Tms logistics ai capacity prediction system based on machine learning

Abstract

This invention relates to the field of intelligent logistics and transportation management technology, specifically a machine learning-based TMS logistics AI capacity prediction system. The system includes a multi-dimensional data acquisition module, a basic demand prediction module, a spatiotemporal friction simulation engine, a global resilience optimization module, and an execution result bridging module. It collects order, weather, road condition, and node terminal data, cleans them to generate a regular dataset and a physical constraint dataset; predicts the ideal capacity demand sequence within a target time window, and calculates queuing delay indicators, node congestion indices, and contract default probabilities based on node throughput capacity; outputs direct scheduling instructions when the default probability is below a safety threshold, otherwise generates a friction-resistant capacity scheduling scheme by minimizing global comprehensive resource overhead; and generates a reward function value based on the actual contract performance deviation to drive the reinforcement learning agent to iteratively update, achieving closed-loop coupling between demand prediction and the physical node throughput boundary.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics and transportation management technology, specifically to a machine learning-based TMS logistics AI capacity prediction system. Background Technology

[0002] TMS logistics capacity forecasting refers to the estimation and scheduling of vehicle capacity demand within a target time window based on order demand, external environment, and logistics node operation. Current TMS logistics capacity forecasting methods include experience-based forecasting based on historical order statistics, scheduling judgment based on fixed rule thresholds, and demand forecasting combined with conventional time series models. However, when forecasting transport capacity based on existing technologies, on the one hand, they often focus on demand-side quantity forecasting, and do not give sufficient consideration to physical constraints such as warehouse platforms, personnel scheduling, loading and unloading rhythm, and on-site traffic capacity, making it difficult to reflect the evolving risks of vehicle queuing, node congestion, and contract default. On the other hand, when faced with complex operating conditions such as abnormal data injection, dynamic decay of node throughput capacity, and cross-node collaborative scheduling, existing technologies generally suffer from insufficient real-time adaptability and imperfect feedback loops. These problems will reduce the reliability of transport capacity forecasting and actual contract scheduling. Summary of the Invention

[0003] The purpose of this invention is to provide a machine learning-based TMS logistics AI capacity prediction system to solve the following technical problems: This avoids situations where high prediction accuracy leads to unstable on-site performance, and enables closed-loop coupling between demand-side forecasts and the actual throughput boundaries of physical nodes. It achieves a technological evolution from static forecasts based on a single demand quantity threshold to multi-dimensional capacity scheduling joint dynamic optimization controlled by the performance capability boundary.

[0004] The objective of this invention can be achieved through the following technical solutions: The TMS logistics AI capacity prediction system based on machine learning includes a multi-dimensional data acquisition module, a basic demand prediction module, a spatiotemporal friction simulation engine, a global resilience optimization module, and an execution result bridging module. The multidimensional data acquisition module connects to the external environment system and physical node terminal to perform data cleaning, generating a first-dimensional regular dataset containing task characteristics and environmental status, and a second-dimensional physical constraint dataset containing node throughput capacity characterization parameters. The basic demand forecasting module calls the time series forecasting model to map the first dimension of the regular dataset to the ideal capacity demand sequence within the preset target time window; The spatiotemporal friction simulation engine includes a queuing simulation unit and a state transition analysis unit. The queuing simulation unit overlays the ideal capacity demand sequence with the second-dimensional physical constraint dataset to calculate the queuing congestion index of physical nodes. The state transition analysis unit calls the Markov chain model to generate the node congestion index and the probability of default. The global resilience tuning module calls a reinforcement learning agent; in response to the default probability being lower than a preset safety threshold, it outputs an ideal capacity demand sequence as a direct scheduling instruction; in response to the default probability being not lower than the preset safety threshold, it generates an anti-friction capacity scheduling scheme with the goal of minimizing global comprehensive resource overhead. The execution result bridging module obtains real performance status data, calculates the deviation between the actual default rate and the default probability based on the real performance status data, generates a reward function value, and drives the agent to iteratively update the network parameters.

[0005] Furthermore, the multidimensional data acquisition module also includes a source ontology verification unit, which performs trend stripping operations on the first-dimensional conventional dataset to extract high-frequency residual sequences and calculates the multi-scale permutation entropy value of the high-frequency residual sequences; The source ontology verification unit performs logical judgment based on a preset feature domain; when the multi-scale permutation entropy value is within the pre-defined normal fluctuation range, the data is deemed valid. When the entropy value of the multi-scale permutation exceeds the normal fluctuation range, the source ontology verification unit determines that the first-dimensional regular dataset contains abnormally injected data and sends a source blocking instruction to the basic demand prediction module to prohibit the execution of prediction logic based on the dataset.

[0006] Furthermore, the spatiotemporal friction simulation engine also includes a benchmark dynamic correction unit, which calculates the actual throughput rate index of physical nodes in real time and monitors the temporal variation characteristics of the actual throughput rate index. When the reference dynamic correction unit detects that the actual throughput rate index of a consecutive preset number of acquisition cycles shows a monotonically decreasing trend and the attenuation rate matches the preset fatigue attenuation model, it generates a dynamic tolerance compensation coefficient. When calculating queuing congestion indices, the queuing simulation unit applies a dynamic tolerance compensation coefficient to nonlinearly correct the node throughput capacity characterization parameters in the second-dimensional physical constraint dataset.

[0007] Furthermore, the node throughput capacity representation parameters in the second-dimensional physical constraint dataset include the number of platforms, available area, average loading and unloading rate, and personnel scheduling parameters. The queuing simulation unit calls a pre-set queuing theory model to transform the ideal capacity demand sequence into an arrival rate Poisson distribution sequence, couples the average loading and unloading rate parameter with the personnel scheduling parameter into a service rate exponential distribution sequence, and calculates the steady-state queue length expectation value and dwell time expectation value of the system in combination with the platform quantity parameter. The steady-state queue length expectation value and dwell time expectation value are combined into a queuing delay index.

[0008] Furthermore, when calling the Markov chain model, the state transition analysis unit maps the queuing delay index to a discrete set of environmental states, and defines the node congestion index and the probability of default as the expected negative revenue value in the state transition process. The state transition analysis unit iteratively calculates the long-term cumulative negative return expectation with time discount factor under each environmental state through the Bellman equation, and extracts the long-term cumulative negative return expectation as the quantitative output benchmark for the probability of default.

[0009] Furthermore, the anti-friction capacity scheduling scheme includes spatial translation strategy, time translation strategy, and physical expansion early warning strategy; The global resilience tuning module executes multi-level traffic splitting logic; for task characteristic parameters where the timeliness tolerance is lower than the preset time threshold and there are backup nodes, the spatial shift strategy is triggered to generate cross-node traffic splitting instructions; For task characteristic parameters where the resource consumption weight coefficient is higher than the preset consumption threshold and the timeliness tolerance is not lower than the preset time threshold, a time shift strategy is triggered to generate a delay scheduling instruction. For the remaining task characteristic parameters that cannot execute spatial translation and temporal translation strategies, a physical expansion warning strategy is triggered, generating cross-system node resource allocation instructions.

[0010] Furthermore, when minimizing the overall global resource overhead, the reinforcement learning agent obtains the expected scheduling resource overhead generated by executing the anti-friction capacity scheduling scheme, the expected vehicle congestion resource loss parameter caused by the queuing and delay index, and the expected default resource loss parameter mapped by the default probability. The objective function is to linearly weight and sum the expected scheduling resource overhead, expected traffic congestion resource loss, and expected default resource loss parameters by using dynamic weight coefficients generated in real time based on the current node congestion index. The reinforcement learning agent updates the network parameters through the policy gradient algorithm to minimize the output value of the objective function.

[0011] Furthermore, the global resilience tuning module also includes an abnormal pattern classification unit, which constructs a spatiotemporal friction feature vector composed of the arrival rate in the arrival rate Poisson distribution sequence and the node throughput capacity characterization parameter when the probability of default is not lower than a preset safety threshold. When the feature vector shows that the rate of change of arrival rate within a preset time window exceeds the first preset rate of change threshold, but the node throughput capacity characterization parameter is within the normal range, it is determined to be an external demand shock event. When the feature vector shows that the arrival rate is within the normal range but the decrease in the node throughput capacity representation parameter exceeds the second preset rate of change threshold, it is determined to be an internal physical node paralysis event; when the feature vector shows that the rate of change of the arrival rate within the preset time window exceeds the first preset rate of change threshold, and the decrease in the node throughput capacity representation parameter exceeds the second preset rate of change threshold, it is determined to be a composite spatiotemporal friction instability event; the abnormal mode classification unit outputs a clearly categorized audit warning instruction accordingly.

[0012] Furthermore, the system is deployed in a layered architecture that includes edge computing nodes and cloud servers; The multidimensional data acquisition module resides on the local edge computing node of the physical node, performing real-time data acquisition and preliminary format standardization processing; The basic demand prediction module, spatiotemporal friction simulation engine, global resilience optimization module, and execution result bridging module reside on the cloud server, performing global logical deduction and strategy generation based on the aggregated first-dimensional conventional dataset and second-dimensional physical constraint dataset.

[0013] The beneficial effects of this invention are: 1. In view of the shortcomings of traditional methods that ignore physical constraints, this invention overlays capacity demand with real physical constraint data such as the number of platforms and staff scheduling; through queuing simulation and Markov state transition analysis, it accurately quantifies queuing delay indicators and default risks, and realizes a technological upgrade from simply predicting the quantity to predicting the evolution of performance congestion. 2. To address the issue of reduced prediction reliability caused by abnormal data injection, this invention performs trend stripping on regular data and calculates the multi-scale permutation entropy value of the high-frequency residual sequence. When the entropy value exceeds the limit, the anomaly is accurately identified and the information source is cut off, preventing false business pulses from interfering with the prediction logic. This ensures the authenticity of the underlying logistics demand data and the accuracy of scheduling from the source. 3. This invention addresses the problem of overestimating load-bearing capacity due to non-constant node throughput capacity. The system monitors the throughput rate in real time and matches it with a preset fatigue attenuation model. By generating a dynamic tolerance compensation coefficient, the throughput parameters are non-linearly adjusted downwards, effectively avoiding the risk of efficiency decline caused by continuous high-load operation of on-site personnel and equipment due to static parameters. 4. To address the problem of rigid scheduling rules, this invention systematically constructs a multi-level diversion strategy that includes spatial shifting, time-shifting, and physical expansion; and utilizes reinforcement learning agents to perform dynamic weighting and closed-loop iteration on resources such as scheduling, congestion, and default, breaking the resource waste caused by rigid single-point scheduling and achieving global resilient scheduling. 5. This invention addresses the problem of unclear causes of high-risk situations leading to difficulties in timely on-site handling. The system analyzes the spatiotemporal friction feature vector composed of arrival rate and node throughput capacity; it can automatically distinguish whether congestion is caused by a sudden increase in external demand or internal physical equipment failure, and output clearly categorized early warning instructions, significantly optimizing the system response time of cross-departmental emergency collaboration. 6. This invention addresses the problem of delayed heterogeneous data collection in complex logistics networks. The system adopts a layered deployment architecture of edge computing nodes and cloud servers. The edge side ensures real-time cleaning and anti-network outage caching of on-site data, while the cloud side aggregates data and performs cross-node inference, realizing the fusion feedback of end-point perception robustness and global capacity scheduling optimization calculation. Attached Figure Description

[0014] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the modules of the TMS logistics AI capacity prediction system based on machine learning provided in the embodiments of this application. Detailed Implementation

[0015] 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.

[0016] Please see Figure 1 The TMS logistics AI capacity prediction system based on machine learning includes a multi-dimensional data acquisition module, a basic demand prediction module, a spatiotemporal friction simulation engine, a global resilience optimization module, and an execution result bridging module. The multidimensional data acquisition module connects to the external environment system and physical node terminal to perform data cleaning, generating a first-dimensional regular dataset containing task characteristics and environmental status, and a second-dimensional physical constraint dataset containing node throughput capacity characterization parameters. The basic demand forecasting module calls the time series forecasting model to map the first dimension of the regular dataset to the ideal capacity demand sequence within the preset target time window; The spatiotemporal friction simulation engine includes a queuing simulation unit and a state transition analysis unit. The queuing simulation unit overlays the ideal capacity demand sequence with the second-dimensional physical constraint dataset to calculate the queuing congestion index of physical nodes. The state transition analysis unit calls the Markov chain model to generate the node congestion index and the probability of default. The global resilience tuning module calls a reinforcement learning agent; in response to the default probability being lower than a preset safety threshold, it outputs an ideal capacity demand sequence as a direct scheduling instruction; in response to the default probability being not lower than the preset safety threshold, it generates an anti-friction capacity scheduling scheme with the goal of minimizing global comprehensive resource overhead. The execution result bridging module obtains real performance status data, calculates the deviation between the actual default rate and the default probability based on the real performance status data, generates a reward function value, and drives the agent to iteratively update the network parameters.

[0017] This embodiment provides a machine learning-based TMS logistics AI capacity prediction mechanism. Specifically, the system is deployed in the peak business fulfillment scenario of a national home appliance retailer. The company has set up a main warehouse W1, a forward distribution node W2, and a backup transit node W3 in East China. It needs to continuously predict the vehicle arrival demand of each node within 24 hours before the promotion date and avoid vehicles accumulating outside the main warehouse platform for a long time. The system operates in a closed loop of prediction-simulation-tuning-feedback, replacing the static prediction mode that outputs a single scalar of capacity demand. The multi-dimensional data acquisition module synchronously collects data from the order management system, weather platform, traffic condition platform, and on-site terminals in various warehouses; among them, the first dimension of the regular dataset includes order waves, product category structure, destination distribution, promised delivery time, weather warnings, road congestion levels, etc. The second-dimensional physical constraint dataset includes platform resources at each node, available yard area, loading and unloading equipment status, shift schedule, and on-site accessibility. In order to make subsequent predictions based on available data, the multi-dimensional data acquisition module first cleans the original time series data. Outlier removal here is not simply deleting large values, but rather identifying data points that are inconsistent with the physical patterns on site. For example, a node may report high throughput during nighttime shutdown periods, or there may be a sudden surge in orders in a certain hour, but the upstream sales system has no corresponding activity record. Missing value imputation prioritizes maintaining temporal continuity so that demand trends and changes in node capabilities are not interrupted. In an exemplary application scenario, the target time window can be divided into three adjacent time periods: S1, S2, and S3. In the cleaned first dimension of the regular dataset, W1 corresponds to medium, high, and high regular order pressure in S1 to S3, respectively, the weather status is sunny, rainstorm warning, and rainstorm, respectively, and the road status is smooth, slow traffic, and congestion, respectively. Based on this, the basic demand forecasting module outputs the ideal capacity demand sequence, such as the need to bring in Class A vehicles, Class A+B vehicles, and a combination of Class B vehicles in three time periods. The ideal capacity demand sequence is a baseline data generated solely based on capacity demand characteristics without introducing physical constraints. Its calculation logic is not coupled with the platform physical resource quota and the actual throughput capacity limit of the loading and unloading team. After receiving the ideal capacity demand sequence, the spatiotemporal friction simulation engine superimposes it with physical constraints; the queuing simulation unit does not rely solely on the static number of vehicles to make threshold judgments, but simulates the complete flow of vehicles from arriving at the park, waiting to dock, loading and unloading operations to leaving the site. If vehicles arrive in a concentrated period of time, but the number of open platforms does not increase accordingly, there will be waiting at the platform; if loading and unloading workers change shifts, although the platform is still open, the actual service pace will decrease, which will also increase congestion; the system generates a queuing congestion index based on this, which is used to characterize the degree of evolution of nodes from smooth flow to critical congestion or even unstable backlog. The state transition analysis unit further observes whether this stagnation state will be transmitted across time periods; for example, the queue formed in time period S2 may continue into time period S3, thus causing orders that should have been loaded on time in time period S3 to be backed up by previous orders and occupy resources. To this end, the system maps queuing and congestion situations to several discrete environmental states, such as high pressure operation that can be digested, continuous backlog, and near default, and then analyzes the possibility of transition between states, outputting the node congestion index and the probability of default. The probability of default is not determined solely by the node capacity saturation in a single time window, but is calculated based on the possibility that congestion will continue to amplify in subsequent windows and lead to overdue performance. The global resilience tuning module receives the default probability and performs conditional arbitration; when the default probability is lower than the safety threshold, it means that the current ideal demand can still be absorbed in the physical network, and the system directly issues scheduling instructions. When the probability of default reaches or exceeds the safety threshold, it indicates that the demand peak has become coupled with the node's throughput limit. If the undifferentiated capacity scheduling instructions continue to be executed, it will trigger excessive delays of capacity entities and induce the risk of default. Therefore, the system changes to outputting a friction-resistant capacity scheduling scheme. This plan may include actions such as diverting traffic, delaying entry, and increasing loading and unloading resources. Its goal is no longer to pursue full load rate at a single point, but to reduce the additional resource consumption caused by global abnormal states. The execution result bridging module collects real performance status data after the solution is executed, such as average vehicle waiting time, actual outbound cycle time of nodes, proportion of overdue orders, and driver dwell time records. The module compares the actual results with the previously given default probability and feeds back the reward function value to the reinforcement learning agent; if the system estimates a high risk and high congestion actually occurs, the model will strengthen the previous warning judgment. If the system anticipates a high risk but it has been mitigated on-site through temporary overtime work, the model will learn to reduce its conservatism in similar working conditions; this will allow the strategy to better align with the company's actual operational boundaries in the long run. As a fault-tolerant mechanism, if the external environment system is temporarily interrupted, such as a delay in the weather interface, the multi-dimensional data acquisition module can enter a degraded mode while retaining the order and node physical data, and supplement it with the most recent effective environmental status or manually maintained regional risk labels, without interrupting the core simulation process. If a node's on-site terminal goes offline, the system marks the node as an incomplete observation node, adopts a conservative value for its throughput capacity, and increases the risk-side weight to avoid amplifying errors due to missing optimistic estimates. If the reinforcement learning agent is still in the early stages of training or there is insufficient historical data for new nodes, an initial anti-friction scheme can be generated using a preset rule base. After accumulating sufficient feedback, the agent's control weights can be gradually increased. Before the pre-set peak period, the W1 main warehouse received a surge in air conditioner and refrigerator orders in East China. The basic demand forecasting module judged that there would be continuous peaks from the early morning to the morning of the next day. At the same time, only 6 warehouses were open at W1 site, and the early morning shift team had not yet fully arrived at their posts. The system first provides an ideal demand sequence. The simulation shows that if all vehicles arrive around 06:00 as originally planned, it will create a continuous queue and affect orders with promised delivery times in the morning. The state transition analysis unit found that the probability of default was no less than the preset safety threshold. Therefore, the global resilience optimization module no longer directly issued the original dispatching volume, but transferred some non-urgent orders to the W3 transfer node and postponed the arrival time of another part of the vehicles until the second wave of shifts arrived in the morning. The execution result bridging module collected data at the end of the day showing a significant reduction in average waiting time and a decrease in the timeout rate, and fed the results back to the intelligent agent. The purpose of this step is to couple demand-side forecasts with the actual throughput boundaries of physical nodes in a closed loop, thereby achieving a technological evolution from static forecasts based on a single demand quantity threshold to multi-dimensional capacity scheduling joint dynamic optimization controlled by the fulfillment capacity boundary, and avoiding situations where high forecast accuracy is achieved but on-site fulfillment instability occurs.

[0018] In a preferred embodiment of the present invention, the multidimensional data acquisition module further includes a source ontology verification unit, which performs trend stripping operation on the first-dimensional conventional dataset to extract high-frequency residual sequences and calculates the multi-scale permutation entropy value of the high-frequency residual sequences; the source ontology verification unit performs logical judgment based on a preset feature domain; when the multi-scale permutation entropy value is within the pre-calibrated normal fluctuation range, the data is determined to be valid; When the entropy value of the multi-scale permutation exceeds the normal fluctuation range, the source ontology verification unit determines that the first-dimensional regular dataset contains abnormally injected data and sends a source blocking instruction to the basic demand prediction module to prohibit the execution of prediction logic based on the dataset.

[0019] This embodiment provides a source ontology verification mechanism. Specifically, in the aforementioned promotional scenario, relying solely on outlier removal and missing value imputation may still be insufficient, because order-side or environment-side data is sometimes not natural noise, but rather contains anomaly injections, such as duplicate marketing activity records, cross-regional order mapping errors caused by crosstalk in interface data transmission, or even human test data mistakenly entering the production chain. Such data may not be extreme in numerical terms, but it can undermine the true basis for subsequent capacity prediction. The source ontology verification unit first performs trend stripping on the time series in the first dimension of the regular dataset, separating the long-term normal growth trend from the short-term abnormal jitter; The system focuses on high-frequency residual sequences because although real logistics demand may change due to promotions, weather, and time of day, its subtle fluctuations are usually still constrained by the business rhythm and will not show disorder at multiple scales at the same time. Multiscale permutation entropy is used to measure the degree of this disorder: if a data stream exhibits jumps that do not conform to the business rhythm at the hourly, half-hourly, and finer-grained levels, it indicates that the information source may have deviated from the actual business physical process. In an exemplary application scenario, three consecutive data segments of a node can be denoted as D1, D2, and D3. Under normal circumstances, D1 reflects the gradual increase in pre-heating orders, D2 reflects the accelerated increase after the start of the activity, and D3 reflects the high-level platform. At this time, the high-frequency residual mainly reflects the high-frequency small fluctuations in the natural increase of promotion volume, and the multi-scale arrangement entropy remains within the pre-calibrated normal fluctuation range. If an abnormal injection occurs, it may manifest as a sudden insertion of a pulse order in stage D2 that is significantly inconsistent with the historical consumption capacity of the region, and there is no upstream promotional evidence to support the pulse at the minute or hour level. In this case, the permutation entropy will deviate from the normal range. At this time, the system does not rush to repair the data, but first blocks the source of information from entering the prediction module to prevent it from polluting subsequent decisions. As a fault-tolerant mechanism, if a certain information source deviates slightly only on a local time scale, while other related information sources such as payment confirmation volume, warehouse pre-allocation volume, and marketing plan can corroborate each other, the system can mark it as suspicious but not block it, and reduce the weight of the information source to continue operating. If the backup data source becomes available after the source is blocked, for example, if there is a backup order stream in the same region in the enterprise master data platform, then switch to the backup source. If there is no backup, the prediction module within the corresponding time window can use the conservative estimate of the most recent stable window and simultaneously send a data verification prompt to the operation and maintenance terminal; if multiple key information sources are abnormal at the same time, the system enters a cautious mode, only allowing the output of conservative scheduling schemes and not executing large-scale expansion instructions. Six hours before the W1 main warehouse promotion, the marketing interface suddenly reported a surge in home appliance orders in a certain city, but the payment side transaction data and historical campaign plans did not show corresponding synchronous changes. After the source ontology verification unit stripped the intraday trend of the city's order flow, it found that the high-frequency residuals exhibited irregular jumps at multiple granularities, which were significantly different from the regular rhythm of promotional volume releases. Therefore, it was determined that the data flow might contain abnormal injections, and a blocking command was immediately sent to the basic demand prediction module. The system switched to using the order flow and warehouse pre-pick plan that had been verified in the previous hour to continue forecasting, avoiding the unnecessary introduction of redundant capacity due to a false peak data. The purpose of this mechanism is to block seemingly reasonable but actually distorted inputs at the source level, thereby protecting the authenticity of the prediction data and preventing subsequent simulations and scheduling from being built on contaminated data.

[0020] In a preferred embodiment of the present invention, the spatiotemporal friction simulation engine further includes a benchmark dynamic correction unit, which calculates the actual throughput rate index of physical nodes in real time and monitors the temporal variation characteristics of the actual throughput rate index. When the benchmark dynamic correction unit detects that the actual throughput rate index shows a monotonically decreasing trend for a consecutive preset number of acquisition cycles and the attenuation rate matches the preset fatigue attenuation model, it generates a dynamic tolerance compensation coefficient. When calculating the queuing delay index, the queuing simulation unit applies the dynamic tolerance compensation coefficient to perform nonlinear correction on the node throughput capacity characterization parameters in the second-dimensional physical constraint dataset.

[0021] This embodiment provides a benchmark dynamic correction mechanism; specifically, in the aforementioned scenario, even if the number of platforms, the number of shift workers, and the average loading and unloading capacity have been collected, it cannot be assumed that these capacities remain constant throughout the entire window. There is a clear fatigue decay phenomenon in actual logistics nodes. For example, the working rhythm of forklift drivers decreases after consecutive night shifts, the time for loading and unloading workers to change pallets and verify due to high-intensity handling is extended, and the efficiency of internal logistics flow is reduced due to adverse weather conditions in the park. If the system still uses static throughput parameters, the queuing risk will be underestimated. The benchmark dynamic correction unit continuously receives actual throughput rate indicators from the node site, such as the number of vehicles that have completed docking per unit time, the average loading and unloading completion time, the forklift round trip cycle, and the warehouse exit release cycle. The system focuses not on a single instantaneous drop, but on the monotonous decrease over multiple consecutive data collection cycles, and determines whether this decrease conforms to the fatigue decay model. The physical meaning is that real fatigue usually manifests as a gradual decline in efficiency, rather than random fluctuations. If the speed slows down for a period of time due to the complexity of loading and unloading individual large items, it will recover and should not be misjudged as continuous decay. In an exemplary application scenario, suppose that the actual throughput performance of node W1 in three consecutive time periods T1, T2, and T3 decreases from normal to below the first preset throughput threshold and then to below the second preset throughput threshold, and the shift has not changed and the equipment has not experienced any new faults, indicating that the processing capacity under the same operating system is declining. At this point, the benchmark dynamic correction unit generates a dynamic tolerance compensation coefficient to lower the previously recorded capacity of absorbing a certain number of vehicles per hour. The nonlinear correction here employs an exponential weight compensation mechanism based on the critical interval. Specifically, the calculation logic for the dynamic tolerance compensation coefficient is as follows: when the number of consecutive decay cycles reaches a preset threshold, the compensation coefficient is generated using the following formula: in, This is the dynamic tolerance compensation coefficient. The number of data collection cycles that continuously exhibit a monotonically decreasing trend. The preset maximum attenuation penalty parameter, This is the fatigue sensitivity decay rate constant unique to the node; As a fault-tolerant mechanism, if the actual throughput rate decreases but a short-term external disturbance is detected at the same time, such as a single batch of large, irregularly shaped goods arriving at the site, a security inspection, or a reordering after a short power outage, the system can identify it as a transient event and not immediately trigger fatigue correction. If there are gaps in the data collection across multiple periods, the previous stable compensation coefficient should be maintained until the observation is restored before updating. If the work team has been changed or new equipment has been added on site, the system should reset the fatigue accumulation state to avoid inheriting the attenuation errors of the old work team to the new work team. During the nighttime peak hours in the W1 main warehouse, the system found that although the number of platforms remained unchanged, the number of loading vehicles completed per hour continued to decrease during the three consecutive data collection cycles from 02:00 to 04:00, and there were no abnormal equipment alarms on site, only continuous overload operation was recorded. Based on this, the benchmark dynamic correction unit judged that the node had entered the fatigue decay stage, so it reduced the throughput capacity for that period and input the corrected capacity into the queuing simulation. The simulation results showed that the originally barely digestible vehicle arrival plan had turned into a high probability of congestion after the correction. Therefore, the subsequent optimization module executed the threshold adaptive reduction instruction for the vehicle arrival density of the corresponding period. The purpose of this mechanism is to correct the nominal capacity of nodes to a more realistic real-time available capacity, thereby enabling timely reflection of fatigue, continuous high load and work cycle decay, and avoiding static parameters from masking the real congestion risks.

[0022] In a preferred embodiment of the present invention, the node throughput capacity characterization parameters in the second-dimensional physical constraint dataset include the number of platforms, available area, average loading and unloading rate, and personnel scheduling parameters. The queuing simulation unit calls a pre-set queuing theory model to transform the ideal capacity demand sequence into an arrival rate Poisson distribution sequence, couples the average loading and unloading rate parameter with the personnel scheduling parameter into a service rate exponential distribution sequence, and calculates the steady-state queue length expectation value and dwell time expectation value of the system in combination with the platform quantity parameter. The steady-state queue length expectation value and dwell time expectation value are combined into a queuing delay index.

[0023] This embodiment provides a node throughput parameter modeling and queuing delay calculation mechanism. Specifically, the aforementioned scheme can identify the coupling risk between demand peaks and node capacity. However, if the composition of physical constraint parameters is not further clarified, it is easy to abstract node capacity into a single number and ignore the linkage relationship between platforms, space, personnel, and cycle time in the logistics site. Therefore, this embodiment decomposes node throughput capacity into multiple observable parameters and expresses them uniformly through queuing theory. The number of platforms reflects the number of vehicles that can be served in parallel, and determines the upper limit of vehicles that can start loading and unloading at the same time. Available area parameter reflects whether there is enough turnover space for goods to be loaded, transfer pallets and returning vehicles in the yard. When the area is close to the upper limit, even if the platform is empty, the actual service rate may decrease due to the obstruction of goods transfer in the yard. Average loading and unloading rate parameter reflects the processing cycle of a single station under standard operation. The personnel scheduling parameters characterize the number of loading, unloading, verification, forklift, and release personnel that can be deployed in different time periods; the system converts the ideal transportation capacity demand sequence into an arrival rate sequence, which technically means mapping how many vehicles are planned to arrive in the future to the density of arrival events per unit time. Meanwhile, the loading and unloading rate is coupled with the shift schedule into a service rate sequence, which reflects how much resource can complete the departure within a unit of time. In order to clarify the implicit mapping process of the algorithm, the above transformation has a strict mapping logic in the system: the queuing simulation unit divides the total number of trains in the ideal capacity demand sequence within the preset statistical time segment by the time span and extracts the arrival rate mean parameter of Poisson distribution. At the same time, the basic average loading and unloading rate is multiplied by the personnel scheduling efficiency coefficient, for example, 1.0 for full staffing and 0.6 for handover vacancies, to derive the single-unit service rate parameter of the exponential distribution. Combining the number of parallel service stations, which is equal to the number of platforms, the system substitutes all three into the multi-service station parameter. The steady-state queuing theory calculation engine calculates the steady-state queue length expectation and dwell time expectation of the system, that is, the average time taken for a single vehicle from arrival to departure. The system performs extreme value normalization processing on the steady-state queue length expectation and dwell time expectation respectively, and applies preset queue weight coefficients and time weight coefficients to linearly add and combine them, ultimately realizing the precise physical quantification of the macro-level shunting plan into the micro-level dimensionless queuing and dwell time index. In an exemplary application scenario, the W1 node can be divided into two sub-periods during the early morning window: the U1 period has 4 racks, the standard shift team is on duty, and there is sufficient available space; the U2 period still has 4 racks, but the temporary storage space is reserved by promotional items, and the forklift team is in the process of shift handover, so the service rate is lower than that of U1. Ideally, if demand arrives evenly in U1 and U2, queuing is controllable; however, if capacity arrival events are highly concentrated in the U2 time period, even if the total number of trips remains constant within the statistical period, a steady-state queue length expectation and dwell time expectation exceeding the preset safety threshold will be generated. The system combines the steady-state queue length expectation and the dwell time expectation through the above normalization and weighted summation process to form a queueing delay index, which is used to distinguish between different states where vehicles are slightly waiting but can be dealt with and where the waiting has eroded the time commitment. As a system fault tolerance mechanism, if the number of platforms at a certain node is fixed but the available area data is temporarily unavailable, the system can generate a conservative estimate of the area based on the yard occupancy rate of similar historical days. If the staff schedule is temporarily changed but has not yet been updated to the system, the on-site supervisor can manually submit the team status on the terminal, prioritizing the coverage of historical schedules; if a certain node does not apply to the standard queuing assumption, such as the mixed operation of oversized parts dedicated platform and ordinary parts platform, it can be split into multiple sub-queues according to the operation type, modeled separately, and then summarized into a comprehensive retention index to avoid distortion caused by simple merging; During the morning shipping window of the W1 main warehouse, the system identified that although the number of open platforms seemed sufficient between 6:00 and 8:00, the available area was significantly limited due to the fact that the pallets that had been returned to the warehouse the previous night had not yet been cleared, and there was a shift handover at 7:00. Based on this, the queuing simulation unit judged that the window's service capacity was lower than its nominal capacity, and thus identified the originally dispersed and seemingly reasonable vehicle arrival plan as a high-risk plan for delays. Conversely, at the W3 backup node, the number of platforms was slightly less, but the space inside the yard was more abundant and the shift rhythm was more stable, thus making it more suitable for the carrying capacity of diverted vehicles. The purpose of this mechanism is to decompose the throughput capacity of logistics nodes into multi-factor constraints with on-site significance, and to uniformly measure their impact on vehicle waiting and loitering using a queuing model, thereby achieving a more credible characterization of congestion risk.

[0024] In a preferred embodiment of the present invention, when the state transition analysis unit calls the Markov chain model, it maps the queuing delay index to a discrete set of environmental states, and defines the node congestion index and the default probability as the expected negative revenue value in the state transition process. The state transition analysis unit iteratively calculates the expected long-term cumulative negative revenue with time discount factor in each environmental state through the Bellman equation, and extracts the expected long-term cumulative negative revenue as the quantitative output benchmark of the default probability.

[0025] This embodiment provides a state transition analysis mechanism. Specifically, simply knowing the queuing congestion level at a certain moment is not enough, because logistics fulfillment failures are often not determined by a single moment, but are caused by the continuous transmission of preceding backlogs. For example, if queuing entities in the preceding time window continue to occupy the physical platform resources of the subsequent time window, it will induce an abnormal increase in the load of the subsequent operation cycle, causing the fulfillment tasks that were originally within the safe time window to default and become overdue. Therefore, it is necessary to transform instantaneous congestion into a continuous state evolution problem. The state transition analysis unit maps the queuing congestion index to several discrete environmental states, such as E1 representing low pressure that can be digested, E2 representing high pressure that can be recovered, E3 representing continuous backlog, and E4 representing near default. The node congestion index can be understood as a characterization of the severity of which state the system is currently in. The probability of default is not based on a single, post-emptive threshold state as the triggering criterion, but rather on a forward-looking state transition path as the evaluation benchmark, quantifying the expected probability of the system transitioning from the current state vector to a queue backlog state and continuously causing punitive resource losses. The so-called expected negative return value is a technical quantification that characterizes the excessive use of system resources caused by abnormal conditions, such as the equivalent of long-term operational burdens such as vehicle delays, redundant rescheduling of transportation capacity caused by overdue delivery, and overloaded operation time of warehouse processing nodes. In quantitative deduction, the unit replaces implicit mapping with structured decomposition of business logic, and visualizes the Bellman equation iteration as follows: the expected long-term cumulative negative return of any given environmental state is equal to the sum of two parts, one of which is the immediate expected negative return directly generated by the state within the current time window, i.e., the current penalty. Second, when the state jumps to all possible states of the next time window through a preset transition probability matrix, the probability weighted sum of the expected long-term cumulative negative returns of each possible state is calculated, and the weighted sum is multiplied by a time discount factor that reflects the decrease in uncertainty. Through repeated iterations of the above logic until the expected value converges, the system uses the converged long-term cumulative negative return expectation as the quantitative output benchmark of the probability of default, thus providing a rigorous mathematical structure to support forward-looking risk judgment. In an exemplary application scenario, if W1 is at E2 in the current time period, and depending on the arrival density, platform occupancy and shift recovery status, it may fall back to E1 or rise to E3 in the next time period, then the system will compare the long-term burden carried by these two evolution paths. Even if the timeout has not yet occurred, if most paths point to a continued backlog, the current moment should be considered a high risk of default. In this way, the system's output of the probability of default is forward-looking, rather than a delayed alarm after a problem has already occurred. As a fault-tolerant mechanism, if a node's historical state transition samples are insufficient, the system can borrow the initialization transition template of a node of the same type and gradually replace it as the node's data accumulates. If the current queuing delay index is close to the boundary between two states, the system can retain the confidence assignment of adjacent states at the same time to avoid policy jitter caused by hard splitting; if there are many missing upstream inputs, the system can only output the congestion trend level and temporarily not output the refined default probability, and indicate that the result is a low confidence result. At 06:30 AM in the W1 main warehouse, the queuing backlog index itself had not yet reached the physical node's maximum capacity threshold. However, the system detected that due to shift handover around 07:00 AM and multiple high-time-sensitive orders being concentrated and shipped out before 08:00 AM, the current state was more likely to slide from high pressure but recoverable to continuous backlog. The state transition analysis unit therefore gives a high probability of default; conversely, although the W3 standby node also has a certain increase in train arrivals, its state is more likely to fall from high pressure to low pressure because there are idle platforms and complete shifts in the following two periods, and the probability of default remains low. The purpose of this mechanism is to elevate the short-term queuing phenomenon into a cross-period performance risk evolution model, thereby enabling early judgment on whether local short-term queuing will evolve into large-scale performance failures in the subsequent global system.

[0026] In a preferred embodiment of the present invention, the anti-friction capacity scheduling scheme includes a spatial translation strategy, a time translation strategy, and a physical expansion early warning strategy; the global resilience optimization module executes multi-level diversion logic; for task characteristic parameters where the timeliness tolerance is lower than a preset time threshold and there are backup nodes, the spatial translation strategy is triggered to generate cross-node diversion instructions; For task characteristic parameters where the resource consumption weight coefficient is higher than the preset consumption threshold and the timeliness tolerance is not lower than the preset time threshold, a time shift strategy is triggered to generate a delayed scheduling instruction; for remaining task characteristic parameters where the spatial shift strategy and time shift strategy cannot be executed, a physical expansion warning strategy is triggered to generate a cross-system node resource allocation instruction.

[0027] This embodiment provides an anti-friction capacity scheduling mechanism; specifically, although the aforementioned scheme can identify risks and make judgments on the need for adjustment, without clear strategy decomposition, the system is prone to simply treating all high-risk situations as reducing the number of vehicles or increasing the number of vehicles, which is not applicable in real business. Different orders have different time sensitivity, resource constraints and node substitutability, so it is necessary to select response paths in a tiered manner; Spatial shifting strategy is suitable for tasks with strict time requirements and backup nodes; its essence is to redirect orders that would otherwise flow into congested nodes to nodes in the network that still have throughput capacity for processing, thereby spatially distributing the operating load of the main node; time shifting strategy is suitable for tasks with stricter requirements on resource consumption limits, but which allow for a certain delay. Its core logic is not to cancel the capacity scheduling task, but to redistribute the capacity throughput demand from the peak congestion period to the resource-rich period by shifting the time window of capacity arrival and loading and unloading operations. The physical expansion early warning strategy is applicable to tasks that cannot be changed or postponed. In this case, a resource increase request should be sent to the warehousing or on-site support system, such as opening temporary platforms, increasing forklifts and loaders, and adjusting security and release channels. In an exemplary application scenario, the tasks to be processed can be divided into three groups: G1, G2, and G3. G1 is a high-time-efficiency installation component. If it cannot be sent on time, it will directly affect the scheduled on-site service. Therefore, it is first determined whether it can be handled by the backup node W3. G2 is price-sensitive general cargo that can be delivered the next morning, and time shifting can be implemented; G3 is dedicated shipments for specific regions that cannot be diverted or delayed due to restrictions on carrier qualifications or inventory ownership, and thus require triggering a capacity expansion warning; the global resilience optimization module diverts traffic in this multi-level manner, and different groups can adopt different strategies at the same time, rather than performing uniform processing on all orders without differentiation; As a system fault tolerance measure, if a high-time-efficiency task has a backup node, but the backup node's inventory is incomplete or the distance exceeds the promised delivery radius, the system should cancel the spatial transfer candidate to avoid increasing the number of last-mile defaults after the transfer. If time shifting overlaps with a larger subsequent order peak time interval, an unconstrained delay operation should not be executed. Instead, a spatial shift or capacity expansion should be determined. If a capacity expansion warning has been issued but the warehouse management system responds that it cannot allocate additional resources, the system needs to return to the diversion logic, execute a more aggressive time shifting for tolerable tasks, and simultaneously upgrade the default risk warning level. As the morning peak approached at the W1 main warehouse, the system categorized orders into three types: orders for high-end air conditioners scheduled for installation were prioritized and transferred to W3, as W3 had inventory of the same model and its local delivery radius could cover the area. Some orders for ordinary small household appliances were delayed until the afternoon shift for loading because customers chose economy delivery; a small number of channel-exclusive parts that had to be shipped directly from W1 triggered additional dispatch requests to the warehouse management system, requiring the addition of forklifts and verification personnel before 09:00; in this way, W1 was not forced to bear all the peak pressure, and different tasks remained fulfillable under their respective constraints. The purpose of this mechanism is to expand risk management from a single shunting action to a coordinated adjustment of space, time, and resources, thereby achieving friction-resistant scheduling that is more in line with the actual constraints of the logistics network.

[0028] In a preferred embodiment of the present invention, the reinforcement learning agent, with the objective function of minimizing global comprehensive resource overhead, obtains the expected scheduling resource overhead generated by executing the anti-friction capacity scheduling scheme, the expected vehicle congestion resource loss parameter caused by the queuing and delay index, and the expected default resource loss parameter mapped by the performance default probability. The objective function is to linearly weight and sum the expected scheduling resource overhead, expected traffic congestion resource loss, and expected default resource loss parameters by using dynamic weight coefficients generated in real time based on the current node congestion index. The reinforcement learning agent updates the network parameters through the policy gradient algorithm to minimize the output value of the objective function.

[0029] This embodiment provides an optimization mechanism for global comprehensive resources; specifically, based on the aforementioned multi-level diversion, if there is no unified resource scheduling measurement standard, the system may excessively pursue saving scheduling costs at certain times, resulting in the amplification of capacity queuing and fulfillment failure degradation indicators. It is also possible that excessive conservatism may lead to frequent diversion and expansion, which may unnecessarily increase the overhead of system resource scheduling; therefore, it is necessary to enable the agent to dynamically balance among various expected resource consumption indicators. The expected scheduling resource overhead can be reflected in the direct resource consumption caused by additional shunting, cross-node diversion, route rerouting, and temporary trunk line switching; the expected congestion penalty index reflects vehicle waiting, driver delays, off-site queuing, and the resulting capacity loss compensation value. The expected penalty indicators for default correspond to the technical indicator degradation equivalent of late delivery, secondary delivery scheduling requests, and performance evaluation downgrade of the delivery node; the system does not use a fixed ratio for the three, but generates dynamic weights in real time based on the current node congestion index. When a node is still under low pressure, the weight of scheduling resources can be appropriately emphasized to avoid unnecessary high-frequency rescheduling in the system; when a node is approaching the instability boundary, the weight coefficients of the congestion and default sides should be significantly increased to prevent large-scale performance anomalies of global nodes caused by excessive restriction on the investment of single shunting resources. It should be noted that, in order to eliminate the logical gap between the preceding prediction and the subsequent execution, this mechanism establishes a linkage closed loop at the algorithm level: In order to ensure that the resource evaluation scale does not drift with the congestion state, the dynamic weight coefficients generated based on the node congestion index must meet the normalization constraint, that is, the sum of the real-time generated scheduling resource weight, traffic congestion penalty weight and default penalty weight is always equal to 1. The agent performs a linear weighted summation based on the three expected resources and the dynamic weight coefficients that meet the normalization constraints to obtain the output value of the objective function and issue the anti-friction scheme. This is the forward policy expectation deduction, i.e. the action expectation at the action execution end. Once the solution is implemented at the physical node, the deviation between the actual performance result calculated by the execution result bridging module and the probability of performance default will be directly mapped as the time-series difference error to the actual reward function value. The reward function value is backpropagated through the policy gradient to apply rewards and penalties to the current objective function weight parameters that cause the deviation, i.e., feedback from the state evaluation end, so that the objective function based on minimizing global comprehensive resource overhead can continuously approach the real friction boundary of the physical entity in long-term dynamic optimization. In an exemplary application scenario, if scheme P1 means maintaining the main warehouse shipment with a small delay, scheme P2 means diverting a large proportion to the backup node, and scheme P3 means initiating expansion and retaining most of the original plan, the system will calculate the three types of resource consumption indicators respectively. If the current congestion index is low, P1 may be better; if the congestion index has increased, although P1 has low direct resource consumption, it will be accompanied by greater subsequent traffic congestion and default risks. In this case, dynamic weighting will raise the overall evaluation of P1, making P2 or P3 more likely to be selected. Policy gradient updates do not require pre-setting the optimal action for each working condition through manual hard coding, but rather learn gradually through long-term feedback which policy combination should be favored under which network conditions; As a system fault tolerance measure, if a certain type of negative resource indicator cannot be accurately quantified temporarily, such as the default indicator degradation data of a newly added node in a certain area not yet forming a stable history, the system can first use the fixed resource consumption baseline value or proxy penalty operator in the preset business rules. If the congestion index fluctuates around the critical edge, a short-term stabilization window can be set to prevent frequent strategy switching. The weights will only be significantly adjusted when the same risk direction is maintained for several consecutive cycles. If the insufficient training samples cause the agent's output to be unstable, the rule baseline scheme will be retained as a fallback. The scheme will only be allowed when the agent's scheme is better than the rule baseline and the confidence level reaches the preset level. During the morning peak in the W1 main warehouse, the system simultaneously evaluated three options: maintaining the original plan to divert traffic to W3, requesting temporary capacity expansion from the warehouse management system, and retaining the main warehouse for shipments. Initially, when the congestion index was moderate, the system believed that a small amount of diversion combined with a slight delay could control resource consumption. As the throughput capacity is reduced after fatigue correction, the congestion index rises, the weight of traffic jams and defaults is increased, and the agent turns to a more proactive combination of diversion and capacity expansion. If, at the end of the day, it turns out that although the diversion cost increased, it successfully avoided large-scale timeouts, then the corresponding strategy will be more favored in similar scenarios in the future. The purpose of this mechanism is to enable the system to no longer perform local optimization around a single scheduling overhead metric, but to adaptively balance the deterioration of scheduling, traffic congestion and default indicators at different stages of congestion, thereby achieving a more stable network-wide performance output.

[0030] In a preferred embodiment of the present invention, the global resilience tuning module further includes an abnormal mode classification unit. When the probability of default is not lower than a preset safety threshold, a spatiotemporal friction feature vector is constructed, which is composed of the arrival rate in the arrival rate Poisson distribution sequence and the node throughput capacity characterization parameter. When the feature vector shows that the rate of change of the arrival rate within a preset time window exceeds a first preset rate of change threshold but the node throughput capacity characterization parameter is in the normal range, it is determined to be an external demand shock event. When the feature vector shows that the arrival rate is within the normal range but the decrease in the node throughput capacity representation parameter exceeds the second preset rate of change threshold, it is determined to be an internal physical node paralysis event; when the feature vector shows that the rate of change of the arrival rate within the preset time window exceeds the first preset rate of change threshold, and the decrease in the node throughput capacity representation parameter exceeds the second preset rate of change threshold, it is determined to be a composite spatiotemporal friction instability event; the abnormal mode classification unit outputs a clearly categorized audit warning instruction accordingly.

[0031] This embodiment provides an abnormal pattern classification mechanism; specifically, after a high risk of default has been identified, if the system only indicates that there is a risk but cannot explain the cause of the risk, on-site managers often find it difficult to take targeted measures. For example, both a surge in external demand and internal node failures can cause congestion, but the former should be prioritized for diversion and staggered peak hours, while the latter requires more emergency equipment repair, temporary lockdown adjustments, and on-site audits; therefore, this embodiment further distinguishes the sources of anomalies. The abnormal pattern classification unit uses arrival rate and node throughput capacity as parameters to form a spatiotemporal friction feature vector. If the arrival rate increases significantly within a short window, while the throughput parameters of the platform, equipment, and personnel remain normal, it indicates that the problem mainly comes from external demand shocks, such as temporary increases in live promotions, pre-ordering due to regional weather, and concentrated replenishment of channels. If the arrival rate is not significantly abnormal, but the throughput capacity suddenly drops, it is more likely to be an internal physical node failure event, such as forklift failure, platform closure, security check suspension, large-scale staff absence, or blocked internal passageway; the audit warning instructions output by the system will directly indicate the category, which is convenient for subsequent responsibility identification and implementation of measures. In an exemplary application scenario, two situations can be set up; in situation A, the demand for W1 to arrive at the train jumps rapidly from medium to extremely high in two consecutive time periods, but the on-site team, equipment and platform are all in normal condition, and the system judges it as an external demand shock. In scenario B, the demand for trucks remains high but stable, but the number of online forklifts decreases sharply, the number of available loading docks decreases, and the outbound release cycle drops, which is judged as an internal physical node anomaly; although both scenarios will increase the probability of default, the handling priorities and audit tracking paths are different. As a system fault tolerance measure, if the arrival rate and throughput change drastically at the same time, the system can mark it as a compound anomaly and output both external shock and internal instability labels in the audit alert, indicating that dual-line verification is required; if the classification evidence is insufficient, such as missing status data of key equipment, the system will not force a classification, but will output a high-risk event to be verified, along with the most suspicious parameter source at present. If the same node frequently switches classification results in a short period of time, a stable window strategy can be enabled to solidify the label only when a consistent trend is maintained for multiple consecutive collection cycles. During an event at the W1 main warehouse, the system detected a surge in demand for trucks arriving in the morning, while all loading docks and forklifts were in normal condition. The system classified this high-risk event as an external demand shock and recommended that the operations team check whether there was additional traffic or temporary replenishment from channels. On another day, the system found that the order flow was stable, but the throughput of W1 suddenly dropped. After classification, it was identified as an internal physical node failure event. The audit warning immediately pointed to the field equipment and team management links. After verification, it was confirmed that the hidden congestion was caused by a failure of a conveyor line in the main warehouse. The purpose of this mechanism is to further trace the risk outcome back to the risk source, thereby enabling targeted scheduling response, management audit, and cross-departmental collaborative handling.

[0032] In a preferred embodiment of the present invention, the system is deployed in a layered architecture that includes edge computing nodes and cloud servers; the multidimensional data acquisition module resides on the local edge computing node of the physical node and performs real-time data acquisition and preliminary format standardization processing; The basic demand prediction module, spatiotemporal friction simulation engine, global resilience optimization module, and execution result bridging module reside on the cloud server, performing global logical deduction and strategy generation based on the aggregated first-dimensional conventional dataset and second-dimensional physical constraint dataset.

[0033] This embodiment provides an edge-cloud layered deployment mechanism; specifically, if all the aforementioned modules are centrally deployed in the cloud, although it is convenient for unified calculation, it is easy to experience collection delays and data format disorder when there are network fluctuations, severe terminal heterogeneity, or sudden increase in data volume. If all deployments are made on local nodes, it becomes difficult to achieve cross-node collaborative optimization; therefore, this embodiment adopts a layered architecture to achieve a balance between real-time performance and global performance. Edge computing nodes reside locally on each physical node and are directly connected to the platform control terminal, forklift vehicle terminal, gate release equipment, team attendance system and local warehouse management system interface. Its main responsibilities are to complete real-time data collection, timestamp alignment, field standardization, and preliminary integrity verification. For example, different warehouses may report different field names for completed loading and unloading. Edge nodes will convert these into a standard event format and then upload them to the cloud. This reduces the complexity of the cloud directly dealing with heterogeneous devices on-site, and also allows for caching during local network jitter to prevent the loss of original events. Cloud servers handle heavier computing tasks, including basic demand forecasting, queuing simulation, state transition analysis, global optimization, and execution result bridging. Because these tasks require comparing loads across nodes, evaluating routing routes, and updating agent parameters uniformly, they are more suitable for a centralized computing environment. The data uploaded from the edge is already structured, and the cloud uses this to form a global view and then distributes the scheduling plan to the corresponding nodes or business systems. In this way, although W1, W2, and W3 run independently, their resource reserves and risk status can be uniformly orchestrated in the cloud. In an exemplary application scenario, edge node W1 can be set to upload local platform occupancy event stream B1, and edge node W3 can upload available yard event stream B2. The cloud will merge B1 and B2 with the order demand stream C to determine whether some tasks of W1 should be moved to W3. Without a layered architecture, W1 can hardly know in real time whether W3 has enough capacity, and the cloud may also experience increased processing latency due to directly receiving raw heterogeneous events. As a system fault tolerance measure, if the edge node temporarily loses connection with the cloud, the edge side can continue to perform basic data collection and short-term caching according to the rule template that has been effectively issued once, and maintain the simplest scheduling protection locally, such as restricting additional vehicles from entering the site during high-risk periods; If the cloud is unavailable but the edge and local warehouse management systems are functioning normally, emergency rules can be enabled on the edge side. For example, the entry rate can be controlled only according to the safe throughput limit of the current node, without cross-node traffic splitting. If the network is restored, the edge node will re-upload the cached data during the breakpoint to the cloud, and the cloud will perform replay training and policy correction. In the early hours of Singles' Day, the W1 main warehouse park experienced a short-term upload delay due to network jitter of the operator. However, the edge nodes continued to collect data on platform occupancy, forklift online status, and gate queuing, and completed standardized caching of resources. After the link was restored, the data was pushed to the cloud in batches. The cloud continued to perform cross-node simulation and optimization, and issued peak-shifting and traffic diversion instructions to W1 and W3 respectively. Throughout the process, the site did not completely lose the basis for scheduling due to the network interruption. The purpose of this mechanism is to achieve layered implementation of real-time on-site perception and unified network-wide simulation, thereby improving the robustness of data collection, cross-node collaboration capabilities, and continuous operation capabilities under network anomaly conditions.

[0034] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A machine learning-based TMS logistics AI capacity prediction system, characterized in that, It includes a multi-dimensional data acquisition module, a basic demand prediction module, a spatiotemporal friction simulation engine, a global resilience optimization module, and an execution result bridging module; The multidimensional data acquisition module connects to the external environment system and physical node terminal to perform data cleaning, generating a first-dimensional regular dataset containing task characteristics and environmental status, and a second-dimensional physical constraint dataset containing node throughput capacity characterization parameters. The basic demand forecasting module calls the time series forecasting model to map the first dimension of the regular dataset to the ideal capacity demand sequence within the preset target time window; The spatiotemporal friction simulation engine includes a queuing simulation unit and a state transition analysis unit. The queuing simulation unit overlays the ideal capacity demand sequence with the second-dimensional physical constraint dataset to calculate the queuing congestion index of physical nodes. The state transition analysis unit calls the Markov chain model to generate the node congestion index and the probability of default. The global resilience tuning module calls a reinforcement learning agent; in response to the default probability being lower than a preset safety threshold, it outputs an ideal capacity demand sequence as a direct scheduling instruction; in response to the default probability being not lower than the preset safety threshold, it generates an anti-friction capacity scheduling scheme with the goal of minimizing global comprehensive resource overhead. The execution result bridging module obtains real performance status data, calculates the deviation between the actual default rate and the default probability based on the real performance status data, generates a reward function value, and drives the agent to iteratively update the network parameters.

2. The TMS logistics AI capacity prediction system based on machine learning according to claim 1, characterized in that, The multidimensional data acquisition module also includes a source ontology verification unit, which performs trend stripping operations on the first-dimensional conventional dataset to extract high-frequency residual sequences and calculates the multi-scale permutation entropy value of the high-frequency residual sequences; The source ontology verification unit performs logical judgment based on a preset feature domain; when the multi-scale permutation entropy value is within the pre-defined normal fluctuation range, the data is deemed valid. When the entropy value of the multi-scale permutation exceeds the normal fluctuation range, the source ontology verification unit determines that the first-dimensional regular dataset contains abnormally injected data and sends a source blocking instruction to the basic demand prediction module to prohibit the execution of prediction logic based on the dataset.

3. The TMS logistics AI capacity prediction system based on machine learning according to claim 1, characterized in that, The spatiotemporal friction simulation engine also includes a benchmark dynamic correction unit, which calculates the actual throughput rate index of physical nodes in real time and monitors the temporal variation characteristics of the actual throughput rate index. When the reference dynamic correction unit detects that the actual throughput rate index of a consecutive preset number of acquisition cycles shows a monotonically decreasing trend and the attenuation rate matches the preset fatigue attenuation model, it generates a dynamic tolerance compensation coefficient. When calculating queuing congestion indices, the queuing simulation unit applies a dynamic tolerance compensation coefficient to nonlinearly correct the node throughput capacity characterization parameters in the second-dimensional physical constraint dataset.

4. The TMS logistics AI capacity prediction system based on machine learning according to claim 1, characterized in that, The node throughput capacity representation parameters in the second-dimensional physical constraint dataset include the number of platforms, available area, average loading and unloading rate, and personnel scheduling parameters. The queuing simulation unit calls a pre-set queuing theory model to transform the ideal capacity demand sequence into an arrival rate Poisson distribution sequence, couples the average loading and unloading rate parameter with the personnel scheduling parameter into a service rate exponential distribution sequence, and calculates the steady-state queue length expectation value and dwell time expectation value of the system in combination with the platform quantity parameter. The steady-state queue length expectation value and dwell time expectation value are combined into a queuing delay index.

5. The TMS logistics AI capacity prediction system based on machine learning according to claim 1, characterized in that, When the state transition analysis unit calls the Markov chain model, it maps the queuing delay index to a discrete set of environmental states and defines the node congestion index and the probability of default as the expected negative revenue value in the state transition process. The state transition analysis unit iteratively calculates the long-term cumulative negative return expectation with time discount factor under each environmental state through the Bellman equation, and extracts the long-term cumulative negative return expectation as the quantitative output benchmark for the probability of default.

6. The TMS logistics AI capacity prediction system based on machine learning according to claim 1, characterized in that, Friction-resistant capacity scheduling schemes include spatial shift strategies, time shift strategies, and physical expansion early warning strategies; The global resilience tuning module executes multi-level traffic splitting logic; for task characteristic parameters where the timeliness tolerance is lower than the preset time threshold and there are backup nodes, the spatial shift strategy is triggered to generate cross-node traffic splitting instructions; For task characteristic parameters where the resource consumption weight coefficient is higher than the preset consumption threshold and the timeliness tolerance is not lower than the preset time threshold, a time shift strategy is triggered to generate a delay scheduling instruction. For the remaining task characteristic parameters that cannot execute spatial translation and temporal translation strategies, a physical expansion warning strategy is triggered, generating cross-system node resource allocation instructions.

7. The TMS logistics AI capacity prediction system based on machine learning according to claim 1, characterized in that, When the reinforcement learning agent takes minimizing the overall global resource overhead as the objective function, it obtains the expected scheduling resource overhead generated by executing the anti-friction capacity scheduling scheme, the expected vehicle congestion resource loss parameter caused by the queuing and delay index, and the expected default resource loss parameter mapped by the default probability. The objective function is to linearly weight and sum the expected scheduling resource overhead, expected traffic congestion resource loss, and expected default resource loss parameters by using dynamic weight coefficients generated in real time based on the current node congestion index. The reinforcement learning agent updates the network parameters through the policy gradient algorithm to minimize the output value of the objective function.

8. The TMS logistics AI capacity prediction system based on machine learning according to claim 4, characterized in that, The global resilience tuning module also includes an abnormal mode classification unit, which constructs a spatiotemporal friction feature vector composed of the arrival rate in the arrival rate Poisson distribution sequence and the node throughput capacity characterization parameter when the probability of default is not lower than a preset safety threshold. When the feature vector shows that the rate of change of arrival rate within a preset time window exceeds the first preset rate of change threshold, but the node throughput capacity characterization parameter is within the normal range, it is determined to be an external demand shock event. When the feature vector shows that the arrival rate is within the normal range but the decrease in the node throughput capacity representation parameter exceeds the second preset change rate threshold, it is determined to be an internal physical node paralysis event. When the feature vector shows that the rate of change of the arrival rate within a preset time window exceeds the first preset rate of change threshold, and the decrease of the node throughput capacity characterization parameter exceeds the second preset rate of change threshold, it is determined to be a composite spatiotemporal friction instability event; the abnormal mode classification unit outputs a clearly categorized audit warning instruction accordingly.

9. The TMS logistics AI capacity prediction system based on machine learning according to claim 1, characterized in that, The system is deployed in a layered architecture that includes edge computing nodes and cloud servers; The multidimensional data acquisition module resides on the local edge computing node of the physical node, performing real-time data acquisition and preliminary format standardization processing; The basic demand prediction module, spatiotemporal friction simulation engine, global resilience optimization module, and execution result bridging module reside on the cloud server, performing global logical deduction and strategy generation based on the aggregated first-dimensional conventional dataset and second-dimensional physical constraint dataset.

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