relationship, a directed edge is established in the loading dependency graph, where the node corresponding to the occluding object is the starting point of the directed edge, and the node corresponding to the occluded object is the ending point of the directed edge.

[0008] Preferably, the process further includes a hierarchical adjustment request step between receiving and processing dynamic adjustment requests in real time and, based on the received dynamic adjustment requests, retrieving the loading dependency graph, identifying all cargo units corresponding to nodes in the loading dependency graph that have directed edges directly or indirectly pointing to the target cargo unit, and defining the cargo unit as the set of preceding dependent units of the target cargo unit: Analyze the urgency parameters of dynamic adjustment requests; classify dynamic adjustment requests into multiple adjustment levels based on preset urgency thresholds; Each adjustment level is associated with an adjustment depth parameter; the adjustment depth parameter is defined in the loading dependency graph and is the maximum level depth at which the order of its preceding dependent units needs to be adjusted along with the order of the target cargo unit when the order of the target cargo unit is adjusted. When generating path adjustment schemes, only solutions are considered where the depth of the cargo units involved in the adjustment sequence in the loading dependency graph does not exceed the allowable adjustment depth of their corresponding adjustment level.

[0009] Preferably, the adjustment levels include at least a first level and a second level; the first level corresponds to an allowable adjustment depth of 0 layers, and its path adjustment scheme only allows checking whether the target cargo unit is already in an accessible state and can be delivered immediately without changing the original delivery order of any other cargo units; if not, the adjustment request for this level is rejected or it is upgraded; the second level corresponds to an allowable adjustment depth of N layers, where N is a positive integer; its path adjustment scheme allows adjusting the delivery order of the target cargo unit and its predecessor dependent units whose depth in the loading dependency graph does not exceed N layers, but the adjustment process must strictly comply with the topological order constraints derived from the loading dependency graph.

[0010] Preferably, when a route adjustment plan that meets the hard constraints and is within the delivery time window cannot be generated, a reconfiguration point planning step is performed: Identify the set of critical conflict cargo units that prevents the generation of a feasible solution; the set of critical conflict cargo units includes the target cargo unit and some or all of its preceding dependent units. From the pre-stored reconstruction point map database, retrieve sites within a preset distance range around the current location that have the conditions for safe parking and cargo rearrangement as candidate reconstruction points; Calculate the total travel time and cost of the current vehicle traveling to the candidate reconfiguration point, completing the physical relocation of the critical conflict cargo unit set at the reconfiguration point, and then traveling to each delivery point; If the total travel time meets the latest time window requirements for all delivery points, a hybrid route adjustment scheme is generated, which includes going to the reconfiguration point, performing reordering, and continuing delivery.

[0011] Preferably, the step of completing the physical location rearrangement of the critical conflict cargo unit set includes: At the reconfiguration point, based on the optimal cargo stacking layout pre-calculated to support the new delivery sequence, the cargo in the critical conflict cargo unit set is unloaded, temporarily stored, and reloaded. After reloading is complete, update the load database and rebuild the load dependency graph.

[0012] Preferably, the method also includes updating the load dependency graph after implementing the path adjustment scheme: Once a cargo unit is successfully delivered and unloaded from the cargo container, delete the node corresponding to that cargo unit in the loading dependency graph, and delete all directed edges that start or end with that node. For the remaining cargo units in the cargo compartment, based on their current actual three-dimensional spatial relationships, their mutual occlusion relationships are reassessed or partially updated to form an updated loading dependency relationship diagram.

[0013] A cold chain delivery dynamic route planning system, applied to any of the cold chain delivery dynamic route planning methods described above, includes: The data acquisition module is used to acquire and record the three-dimensional spatial coordinates, three-dimensional dimensions, and loading completion timestamp sequence of each cargo unit within the vehicle cargo compartment; The building module is used to construct a loading dependency graph of cargo units based on three-dimensional spatial coordinates and three-dimensional dimension data; The processing module is used to receive and process dynamic adjustment requests in real time; the dynamic adjustment request includes at least the target cargo unit identifier whose delivery order needs to be adjusted and the adjusted expected delivery time window; The analysis module is used to retrieve the loading dependency graph based on the received dynamic adjustment request, identify the cargo units corresponding to all nodes in the loading dependency graph that have directed edges that directly or indirectly point to the target cargo unit, and define the cargo units as the set of the preceding dependent units of the target cargo unit. The route update module is used to generate a route adjustment plan; the generation of the route adjustment plan must meet the following hard constraint: in the adjusted delivery sequence, the delivery order of each cargo unit in the set of preceding dependent units is earlier than the delivery order of the target cargo unit. The delivery module is used to execute a route adjustment scheme that satisfies the hard constraints, and to control the delivery vehicles to go to each delivery point in the adjusted order.

[0014] Compared with the prior art, the beneficial effects of the present invention are: By introducing matching rules between adjustment levels and allowed adjustment depths in the dynamic adjustment request processing flow, an intelligent balance can be achieved between responding to sudden demands and maintaining the stability of the overall plan. Adjustment requests are categorized based on factors such as order urgency and customer level, with different allowed adjustment depths set for each level. Ordinary requests can only be adjusted without affecting the order of other goods; higher-level requests have the authority to make more in-depth adjustments to the order of more preceding goods, enabling differentiated resource allocation strategies. This ensures that high-priority demands are strongly guaranteed while preventing low-priority requests from causing unnecessary disruption to the overall delivery plan. This reflects the organic combination of management wisdom and technology, improving the efficiency of cold chain delivery. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Fig. 1 This is a schematic diagram of the method flow of the present invention; Fig. 2 This is a system structure block diagram of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example Please see Figs. 1-2 This invention provides a method and system technical solution for dynamic route planning in cold chain distribution: a method for dynamic route planning in cold chain distribution includes the following steps: S1: Acquire and record the three-dimensional spatial coordinates, three-dimensional dimensions, and loading completion timestamp sequence of each cargo unit within the vehicle cargo compartment; The steps for acquiring and recording the three-dimensional spatial coordinates and three-dimensional dimensions of each cargo unit within the vehicle cargo compartment include: during the loading operation, performing a three-dimensional contour scan on each cargo unit loaded into the cargo compartment using multiple monitoring points fixed to the top corner or side wall inside the cargo compartment; assigning a unique identifier to each scanned cargo unit and calculating its minimum bounding cuboid; defining the geometric center coordinates of the minimum bounding cuboid as the three-dimensional spatial coordinates of the cargo unit, and defining the length, width, and height of the minimum bounding cuboid as the three-dimensional dimensions of the cargo unit; and storing the unique identifier, three-dimensional spatial coordinates, three-dimensional dimensions, and the system timestamp when the scan of the cargo unit is completed in the loading database. Specifically, the monitoring points can be depth cameras and / or lidar scanners. Multiple three-dimensional sensing devices (e.g., four depth cameras based on the time-of-flight (ToF) principle, deployed at the four corners of the top of the cargo compartment, can cover the entire interior space of the cargo compartment) are fixedly installed on the top corners and side walls inside the cargo compartment to perform synchronous three-dimensional scanning of the stacking status of the goods inside the cargo compartment. After acquiring point cloud data of the cargo compartment, the 3D sensing device group transmits it to the edge computing module (which can be integrated into the vehicle terminal). The edge computing module runs point cloud segmentation and target recognition algorithms to cluster the point cloud into individual cargo units. For each identified cargo unit, the system performs the following operations: Assign a unique identifier: This identifier can be bound to the cargo's tracking number or RFID tag information. Calculate the minimum bounding box: Using principal component analysis (PCA) or axial bounding box (AABB) algorithms, a minimum cuboid that can completely enclose the cargo point cloud and whose sides are parallel to the axes of the cargo compartment coordinate system is fitted. The geometric center coordinates (x, y, z) of this cuboid are defined as the three-dimensional spatial coordinates of the cargo in the cargo compartment coordinate system (with the center of the cargo compartment loading / unloading hatch floor as the origin, the X-axis pointing to the right side of the cargo compartment, the Y-axis pointing to the front of the cargo compartment, and the Z-axis pointing vertically upwards). The length L, width W, and height H of this cuboid are defined as the three-dimensional dimensions of the cargo. Record a timestamp: Record the moment when the cargo unit is scanned and entered into the system, as an item in the timestamp sequence of loading completion. Each cargo unit's unique identifier, 3D spatial coordinates, 3D dimensions, timestamp, vehicle identification, and batch number are structurally stored in the loading status database, forming a complete loading snapshot. Upon completion of loading, multiple depth cameras / LiDARs deployed inside the cargo compartment perform a 3D scan of each cargo unit, calculating its minimum bounding cuboid spatial coordinates and dimensions, and recording the loading time sequence. This transforms the ambiguous "cargo stacking" state into a precise digital model, providing an accurate and objective data foundation for subsequent analysis and avoiding subjective errors and omissions from manual judgment.

[0019] S2: Based on three-dimensional spatial coordinates and three-dimensional dimension data, construct a loading dependency graph for cargo units; The steps for constructing a loading dependency graph of cargo units based on three-dimensional spatial coordinates and three-dimensional dimension data include: reconstructing the stacking and arrangement state model of cargo units in the cargo compartment in a three-dimensional spatial coordinate system based on the three-dimensional spatial coordinates and three-dimensional dimensions of all cargo units; generating virtual take-out radial lines along the depth direction of the cargo compartment with the opening plane of the cargo compartment door as the reference plane; simulating a straight-line movement path directly taken out from the cargo compartment door for any cargo unit B; determining whether there are entities of other cargo units A occupying the path space on the virtual take-out radial line; if so, determining that cargo unit A occupies cargo unit B; and establishing directed edges in the loading dependency graph for all cargo unit pairs with the occlusion relationship, where the node corresponding to the occluding object is the starting point of the directed edge, and the node corresponding to the occluded object is the ending point of the directed edge. Specifically, the nodes in the loading dependency graph represent cargo units, and the directed edges represent the spatial occlusion relationships between cargo units; if cargo unit A physically blocks the direct pick-up and drop-off path of cargo unit B from the cargo compartment door, then a directed edge from node A to node B is generated in the loading dependency graph. Based on the "loading snapshot" obtained in step S101, a directed graph describing the physical occlusion relationships between goods, i.e., a loading dependency graph, is constructed at the logical level. The construction process is as follows: Spatial Relationship Modeling: In memory, based on the 3D spatial coordinates and dimensions of all goods, a cargo stacking model is reconstructed within a virtual 3D carriage coordinate system. Accessibility Path Simulation: An "accessibility determination rule" is defined. Taking the most common rear-opening carriage as an example, the system's determination rule is: assuming a virtual ray (straight line) is emitted from the center point of the carriage's loading / unloading port, pointing towards the geometric center of the target cargo. This ray represents the ideal path for unobstructed direct retrieval of the cargo from the doorway. Occlusion Relationship Determination: For any two different goods A and B, the system simulates a virtual ray from the loading / unloading port to cargo B. Then, it checks whether this ray path interferes with (i.e., intersects or penetrates) the 3D circumscribed cuboid model of cargo A in 3D space. If interference occurs, cargo A is determined to physically obstruct cargo B. This means that cargo B cannot be directly retrieved without first removing cargo A. Graph Structure Construction: Based on the above determinations, an empty directed graph is initialized. A graph node is created for each cargo unit in the database, with the node ID being its unique identifier. For each pair of goods with an "A occludes B" relationship, a directed edge is added to the graph, pointing from the node representing goods A to the node representing goods B. Once this graph is constructed, its topological structure precisely encodes the physical access dependencies of all goods within the cargo compartment. Any directed path from node X to node Y in the graph means that, physically, X (and its other preceding nodes on the path) must be removed first before Y can be safely removed. Using the cargo compartment door as a reference, a virtual ray collision detection algorithm is used to determine the "occlusion-occlusion" relationship between any two goods, and a directed graph is constructed based on this. This graph encodes the physical principle that "A must be moved before B can be retrieved" as a "directed edge from node A to node B." By setting a mechanism for constructing and maintaining the loading dependency graph, the physical access order constraints can be transformed into computable and reasonable graph theory topological constraints.

[0020] S3: Receive and process dynamic adjustment requests in real time; the dynamic adjustment request shall at least include the target cargo unit identifier whose delivery order needs to be adjusted and the adjusted expected delivery time window; During the delivery task execution, the system receives dynamic adjustment requests from the dispatch center or client in real time via a communication module (such as 4G / 5G). This request must contain at least the following core fields: request_id: Unique identifier for the request; vehicle_id: Identifier for the target vehicle; target_goods_id: Identifier for the target goods unit whose delivery order needs to be adjusted; new_time_window: Expected delivery time window after adjustment [earliest, latest]; priority_level (optional): Identifier of the urgency level of the request; S4: Based on the received dynamic adjustment request, retrieve the loading dependency graph, identify all cargo units corresponding to nodes in the loading dependency graph that have directed edges that directly or indirectly point to the target cargo unit, and define the cargo unit as the set of the target cargo unit's preceding dependent units. The process also includes a hierarchical adjustment request step involving real-time reception and processing of dynamic adjustment requests, retrieval of the loading dependency graph based on the received dynamic adjustment requests, identification of all cargo units corresponding to nodes in the loading dependency graph that have directed edges directly or indirectly pointing to the target cargo unit, and defining the cargo unit as the set of preceding dependent units of the target cargo unit. This step involves analyzing the urgency parameter of the dynamic adjustment request, which is derived from at least one of order priority, customer level, and remaining shelf life of the goods; dividing the dynamic adjustment request into multiple adjustment levels according to a preset urgency threshold; associating each adjustment level with an allowed adjustment depth parameter; the allowed adjustment depth parameter is defined in the loading dependency graph as the maximum hierarchical depth at which the order of preceding dependent units needs to be adjusted in order for the target cargo unit; and when generating a path adjustment scheme, only solutions where the depth of the cargo units involved in the adjustment order in the loading dependency graph does not exceed the allowed adjustment depth of their corresponding adjustment level are considered. The adjustment levels include at least a first level and a second level. The first level allows an adjustment depth of 0 layers, and its path adjustment scheme only allows checking whether the target cargo unit is already in an accessible state and can be delivered immediately without changing the original delivery order of any other cargo units. If not, the adjustment request for this level is rejected or it is upgraded. The second level allows an adjustment depth of N layers, where N is a positive integer. Its path adjustment scheme allows adjusting the delivery order of the target cargo unit and its predecessor dependent units with a depth not exceeding N layers in the loading dependency graph, but the adjustment process must strictly comply with the topological order constraints derived from the loading dependency graph. Specifically, before generating a specific adjustment plan, a preprocessing step is performed to balance adjustment needs with operational costs: Adjustment request grading: The `priority_level` in the request is parsed, or the urgency level is automatically calculated based on order information associated with `target_goods_id` (such as customer level, whether the goods are special medicines like vaccines). Based on preset thresholds, requests are divided into multiple adjustment levels (e.g., Level 1: Standard Expedited; Level 2: VIP Expedited; Level 3: Emergency Medical Supplies). Determining the allowed adjustment depth: Each adjustment level is associated with a preset "allowed adjustment depth" parameter `D`. `D=0` means only the target goods themselves are allowed to be adjusted, and the order of any other goods is not allowed to be disturbed; `D=1` means the target goods and all their directly preceding goods (i.e., nodes directly pointing to them in the graph) are allowed to be adjusted; `D=2` means two levels of dependency relationships are allowed to be adjusted, and so on. Retrieving the preceding dependency set: Based on `vehicle_id` and the current task batch number, the corresponding loading dependency graph is retrieved from memory or cache. Starting from the target goods node, the directed graph is traversed in reverse, collecting all nodes that can be reached by directed paths. The goods represented by these nodes constitute the complete set of preceding dependent units for the target goods. Set filtering: Based on the allowable adjustment depth D corresponding to the current request level, a subset of nodes with a dependency depth (i.e., the longest directed path length from the current node to the target node) less than or equal to D is selected from the complete set of preceding dependent units. This subset forms the set of preceding dependent units that can be changed in this adjustment. Goods represented by nodes with a depth exceeding D have their delivery order locked in this round of adjustment and cannot be changed. By introducing a matching rule between adjustment level and allowable adjustment depth in the dynamic adjustment request processing flow, an intelligent balance can be achieved between responding to sudden demands and maintaining the stability of the overall plan. Adjustment requests are categorized based on factors such as order urgency and customer level, and different "allowable adjustment depths" are set for each level. Ordinary requests can only be adjusted without affecting the order of other goods; higher-level requests have the authority to "deeper" the order of more preceding goods. This setting implements a differentiated resource allocation strategy, ensuring that high-priority demands are strongly guaranteed while preventing low-priority requests from causing unnecessary disturbances to the overall delivery plan, reflecting the organic combination of management wisdom and technology. S5: Generate a route adjustment plan; the generation of the route adjustment plan must meet the following hard constraint: in the adjusted delivery sequence, the delivery order of each cargo unit in the set of preceding dependent units is earlier than the delivery order of the target cargo unit. Hard constraints are implemented in the path planning algorithm as follows: the loading dependency graph is transformed into a topological sorting constraint in the form of a directed acyclic graph; when solving the order sequence of the adjusted delivery routes, the topological sorting constraint is embedded as a prerequisite condition that must be followed into the cost function or constraint set of the path planning; any path sequence that causes the delivery order of the target cargo unit to be earlier than the delivery order of any of its preceding dependent units has its cost function value set to infinity or is directly filtered out by the algorithm; Specifically, the target goods, the set of allowed dependencies, the new time window, and the list of remaining delivery points for the current vehicle, its geographical location, and traffic information are all input into a constrained path planning algorithm (e.g., a variant of the vehicle routing problem with time windows and priority constraints). Hard constraint modeling: The algorithm formally defines the physical constraints expressed by the dependency graph as hard constraints for the planning problem. Specifically, it transforms the allowed goods nodes (including the target node and selected predecessor nodes) and their partial order relationships in the original dependency graph into a series of order constraint pairs. For example, if the graph shows "goods P -> goods Q", the generated hard constraint is: "The delivery order of goods P must be earlier than the delivery order of goods Q". Solution finding: When searching for a new delivery sequence (i.e., the order in which delivery points are visited), the algorithm must strictly satisfy all the above order constraint pairs. Any candidate sequence that violates these constraints is immediately excluded. Under the premise of satisfying all hard constraints, the algorithm aims to find the optimal or near-optimal new delivery path sequence with the shortest total mileage, the highest time window satisfaction, and the fastest emergency request response. Solution Evaluation and Output: The solved new sequence, along with the estimated arrival time at each point, constitutes a preliminary route adjustment plan. The system evaluates whether the plan meets the latest time window requirements for all delivery points (including unadjusted goods). By setting the topological sorting constraint derived from the loading dependency graph as a hard prerequisite for the route planning algorithm, it fundamentally prevents the generation of physically unexecutable delivery instructions. During the route planning process, any candidate delivery sequence must satisfy the partial order relationship specified by the dependency graph. Any sequence that attempts to deliver the obscured goods before the obscured goods will be automatically deemed invalid and eliminated by the algorithm. This hard constraint ensures that every adjustment instruction ultimately issued to the vehicle is completely consistent in both logical order and physical operation, effectively resolving the contradiction of "planning is feasible, but operation is infeasible."

[0021] The process also includes a reconfiguration point planning step when a route adjustment plan that meets the hard constraints and is within the delivery time window cannot be generated: identifying a set of critical conflict cargo units that prevent the generation of a feasible plan; the set of critical conflict cargo units includes the target cargo unit and some or all of its preceding dependent units; retrieving from a pre-stored reconfiguration point map database sites within a preset distance range around the current location that have the conditions for safe parking and cargo rearrangement as candidate reconfiguration points; calculating the total travel time and cost for the current vehicle to travel to the candidate reconfiguration point, complete the physical relocation of the set of critical conflict cargo units at the reconfiguration point, and then travel to each delivery point; the goal of the physical relocation is to change the loading dependency graph so that the new dependency can support the required sequence adjustment; if the total travel time meets the latest time window requirements of all delivery points, a hybrid route adjustment plan including "travel to the reconfiguration point - perform relocation - continue delivery" is generated; The steps for completing the physical location rearrangement of the critical conflict cargo unit set include: at the reconfiguration point, based on the optimal cargo stacking layout pre-calculated to support the new delivery sequence, instructing operators or automated loading and unloading equipment to unload, temporarily store, and reload the cargo in the critical conflict cargo unit set; after reloading is completed, updating the loading database and reconstructing the loading dependency graph to reflect the physical accessibility status after rearrangement. Specifically, when a feasible path solution that meets the new time window cannot be found within the allowed adjustment depth D, it indicates that adjusting the logical sequence alone cannot meet the current urgent needs. At this point, the system will initiate the "Safe Reconfiguration Point" emergency planning process: Conflict Analysis: The system identifies a set of key conflicting cargo units that lead to an unsolvable problem. This typically includes the target cargo, as well as a few preceding cargoes whose dependency depth is slightly greater than D and whose "locked" state renders the solution infeasible. Reconfiguration Point Retrieval: The system accesses a pre-stored safe reconfiguration point geographic database. This database contains site information that meets the following criteria: located within the urban delivery network, possessing legal parking permits, having sufficient flat space for cargo temporary storage and rearrangement, and having a safe environment (e.g., equipped with surveillance). The system searches with the vehicle's current location as the center and a preset "acceptable detour radius" (e.g., 5 kilometers) to obtain several candidate reconfiguration points. Hybrid Path Planning: For each candidate reconfiguration point R, the system plans a hybrid path: current location -> reconfiguration point R -> all subsequent delivery points. During planning, an estimated cargo rearrangement operation time T_rearrange is added for the stop at point R. T_rearrange estimates the quantity and volume of goods based on the key conflict set and the preset manual / mechanical loading and unloading efficiency. Solution generation and selection: The system calculates the total travel time for each hybrid route and checks whether it meets the time windows of all delivery points (especially after the target goods are adjusted). It also calculates additional fuel consumption, time costs, etc. The system selects the most cost-effective solution from all feasible hybrid routes. Rearrangement guidance: When the vehicle arrives at the selected safe relocation point, the system provides detailed cargo rearrangement guidance instructions via the in-vehicle screen or the delivery person's mobile app. For example: "Please first unload goods [ID: 123] and [ID: 456] and temporarily place them in the designated area; then move goods [ID: 789] to the front of the vehicle; finally, reload goods [ID: 123] and [ID: 456] to location X." This set of instructions is the optimal loading layout pre-calculated by the system based on the new expected delivery order. State Reset: After the rearrangement is completed, the delivery person scans the rearranged goods using a handheld terminal, or the vehicle automatically starts a new round of 3D scanning (as in step S101). Based on the new scan data, the system resets the loading status database and reconstructs the loading dependency graph. Subsequently, based on the new loading status that supports priority delivery of the target goods, the vehicle continues to execute the subsequent delivery route planned in step 3. By setting up emergency planning and guidance execution processes for safe reconfiguration points, it can provide an ultimate solution that transcends the constraints of the vehicle's internal system for extreme emergencies, improving business coverage flexibility. When the constraints of the vehicle's internal system cannot meet the needs of emergency adjustments, it proactively plans to go to a preset reconfiguration point with safe operating conditions and generates a hybrid route plan that includes driving, rearranging, and continuing delivery. At the reconfiguration point, it provides specific visual guidance for unloading, temporarily storing, and reloading goods.This setup breaks through the static thinking of "one vehicle, one load" and incorporates "fixed site rearrangement" into the scope of dynamic resource scheduling. This enables the system to handle extremely complex scenarios that would inevitably lead to order rejection by traditional methods, thus expanding the capability boundaries of the intelligent logistics system.

[0022] S6: Execute the path adjustment scheme that satisfies the hard constraint conditions, and control the delivery vehicles to go to each delivery point in the adjusted order; It also includes the step of updating the loading dependency graph after executing the path adjustment scheme in step S6: when a cargo unit is successfully delivered and unloaded from the cargo compartment, the node corresponding to the cargo unit is deleted from the loading dependency graph, and all directed edges with that node as the start or end point are deleted; for the remaining cargo units in the cargo compartment, the occlusion relationships between them are re-evaluated or locally updated according to their current actual three-dimensional spatial relationships, forming a new loading dependency graph that reflects the current accessibility status of the remaining cargo in the cargo compartment; the updated loading dependency graph is used to handle subsequent dynamic adjustment requests that may occur. Specifically, if the generated solution is feasible, it is sent to the vehicle's navigation system, and the driver or autonomous driving system executes the delivery according to the new route and sequence. Dynamic updates to loading status: This is a crucial feedback loop. When a vehicle arrives at a delivery point and successfully unloads a cargo unit from the cargo compartment, sensors on the vehicle (or confirmation by the delivery person via a handheld terminal) send a "goods delivered" signal to the system. The system then performs the following operations: In the current loading dependency graph, delete the node corresponding to the delivered goods. Delete all directed edges originating from or ending at that node. Due to the removal of a cargo, the spatial relationships of the remaining goods in the cargo compartment may change (for example, removing an obstruction may make previously obscured goods directly accessible). The system can invoke a lightweight local update algorithm: only perform the "accessibility path simulation" in step S102 on nodes adjacent to the original node (i.e., goods previously obscured by it or obscuring it) to update the edge relationships between them. This results in an updated loading dependency graph that accurately reflects the current accessibility status of the remaining goods in the cargo compartment. This updated graph will be used to handle the next possible dynamic adjustment request, forming a closed loop of "planning-execution-state update-replanning." This ensures that the system's perception remains consistent with physical reality throughout the delivery process. By setting up a closed-loop feedback mechanism that dynamically updates the loading dependency graph after each delivery, the system's cognitive model and the actual physical state of the cargo compartment are kept synchronized in real time. Whenever goods are unloaded, the corresponding node and related edges are immediately removed from the graph, and the accessibility of goods in the affected area is reassessed. This makes the dependency graph a dynamically evolving "living model," rather than a one-time static snapshot. It ensures that every subsequent dynamic adjustment decision is based on the most accurate current accessibility state, avoiding decision-making errors caused by outdated information.

[0023] A cold chain delivery dynamic route planning system, applied to any one of the above-described cold chain delivery dynamic route planning methods, includes: The data acquisition module is used to acquire and record the three-dimensional spatial coordinates, three-dimensional dimensions, and loading completion timestamp sequence of each cargo unit within the vehicle cargo compartment; The building module is used to construct a loading dependency graph of cargo units based on three-dimensional spatial coordinates and three-dimensional dimension data; The processing module is used to receive and process dynamic adjustment requests in real time; the dynamic adjustment request includes at least the target cargo unit identifier whose delivery order needs to be adjusted and the adjusted expected delivery time window; The analysis module is used to retrieve the loading dependency graph based on the received dynamic adjustment request, identify the cargo units corresponding to all nodes in the loading dependency graph that have directed edges that directly or indirectly point to the target cargo unit, and define the cargo units as the set of the preceding dependent units of the target cargo unit. The route update module is used to generate a route adjustment plan; the generation of the route adjustment plan must meet the following hard constraint: in the adjusted delivery sequence, the delivery order of each cargo unit in the set of preceding dependent units is earlier than the delivery order of the target cargo unit. The delivery module is used to execute a route adjustment scheme that satisfies the hard constraints, and to control the delivery vehicles to go to each delivery point in the adjusted order.

[0024] This invention achieves differentiated and refined responses to requests of varying urgency levels by setting a matching correspondence between adjustment levels and allowed adjustment depths. It strikes an optimal balance between meeting urgent needs and maintaining overall plan stability. By setting safety reconfiguration points, it provides a last resort for dealing with extreme emergencies, expanding the capabilities and application flexibility of the dynamic path planning system. Dynamically updating the loading dependency graph after delivery ensures real-time synchronization between the cognitive model and the physical world, providing an accurate foundation for continuous dynamic decision-making. This avoids vehicle waiting, plan rework, and high-risk roadside refurbishment caused by invalid adjustment instructions, reducing time waste, cargo damage, and safety hazards, thereby improving the overall operational efficiency and service quality of cold chain logistics. After loading, a "loading dependency graph" reflecting the spatial occlusion relationships between goods is established and continuously maintained, and the physical constraints expressed in this graph serve as inviolable "hard constraints" for the dynamic path adjustment algorithm. By transforming the access order problem in the physical world into a graph-theoretic topological sorting problem in the digital world, we can achieve self-verification of the physical feasibility of path planning. This method considers the physical accessibility of goods within the carriage as a core constraint to ensure that any adjustment instructions generated are both logically and physically executable.

[0025] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0026] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A dynamic route planning method for cold chain distribution, characterized in that, Includes the following steps: Acquire and record the three-dimensional spatial coordinates, three-dimensional dimensions, and loading completion timestamp sequence of each cargo unit within the vehicle cargo compartment; Based on three-dimensional spatial coordinates and three-dimensional dimension data, a loading dependency graph of cargo units is constructed; Receive and process dynamic adjustment requests in real time; a dynamic adjustment request must include at least the target cargo unit identifier whose delivery order needs to be adjusted and the adjusted expected delivery time window. Based on the received dynamic adjustment request, retrieve the loading dependency graph, identify all cargo units corresponding to nodes in the loading dependency graph that have directed edges that directly or indirectly point to the target cargo unit, and define the cargo unit as the set of the target cargo unit's preceding dependent units. Generate a route adjustment plan; the generation of the route adjustment plan must meet the following hard constraint: in the adjusted delivery sequence, the delivery order of each cargo unit in the set of preceding dependent units is earlier than the delivery order of the target cargo unit.

2. The dynamic route planning method for cold chain distribution according to claim 1, characterized in that: The steps of acquiring and recording the three-dimensional spatial coordinates and three-dimensional dimensions of each cargo unit within the vehicle cargo compartment include: During the loading process, a three-dimensional contour scan is performed on each cargo unit loaded into the cargo compartment by using multiple monitoring points fixed to the top corner or side wall inside the cargo compartment. Assign a unique identifier to each scanned and identified cargo unit, and calculate the minimum bounding cube; the geometric center coordinates of the minimum bounding cube are defined as the three-dimensional spatial coordinates of the cargo unit, and the length, width, and height of the minimum bounding cube are defined as the three-dimensional dimensions of the cargo unit; The unique identifier, three-dimensional spatial coordinates, three-dimensional dimensions, and system timestamp of the completed scan of each cargo unit are associated and stored in the loading database.

3. The dynamic route planning method for cold chain distribution according to claim 1, characterized in that: The step of constructing a loading dependency graph of cargo units based on three-dimensional spatial coordinates and three-dimensional dimension data includes: Based on the three-dimensional spatial coordinates and three-dimensional dimensions of all cargo units, a model of the stacking and arrangement of cargo units within the cargo compartment is reconstructed in a three-dimensional spatial coordinate system. Using the opening plane of the cargo door as the reference plane, a virtual radial line is generated along the depth direction of the cargo box; for any cargo unit B, the straight-line movement path directly taken out from the cargo door is simulated. Determine whether there are other cargo unit A entities occupying the path space on the virtual radial path; If it exists, then it is determined that cargo unit A occludes cargo unit B; for all cargo unit pairs with occlusion relationship, a directed edge is established in the loading dependency graph, where the node corresponding to the occluding object is the starting point of the directed edge, and the node corresponding to the occluded object is the ending point of the directed edge.

4. The dynamic route planning method for cold chain distribution according to claim 1, characterized in that: The process also includes a hierarchical adjustment request execution step between receiving and processing dynamic adjustment requests in real time and, based on the received dynamic adjustment requests, retrieving the loading dependency graph, identifying all cargo units corresponding to nodes in the loading dependency graph that have directed edges directly or indirectly pointing to the target cargo unit, and defining the cargo unit as the set of preceding dependent units of the target cargo unit: Analyze the urgency parameters of dynamic adjustment requests; classify dynamic adjustment requests into multiple adjustment levels based on preset urgency thresholds; Each adjustment level is associated with an adjustment depth parameter; the adjustment depth parameter is defined in the loading dependency graph and is the maximum level depth at which the order of its preceding dependent units needs to be adjusted along with the order of the target cargo unit when the order of the target cargo unit is adjusted. When generating path adjustment schemes, only solutions are considered where the depth of the cargo units involved in the adjustment sequence in the loading dependency graph does not exceed the allowable adjustment depth of their corresponding adjustment level.

5. The dynamic route planning method for cold chain distribution according to claim 4, characterized in that: The adjustment levels include at least a first level and a second level; the first level corresponds to an allowable adjustment depth of 0 layers, and its path adjustment scheme only allows checking whether the target cargo unit is already in an accessible state and can be delivered immediately without changing the original delivery order of any other cargo units; if not, the adjustment request for this level is rejected or it is upgraded; the second level corresponds to an allowable adjustment depth of N layers, where N is a positive integer. Its path adjustment scheme allows for the adjustment of the delivery order of the target cargo unit and its predecessor dependent units at a depth of no more than N layers in the loading dependency graph, but the adjustment process must strictly comply with the topological order constraints derived from the loading dependency graph.

6. The dynamic route planning method for cold chain distribution according to claim 5, characterized in that: This also includes performing a reconfiguration point planning step when a route adjustment plan that meets the hard constraints and is within the delivery time window cannot be generated: Identify the set of critical conflict cargo units that prevents the generation of a feasible solution; the set of critical conflict cargo units includes the target cargo unit and some or all of its preceding dependent units. From the pre-stored reconstruction point map database, retrieve sites within a preset distance range around the current location that have the conditions for safe parking and cargo rearrangement as candidate reconstruction points; Calculate the total travel time and cost of the current vehicle traveling to the candidate reconfiguration point, completing the physical relocation of the critical conflict cargo unit set at the reconfiguration point, and then traveling to each delivery point; If the total travel time meets the latest time window requirements for all delivery points, a hybrid route adjustment scheme is generated, which includes going to the reconfiguration point, performing reordering, and continuing delivery.

7. The dynamic route planning method for cold chain distribution according to claim 6, characterized in that: The steps for completing the physical location rearrangement of the critical conflict cargo unit set include: At the reconfiguration point, based on the optimal cargo stacking layout pre-calculated to support the new delivery sequence, the cargo in the critical conflict cargo unit set is unloaded, temporarily stored, and reloaded. After reloading is complete, update the load database and rebuild the load dependency graph.

8. The dynamic route planning method for cold chain distribution according to claim 7, characterized in that: This also includes the step of updating the load dependency graph after implementing the path adjustment scheme: Once a cargo unit is successfully delivered and unloaded from the cargo container, delete the node corresponding to that cargo unit in the loading dependency graph, and delete all directed edges that start or end with that node. For the remaining cargo units in the cargo compartment, based on their current actual three-dimensional spatial relationships, their mutual occlusion relationships are reassessed or partially updated to form an updated loading dependency relationship diagram.

9. A cold chain distribution dynamic route planning system, applied to the cold chain distribution dynamic route planning method as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire and record the three-dimensional spatial coordinates, three-dimensional dimensions, and loading completion timestamp sequence of each cargo unit within the vehicle cargo compartment; The building module is used to construct a loading dependency graph of cargo units based on three-dimensional spatial coordinates and three-dimensional dimension data; The processing module is used to receive and process dynamic adjustment requests in real time; the dynamic adjustment request includes at least the target cargo unit identifier whose delivery order needs to be adjusted and the adjusted expected delivery time window; The analysis module is used to retrieve the loading dependency graph based on the received dynamic adjustment request, identify the cargo units corresponding to all nodes in the loading dependency graph that have directed edges that directly or indirectly point to the target cargo unit, and define the cargo units as the set of the preceding dependent units of the target cargo unit. The route update module is used to generate a route adjustment plan; the generation of the route adjustment plan must meet the following hard constraint: in the adjusted delivery sequence, the delivery order of each cargo unit in the set of preceding dependent units is earlier than the delivery order of the target cargo unit. The delivery module is used to execute a route adjustment scheme that satisfies the hard constraints, and to control the delivery vehicles to go to each delivery point in the adjusted order.

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