Multi-task oriented logistics dynamic scheduling and path optimization method and system

Abstract

The application belongs to the technical field of intelligent logistics scheduling and relates to a multi-task-oriented logistics dynamic scheduling and path optimization method and system, which comprises the following steps: analyzing an initial task data set to construct a task core state vector; calling a position mapping matrix to transform an initial coordinate sequence and eliminate redundancy, thereby generating a standard coordinate sequence; taking a given distribution path as a topological main shaft to construct a multi-stage task flow state tree, truncating a specific branch according to a predicted time loss to output a virtual path node sequence; comparing actual node data to generate a space-time residual value, and reorganizing a queue through a dynamic gain correction rule to generate a logistics dynamic scheduling instruction. The application inversely corrects the business layer priority evaluation value through spatial topological characteristics, reprograms the time sequence of the scheduling queue before the construction of the state tree, eliminates memory allocation deadlocks and improves the calculation convergence speed.

Description

Technical Field

[0001] This invention belongs to the field of intelligent logistics scheduling technology, and relates to a method and system for dynamic logistics scheduling and path optimization oriented to multiple tasks. Background Technology

[0002] Currently, using intelligent scheduling algorithms for dynamic planning of task allocation and route optimization for delivery vehicles has become a core approach in smart logistics. However, existing technologies still have limitations in handling multi-dimensional spatiotemporal boundary convergence, rigid performance constraints, and global capacity elastic coordination in large-scale, high-concurrency scenarios.

[0003] First, discrete path optimization technology based on static spatiotemporal window tree retrieval; for example, Chinese invention patent CN104598979A discloses a time and location-based delivery optimization method and system, which uses the depth or width priority of the tree structure to retrieve feasible delivery paths, and performs path pruning and optimal ranking based on profit targets. Its shortcomings are: the solution is essentially an exhaustive or greedy tree retrieval in discrete state space, and the decision logic faces a hard bottleneck of exponential explosion of computational scale (NP-hard) when facing a large number of delivery points. It does not map the elastic spatiotemporal ripple effect generated by highly dynamic sudden orders into the global boundary constraints of the search tree, causing the algorithm to lose its practical value in strong real-time dynamic scheduling scenarios due to excessive time consumption.

[0004] Second, there is the area planning technology based on local geographic information aggregation and individual soldier multi-weight alignment. For example, Chinese invention patent CN112257930A discloses a method, device and equipment for dynamically planning the delivery route of express parcels. It aggregates POI geographic information points in a fixed business area and performs weighted route optimization by combining timeliness, parcel volume and assessment requirements. Its shortcomings are: the technology focuses on local optimization within the grid area and alignment with individual soldier indicators. Although it standardizes the offline routes of couriers, it fails to establish a closed-loop connection between the surface capacity status of each independent area and the global carrying capacity of the logistics network. It cannot map in real time the physical compression of the inherent elastic space of the area by cross-regional sudden tasks, and it is difficult to cope with the risk of collaborative overdraft under global strong concurrent disturbances.

[0005] Third, geometric space pre-allocation technology based on vehicle-side IoT status; for example, Chinese invention patent CN103473659A discloses a dynamic optimization allocation method for logistics tasks driven by real-time status information of delivery vehicles. It dynamically senses vehicle status through vehicle terminals and performs regional pre-allocation and cost optimization based on the principle of minimizing the angle between task vector and traffic flow vector. Its shortcomings are: the method usually regards task allocation as a discrete decision-making unit triggered by fixed-step long-cycle polling, ignoring the continuous impact of the dynamic flow of logistics orders; when performing physical optimization, if there is a lack of rigid boundary constraint mechanism based on real complex road network topology constraints and multi-dimensional traffic impedance, its matching trajectory based on simple geometric plane vectors is prone to spatial distortion and computational drift, causing the output solution to lose its practical value due to exceeding the physical bottom line of the actual road network.

[0006] Therefore, how to construct a scheduling architecture with multi-dimensional spatiotemporal streaming collaborative solution capabilities, so that the path and task allocation logic converges to the real complex road network physical safety boundary throughout the entire cycle, is the technical problem to be solved by this invention. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the prior art and solve the technical problems of processor computing power overload caused by heterogeneous spatiotemporal coordinate sequence misalignment and state tree search branch divergence during high-concurrency task flow scheduling. It provides a method and system for dynamic logistics scheduling and path optimization for multi-task applications.

[0008] To achieve the above-mentioned objectives, this invention provides a multi-task-oriented dynamic logistics scheduling and route optimization method, comprising the following steps: Step S1, parse task data and construct core state vector: The data processing system receives the initial task dataset containing the time nodes, task types and initial coordinate sequences of concurrent tasks, parses the initial task dataset to extract maintenance task attributes and inspection task attributes, and combines them to construct the core state vector of the task. Step S2, Projecting coordinates and removing redundancy to generate a standard sequence: The data processing system calls the location mapping matrix to uniformly project the initial coordinate sequence onto the standardized logistics operation spatiotemporal reference, compares the spatial location residual values ​​of adjacent timestamps to remove redundant coordinate records, and generates a standard coordinate sequence; Step S3: Construct a state tree and truncate branches to output node sequence: The data processing system uses the predetermined delivery path as the topological axis, attaches the standard coordinate sequence to evolve and construct a multi-stage task flow state tree, monitors the predicted time loss of each state branch in the multi-stage task flow state tree, and truncates the corresponding state branch when the predicted time loss is greater than the global dynamic mean line, and outputs the virtual path node sequence. Step S4: Reorganize the queue based on residuals to generate scheduling instructions: The data processing system collects the actual completed node data of the task, compares the difference between the actual completed node data of the task and the estimated node in the virtual path node sequence to generate spatiotemporal residual values. When the spatiotemporal residual value crosses the set window threshold three times in a row, the dynamic gain correction rule is called to lower the comprehensive evaluation value of the current disturbed flow channel, and the task priority queue is reorganized according to the monotonically decreasing discrete order of the comprehensive evaluation value from large to small to output the optimized task chain and generate logistics dynamic scheduling instructions.

[0009] The step S2 of this invention, which compares the spatial location residuals of adjacent timestamps to eliminate redundant coordinate records and generate a standard coordinate sequence, includes the following sub-steps: Step S21, the data processing system calculates the Euclidean distance and the mean distance between adjacent task nodes under the standardized logistics operation spatiotemporal benchmark; Step S22, the data processing system calculates the discrete variance of the Euclidean distance between adjacent task nodes based on the mean distance. When the discrete variance of the Euclidean distance is lower than a set fluctuation threshold, it is determined that there is misalignment and redundancy in the corresponding adjacent timestamps and the coordinate records are eliminated. The remaining coordinates are then reassembled to obtain the standard coordinate sequence.

[0010] Preferably, in step S4, the comprehensive evaluation value of the current disturbed flow channel is lowered, calculated according to the following formula: ,in, This is the comprehensive evaluation value of the current disturbed flow channel; This serves as the baseline evaluation value for the currently disturbed flow channel; To fix the penalty step size; The spatiotemporal residual value is normalized; in step S4, the task priority queue is reorganized in a monotonically decreasing discrete order of comprehensive evaluation value to output an optimized task chain, including: reordering concurrent tasks in the current disturbed flow channel according to the order of comprehensive evaluation value from largest to smallest, and generating a corrected scheduling queue.

[0011] The step S1 of this invention, which parses the initial task dataset to extract maintenance task attributes and inspection task attributes and combines them to construct a task core state vector, includes the following sub-steps: Step S11, the data processing system extracts the urgency parameters and workload parameters of each concurrent task from the initial task dataset; Step S12, the data processing system uses the urgency parameters as the first state component and the workload parameters as the second state component, combines them to construct a two-dimensional feature vector, and obtains the task core state vector used to characterize the core attributes of the task after normalization mapping.

[0012] In step S2 of this invention, the initial coordinate sequence is uniformly projected onto a standardized logistics operation spatiotemporal reference by calling the position mapping matrix. This includes the following sub-steps: Step S23, the data processing system acquires a multi-source heterogeneous spatiotemporal coordinate sequence; Step S24, the data processing system calls the position mapping matrix composed of a combination of translation and rotation operators to perform spatial alignment transformation on the multi-source heterogeneous spatiotemporal coordinate sequence, uniformly projecting it onto a standardized logistics operation spatiotemporal reference with the same predetermined reference axis, thereby eliminating spatial topological overlap.

[0013] The construction of a multi-stage task flow state tree in step S3 of the present invention includes the following sub-steps: Step S31, the data processing system performs forward data traversal on each node branch of the current evolution level in the multi-stage task flow state tree to deduce the estimated time for each path branch to reach the termination state; Step S32, the data processing system calculates the deviation between the estimated time of each node branch and the corresponding standard time consumption benchmark, and defines the deviation as the predicted time loss.

[0014] The step S3 of this invention, which involves truncating the corresponding state branch when the prediction time loss exceeds the global dynamic mean, includes the following sub-steps: Step S33: The data processing system statistically analyzes the prediction time loss of all unclosed state branches in the current evolution level in real time, calculates the arithmetic mean of the prediction time loss, and confirms the arithmetic mean as the global dynamic mean; Step S34: When the data processing system detects that the prediction time loss of a certain node branch exceeds the global dynamic mean, it triggers a discrete truncation event, stops the downward evolution of the node branch, and discards the background search space of the corresponding branch. After generating the logistics dynamic scheduling instruction in step S4, the process also includes data processing. The system's performance monitoring steps are as follows: Step S41, the data processing system continuously records the time consumed by the scheduling decision output of the data processing system within multiple consecutive scheduling cycles, and constructs a historical evolution sequence of the time consumption; Step S42, the data processing system uses waveform analysis to extract the mean volatility of the historical evolution sequence of the time consumption, and uses the mean volatility as an adaptive evolution indicator to characterize the deadlock rate of computing architecture resources; Step S43, when the adaptive evolution indicator continuously exceeds the predetermined safety threshold, the data processing system reduces the maximum multi-stage level depth of a single state tree evolution construction and increases the filtering sensitivity used for comparing spatiotemporal time series, so that the adaptive evolution indicator stabilizes within the predetermined safety threshold.

[0015] The setting window threshold described in this invention is a deviation range of 800ms to 1500ms for the spatiotemporal residual value; the fixed penalty step size is the basic penalty score for adjusting the comprehensive evaluation value of the current disturbed flow channel, and the basic penalty score ranges from 5 to 15.

[0016] The generation of dynamic logistics scheduling instructions in step S4 of the present invention includes the following steps: Step S44, the data processing system converts the sequence of virtual path nodes that meet the time overhead limit into a deterministic dynamic flow task data stream; Step S45, the data processing system encapsulates the dynamic flow task data stream into a standard scheduling data packet and distributes it to the multi-task data monitoring side through the data distribution interface of the logical processing architecture, providing an instant spatial geometric alignment reference.

[0017] This invention also provides a multi-task-oriented logistics dynamic scheduling and route optimization system, which implements a multi-task-oriented logistics dynamic scheduling and route optimization method, including: The data parsing and attribute extraction module receives the initial task dataset at the input end, and is used to parse the initial task dataset to extract maintenance task attributes and inspection task attributes, and combine them to construct a two-dimensional feature vector. After normalization mapping, a standard state vector is obtained to represent the core attributes of the task. The spatiotemporal geometric transformation alignment module, whose input end is connected to the data parsing and attribute extraction module, is used to call the position mapping matrix composed of translation and rotation operators, to uniformly project the initial coordinate sequence in the initial task dataset onto the standardized logistics operation spatiotemporal reference to eliminate spatial topological overlap, and to compare the spatial position residual values ​​of adjacent timestamps through the time-series cleaning operator to remove redundant coordinate records and generate a standard coordinate sequence. The multi-stage state tree search and pruning module has its inputs connected to the data parsing and attribute extraction module and the spatiotemporal geometric transformation alignment module, respectively. It is used to build a multi-stage task flow state tree by using the optimized task chain as the topology axis and attaching standard coordinate sequences. It monitors the prediction time loss of each state branch through the feedforward topology pruning program. When the prediction time loss is greater than the global dynamic mean line, it triggers a discrete truncation event to discard the background search space of the corresponding branch and outputs a sequence of virtual path nodes. The delivery flow queue deadlock compensation module is connected to the multi-stage state tree search and pruning module at its input end. It is used to collect the actual task completion node data and calculate the difference between the actual task completion node data and the estimated node in the virtual path node sequence by the differential to generate the spatiotemporal residual value. When the spatiotemporal residual value crosses the set window threshold three times in a row, the backup step-by-step adjustment mechanism is activated and the dynamic gain correction rule is called to lower the comprehensive evaluation value of the current disturbed flow channel. Based on the monotonically decreasing discrete order of the comprehensive evaluation value from large to small, the task priority queue is reorganized to output the optimized task chain and generate logistics dynamic scheduling instructions.

[0018] Compared with the prior art, the present invention has at least the following beneficial effects: 1. In multi-task-oriented dynamic logistics scheduling, the standard spatial reference of the initial coordinate sequence is projected by the coordinate system algorithm transformation matrix. Combined with the time-series cleaning operator, the spatial position residual values ​​of adjacent timestamps are compared to erase redundant and duplicate records in the buffer, generating a standard coordinate sequence. This is then used as a constraint input to inversely access the multi-level priority rule operator. The updated dynamic overlap coefficient is used to cover the business type parameters in situ, reorganizing the disordered concurrent heterogeneous attribute task flow into a monotonically decreasing one-dimensional optimized task chain in memory. This completes the time-series re-editing of the scheduling queue before the task flow transitions to the state tree construction, eliminating memory allocation deadlock caused by disordered crossover of multi-source data flows and controlling the evolution branches of the background search space.

[0019] 2. Utilizing the computing unit with the reorganized one-dimensional optimized task chain as the topological axis, and mounting a standard coordinate sequence aligned by spatial geometric transformation to evolve and construct a multi-stage task flow state tree, the high-dimensional combinatorial optimization is reshaped into a discrete event-driven structure. In conjunction with an active feedforward topology pruning program, the prediction time consumption of each state branch is monitored. During the topology search phase, branches exceeding the global dynamic mean line are automatically identified and discrete truncation events are triggered to stop the downward extrapolation of invalid state branches. This keeps the calculation of tens of millions of state branches caused by ultra-large-scale datasets within the range of low-order linear growth, reduces processor energy consumption, and ensures low-latency output of scheduling prediction decisions.

[0020] 3. By configuring a dynamic delay compensation mechanism composed of differentials, the actual completion node data of the task is collected in real time, and the spatiotemporal residual value between the actual completion node and the estimated node in the virtual path node sequence is calculated. Under the edge condition of crossing the set window threshold three times in a row, the backup step-by-step adjustment mechanism is forcibly activated. The compensation operator is called to lower the comprehensive evaluation value of the current disturbed channel with a fixed step size, and the queuing sequence is reconstructed to enter the safe self-healing optimization boundary. This avoids the data processing system from getting stuck in an infinite loop in the state tree search stage due to loss of convergence benchmark when facing external disturbances such as sudden order insertion or parallel task flow interruption, thereby improving the anti-interference self-healing stability of the path scheduling strategy output. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the multi-task-oriented dynamic logistics scheduling method of the present invention; Figure 2 This is a schematic diagram of the functional modules of the logistics dynamic scheduling system of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments and accompanying drawings.

[0023] Example 1: This embodiment discloses a multi-task-oriented dynamic logistics scheduling and route optimization method, including the following steps: Step S1, parse task data and construct core state vector: The data processing system receives the initial task dataset containing the time nodes, task types and initial coordinate sequences of concurrent tasks, parses the initial task dataset to extract maintenance task attributes and inspection task attributes, and combines them to construct the core state vector of the task. Step S2, Projecting coordinates and removing redundancy to generate a standard sequence: The data processing system calls the location mapping matrix to uniformly project the initial coordinate sequence onto the standardized logistics operation spatiotemporal reference, compares the spatial location residual values ​​of adjacent timestamps to remove redundant coordinate records, and generates a standard coordinate sequence; Step S3: Construct a state tree and truncate branches to output node sequence: The data processing system uses the predetermined delivery path as the topological axis, attaches the standard coordinate sequence to evolve and construct a multi-stage task flow state tree, monitors the predicted time loss of each state branch in the multi-stage task flow state tree, and truncates the corresponding state branch when the predicted time loss is greater than the global dynamic mean line, and outputs the virtual path node sequence. Step S4: Reorganize the queue based on residuals to generate scheduling instructions: The data processing system collects the actual completed node data of the task, compares the difference between the actual completed node data of the task and the estimated node in the virtual path node sequence to generate spatiotemporal residual values. When the spatiotemporal residual value crosses the set window threshold three times in a row, the dynamic gain correction rule is called to lower the comprehensive evaluation value of the current disturbed flow channel, and the task priority queue is reorganized according to the monotonically decreasing discrete order of the comprehensive evaluation value from large to small to output the optimized task chain and generate logistics dynamic scheduling instructions.

[0024] In step S2 of this embodiment, comparing the spatial location residuals of adjacent timestamps to eliminate redundant coordinate records and generate a standard coordinate sequence includes the following sub-steps: Step S21, the data processing system calculates the Euclidean distance and the mean distance between adjacent task nodes under the standardized logistics operation spatiotemporal benchmark; Step S22, the data processing system calculates the discrete variance of the Euclidean distance between adjacent task nodes based on the mean distance. When the discrete variance of the Euclidean distance is lower than the set fluctuation threshold, it is determined that there is misalignment and redundancy in the corresponding adjacent timestamps and the coordinate records are eliminated. The remaining coordinates are then reassembled to obtain the standard coordinate sequence.

[0025] In step S4 of this embodiment, the comprehensive evaluation value of the current disturbed flow channel is lowered, calculated according to the following formula: ,in, This is the comprehensive evaluation value of the current disturbed flow channel; This serves as the baseline evaluation value for the currently disturbed flow channel; To fix the penalty step size; The spatiotemporal residual value is normalized; in step S4, the task priority queue is reorganized in a monotonically decreasing discrete order of comprehensive evaluation value to output an optimized task chain, including: reordering concurrent tasks in the current disturbed flow channel according to the order of comprehensive evaluation value from largest to smallest, and generating a corrected scheduling queue.

[0026] In this embodiment, step S1 involves parsing the initial task dataset to extract maintenance task attributes and inspection task attributes, and then combining them to construct a task core state vector. This includes the following sub-steps: Step S11: The data processing system extracts the urgency parameters and workload parameters of each concurrent task from the initial task dataset; Step S12: The data processing system uses the urgency parameters as the first state component and the workload parameters as the second state component, combines them to construct a two-dimensional feature vector, and after normalization mapping, obtains the task core state vector used to characterize the core attributes of the task.

[0027] In this embodiment, step S2 involves calling a position mapping matrix to uniformly project the initial coordinate sequence onto a standardized logistics operation spatiotemporal reference. This includes the following sub-steps: Step S23: The data processing system acquires a multi-source heterogeneous spatiotemporal coordinate sequence; Step S24: The data processing system calls a position mapping matrix composed of a translation operator and a rotation operator to perform a spatial alignment transformation on the multi-source heterogeneous spatiotemporal coordinate sequence, uniformly projecting it onto a standardized logistics operation spatiotemporal reference with the same predetermined reference axis, thereby eliminating spatial topological overlap.

[0028] In this embodiment, step S3, which constructs a multi-stage task flow state tree, includes the following sub-steps: Step S31, the data processing system performs forward data traversal on each node branch of the current evolution level in the multi-stage task flow state tree to deduce the estimated time for each path branch to reach the termination state; Step S32, the data processing system calculates the deviation between the estimated time of each node branch and the corresponding standard time consumption benchmark, and defines the deviation as the predicted time loss.

[0029] In this embodiment, step S3, which involves truncating the corresponding state branch when the predicted time loss exceeds the global dynamic mean, includes the following sub-steps: Step S33: The data processing system statistically analyzes the predicted time loss of all unclosed state branches in the current evolution level in real time, calculates the arithmetic mean of the predicted time loss, and confirms the arithmetic mean as the global dynamic mean; Step S34: When the data processing system detects that the predicted time loss of a certain node branch exceeds the global dynamic mean, it triggers a discrete truncation event, stops the downward evolution of the node branch, and discards the background search space of the corresponding branch. After generating the logistics dynamic scheduling instruction in step S4, the system also includes data processing... The performance monitoring steps of the data processing system are as follows: Step S41, the data processing system continuously records the time consumed by the scheduling decision output of the data processing system within multiple consecutive scheduling cycles, and constructs a historical evolution sequence of the time consumption; Step S42, the data processing system uses waveform analysis to extract the mean volatility of the historical evolution sequence of the time consumption, and uses the mean volatility as an adaptive evolution indicator to characterize the deadlock rate of computing architecture resources; Step S43, when the adaptive evolution indicator continuously exceeds the predetermined safety threshold, the data processing system reduces the maximum multi-stage level depth of a single state tree evolution construction and increases the filtering sensitivity used for comparing spatiotemporal time series, so that the adaptive evolution indicator stabilizes within the predetermined safety threshold.

[0030] In this embodiment, the set window threshold is the deviation range of the spatiotemporal residual value between 800ms and 1500ms; the fixed penalty step size is the basic penalty score for adjusting the comprehensive evaluation value of the current disturbed flow channel, and the basic penalty score ranges from 5 to 15.

[0031] In this embodiment, step S4 generates a dynamic logistics scheduling instruction, which includes the following steps: Step S44, the data processing system converts the sequence of virtual path nodes that meet the time overhead limit into a defined dynamic flow task data stream; Step S45, the data processing system encapsulates the dynamic flow task data stream into a standard scheduling data packet and distributes it to the multi-task data monitoring side through the data distribution interface of the logical processing architecture, providing an instant spatial geometric alignment reference.

[0032] This embodiment also provides a multi-task-oriented logistics dynamic scheduling and route optimization system, which is used to implement a multi-task-oriented logistics dynamic scheduling and route optimization method, including: The data parsing and attribute extraction module receives the initial task dataset at the input end, and is used to parse the initial task dataset to extract maintenance task attributes and inspection task attributes, and combine them to construct a two-dimensional feature vector. After normalization mapping, a standard state vector is obtained to represent the core attributes of the task. The spatiotemporal geometric transformation alignment module, whose input end is connected to the data parsing and attribute extraction module, is used to call the position mapping matrix composed of translation and rotation operators, to uniformly project the initial coordinate sequence in the initial task dataset onto the standardized logistics operation spatiotemporal reference to eliminate spatial topological overlap, and to compare the spatial position residual values ​​of adjacent timestamps through the time-series cleaning operator to remove redundant coordinate records and generate a standard coordinate sequence. The multi-stage state tree search and pruning module has its inputs connected to the data parsing and attribute extraction module and the spatiotemporal geometric transformation alignment module, respectively. It is used to build a multi-stage task flow state tree by using the optimized task chain as the topology axis and attaching standard coordinate sequences. It monitors the prediction time loss of each state branch through the feedforward topology pruning program. When the prediction time loss is greater than the global dynamic mean line, it triggers a discrete truncation event to discard the background search space of the corresponding branch and outputs a sequence of virtual path nodes. The delivery flow queue deadlock compensation module is connected to the multi-stage state tree search and pruning module at its input end. It is used to collect the actual task completion node data and calculate the difference between the actual task completion node data and the estimated node in the virtual path node sequence by the differential to generate the spatiotemporal residual value. When the spatiotemporal residual value crosses the set window threshold three times in a row, the backup step-by-step adjustment mechanism is activated and the dynamic gain correction rule is called to lower the comprehensive evaluation value of the current disturbed flow channel. Based on the monotonically decreasing discrete order of the comprehensive evaluation value from large to small, the task priority queue is reorganized to output the optimized task chain and generate logistics dynamic scheduling instructions.

[0033] Example 2: In this embodiment, within the continuous operation environment of a commercial logistics center dispatch management system that includes multi-source high-frequency concurrent equipment maintenance and site inspection operations, the data processing system receives an initial task dataset from the complex operation scenario. This initial task dataset includes multi-source concurrent time nodes, task types, and initial coordinate sequences. Due to overlaps and deviations in the time and spatial dimensions of the multi-source input task data sequences, misalignment of the multi-source heterogeneous spatiotemporal coordinate sequences occurs. Because the system lacks a control loop at the business logic layer to provide unidirectional feedback and a unified spatiotemporal reference for multi-task attributes, the memory allocation area experiences computation queue deadlock when parsing high-concurrency task flows, causing processor running state divergence and processing latency. To process the high-concurrency task flows, the data parsing and attribute extraction module in the data processing system parses the initial task dataset, separating maintenance task attributes and inspection task attributes, obtaining the urgency parameters and workload parameters of each concurrent task, and combining the urgency parameters as the first state component and the workload parameters as the second state component into a two-dimensional feature vector to obtain the core task state vector. The normalization mapping processing used in this invention is a maximum-minimum standardization algorithm.

[0034] During the mapping process, the computing unit obtains the maximum and minimum values ​​of the emergency timeliness parameters in all concurrent tasks of the current batch. It subtracts the minimum value from the specific value of the emergency timeliness parameter of the currently processed task, and then divides the difference by the absolute difference between the maximum and minimum values. This maps the time-dimensional emergency timeliness parameters to dimensionless pure numbers between 0 and 1, serving as the first state component. Similarly, the same maximum-minimum division operation is performed on the workload parameter and the spatiotemporal residual value in step S4 to eliminate the physical dimension differences of multi-source heterogeneous data and construct standardized feature components. The spatiotemporal geometric transformation alignment module calls the position mapping matrix to project the initial coordinate sequence onto the standardized logistics operation spatiotemporal reference. The time-series cleaning operator compares the spatial position residual values ​​of adjacent timestamps to erase duplicate coordinate records in the memory buffer and outputs a standard coordinate sequence. The computing unit reads the standard coordinate sequence and calculates the Euclidean distance and mean distance between adjacent task nodes under the standardized logistics operation spatiotemporal reference, and calculates the discrete variance of the Euclidean distance between adjacent task nodes. When the Euclidean distance is discrete variance When the fluctuation value is below the preset fluctuation threshold of 25.0 and the actual operating value is 8.5, the system clears the corresponding timestamp misaligned redundant records in the memory buffer to update the standard coordinate sequence. After the data processing system receives the standard coordinate sequence...

[0035] Before constructing the multi-stage task flow state tree evolution, initial state sorting is initiated. The system runs in a dual-processor server environment with a processor core frequency of 2.5GHz and 16GB of available memory. The initial input standard coordinate sequence contains a spatiotemporal three-dimensional data structure of 500 discrete logistics task nodes. The system reads the core task state vector, extracts the urgency parameter of the first state component and the workload parameter of the second state component. To determine the recompilation decision boundary, parameter values ​​within the range of 0 to 100% are injected during the system initialization and debugging phase, and the memory conflict rate is monitored to complete the experimental calibration. The calibration results show that when the weighted comprehensive evaluation value reaches 75%, the buffer overflow probability reaches an inflection point. This is used to determine the pre-recombination feature threshold. The weighted comprehensive evaluation value refers to the dimensionless pure number calculated by linearly combining the urgency parameter and the workload parameter. The value range is limited to 0 to 100% to characterize the potential consumption intensity of computing resources by concurrent data streams. When the value exceeds 75%, the system skips the state tree search and directly extracts the batch of high-concurrency task streams. It re-edits the concurrent task sequence using monotonically decreasing pointers in the memory stack and reconstructs a one-dimensional optimized task chain in situ in the memory buffer. The task chain refers to a linear data table of logistics concurrent tasks stored in a contiguous memory space, arranged monotonically in decreasing order according to the combined weights of timeliness and intensity. The total length of the space is fixed at 24,000 bytes and is used as the topological axis for subsequent state tree evolution. The system completes the re-editing of the scheduling queue in the initial state before the state tree is built, controls the background search space for subsequent state tree evolution, and eliminates memory allocation deadlock caused by disordered intersection of multi-source data streams. In this construction process, in order to eliminate the disconnect between business layer attributes and spatial topology optimization.

[0036] This invention introduces the core state vector of the task in step S1 to dynamically guide and correct the topology evolution hierarchy and search space of each node branch; the first state component (urgent timeliness parameter) and the second state component (work intensity parameter) in the core state vector of the task are linearly weighted and summed. When the calculated weighted sum exceeds a preset safety threshold of 75%, it indicates that the concurrent task has extremely high execution urgency and resource consumption. During the multi-stage state tree evolution, the system will forcibly activate, attach, and expand the topology path branches corresponding to the nodes, so that they are freed from simple time loss control. Thus, the core state vector is directly embedded as a control variable into the growth and search convergence loop of the state tree. The multi-stage state tree search pruning module uses the determined delivery path as the topology axis and attaches a standard coordinate sequence to... A multi-stage task flow state tree is constructed by combining and constructing the tree. Its internal program performs forward data traversal on the nodes of the current evolution level in the multi-stage task flow state tree, calculating the estimated time for each path branch to reach the termination state. In the specific estimation of time, this invention sequentially accumulates the travel time between adjacent virtual nodes on the currently traversed path branch and the task execution time of each node. Specifically, the travel time is calculated by obtaining the standard graph topology distance between two nodes and dividing it by the preset average travel speed of the logistics vehicle, where the preset average travel speed of the logistics vehicle is set to 5 m / s. The task execution time is obtained by matching the baseline operation time according to the task type of the corresponding node and multiplying it by the task's workload parameter, where the baseline operation time is set to 300 seconds.

[0037] Finally, the computational unit sums the travel time and task execution time of all nodes on the path branch to obtain a highly certain estimated time to reach the termination state. This is used as the precise input for subsequent time series comparisons. The difference between the estimated time of each node branch and the standard time benchmark is defined as the predicted time loss. At the same time, the predicted time loss of all unclosed branches in the current evolution level is calculated and the arithmetic mean is used as the global dynamic mean. When the predicted time loss of a node branch is detected to be greater than the global dynamic mean, a discrete truncation event is triggered to stop the downward evolution of the node branch and discard the background search storage space of the corresponding branch, outputting the virtual path node sequence.

[0038] When data insertion disturbances occur within the standardized logistics operation time-space baseline, the delivery flow queue deadlock compensation module collects the actual task completion node data, calculates the difference between the actual task completion node data and the estimated nodes in the virtual path node sequence using a differential, and outputs the time-space residual value. When the time-space residual value exceeds the set window threshold between 800ms and 1500ms for three consecutive times, the system calls the dynamic gain correction rule to lower the comprehensive evaluation value of the currently disturbed flow channel. The calculation relationship conforms to the following formula: ,in, This is the comprehensive evaluation value of the current disturbed flow channel, expressed in points; This serves as the baseline evaluation value for the current disrupted circulation channel. To ensure a fixed penalty step size, its value is a fixed base penalty score of 10; The spatiotemporal residual value is the result of dimensionless processing. The significance of setting the aforementioned window threshold between 800ms and 1500ms for the engineering boundary is that if the spatiotemporal residual value is lower than the lower limit of 800ms, it indicates that the network transmission is experiencing slight jitter or hardware transient delay within the normal range. The system maintains its original operation and does not need to activate the penalty degradation logic, thereby avoiding the scheduling system from becoming overly sensitive and causing frequent oscillations in the scheduling queue.

[0039] If the spatiotemporal residual value exceeds the upper limit of 1500ms, it means that a substantial sudden blockage or order insertion disturbance has occurred in the flow channel, forcibly activating the step adjustment mechanism to make the scheduling converge quickly. The set fluctuation threshold of 25.0 square meters for the Euclidean distance discrete variance is determined based on the statistical steady-state value of the coordinate measurement variance under historical high-concurrency conditions. A value higher than the threshold indicates that the geometric spatial dispersion between nodes is normal, while a value lower than the threshold can accurately determine the existence of overlapping and misaligned records. The specific value can balance the cleaning accuracy of the standard sequence and the computational load of the system background. Based on the monotonically decreasing discrete order of the comprehensive evaluation value from large to small, the system performs time-series reordering of concurrent tasks in the currently disturbed flow channel to reorganize the task priority queue, outputs the optimized task chain as a logistics dynamic scheduling instruction and sends it to the multi-task data monitoring side to provide a spatial geometric alignment benchmark. The multi-task data monitoring side includes a central gateway router and an in-vehicle embedded terminal. The central gateway router is configured with gigabit Ethernet. The network physical interface and data processing system convert logistics dynamic scheduling instructions containing one-dimensional optimized task chains into standard downlink control message streams through the data distribution interface. These streams are then sent to the central gateway router via the network interconnection bus. The central gateway router parses the spatial geometric alignment reference in the message stream and extracts the target speed limit parameters and task refresh cycle for each node. The target speed limit parameters refer to the maximum allowable value of the logistics vehicle's moving speed, which is calculated by the logistics dynamic scheduling instructions and used as the saturation cutoff boundary of the underlying motor speed control system. The central gateway router sends the parameters to the vehicle-mounted embedded terminal through the wireless communication base station. After receiving the parameters, the controller of the vehicle-mounted embedded terminal rewrites the pulse width modulation duty cycle output of the driver chip to limit the physical driving speed of the logistics vehicle to within 5m / s. Simultaneously, the wireless communication retransmission timer is locked to 50ms, thus realizing the rigid constraint of the logistics dynamic scheduling instructions on the underlying communication and the actions of the vehicle-mounted actuators.

[0040] The data processing system continuously records the time consumed by its scheduling decision outputs over multiple consecutive scheduling cycles through a built-in monitoring program to construct a historical evolution sequence of time consumption. Waveform analysis technology is used to extract the mean volatility of this historical evolution sequence. Specifically, the waveform analysis technique employs a sliding window statistical method, using 10 consecutive scheduling cycles as a fixed pane. The historical evolution sequence of time consumption is discretized into a time-domain signal sequence. The calculation unit extracts the decision output time consumed by each scheduling cycle within the current pane, calculates its absolute deviation relative to the average decision time consumed within the current pane, and sums the squares of all absolute deviations within the window before dividing by the total number of samples. This calculates the standard deviation within a specific time pane. Finally, this standard deviation is divided by the mean to obtain the dimensionless variance fluctuation ratio, which serves as the specific value of the mean volatility. This allows for the precise extraction of the discrete oscillations of the time consumption sequence through non-parametric statistical waveform characteristics. The mean volatility is used as an adaptive evolution indicator to characterize the occurrence rate of deadlock in computing architecture resources. The underlying physical mechanism of this adaptive evolution indicator is that when the search evolution space of the multi-stage state tree faces the impact of tens of millions of high-concurrency tasks and suddenly expands, the system memory buffer of the data processing system will frequently experience high-frequency concurrent access conflicts, disordered swapping of memory pages, and intense contention for thread mutex locks. This underlying operating system kernel resource lock mutual exclusion and blocking will directly reflect the dramatic fluctuations in the output time of the scheduling decision algorithm and the increase in latency. Therefore, an abnormally high mean volatility of the time evolution sequence can scientifically and accurately predict that the current computing architecture of the system is on the verge of thread deadlock and buffer overflow. When the volatility index continues to exceed 20%, it means that the deadlock occurrence rate is rising. The two have a completely consistent physical and logical causal correspondence.

[0041] To eliminate crosstalk between network transmission jitter and background processes preempting time slices on the time-consuming waveform, the system establishes interference data cleaning logic. The data processing system utilizes the built-in kernel performance monitor to synchronously collect the system's background reference level delay at a 1ms sampling period. The background interference reference refers to the performance consumption index when the system maintains the microkernel self-loop. The sum of hardware interrupt response time and context switch time measured by accumulating through the performance register is used as a static calibration reference line to subtract environmental disturbances. The average context switch time and bus wait time measured by the kernel performance monitor in an idle state are identified as the background interference reference. After the computing unit extracts the total decision output time of the current cycle, it subtracts the reference through differential subtraction to obtain clean search time data, realizing the decoupling of real latency fluctuations and random noise from the external environment at the physical and logical layers. When the adaptive evolution index is detected to continuously exceed the predetermined safety threshold, the system adaptively reduces the single state tree. The system evolves to the maximum multi-stage hierarchical depth and increases the filtering sensitivity used for comparing spatiotemporal time series, thereby reducing the background computing load and ensuring that the adaptive evolution index converges stably within a predetermined safety threshold. The data processing system relies on the collaborative operation of the data parsing and attribute extraction module, the spatiotemporal geometric transformation alignment module, the multi-stage state tree search and pruning module, and the delivery flow queue deadlock prevention compensation module. It uses the task core state vector output by the data parsing and attribute extraction module as the control variable of the spatiotemporal geometric transformation alignment module. Combined with the discrete truncation action of the multi-stage state tree search and pruning module for invalid nodes, it reduces the computational overhead of high-concurrency task flow during state space evolution. Through the dynamic feedback constraint of spatiotemporal residual values, it controls the power consumption of the computing unit and the deadlock probability of the memory buffer, and converges the complex concurrent task conflicts into a monotonic one-dimensional optimized task chain. It establishes a two-way dependency closed loop within the system, in which the priority evaluation value of the business layer is corrected by spatial topological features.

[0042] Example 3: In this embodiment, in a high-concurrency automated retail logistics distribution center hybrid operation scheduling management scenario, the data processing system handles 1000 concurrent equipment maintenance and site inspection task requests per hour. The data processing system obtains the initial task dataset from the running instance of the discrete event logistics enterprise simulation platform. It actively injects a coordinate positioning measurement error with a deviation range of ±15.4m into the initial coordinate sequence of the initial task dataset and superimposes a timestamp transmission jitter disturbance of 45.2ms at the time node to test the stability of the scheduling management platform. The computing unit reads the initial task dataset, and the data parsing and attribute extraction module extracts the standard state vector containing maintenance task attributes and inspection task attributes from it. In the spatiotemporal geometric transformation alignment module, the initial coordinate sequence is uniformly projected to the preset standardized logistics operation spatiotemporal benchmark through the position mapping matrix. The time-series cleaning operator compares the spatial position residual values ​​of adjacent timestamps to process and clean the original input data.

[0043] Regarding the discrete variance of Euclidean distance within the data processing system The process of setting the fluctuation threshold involves identifying key process parameters as the threshold values. These threshold values ​​are controlled by the input density of the concurrent data stream and the variance level of the coordinate positioning measurement error. The technical trade-off in setting these key process parameters lies in balancing the cleaning accuracy of the standard coordinate sequence with the search load of the computing unit's background multi-stage task flow state tree. If the fluctuation threshold is set too low, misaligned and redundant coordinate records caused by timestamp transmission jitter cannot be cleared, leading to an exponential expansion of the number of branches in the multi-stage task flow state tree with high-concurrency task flows, resulting in increased memory allocation issues for the computing unit. If the fluctuation threshold is set too high, the timing cleaning operator will over-erase, mistakenly deleting normal task coordinate records, resulting in geometric path deviations in the output optimized task chain. The functional relationship model shows that when the variance of the coordinate positioning measurement error increases, the filter window of the fluctuation threshold should tend to the upper middle of its value range of 20.0 to 30.0. For the typical working condition of 1000 concurrent tasks per hour, the fluctuation threshold is set to a fixed value of 25.0 as a deterministic process operation benchmark after applying decision rules for specific working conditions.

[0044] To verify the synergistic effect of the technical solutions and the rationality of the numerical range boundaries, a multi-dimensional control system was constructed in the experimental design. This system included an experimental group using the complete technical solution, a control group using existing technology, a partially missing control group (partially missing functional components), a partially missing control group (partially missing feedback loops), an out-of-range control group (parameters exceeding the lower limit), and an out-of-range control group (parameters exceeding the upper limit). Three severity levels were set: low, medium, and high, corresponding to concurrent task input scales of 200 tasks per hour, 600 tasks per hour, and 1200 tasks per hour, respectively. At the low-intensity level, the processor utilization rate of the experimental group was 12.4%. The scheduling decision output latency was 45.2ms, and the number of deadlocks in the computation queue was 0. In contrast, the control group had a processor utilization rate of 28.6% and a scheduling decision output latency of 142.5ms. Under medium intensity, the experimental group's processor utilization rate remained stable at 24.3%, with a scheduling decision output latency of 88.6ms. The control group's processor utilization rate rose to 56.4%, and the scheduling decision output latency reached 412.3ms. Under high intensity, when the system faced a sudden influx of tasks, the experimental group used a data parsing and attribute extraction module to convert the initial task dataset into a cleaned standard coordinate sequence, calculating the discrete variance of the Euclidean distance between adjacent task nodes. The actual measured value was 8.5. Since it was lower than the set fluctuation threshold of 25.0, the system cleared the corresponding misaligned record in place. At this time, the multi-stage state tree search and pruning module evolved to construct a multi-stage task flow state tree.

[0045] When the prediction time loss at the current evolution level crosses the global dynamic mean, a discrete truncation event is triggered, discarding invalid branches. Ultimately, the processor utilization rate of the experimental group converges to 38.6%, and the scheduling decision output latency is 156.4ms, without memory allocation region lock-up. However, the control group, under high-intensity conditions, exhausts computing resources, with processor utilization rising to 92.4%, computation diverging, and scheduling decision output latency increasing to 2450.2ms, resulting in system shutdown. Simultaneously, the partial missing control group one, under high-intensity conditions, achieves a processor utilization rate of 78.3% and a scheduling decision output latency of 894.2ms, indicating that the lack of an active truncation mechanism leads to search branch overload. The path space alignment geometric deviation of the partial missing control group two increases to 34.6m, indicating that the lack of spatiotemporal residual feedback degrades the projection accuracy of the position mapping matrix. Further analysis of the out-of-range control group data shows that the out-of-range control group one suffers from an excessively low fluctuation threshold. The inability to clear jitter records caused the processor's computing power utilization rate to rise to 85.3%, and the scheduling decision output latency to be 712.4ms. Data from the out-of-range control group two showed that when parameters exceeded the upper limit, due to the timing cleaning operator mistakenly deleting valid coordinates, the final output virtual path node sequence deviated from the actual required geometric path by 48.7m. The scheduling success rate of logistics dynamic scheduling instructions plateaued after crossing the performance inflection point and remained at 58.2%. Further increasing the value did not improve performance, thus confirming that the parameter range defined in this invention belongs to a process working window that can balance multi-dimensional indicators. Based on the feature evolution of the above multi-dimensional experimental data, the data processing system confirmed the effectiveness of the control loop by extracting feature vectors to construct the core state vector of the task, using the position mapping matrix to implement standardized logistics operation spatiotemporal benchmark alignment, and relying on the global dynamic mean line to truncate the invalid search space of the multi-stage task flow state tree. The calculation relationship conforms to the following formula: ,in, This is the comprehensive evaluation value of the current disturbed flow channel. This serves as the baseline evaluation value for the current disrupted circulation channel. To ensure a fixed penalty step size, its value is a fixed base penalty score of 10; This represents the spatiotemporal residual value after dimensionless processing.

[0046] Based on a monotonically decreasing discrete order of comprehensive evaluation values ​​from largest to smallest, the system performs temporal reordering on concurrent tasks within the currently disturbed flow channel to reorganize the task priority queue. It outputs an optimized task chain as a dynamic logistics scheduling instruction and sends it to the multi-task data monitoring side, providing a spatial geometric alignment benchmark. During the aforementioned task priority queue reordering process, the system internally calls multi-level priority rule operators. These operators extract maintenance and inspection tasks with significant spatiotemporal overlap in the current high-concurrency task flow, calculate the intersection area ratio between the temporal overlap window and the geographic spatial range of different tasks, and confirm this ratio as the dynamic overlap coefficient. Multi-level priority... The level rule operator uses the dynamic overlap coefficient as a correction weight factor to overwrite the original business type parameters in memory in situ, so that concurrent tasks with high overlap spatiotemporal characteristics can obtain higher queuing weights. Thus, before the state tree is generated, the disordered concurrent task attributes are quickly reconstructed in memory into a monotonically decreasing one-dimensional optimized task chain. The data processing system continuously records the time consumption of its own scheduling decision output in multiple consecutive scheduling cycles through the built-in monitoring program to construct the time consumption history evolution sequence. Waveform analysis technology is used to extract the mean volatility of the time consumption history evolution sequence and use it as an adaptive evolution index to characterize the deadlock occurrence rate of computing architecture resources.

[0047] When the adaptive evolution index is detected to continuously exceed the predetermined safety threshold, the system adaptively reduces the maximum multi-stage level depth of a single state tree evolution and increases the filtering sensitivity used for comparing spatiotemporal time series. This reduces the background computing load and makes the adaptive evolution index stably converge within the predetermined safety threshold. The data processing system relies on the collaborative operation of the data parsing and attribute extraction module, the spatiotemporal geometric transformation alignment module, the multi-stage state tree search and pruning module, and the delivery flow queue deadlock prevention compensation module. It uses the task core state vector output by the data parsing and attribute extraction module as the control variable of the spatiotemporal geometric transformation alignment module. Combined with the discrete truncation action of the multi-stage state tree search and pruning module on invalid nodes, it reduces the computational overhead of high-concurrency task flow during state space evolution. Through the dynamic feedback constraint of spatiotemporal residual values, it controls the power consumption of the computing unit and the deadlock probability of the memory buffer, and converges concurrent task conflicts into a monotonic one-dimensional optimized task chain. A two-way dependency closed loop is established within the system, which corrects the priority evaluation value of the business layer by spatial topological features.

[0048] Example 4: This embodiment combines Figures 1 to 2 This section describes methods and systems for dynamic logistics scheduling and route optimization oriented to multiple tasks, such as... Figure 1As shown, step S1 is executed to parse task data and construct core state vector. The process then proceeds to step S2 to execute projected coordinates and remove redundancy to generate standard sequence. Next, step S3 is executed to construct state tree and truncate branch output node sequence. Finally, step S4 is executed to reorganize queue based on residuals to generate scheduling instructions.

[0049] like Figure 2 As shown, in the corresponding execution system architecture, the logical branches of each specific processing module converge towards the terminal goal of generating dynamic logistics scheduling instructions. The data parsing and attribute extraction module includes three input-side branches: extracting maintenance and inspection task attributes, constructing two-dimensional feature vectors, and normalizing the core state vector. The spatiotemporal geometric transformation alignment module includes three processing branches: calling the position mapping matrix, projecting the standardized spatiotemporal reference, and removing redundant coordinate records. The multi-stage state tree search and pruning module includes three evaluation branches: using the predetermined delivery path as the main axis, traversing forward data to extrapolate time, and truncating branches to output virtual paths. The delivery flow queue deadlock prevention compensation module includes three feedback compensation branches: generating spatiotemporal residual values ​​with a differential generator, continuously crossing the set window threshold, and reorganizing the queue to output optimized task chains.

[0050] Example 5: In this embodiment, within a high-concurrency automated retail logistics distribution center hybrid operation scheduling management scenario, the data processing system obtains the initial task dataset from the simulation platform and injects a 45.2ms timestamp transmission jitter and a 15.4m coordinate positioning measurement error into the time nodes and initial coordinate sequence, respectively, to simulate data fluctuations under extreme working conditions. The data parsing and attribute extraction module of the data processing system separates the maintenance task attributes and inspection task attributes from these data, and obtains the emergency timeliness parameters and workload parameters as the first state components. With the second state component The parameters are normalized and mapped to the core state vector of the task through a vector construction operator. The spatiotemporal geometric transformation alignment module calls the position mapping matrix to project the initial coordinate sequence onto the standardized logistics operation spatiotemporal reference. The time-series cleaning operator compares the spatial position residual values ​​of adjacent timestamps to erase duplicate records in the memory buffer, thus obtaining the standard coordinate sequence. The computing unit calculates the Euclidean distance and mean distance between adjacent task nodes under the spatiotemporal reference, and then obtains the discrete variance of the Euclidean distance between adjacent task nodes. ,when When the value falls below the preset fluctuation threshold of 25.0, the system automatically releases the corresponding misaligned redundant coordinate records in the memory buffer.

[0051] The multi-stage state tree search and pruning module uses the determined delivery path as the main axis and attaches a standard coordinate sequence to construct a multi-stage task flow state tree. Its processing program performs a forward traversal of the current evolution level of the multi-stage task flow state tree, deduces the estimated time for each path branch to reach the termination state, and determines the difference between the estimated time of each branch and the standard time consumption benchmark as the predicted time loss. The system calculates the arithmetic mean of the predicted time loss of all unclosed branches in the current level in real time as the global dynamic mean line. When the predicted time loss of a certain node branch is detected to exceed the global dynamic mean line, the processor executes a discrete truncation instruction to discard the background search space of the corresponding branch, constraining the high-dimensional combinatorial optimization space within a monotonically increasing linear range, and outputting a virtual path node sequence that meets the time cost limit.

[0052] When data insertion disturbances occur within the standardized logistics operation time-space baseline, the delivery flow queue deadlock compensation module calculates the difference between the actual completed node data and the estimated nodes in the virtual path node sequence using a differential converter to output the time-space residual value. In the spatiotemporal residual value When three consecutive sampling periods cross the set window threshold of 800ms to 1500ms and the average measurement reaches 1150.4ms, the system activates the backup step adjustment logic and dynamically corrects the comprehensive evaluation value of the current disturbed flow channel according to the following formula: ,in, This is the comprehensive evaluation value of the current disturbed flow channel. This serves as the baseline evaluation value for the current disrupted circulation channel. The penalty step size is fixed and is set to 10. These are the spatiotemporal residual values ​​after dimensionless processing; the system is based on... The monotonically decreasing discrete order of task priority queues is recompiled, and the optimized task chain is output as a dynamic scheduling instruction for logistics. It is then sent to the multi-task data monitoring side via the data distribution interface. The data processing system records the time consumption of scheduling decision output in multiple consecutive scheduling cycles through the built-in monitoring program, constructs a time consumption history evolution sequence, and extracts the mean volatility of the sequence as an adaptive evolution index characterizing the deadlock rate of computing architecture resources.

[0053] When the adaptive evolution index is detected to continuously exceed the preset safety threshold, the system reduces the maximum multi-stage level depth of a single state tree evolution and increases the filtering sensitivity used for comparing spatiotemporal time series, so that the adaptive evolution index converges to within the preset safety threshold. The data processing system relies on the collaboration of various functional modules, uses the task core state vector as the control variable, and coordinates with discrete truncation actions to reduce the computational overhead of high-concurrency task flow during state space evolution. Through the dynamic feedback constraint of spatiotemporal residual values, the overflow probability of the memory buffer is constrained, and concurrent task conflicts are converged into a monotonic one-dimensional optimized task chain. A two-way dependency closed loop is established within the system, which corrects the priority evaluation value of the business layer by spatial topology features, so as to realize the low-latency output of logistics dynamic scheduling instructions. Under the extreme working condition of 1200 high-intensity conflict requests per hour, the processor computing power utilization rate is stable at 38.6%, and the scheduling decision output latency is 156.4ms, realizing the stable output of scheduling decisions under high-concurrency business flow.

[0054] Example 6: In this embodiment, to ensure the logical robustness of the multi-task scheduling data processing system before deployment to a specific warehousing environment in a logistics center sorting operation management scenario, the technical solution executes a discretized initial state definition procedure during the initialization phase. This involves obtaining the current task node distribution density of the logistics center and discretizing the geospatial information corresponding to the geographical coordinates of each task node into a form containing… shaft and A two-dimensional planar matrix with axial coordinates is used to define the baseline computing power parameters of the computing unit. These parameters include a processor clock speed of no less than 2.5 GHz and a memory buffer capacity of no less than 8 GB. This establishes the initial state and enabling environment specifications of the system. The execution process is judged by a quantitative procedure. When the data processing system calculates the priority of each task, it monitors the memory usage and CPU time slice consumption. The standard for judging redundant tasks is set as the average processor load rate deviation exceeding a preset 15.0% within 5 consecutive processing clock cycles. This threshold is derived from stress test experiments under historical high load conditions. The quantitative indicators determine whether the system is in the risk range of computational divergence. When the judgment result shows that the system load continues to exceed the standard, the data flow reset mechanism is automatically triggered to temporarily store some low-priority discrete task data in the spare storage area to prevent deadlock of the memory buffer.

[0055] Before deploying the system to the actual operating environment, a standardized deployment and debugging procedure must be performed to calibrate the system's response sensitivity. The procedure includes the following specific steps: Input a test dataset containing known optimal scheduling paths into the simulator. The total number of tasks to be processed is 500. The initial task scheduling sequence is set to a completely random distribution. The system automatically calculates the comprehensive evaluation value of each task based on the task's core state vector. The system calculation formula is set as follows: ,in, This is the overall evaluation value of the task. This is the weighting coefficient for the emergency timeliness parameter, with a value of 0.6; Standardized values ​​for emergency timeliness parameters. This is the weighting coefficient for the work intensity parameter, with a value of 0.4. The standardized values ​​for the work intensity parameters are based on... The task priority queue is reorganized in a discrete order from largest to smallest. The reorganized scheduling path is compared with the known optimal scheduling path in the test dataset, and the deviation index is calculated. If the deviation index exceeds 5.0%, adjustments are made. and The value of the parameter is adjusted in reverse with a step size of 0.05 until the deviation index drops to within 5.0%. The set of debugging parameters in the output state serves as the final operating reference benchmark for the logistics dynamic scheduling system, thereby ensuring the consistency of the system's decision-making logic in complex and ever-changing working conditions. In this deployment and debugging procedure, the standardized values ​​of the emergency timeliness parameter and the standardized values ​​of the workload parameter essentially correspond to the first and second state components in the task core state vector in step S1, respectively. The weight coefficients of the emergency timeliness parameter and the workload parameter determined in the initialization and debugging phase are used to solidify the underlying mapping allocation ratio of the multi-level priority rule operators in the normal operation process of the system. This enables the data processing system to generate an initial benchmark evaluation value based on this consistent set of task attribute weights when entering the formal scheduling phase. This value is then used in conjunction with the spatiotemporal residual value calculated in real time by the differential in step S4 for dynamic negative correction, thereby providing a unified weight quantification benchmark for the step-by-step penalty self-healing of the comprehensive evaluation value.

Claims

1. A multi-task oriented logistics dynamic scheduling and path optimization method, characterized in that, Includes the following steps: Step S1: The data processing system receives an initial task dataset containing the time nodes, task types, and initial coordinate sequences of concurrent tasks, parses the initial task dataset to extract maintenance task attributes and inspection task attributes, and combines them to construct the core state vector of the task. Step S2: The data processing system calls the location mapping matrix to uniformly project the initial coordinate sequence onto the standardized logistics operation spatiotemporal reference, compares the spatial location residual values ​​of adjacent timestamps to eliminate redundant coordinate records, and generates a standard coordinate sequence. Step S3: The data processing system uses the predetermined delivery route as the topological axis, attaches the standard coordinate sequence to evolve and construct a multi-stage task flow state tree, monitors the predicted time loss of each state branch in the multi-stage task flow state tree, and cuts off the corresponding state branch when the predicted time loss is greater than the global dynamic mean line, and outputs the virtual path node sequence. Step S4: The data processing system collects the actual completed node data of the task, compares the difference between the actual completed node data of the task and the estimated node in the virtual path node sequence to generate a spatiotemporal residual value. When the spatiotemporal residual value crosses the set window threshold three times in a row, the dynamic gain correction rule is called to lower the comprehensive evaluation value of the current disturbed flow channel, and the task priority queue is reorganized according to the monotonically decreasing discrete order of the comprehensive evaluation value from large to small to output the optimized task chain and generate a logistics dynamic scheduling instruction. Furthermore, in step S4, the overall evaluation value of the currently disturbed flow channel is lowered, calculated according to the following formula: ,in, This is the comprehensive evaluation value of the current disturbed flow channel; This serves as the baseline evaluation value for the currently disturbed flow channel; To fix the penalty step size; The spatiotemporal residual value after normalization; in step S4, the task priority queue is reorganized according to the monotonically decreasing discrete order of the comprehensive evaluation value from large to small to output the optimized task chain, including: reordering the concurrent tasks in the current disturbed flow channel according to the order of the comprehensive evaluation value from large to small to generate the corrected scheduling queue. Step S3 involves constructing a multi-stage task flow state tree, which includes the following sub-steps: Step S31, the data processing system performs forward data traversal on each node branch of the current evolution level in the multi-stage task flow state tree to deduce the estimated time for each path branch to reach the termination state; Step S32, the data processing system calculates the deviation between the estimated time of each node branch and the corresponding standard time consumption benchmark, and defines the deviation as the predicted time loss. Step S3, which involves truncating the corresponding state branch when the prediction time loss exceeds the global dynamic mean, includes the following sub-steps: Step S33, the data processing system statistically analyzes the prediction time loss of all unclosed state branches in the current evolution level in real time, calculates the arithmetic mean of the prediction time loss, and confirms the arithmetic mean as the global dynamic mean; Step S34, when the data processing system detects that the prediction time loss of a certain node branch exceeds the global dynamic mean, it triggers a discrete truncation event, stops the downward evolution of the node branch, and discards the background search space of the corresponding branch. After generating the logistics dynamic scheduling instruction in step S4, the system also includes processing the data processing system... Performance monitoring steps: Step S41, the data processing system continuously records the time consumed by the scheduling decision output of the data processing system within multiple consecutive scheduling cycles, and constructs a historical evolution sequence of the time consumption; Step S42, the data processing system uses waveform analysis to extract the mean volatility of the historical evolution sequence of the time consumption, and uses the mean volatility as an adaptive evolution indicator to characterize the deadlock rate of computing architecture resources; Step S43, when the adaptive evolution indicator continuously exceeds the predetermined safety threshold, the data processing system reduces the maximum multi-stage level depth of a single state tree evolution construction and increases the filtering sensitivity used for comparing spatiotemporal time series, so that the adaptive evolution indicator stabilizes within the predetermined safety threshold.

2. The method for dynamic logistics scheduling and path optimization oriented to multiple tasks according to claim 1, characterized in that, Step S2 compares the spatial location residuals of adjacent timestamps to remove redundant coordinate records and generate a standard coordinate sequence, including the following sub-steps: Step S21, the data processing system calculates the Euclidean distance and the mean distance between adjacent task nodes under the standardized logistics operation spatiotemporal benchmark; Step S22, the data processing system calculates the discrete variance of the Euclidean distance between adjacent task nodes based on the mean distance. When the discrete variance of the Euclidean distance is lower than the set fluctuation threshold, it is determined that there is misalignment and redundancy in the corresponding adjacent timestamps and the coordinate records are removed. The remaining coordinates are then reassembled to obtain the standard coordinate sequence.

3. The method for dynamic logistics scheduling and path optimization oriented to multiple tasks according to claim 1, characterized in that, Step S1 involves parsing the initial task dataset to extract maintenance task attributes and inspection task attributes, and combining them to construct a task core state vector. This includes the following sub-steps: Step S11: The data processing system extracts the urgency parameters and workload parameters of each concurrent task from the initial task dataset; Step S12: The data processing system uses the urgency parameters as the first state component and the workload parameters as the second state component, combines them to construct a two-dimensional feature vector, and after normalization mapping, obtains the task core state vector used to characterize the core attributes of the task.

4. The method for dynamic logistics scheduling and path optimization oriented to multiple tasks according to claim 1, characterized in that, Step S2 calls the location mapping matrix to uniformly project the initial coordinate sequence onto the standardized logistics operation spatiotemporal reference, including the following sub-steps: Step S23, the data processing system obtains the multi-source heterogeneous spatiotemporal coordinate sequence; In step S24, the data processing system calls the position mapping matrix composed of translation and rotation operators to perform spatial alignment transformation on the multi-source heterogeneous spatiotemporal coordinate sequence, and projects it uniformly onto the standardized logistics operation spatiotemporal reference with the same predetermined reference axis to eliminate spatial topological overlap.

5. The method for dynamic logistics scheduling and path optimization oriented to multiple tasks according to claim 1, characterized in that, The window threshold is set as the deviation range of the spatiotemporal residual value between 800ms and 1500ms; the fixed penalty step size is the basic penalty score for adjusting the comprehensive evaluation value of the current disturbed flow channel, and the basic penalty score ranges from 5 to 15.

6. The method for dynamic logistics scheduling and path optimization oriented to multiple tasks according to claim 1, characterized in that, Step S4 generates a dynamic logistics scheduling instruction, including the following steps: Step S44, the data processing system converts the sequence of virtual path nodes that meet the time cost constraints into a deterministic dynamic flow task data stream; Step S45, the data processing system encapsulates the dynamic flow task data stream into a standard scheduling data packet, and distributes it to the multi-task data monitoring side through the data distribution interface of the logical processing architecture, providing an instant spatial geometric alignment benchmark.

7. A multi-task-oriented logistics dynamic scheduling and route optimization system, used to implement the multi-task-oriented logistics dynamic scheduling and route optimization method described in claim 1, characterized in that, include: The data parsing and attribute extraction module receives the initial task dataset at the input end, and is used to parse the initial task dataset to extract maintenance task attributes and inspection task attributes, and combine them to construct a two-dimensional feature vector. After normalization mapping, the core state vector of the task is obtained to represent the core attributes of the task. The spatiotemporal geometric transformation alignment module, whose input end is connected to the data parsing and attribute extraction module, is used to call the position mapping matrix composed of translation and rotation operators, to uniformly project the initial coordinate sequence in the initial task dataset onto the standardized logistics operation spatiotemporal reference to eliminate spatial topological overlap, and to compare the spatial position residual values ​​of adjacent timestamps through the time-series cleaning operator to remove redundant coordinate records and generate a standard coordinate sequence. The multi-stage state tree search and pruning module has its inputs connected to the data parsing and attribute extraction module and the spatiotemporal geometric transformation alignment module, respectively. It is used to construct a multi-stage task flow state tree by attaching a standard coordinate sequence with the given delivery path as the topology axis. The module monitors the prediction time loss of each state branch through the feedforward topology pruning program. When the prediction time loss is greater than the global dynamic mean line, a discrete truncation event is triggered to discard the background search space of the corresponding branch and output the virtual path node sequence. The delivery flow queue deadlock compensation module is connected to the multi-stage state tree search and pruning module at its input end. It is used to collect the actual task completion node data and calculate the difference between the actual task completion node data and the estimated node in the virtual path node sequence by the differential to generate the spatiotemporal residual value. When the spatiotemporal residual value crosses the set window threshold three times in a row, the backup step-by-step adjustment mechanism is activated and the dynamic gain correction rule is called to lower the comprehensive evaluation value of the current disturbed flow channel. Based on the monotonically decreasing discrete order of the comprehensive evaluation value from large to small, the task priority queue is reorganized to output the optimized task chain and generate logistics dynamic scheduling instructions.

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