Global material management and control platform based on digital twin technology

Abstract

The application discloses a global material management and control platform based on digital twin technology, and relates to the technical field of material management and control.The platform comprises a window construction module, an opportunity scoring module and a conflict resolution module.The window construction module is used for constructing a digital twin of an in-transit vehicle, generating a pre-return trajectory chain and delimiting an opportunity search window.The opportunity scoring module filters demand to be allocated through double verification and calculates a matching score by using a multi-dimensional spatiotemporal opportunity matching degree function.The conflict resolution module completes demand matching and conflict resolution, and forms a mutually exclusive demand sequence.The application can effectively activate return idle capacity, avoid scheduling conflicts, and realize efficient global material management and control and global benefit optimization.

Description

Technical Field

[0001] This invention relates to the field of materials management technology, specifically a comprehensive materials management platform based on digital twin technology. Background Technology

[0002] The existing nationwide logistics network is beginning to develop towards large-scale operations, and the requirements for timeliness and economy in cross-regional material allocation are continuously increasing. However, the existing nationwide material management still has the following shortcomings:

[0003] Firstly, existing comprehensive material management technologies lack the ability to proactively utilize the return empty-running capacity of transport vehicles in transit, and cannot construct the opportunity retrieval boundary of return routes in advance based on the real-time operating status of the vehicles, resulting in a large amount of idle and wasted return transport capacity resources.

[0004] Secondly, existing technologies are unable to accurately and in real time digitally map the physical boundaries, special transport capabilities, and dynamic load status of transport vehicles, and cannot construct standardized transport capacity profiles. This results in a lack of rigorous compatibility and constraint verification between material demand and vehicle capacity, leading to frequent supply-demand mismatch problems.

[0005] Third, the existing multi-vehicle, multi-demand global dispatch model lacks a conflict resolution mechanism, which easily leads to the problem of demand competition and makes it difficult to balance the urgency of material allocation, capacity utilization, and time and space constraints.

[0006] Therefore, there is an urgent need for a comprehensive material management platform based on digital twin technology. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a comprehensive material management platform based on digital twin technology, which solves the problems of idle return transport capacity and imbalance between supply and demand in existing comprehensive material management systems.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a comprehensive material management platform based on digital twin technology, comprising:

[0009] The window construction module acquires the real-time location coordinates, destination coordinates, planned release time, and vehicle attribute parameters of all vehicles in transit. This allows for the construction of a digital twin containing a transportation capacity profile for each vehicle in transit. When the digital twin detects that the remaining travel time of a vehicle is less than a preset empty return activation threshold, it generates a pre-return trajectory chain corresponding to that vehicle based on Map as a Service and real-time traffic data. This pre-return trajectory chain radiates outwards at preset distances to both sides, forming a path radiation zone, which serves as the opportunity retrieval window for that vehicle in transit. The vehicle attribute parameters consist of physical boundary parameters, special capability parameters, and current status parameters. The physical boundary parameters include the vehicle's rated load capacity, total internal volume of the cargo compartment, and internal dimensions of the cargo compartment. The special capability parameters include a cargo transportation permit category code table, temperature control range for each temperature zone, and loading / unloading equipment type table. The current status parameters include the currently loaded weight, occupied volume, and current temperature zone usage.

[0010] The opportunity scoring module extracts the original attributes of each request to be allocated within the opportunity retrieval window, and performs a dual verification of spatial compatibility and capacity constraints between the original attributes and the transportation capacity profile of the digital twin. For requests to be allocated that pass the dual verification, the multidimensional spatiotemporal opportunity matching degree function is called to calculate their matching score.

[0011] The conflict resolution module selects the request with the highest matching score from all the requests to be transferred for the same vehicle in transit that have passed dual verification, and obtains the request sequence corresponding to all vehicles in transit. If the same request to be transferred appears in the request sequence, the allocation of the request to be transferred is determined according to the matching score, and finally a mutually exclusive request sequence is formed and transmitted to the global control platform.

[0012] As a further aspect of the present invention, the specific operation of constructing a digital twin containing a transportation capacity profile for each vehicle in transit is as follows:

[0013] Obtain the vehicle identification code of each vehicle on the road, use the physical boundary parameters as the physical capability dimension, the special capability parameters as the special capability dimension, and the current state parameters as the dynamic load dimension, combine them into a capability description matrix, and write this capability description matrix as the initial static attribute of the digital twin.

[0014] An array of resistance strain gauge weighing sensors is installed at the load-bearing position at the bottom of the vehicle compartment, an array of infrared time-of-flight sensors is installed on the roof inside the compartment, and a data preprocessing module is deployed in the vehicle terminal.

[0015] Meanwhile, the vehicle terminal reads the vehicle's mileage accumulation and fuel consumption rate through the controller local area network bus, and calculates the remaining driving range of the vehicle to its current destination by combining the real-time positioning coordinates output by the global navigation satellite system.

[0016] The data obtained above is transmitted back to the data processing interface of the digital twin via the vehicle communication unit.

[0017] As a further aspect of the present invention, the specific operation for constructing the pre-return trajectory chain is as follows:

[0018] Extract the destination coordinates and home coordinates of the vehicle in transit, retrieve the vector road network data covering the area between the two coordinates, and enumerate all connected paths in the vector road network with the destination coordinates as the starting point and the home coordinates as the ending point to form an initial path set.

[0019] Based on real-time traffic data, impassable paths are removed from the initial path set, and the total mileage of each remaining path is calculated. The path with the shortest total mileage is selected as the pre-return trajectory chain, and the planned release time of the vehicle in transit is simultaneously marked on the pre-return trajectory chain.

[0020] As a further aspect of the present invention, the specific operation of removing impassable paths from the initial path set is as follows: obtain the status identifier of each road segment in the initial path set and the expected end time of the corresponding event from real-time traffic data; for road segments with the status identifier "road closed", they are directly determined to be impassable paths and removed; for road segments with the status identifier "temporary construction" or "traffic accident", if the expected end time of the event is later than the sum of the planned release time of the vehicle in transit and the preset waiting time, the road segment is determined to be an impassable path and removed, otherwise it is retained.

[0021] As a further aspect of the present invention, the specific rules for forming the path radiation band are as follows:

[0022] Extract the coordinate sequence of continuous trajectory points in the pre-return trajectory chain, calculate the direction vector of the line segment formed by two adjacent trajectory points, and obtain the unit normal vector perpendicular to the direction vector.

[0023] At both ends of the line segment, offset by a preset distance in the direction of the normal vector and its opposite direction to obtain the corresponding points on both sides after offset, and connect each offset point in sequence to form an initial strip polygon;

[0024] For the intersection or breakage areas in the initial strip polygon caused by the trajectory chain bends, a polygon self-intersection repair algorithm is used to generate closed polygon regions without self-intersections, which are then used as the path radiation zone of the vehicle.

[0025] As a further aspect of the present invention, the original attributes include physical attributes, category attributes, location attributes, time constraints, and priority tags. The physical attributes include the volume, total weight, outer length, outer width, and outer height of the materials. The category attributes include the name, category code, temperature control range, and loading / unloading equipment type of the materials. The location attributes include the pickup coordinates and delivery coordinates. The time constraints include the timeliness requirement type, latest pickup time, and latest delivery time. The priority tag is the urgency weight value assigned to the demand in the global allocation plan.

[0026] As a further aspect of the present invention, the specific rules for performing space compatibility verification are as follows:

[0027] Extract physical attributes from the original attributes of the demand to be allocated, and read the remaining load value, net length, net width, and net height of the vehicle from the transport capacity profile of the digital twin of the vehicle in transit.

[0028] If the total weight of the demand to be transferred is greater than the remaining load value, the space compatibility check is deemed to have failed, and subsequent checks are terminated. If the total weight of the demand to be transferred is less than or equal to the remaining load value, the outer length, outer width, and outer height are compared with the net length, net width, and net height inside the cargo compartment, respectively. If any of the three parameters is greater than the corresponding cargo compartment constraint value, the space compatibility check is deemed to have failed; otherwise, the space compatibility check is deemed to have passed.

[0029] As a further aspect of the present invention, the specific rules for performing capability constraint verification are as follows: extract the category attribute from the original attributes of the demand to be allocated, and then read the special capability parameters of the vehicle from the transportation capability profile of the vehicle in transit, and execute them in the following three-level order: First level, determine whether the category code of the material falls into the cargo transportation permit category code table of the vehicle in transit; if it does not fall into the table, it fails; Second level, determine whether the temperature control range is completely covered by the temperature control range of at least one temperature zone; if not, it fails; Third level, determine whether the loading and unloading equipment type belongs to the loading and unloading equipment type table; if it does not belong, it fails; when all three levels are satisfied, the capability constraint verification is deemed to have passed.

[0030] As a further aspect of the present invention, the specific steps for calculating its matching score are as follows:

[0031] Extract the urgency weight value E from the original attributes of the demand to be allocated; read the shortest vertical distance D between the pickup coordinates of the demand to be allocated and the pre-return trajectory chain from the digital twin of the vehicle in transit; read the current remaining volume from the transportation capacity profile of the vehicle in transit, and calculate the ratio U of the volume of the demand to be allocated to the remaining volume; extract the latest pickup time from the original attributes of the demand to be allocated, and calculate the absolute value |△T| of the difference between the latest pickup time and the estimated time of arrival of the vehicle at the pickup point.

[0032] Substituting E, D, U, and |△T| into the multidimensional spatiotemporal chance matching function, the matching score S is calculated. The mathematical expression of the multidimensional spatiotemporal chance matching function is: ,in, This is the path offset attenuation coefficient. This is the time offset attenuation coefficient. This is the power exponent of utilization.

[0033] As a further aspect of the present invention, the specific operation of determining the ownership of the allocation request based on the matching score is as follows:

[0034] Extract all vehicles en route that are involved in the conflicting allocation request, calculate the opportunity cost value of each vehicle en route, compare the opportunity cost values ​​of each vehicle en route, and assign the conflicting allocation request to the vehicle en route with the highest opportunity cost. If there are multiple vehicles en route with the same highest opportunity cost, the one with the highest matching score is selected as the assignee.

[0035] The opportunity cost is equal to the matching score of the vehicle in transit for the conflicting allocation demand minus the score of the highest-scoring alternative demand in its demand sequence, excluding the conflicting allocation demand. If there is no alternative demand, the score is recorded as zero.

[0036] Once the attribution is determined, the on-the-way vehicle corresponding to the attribution is marked as adjudicated, and other demands in its demand sequence are released back to the global demand pool. For the remaining on-the-way vehicles that have not been adjudicated, the conflicting demand to be allocated is removed from their demand sequence. If there are still other demands to be allocated in the sequence, the demand with the highest score is automatically added as the new execution demand for the on-the-way vehicle. After the addition, a new round of conflict detection is triggered. If there are no other demands to be allocated in the demand sequence, the on-the-way vehicle exits the current round of return matching, and its digital twin is restored to the empty return standby state.

[0037] The new execution requirements generated by the supplement may overlap with the current execution requirements of other vehicles in transit within the entire domain. The system will automatically substitute the set of vehicles in transit corresponding to the new conflicting allocation requirements into the above-mentioned adjudication process, and perform opportunity cost comparison and attribution adjudication repeatedly until there is no situation in the entire domain's demand sequence where the same allocation requirement is listed as an execution requirement by multiple vehicles in transit at the same time.

[0038] This invention provides a comprehensive materials management platform based on digital twin technology, which has the following advantages compared with existing technologies:

[0039] (1) This invention constructs a digital twin of a vehicle in transit that includes a transportation capacity profile, thereby achieving a multi-dimensional digital and accurate mapping of the vehicle's transportation capacity and real-time status. Furthermore, by constructing a pre-return trajectory chain and a path radiation zone, it pre-defines the opportunity retrieval window for return transportation capacity, uncovers and activates idle return transportation capacity, and improves the utilization rate of logistics resources across the entire domain.

[0040] (2) This invention achieves pre-compliance screening of the demand to be allocated and the vehicle capacity through dual verification of spatial compatibility and capacity constraints. At the same time, it achieves accurate assessment of supply and demand matching through a multi-dimensional spatiotemporal opportunity matching function, effectively solving the problem of supply and demand mismatch.

[0041] (3) This invention uses an opportunity cost-based conflict resolution mechanism to make closed-loop attribution decisions for scenarios where multiple vehicles compete for the same demand, forming a globally mutually exclusive demand sequence, and effectively avoids scheduling conflicts and resource contention by using demand replenishment and cyclic conflict detection mechanisms. Attached Figure Description

[0042] Figure 1 This is the system principle block diagram of the present invention;

[0043] Figure 2 This is a flowchart illustrating the steps involved in calculating the matching score in this invention. Detailed Implementation

[0044] 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.

[0045] like Figure 1 This invention provides a comprehensive material management platform based on digital twin technology;

[0046] As an embodiment of this application, it includes:

[0047] The window construction module acquires the real-time location coordinates, destination coordinates, planned release time, and vehicle attribute parameters of all vehicles in transit. This allows for the construction of a digital twin containing a transportation capacity profile for each vehicle in transit. When the digital twin detects that the remaining travel time of a vehicle is less than a preset empty return activation threshold, it generates a pre-return trajectory chain corresponding to that vehicle based on Map as a Service and real-time traffic data. This pre-return trajectory chain radiates outwards at preset distances to both sides, forming a path radiation zone, which serves as the opportunity retrieval window for that vehicle in transit. The vehicle attribute parameters consist of physical boundary parameters, special capability parameters, and current status parameters. The physical boundary parameters include the vehicle's rated load capacity, total internal volume of the cargo compartment, and internal dimensions of the cargo compartment. The special capability parameters include a cargo transportation permit category code table, temperature control range for each temperature zone, and loading / unloading equipment type table. The current status parameters include the currently loaded weight, occupied volume, and current temperature zone usage.

[0048] The opportunity scoring module extracts the original attributes of each request to be allocated within the opportunity retrieval window, and performs a dual verification of spatial compatibility and capacity constraints between the original attributes and the transportation capacity profile of the digital twin. For requests to be allocated that pass the dual verification, the multidimensional spatiotemporal opportunity matching degree function is called to calculate their matching score.

[0049] The conflict resolution module selects the request with the highest matching score from all the requests to be transferred for the same vehicle in transit that have passed dual verification, and obtains the request sequence corresponding to all vehicles in transit. If the same request to be transferred appears in the request sequence, the allocation of the request to be transferred is determined according to the matching score, and finally a mutually exclusive request sequence is formed and transmitted to the global control platform.

[0050] As a second embodiment of this application, it is implemented based on the first embodiment, except that this embodiment includes:

[0051] The window construction module obtains the real-time location coordinates, destination coordinates, planned release time, and vehicle attribute parameters of all vehicles in transit, thereby constructing a digital twin of each vehicle in transit containing a transportation capacity profile.

[0052] The planned release time refers to when a vehicle currently performing a forward delivery task is expected to complete unloading. This value is specifically obtained by adding up the current time, the remaining travel time to the destination, and the historical average unloading time at the destination.

[0053] The remaining travel time is calculated based on the real-time location of the vehicle on the road, the destination coordinates, and the current average speed, while the historical average unloading time comes from the platform's long-term accumulated operational data statistics for the same destination or similar unloading points.

[0054] The vehicle attribute parameters consist of three categories: physical boundary parameters, special capability parameters, and current state parameters.

[0055] Physical boundary parameters include the vehicle's rated load capacity, total internal volume of the cargo compartment, and internal dimensions of the cargo compartment (including net length, net width, and net height).

[0056] Special capability parameters include a cargo transport permit category coding table, temperature control range for each temperature zone, and a loading and unloading equipment type table;

[0057] Current status parameters, including current loaded weight, occupied volume, and current temperature zone usage, are dynamic, meaning that each unloading will release a portion of the load and volume.

[0058] The specific operations for constructing a digital twin containing a transportation capacity profile for each vehicle in transit are as follows:

[0059] Obtain the vehicle identification code of each vehicle on the road, use the physical boundary parameters as the physical capability dimension, the special capability parameters as the special capability dimension, and the current state parameters as the dynamic load dimension, combine them into a capability description matrix, and write this capability description matrix as the initial static attribute of the digital twin.

[0060] In the capability description matrix, each field is stored in key-value pairs. The physical capability dimension records the upper limit constraint value with a numerical key value, the special capability dimension records the permission status or the presence or absence of the device with an enumerated key value, and the dynamic load dimension records the real-time collected values ​​of each parameter with a numerical key value.

[0061] An array of resistance strain gauge weighing sensors is installed at the load-bearing position at the bottom of the vehicle compartment, an array of infrared time-of-flight sensors is installed on the roof inside the compartment, and a data preprocessing module is deployed in the vehicle terminal.

[0062] The data preprocessing module collects the load values ​​of each axle output by the weighing sensor and the remaining space point cloud data inside the carriage output by the infrared time-of-flight sensor at fixed intervals.

[0063] Meanwhile, the vehicle terminal reads the vehicle's mileage accumulation and fuel consumption rate through the controller local area network bus, and calculates the remaining driving range of the vehicle to its current destination by combining the real-time positioning coordinates output by the global navigation satellite system.

[0064] The data obtained above is transmitted back to the data processing interface of the digital twin via the vehicle communication unit;

[0065] When the digital twin detects that the remaining travel time of the vehicle is less than the preset empty return activation threshold, it enters the empty return pre-rehearsal state and generates the pre-return trajectory chain corresponding to the vehicle based on Map as a Service and real-time traffic data.

[0066] The pre-return trajectory chain is different from a static navigation route. It is the most likely return route planned in advance in the virtual space for the vehicle before it has been executed, based on the current road network status and the road conditions at the future return time. If the search scope is simply "straight-line distance from destination to base" or "historically used routes", the matching results will be out of touch with the actual situation.

[0067] Map as a Service provides a standardized route planning interface, which shields the complexity of the underlying road network data, allowing digital twins to request route services in a unified manner at the technical level. At the same time, selecting real-time traffic data can be further combined with the actual route situation, making the final pre-return trajectory chain more realistic.

[0068] The specific operation of generating the pre-return trajectory chain corresponding to the vehicle based on Map as a Service and real-time traffic data is as follows:

[0069] Extract the destination coordinates and home coordinates of the vehicle in transit, retrieve the vector road network data covering the area between the two coordinates, and enumerate all connected paths in the vector road network with the destination coordinates as the starting point and the home coordinates as the ending point to form an initial path set.

[0070] Based on real-time traffic data, impassable paths are removed from the initial path set, and the total mileage of each remaining path is calculated. The path with the shortest total mileage is selected as the pre-return trajectory chain, and the planned release time of the vehicle in transit is simultaneously marked on the pre-return trajectory chain.

[0071] The specific operation of removing impassable paths from the initial path set based on real-time traffic data is as follows:

[0072] The status indicators of each road segment in the initial path set and the expected end time of the corresponding event are obtained from real-time traffic data: For road segments with the status indicator "road closed", they are directly determined as impassable paths and removed; For road segments with the status indicator "temporary construction" or "traffic accident", if the expected end time of the event is later than the sum of the planned release time of the vehicle in transit and the preset waiting time, the road segment is determined as an impassable path and removed, otherwise it is retained.

[0073] The preset waiting time refers to the maximum travel time allowed by the system for vehicles in transit from the planned release time to the expected arrival at the location of the event segment. This value is calculated by the digital twin of the vehicle in transit based on the distance from the destination coordinates to the starting point of the event segment, divided by the normal travel speed of the segment under non-congested conditions.

[0074] And radiate a preset distance to both sides along the pre-return trajectory chain to form a path radiation zone, which serves as the opportunity retrieval window for the vehicle in transit;

[0075] The specific rules for forming the path radiation zone are as follows: extract the coordinate sequence of continuous trajectory points in the pre-return trajectory chain, calculate the direction vector of the line segment formed by two adjacent trajectory points, and obtain the unit normal vector perpendicular to the direction vector.

[0076] At both ends of the line segment, offset by a preset distance in the direction of the normal vector and its opposite direction to obtain the corresponding points on both sides after offset, and connect each offset point in sequence to form an initial strip polygon;

[0077] For the intersection or breakage areas in the initial strip polygon caused by the trajectory chain bends, a polygon self-intersection repair algorithm is used to generate closed polygon regions without self-intersections, and these regions are used as the path radiation zone of the vehicle.

[0078] In this scheme, the polygon self-intersection repair algorithm can adopt the Bentley-Ottmann algorithm. The Bentley-Ottmann algorithm first detects all self-intersection points in the initial strip polygon generated by the offset. Then, at the intersection point, the polygon is divided into several simple sub-polygons that do not contain self-intersections. The sub-polygons traversed by the pre-return trajectory chain are retained as valid path radiation zones, and the remaining sub-polygons are discarded.

[0079] The opportunity scoring module extracts the original attributes of each allocation demand within the opportunity retrieval window and performs dual verification of spatial compatibility and capacity constraints between the original attributes and the transportation capacity profile of the digital twin.

[0080] There may be dozens or even hundreds of pending allocation requests within the opportunity retrieval window at the same time. These requests may come from different material categories, delivery nodes, and time requirements. If all of them are substituted into the subsequent matching process without screening, the amount of calculation will increase dramatically. Moreover, the vast majority of them will be rejected due to the capacity limitations of the vehicles in transit, resulting in invalid calculations. Therefore, after the opportunity retrieval window is defined and before the matching calculation, it is necessary to extract standardized information for each pending allocation request within the window.

[0081] The original attributes include physical attributes, category attributes, location attributes, time constraints, and priority tags:

[0082] Physical properties, namely the volume, total weight, outer length, outer width, and outer height of a single item or a stack of items;

[0083] Category attributes, namely the name of the material, category code, temperature control range (enter null if not specified), and loading / unloading equipment type (enter null if not specified).

[0084] Location attributes, namely pickup coordinates and delivery coordinates;

[0085] Time constraints, namely, the type of delivery requirement (such as same-day delivery, next-day delivery, delivery within a specified time period), the latest pickup time, and the latest delivery time;

[0086] Priority tag, which is the urgency weight value assigned to this request in the global allocation plan;

[0087] The specific rules for performing spatial compatibility verification between the original attributes and the digital twin's transportation capacity profile are as follows:

[0088] Extract physical attributes (including total weight, outer length, outer width, and outer height) from the original attributes of the demand to be allocated, and read the remaining load value, net length, net width, and net height of the vehicle from the transport capacity profile of the digital twin of the vehicle in transit.

[0089] If the total weight of the demand to be transferred is greater than the remaining load value, the space compatibility check is deemed to have failed and subsequent checks are terminated. If the total weight of the demand to be transferred is less than or equal to the remaining load value, the outer length, outer width, and outer height are compared with the net length, net width, and net height inside the cargo compartment, respectively. If any of the three parameters is greater than the corresponding cargo compartment constraint value, the space compatibility check is deemed to have failed; otherwise, the space compatibility check is deemed to have passed.

[0090] The specific rules for performing capability constraint verification between the original attributes and the digital twin's transportation capability profile are as follows:

[0091] Extract category attributes (i.e., material category code, temperature control range, and loading / unloading equipment type) from the original attributes of the demand to be allocated. Then, read the special capability parameters of the vehicle (i.e., cargo transportation permit category code table, temperature control range of each temperature zone, and loading / unloading equipment type table) from the transportation capacity profile of the vehicle in transit. Perform the following three-level checks in the following order: First, determine whether the material category code falls within the cargo transportation permit category code table of the vehicle in transit. If it does not, the check fails. Second, determine whether the temperature control range is completely covered by the temperature control range of at least one temperature zone. If not, the check fails. Third, determine whether the loading / unloading equipment type belongs to the loading / unloading equipment type table. If not, the check fails. If all three levels are satisfied, the capability constraint check is considered to have passed.

[0092] To meet the requirement of passing dual verification, the multidimensional spatiotemporal chance matching degree function is invoked to calculate its matching score.

[0093] The conflict resolution module selects the request with the highest matching score from all the transfer requests that have passed dual verification for the same vehicle in transit, and uses it as the execution request for that vehicle in transit.

[0094] For a vehicle that has completed dual verification, it may simultaneously face multiple allocation requests that are "capable and qualified to carry". However, these requests are distributed in different locations within its return route radiation zone, and each has different levels of urgency, capacity occupancy ratio, and time window. If multiple requests are assigned to the same vehicle without screening, a complex multi-task combination optimization problem will be faced, namely, whether these requests are on the same route, whether they conflict in time, and whether the total capacity exceeds the limit. This is uneconomical in return matching scenarios with high real-time requirements. Therefore, the solution is to retain only one optimal solution for each vehicle in transit.

[0095] Obtain the demand sequence corresponding to all vehicles en route. If there are conflicting demand sequences (i.e., identical demand sequences), determine the ownership of the demand sequence based on the matching score, ultimately forming mutually exclusive demand sequences, which are then transmitted to the overall control platform. The specific operation is as follows:

[0096] First, extract all vehicles en route that are involved in the conflicting allocation request, calculate the opportunity cost value of each vehicle en route, compare the opportunity cost values ​​of each vehicle en route, and assign the conflicting allocation request to the vehicle en route with the highest opportunity cost. If there are multiple vehicles en route with the same highest opportunity cost, the one with the highest matching score is selected as the assignee.

[0097] The opportunity cost is equal to the matching score of the vehicle in transit for the conflicting allocation demand minus the score of the highest-scoring alternative demand in its demand sequence, excluding the conflicting allocation demand. If there is no alternative demand, the score is recorded as zero.

[0098] Once the attribution is determined, the on-the-way vehicle corresponding to the attribution is marked as adjudicated, and other demands in its demand sequence are released back to the global demand pool. For the remaining on-the-way vehicles that have not been adjudicated, the conflicting demand to be allocated is removed from their demand sequence. If there are still other demands to be allocated in the sequence, the demand with the highest score is automatically added as the new execution demand for the on-the-way vehicle. After the addition, a new round of conflict detection is triggered. If there are no other demands to be allocated in the demand sequence, the on-the-way vehicle exits the current round of return matching, and its digital twin is restored to the empty return standby state.

[0099] The new execution requirements generated by the supplement may overlap with the current execution requirements of other vehicles in transit within the entire domain. The system will automatically substitute the set of vehicles in transit corresponding to the new conflicting allocation requirements into the above-mentioned adjudication process, and perform opportunity cost comparison and attribution adjudication repeatedly until there is no situation in the entire domain's demand sequence where the same allocation requirement is listed as an execution requirement by multiple vehicles in transit at the same time.

[0100] As a third embodiment of this application, this embodiment further discloses a method for calculating a matching score by calling a multidimensional spatiotemporal chance matching degree function, based on embodiments one and two. Figure 2 As shown, the specific content includes:

[0101] Extract the urgency weight value E from the original attributes of the demand to be allocated (i.e., obtained from the priority label); read the shortest vertical distance D between the pickup coordinates of the demand to be allocated and the pre-return trajectory chain from the digital twin of the vehicle in transit; read the current remaining volume from the transportation capacity profile of the vehicle in transit, and calculate the ratio U of the volume of the demand to be allocated to the remaining volume; extract the latest pickup time from the original attributes of the demand to be allocated (i.e., obtained from the time constraint), and calculate the absolute value |△T| of the difference between the latest pickup time and the estimated time of the vehicle to arrive at the pickup point.

[0102] Substituting E, D, U, and |△T| into the multidimensional spatiotemporal chance matching function, the matching score S is calculated. The mathematical expression for the multidimensional spatiotemporal chance matching function is: ,in, This is the path offset attenuation coefficient. This is the time offset attenuation coefficient. The utilization rate is the power exponent, and the range of values ​​for the three values ​​is (0,∞).

[0103] For the multidimensional spatiotemporal opportunity matching function constructed above, the first term uses E as the multiplication basis, mainly to ensure that under any conditions, under the same spatiotemporal conditions, priority is given to ensuring urgently needed materials.

[0104] The second option, the exponential decay function, accurately describes the law of diminishing marginal tolerance: within a short distance or short time difference, if the deviation has a slight impact, the score decreases slowly; conversely, if the deviation has a large impact, the score decays faster and quickly approaches zero. At the same time, D and |△T| are added in the exponent rather than multiplied, achieving the effect of the weakest link being superimposed. If one indicator performs poorly, even if the other indicator performs well, the overall time and space cost is still high, and the score is very low.

[0105] The third item uses a power function to flexibly adjust the response characteristics of the rating to changes in utilization rate. When U > 1, the function's slope increases sharply in the interval where U is close to 1, and the system significantly improves the score for high utilization demands. In the interval where U is small, the curve is flat, and low utilization demands are not excessively penalized. When ∈(0,1), even if the utilization rate is not high, the score will not be too low, which is suitable for scenarios where resources are scarce and transportation capacity is generally abundant.

[0106] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0107] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A comprehensive materials management platform based on digital twin technology, characterized in that: include: The window construction module acquires the real-time location coordinates, destination coordinates, planned release time, and vehicle attribute parameters of all vehicles in transit. This allows for the construction of a digital twin containing a transportation capacity profile for each vehicle in transit. When the digital twin detects that the remaining travel time of a vehicle is less than a preset empty return activation threshold, it generates a pre-return trajectory chain corresponding to that vehicle based on Map as a Service and real-time traffic data. This pre-return trajectory chain radiates outwards at preset distances to both sides, forming a path radiation zone, which serves as the opportunity retrieval window for that vehicle in transit. The vehicle attribute parameters consist of physical boundary parameters, special capability parameters, and current status parameters. The physical boundary parameters include the vehicle's rated load capacity, total internal volume of the cargo compartment, and internal dimensions of the cargo compartment. The special capability parameters include a cargo transportation permit category code table, temperature control range for each temperature zone, and loading / unloading equipment type table. The current status parameters include the currently loaded weight, occupied volume, and current temperature zone usage. The opportunity scoring module extracts the original attributes of each request to be allocated within the opportunity retrieval window, and performs a dual verification of spatial compatibility and capacity constraints between the original attributes and the transportation capacity profile of the digital twin. For requests to be allocated that pass the dual verification, the multidimensional spatiotemporal opportunity matching degree function is called to calculate their matching score. The conflict resolution module selects the request with the highest matching score from all the requests to be transferred for the same vehicle in transit that have passed dual verification, and obtains the request sequence corresponding to all vehicles in transit. If the same request to be transferred appears in the request sequence, the allocation of the request to be transferred is determined according to the matching score, and finally a mutually exclusive request sequence is formed and transmitted to the global control platform.

2. The full-domain material management platform based on digital twin technology according to claim 1, characterized in that, The specific steps for constructing a digital twin of each vehicle in transit, including a portrait of its transportation capabilities, are as follows: Obtain the vehicle identification code of each vehicle on the road, use the physical boundary parameters as the physical capability dimension, the special capability parameters as the special capability dimension, and the current state parameters as the dynamic load dimension, combine them into a capability description matrix, and write this capability description matrix as the initial static attribute of the digital twin. An array of resistance strain gauge weighing sensors is installed at the load-bearing position at the bottom of the vehicle compartment, an array of infrared time-of-flight sensors is installed on the roof inside the compartment, and a data preprocessing module is deployed in the vehicle terminal. Meanwhile, the vehicle terminal reads the vehicle's mileage accumulation and fuel consumption rate through the controller local area network bus, and calculates the remaining driving range of the vehicle to its current destination by combining the real-time positioning coordinates output by the global navigation satellite system. The data obtained above is transmitted back to the data processing interface of the digital twin via the vehicle communication unit.

3. The full-domain material management platform based on digital twin technology according to claim 1, characterized in that, The specific steps for constructing the pre-return trajectory chain are as follows: Extract the destination coordinates and home coordinates of the vehicle in transit, retrieve the vector road network data covering the area between the two coordinates, and enumerate all connected paths in the vector road network with the destination coordinates as the starting point and the home coordinates as the ending point to form an initial path set. Based on real-time traffic data, impassable paths are removed from the initial path set, and the total mileage of each remaining path is calculated. The path with the shortest total mileage is selected as the pre-return trajectory chain, and the planned release time of the vehicle in transit is simultaneously marked on the pre-return trajectory chain.

4. The full-domain material management platform based on digital twin technology according to claim 3, characterized in that, The specific operation for removing impassable paths from the initial path set is as follows: Obtain the status identifier of each road segment in the initial path set and the expected end time of the corresponding event from the real-time traffic data: For road segments with the status identifier "road closed", they are directly determined as impassable paths and removed; For road segments with the status identifier "temporary construction" or "traffic accident", if the expected end time of the event is later than the sum of the planned release time of the vehicle in transit and the preset waiting time, the road segment is determined as an impassable path and removed, otherwise it is retained.

5. The full-domain material management platform based on digital twin technology according to claim 1, characterized in that, The specific rules for forming path radiation zones are as follows: Extract the coordinate sequence of continuous trajectory points in the pre-return trajectory chain, calculate the direction vector of the line segment formed by two adjacent trajectory points, and obtain the unit normal vector perpendicular to the direction vector. At both ends of the line segment, offset by a preset distance in the direction of the normal vector and its opposite direction to obtain the corresponding points on both sides after offset, and connect each offset point in sequence to form an initial strip polygon; For the intersection or breakage areas in the initial strip polygon caused by the trajectory chain bends, a polygon self-intersection repair algorithm is used to generate closed polygon regions without self-intersections, which are then used as the path radiation zone of the vehicle.

6. The full-domain material management platform based on digital twin technology according to claim 1, characterized in that, The original attributes include physical attributes, category attributes, location attributes, time constraints, and priority tags. Among them, physical attributes include the volume, total weight, outer length, outer width, and outer height of the material; category attributes include the name, category code, temperature control range, and loading / unloading equipment type of the material; location attributes include pickup coordinates and delivery coordinates; time constraints include timeliness requirement type, latest pickup time, and latest delivery time; and priority tags are the urgency weight value assigned to the demand in the global allocation plan.

7. The full-domain material management platform based on digital twin technology according to claim 1, characterized in that, The specific rules for performing space compatibility verification are as follows: Extract physical attributes from the original attributes of the demand to be allocated, and read the remaining load value, net length, net width, and net height of the vehicle from the transport capacity profile of the digital twin of the vehicle in transit. If the total weight of the demand to be transferred is greater than the remaining load value, the space compatibility check is deemed to have failed, and subsequent checks are terminated. If the total weight of the demand to be transferred is less than or equal to the remaining load value, the outer length, outer width, and outer height are compared with the net length, net width, and net height inside the cargo compartment, respectively. If any of the three parameters is greater than the corresponding cargo compartment constraint value, the space compatibility check is deemed to have failed; otherwise, the space compatibility check is deemed to have passed.

8. The full-domain material management platform based on digital twin technology according to claim 1, characterized in that, The specific rules for capacity constraint verification are as follows: Extract the category attribute from the original attributes of the demand to be allocated, and then read the special capacity parameters of the vehicle from the transportation capacity profile of the vehicle in transit. The verification is performed in the following three-level order: First, determine whether the category code of the material falls into the cargo transportation permit category code table of the vehicle in transit. If it does not fall into the table, the verification fails. Second, determine whether the temperature control range is completely covered by the temperature control range of at least one temperature zone. If not, the verification fails. Third, determine whether the loading and unloading equipment type belongs to the loading and unloading equipment type table. If it does not belong, the verification fails. If all three levels are satisfied, the capacity constraint verification is deemed to have passed.

9. The full-domain material management platform based on digital twin technology according to claim 1, characterized in that, The specific steps for calculating its matching score are as follows: Extract the urgency weight value E from the original attributes of the demand to be allocated; read the shortest vertical distance D between the pickup coordinates of the demand to be allocated and the pre-return trajectory chain from the digital twin of the vehicle in transit; read the current remaining volume from the transportation capacity profile of the vehicle in transit, and calculate the ratio U of the volume of the demand to be allocated to the remaining volume; extract the latest pickup time from the original attributes of the demand to be allocated, and calculate the absolute value |△T| of the difference between the latest pickup time and the estimated time of arrival of the vehicle at the pickup point. Substituting E, D, U, and |△T| into the multidimensional spatiotemporal chance matching function, the matching score S is calculated. The mathematical expression of the multidimensional spatiotemporal chance matching function is: ,in, This is the path offset attenuation coefficient. This is the time offset attenuation coefficient. This is the power exponent of utilization.

10. The full-domain material management platform based on digital twin technology according to claim 1, characterized in that, The specific procedure for determining the allocation of this demand based on the matching score is as follows: Extract all vehicles en route that are involved in the conflicting allocation request, calculate the opportunity cost value of each vehicle en route, compare the opportunity cost values ​​of each vehicle en route, and assign the conflicting allocation request to the vehicle en route with the highest opportunity cost. If there are multiple vehicles en route with the same highest opportunity cost, the one with the highest matching score is selected as the assignee. The opportunity cost is equal to the matching score of the vehicle in transit for the conflicting allocation demand minus the score of the highest-scoring alternative demand in its demand sequence, excluding the conflicting allocation demand. If there is no alternative demand, the score is recorded as zero. Once the attribution is determined, the on-the-way vehicle corresponding to the attribution is marked as adjudicated, and other demands in its demand sequence are released back to the global demand pool. For the remaining on-the-way vehicles that have not been adjudicated, the conflicting demand to be allocated is removed from their demand sequence. If there are still other demands to be allocated in the sequence, the demand with the highest score is automatically added as the new execution demand for the on-the-way vehicle. After the addition, a new round of conflict detection is triggered. If there are no other demands to be allocated in the demand sequence, the on-the-way vehicle exits the current round of return matching, and its digital twin is restored to the empty return standby state. The new execution requirements generated by the supplement may overlap with the current execution requirements of other vehicles in transit within the entire domain. The system will automatically substitute the set of vehicles in transit corresponding to the new conflicting allocation requirements into the above-mentioned adjudication process, and perform opportunity cost comparison and attribution adjudication repeatedly until there is no situation in the entire domain's demand sequence where the same allocation requirement is listed as an execution requirement by multiple vehicles in transit at the same time.

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