Order logistics timeliness management system

Abstract

The invention discloses an order logistics timeliness management system, and relates to the technical field of logistics intelligence, and the system comprises a risk prediction module which collects order logistics full-link data, carries out the preprocessing of the data, calculates the logistics node delay risk probability through a virtual intervention scene and a causal graph model, and obtains a delay risk prediction result; the visualization module is used for performing overlay analysis on the delay risk prediction result and the preprocessed order logistics full-link data to generate a delay risk thermodynamic diagram; the destroy-resistant module is used for carrying out vulnerability evaluation on the delay risk thermodynamic diagram to generate a vulnerability analysis report, and carrying out multi-objective optimization evaluation through a k-shortest path algorithm to generate a destroy-resistant strategy table; the scheduling module is used for carrying out transport capacity resource matching and scheduling scheme selection by utilizing a multi-target resource optimization model based on the survivability strategy table to obtain a scheduling instruction set; according to the invention, causal traceability and probability quantification of logistics delay risks are realized by constructing a collaborative prediction mechanism of a virtual intervention scene and a causal graph model.

Description

Technical Field

[0001] The present invention relates to the field of intelligent logistics technology, and in particular to an order logistics time management system. Background Art

[0002] Logistics time management is gradually evolving towards a data-driven and intelligent approach. In recent years, with advances in big data analytics and machine learning, the industry has widely adopted predictive models based on historical data to optimize shipping times. Typical applications include predicting shipping times using time series analysis, identifying delay patterns through supervised learning algorithms, and dynamically adjusting shipping routes using real-time GPS data.

[0003] Existing logistics time management typically uses a static resource allocation model, lacking consideration for dynamic network reconfiguration capabilities and resulting in low fault tolerance in the face of unexpected events. In particular, in multi-objective optimization processes, existing technologies struggle to effectively balance key metrics such as timeliness, cost-efficiency, and reliability, impacting the global optimality of the final decision. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an order logistics time management system to solve the problems of insufficient fault tolerance of logistics time management under sudden abnormal events and difficulty in achieving global optimization in multi-objective decision-making.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] The present invention provides an order logistics time management system, which includes:

[0008] The risk prediction module collects and preprocesses data from the entire order logistics chain, calculates the probability of delay risk at logistics nodes through virtual intervention scenarios and causal graph models, and obtains delay risk prediction results;

[0009] The visualization module overlays and analyzes the delay risk prediction results with the pre-processed order logistics data to generate a delay risk heat map.

[0010] The invulnerability module performs vulnerability assessment on the delay risk heat map, generates a vulnerability analysis report, and uses the k-shortest path algorithm to perform multi-objective optimization assessment and generate an invulnerability strategy table.

[0011] The scheduling module uses a multi-objective resource optimization model to match transport resources and select scheduling solutions based on the invulnerability strategy table to obtain a scheduling instruction set.

[0012] The optimization module, based on the scheduling instruction set, dynamically adjusts multimodal operating parameters through the intelligent collaborative control engine, reconstructs the logistics topology network, forms a highly fault-tolerant network, and obtains the optimal three-dimensional decision-making time management solution through real-time operation data monitoring and dynamic optimization decision-making mechanism.

[0013] As a preferred solution of the order logistics time management system of the present invention, wherein: the order logistics full link data includes product feature data, transportation environment data, time history data and logistics topology network;

[0014] The preprocessing includes outlier cleaning, missing value filling, feature normalization and spatiotemporal alignment.

[0015] As a preferred solution of the order logistics time management system of the present invention, wherein: the delay risk probability of the logistics node is calculated by using the virtual intervention scenario and the causal graph model to obtain the delay risk prediction result, the specific steps are as follows:

[0016] Based on the pre-processed order logistics data, we define transportation acceleration intervention measures, route change intervention measures, and resource addition intervention measures through intervention variables, and then combine them to construct a virtual intervention scenario.

[0017] Based on historical virtual intervention scenarios, the causal graph model is incrementally trained using machine learning methods to obtain a trained causal graph model.

[0018] The virtual intervention scenario is input into the trained causal graph model, forward reasoning is performed, the delay risk probability matrix is ​​output, and risk classification and reasoning confidence verification are performed to obtain the delay risk prediction results.

[0019] As a preferred solution of the order logistics time management system of the present invention, the delay risk prediction results are superimposed and analyzed with the pre-processed order logistics full-link data to generate a delay risk heat map. The specific steps are as follows:

[0020] The delay risk prediction results are integrated with the pre-processed order logistics data through spatial coordinate system conversion and attribute field association to generate spatiotemporal benchmark fusion data. The risk intensity is then quantified to generate a risk space matrix.

[0021] Perform color mapping and spatial interpolation on the risk space matrix to obtain a three-level delay risk heat map;

[0022] Through the spatial clustering algorithm, the key risk areas and main transmission paths of the three-level delay risk heat map are identified to generate a delay risk heat map.

[0023] As a preferred solution of the order logistics time management system of the present invention, the vulnerability assessment of the delay risk heat map is performed to generate a vulnerability analysis report. The specific steps are as follows:

[0024] Extract delay risk thermal values ​​based on the delay risk heat map. Generate a delay risk thermal value map through spatial cluster analysis and time series trend prediction. Also, conduct logistics node connectivity analysis and load pressure assessment to generate logistics node vulnerability indicators.

[0025] Conducting survivability tests on logistics topology networks based on vulnerability indicators of logistics nodes, generating resilience assessment reports, and conducting cascading failure analysis of logistics topology networks and locating key logistics nodes, generating recommendations on delay risk transmission paths and optimal blocking points.

[0026] Integrate the resilience assessment report, delay risk transmission path and optimal blocking point recommendations to generate a vulnerability analysis report.

[0027] As a preferred solution of the order logistics time management system of the present invention, wherein: the multi-objective optimization evaluation is performed by the k-shortest path algorithm to generate an anti-destruction strategy table, the specific steps are as follows:

[0028] Based on the vulnerability analysis report, multi-dimensional path evaluation and optimization screening are performed using the k-shortest path algorithm to form a path optimization plan;

[0029] The non-inferior solution set of the path optimization scheme is screened through the Pareto front to generate an anti-destruction strategy table.

[0030] As a preferred solution of the order logistics time management system of the present invention, wherein: based on the anti-destruction strategy table, the multi-objective resource optimization model is used to match the transportation resources and select the scheduling plan to obtain the scheduling instruction set. The specific steps are as follows:

[0031] Based on the historical invulnerability strategy table, the multi-objective resource optimization model is incrementally trained through a deep reinforcement learning framework to obtain a trained multi-objective resource optimization model. The model then performs capacity resource matching and scheduling scheme selection to obtain a resource matching solution.

[0032] Perform resource conflict detection and timeliness verification on the resource matching plan, generate a resource conflict detection report, adjust the resource matching plan to generate an optimized scheduling plan, and generate a scheduling instruction set through instruction encoding and protocol conversion.

[0033] As a preferred solution of the order logistics time management system of the present invention, wherein: based on the scheduling instruction set, the multimodal operation parameters are dynamically adjusted through the intelligent collaborative control engine, the logistics topology network is reconstructed, and a high fault-tolerant network is formed. The specific steps are as follows:

[0034] Based on the scheduling instruction set, the intelligent collaborative control engine dynamically adjusts the multi-modal operating parameters to form a coordinated operating parameter set;

[0035] Perform topological relationship analysis and service association feature extraction on the coordinated operation parameter set to obtain logistics node associations, reconstruct the service mapping relationship between the sorting center and the distribution station, and generate a new logistics topology network;

[0036] Perform stress testing on the new logistics topology network, evaluate the connectivity retention rate and time attenuation under logistics node failure scenarios, and output a highly fault-tolerant network.

[0037] As a preferred solution of the order logistics time efficiency management system described in the present invention, the multimodal operation parameters include sorting equipment parameters, transport vehicle parameters, path guidance parameters and environmental adaptation parameters.

[0038] As a preferred solution of the order logistics time management system of the present invention, wherein: the optimal three-dimensional decision-making time management solution is obtained through real-time operation data monitoring and dynamic optimization decision-making mechanism. The specific steps are as follows:

[0039] Based on a highly fault-tolerant network, it uses outlier detection to identify abnormal readings, path deviation events, and time deviation signals, and outputs abnormal event reports.

[0040] Conduct Pareto frontier analysis on abnormal event reports in the key dimensions of timeliness, cost efficiency, and reliability to form the optimal three-dimensional decision-making timeliness management plan.

[0041] The beneficial effects of the present invention are as follows: by constructing a collaborative prediction mechanism of virtual intervention scenarios and causal graph models, the causal traceability and probabilistic quantification of logistics delay risks are realized, and the problem of insufficient explanatory power of statistical predictions in complex scenarios is solved; at the same time, through the dynamic parameter adjustment and topology reconstruction of the intelligent collaborative control engine, the multimodal collaborative optimization and online self-healing capabilities of the logistics network are realized, overcoming the rigid defects of the static resource allocation model. This two-way enhancement mechanism can not only proactively identify potential delay risks, but also actively build a defense system through dynamic network reconstruction, and ultimately achieve the global optimization of the three-dimensional management indicators of timeliness, cost efficiency and reliability in complex scenarios such as business peak periods, significantly improving the overall effectiveness of the system in responding to abnormal events. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a schematic diagram of the order logistics time management system.

[0044] Figure 2 Flowchart for generating delay risk prediction results.

[0045] Figure 3 Flowchart for vulnerability analysis report.

[0046] Figure 4 Flowchart for generating the optimal three-dimensional decision solution. DETAILED DESCRIPTION

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0048] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0049] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0050] Reference Figures 1 to 4 , is an embodiment of the present invention, which provides an order logistics time management system, including the following steps:

[0051] The risk prediction module collects and pre-processes data from the entire order logistics chain, calculates the delay risk probability of logistics nodes through virtual intervention scenarios and causal graph models, and obtains delay risk prediction results.

[0052] The full-link data of order logistics includes product feature data, transportation environment data, transportation resource data, timeliness history data and logistics topology network.

[0053] Preprocessing includes outlier cleaning, missing value filling, feature normalization, and spatiotemporal alignment.

[0054] It should be noted that outlier cleaning uses the 3σ criterion to eliminate data points in the entire order logistics chain that deviate from the mean by three times the standard deviation;

[0055] Missing values ​​are filled using the moving average of the entire logistics chain data of historical orders on the same delivery route;

[0056] Feature standardization is to normalize the product feature data to the range of 0-1 using the maximum and minimum values; transport environment data is standardized using decimal calibration; timeliness historical data is standardized using Z-score; logistics topology network is standardized using logarithmic transformation;

[0057] Spatiotemporal alignment converts the timestamps of commodity feature data, transportation environment data, transportation resource data, timeliness history data and logistics topology network from different sources into UTC time, and converts the geographic coordinates into the WGS84 coordinate system.

[0058] Based on the pre-processed order logistics full-link data, transportation acceleration intervention measures, route change intervention measures and resource addition intervention measures are defined through intervention variables to combine and construct a virtual intervention scenario.

[0059] Specifically, within the pre-processed order logistics data, the transport acceleration intervention extracts the maximum speed value from the transport vehicle parameters and uses an example of 1.2 times the standard speed value as the accelerated speed. The route change intervention calculates the mileage difference between the original route and the alternative route in the logistics topology network, generating new route coordinates when the difference exceeds an example of 5 kilometers. The resource addition intervention uses the maximum processing capacity in the transport environment data and uses an example of 1.5 times the standard resource allocation as the added resource quantity. The triggering conditions for the three types of interventions are: transport acceleration is activated when the vulnerability label in the product feature data is marked as "high risk"; route change is activated when the probability of precipitation in the transport environment data exceeds an example of 70%; and resource addition is activated when the average delay duration in the timeliness history data exceeds an example of 2 hours. Transport acceleration interventions, route change interventions, and resource addition interventions are combined to form a complete virtual intervention scenario.

[0060] Based on historical virtual intervention scenarios, the causal graph model is incrementally trained through machine learning methods to obtain the trained causal graph model.

[0061] Specifically, the historical virtual intervention scenario data is divided into a training set and a validation set in a ratio of 7:3. The training set is divided into fixed-length batches in chronological order, with an exemplary batch length of 24 hours. Each training batch contains the baseline speed value and the accelerated speed value in the transportation acceleration intervention measure, the original path mileage and the new path coordinates in the path change intervention measure, and the baseline resource quantity and the additional resource quantity in the resource addition intervention measure. The training process adopts the stochastic gradient descent algorithm, and the intervention parameter combination in the training batch is used as the feature vector, and the corresponding actual delay result is used as the supervision signal. The validation set is used to monitor the training process. After each training batch is completed, the validation set is used to calculate the causal reasoning accuracy, an exemplary indicator of which is the F1 value. The causal graph model edge weight update adopts the backpropagation mechanism, an exemplary step size of 0.001, and retains the causal path weight from transportation acceleration to path change, and the influence coefficient from path change to resource addition. The training is terminated when the fluctuation of the validation set F1 value for five consecutive batches is less than an exemplary 1%. The final output is a trained causal graph model that retains the complete causal topology structure and parameter weights;

[0062] It should be noted that the expression for calculating the causal reasoning accuracy index F1 value is:

[0063]

[0064] Among them, F1 is the causal reasoning accuracy indicator F1 value, A is an exemplary value of precision (0-1), and B is an exemplary value of recall (0-1).

[0065] The virtual intervention scenario is input into the trained causal graph model, forward reasoning is performed, the delay risk probability matrix is ​​output, and risk classification and reasoning confidence verification are performed to obtain the delay risk prediction results.

[0066] Specifically, the transport acceleration intervention measure values, route change intervention measure values, and resource addition intervention measure values ​​in the virtual intervention scenario are encoded and input in the feature vector format during training. The trained causal graph model calculates the causal effect value of the transport acceleration intervention measure on the route change intervention measure, and the transmission impact value of the route change intervention measure on the resource addition intervention measure based on the stored causal topology structure using the Do-Calculus mathematical tool. The calculation process adopts the forward propagation algorithm of the probabilistic graph model, and the exemplary calculation iteration number is 100. The output result is a delay risk probability matrix containing the delay probability value of each logistics node. Each element in the delay risk probability matrix represents the delay probability of the corresponding logistics node within the exemplary time window of 24 hours. The risk classification adopts a fixed division, and the exemplary setting is 0-30% for low risk, 30-70% for medium risk, and 70-100% for high risk. The reasoning confidence verification is completed by calculating the deviation value between the reasoning result and the historical prediction accuracy of the trained causal graph model, and the exemplary reasoning confidence threshold is set to 85%. Finally, the delay risk prediction result with risk level labeling and reasoning confidence identification is output.

[0067] It should be noted that the expression of the output delay risk probability matrix is:

[0068] p ij =σ(w i ·h j +b i );

[0069] Among them, P is the delay risk probability matrix, p ij is the delay probability of the i-th logistics node in the j-th time window, σ is the sigmoid function, w i is the output layer weight vector of the i-th logistics node, h j is the hidden layer feature representation of the jth time window, b i is the output layer bias term of the i-th logistics node, N is the total number of logistics nodes, M is the total number of time windows, and p 11 is the delay probability of the time window in the first row and first column of the delay risk probability matrix P, p 1M is the delay probability of the time window in the 1st row and Mth column of the delay risk probability matrix P, p N1 is the delay probability of the time window in the Nth row and first column of the delay risk probability matrix P, p NM is the delay probability of the time window in the Nth row and Mth column of the delay risk probability matrix P;

[0070] It should be explained that the process of setting the inference confidence threshold is as follows: by analyzing the causal reasoning accuracy of the trained causal graph model on the validation set, the standard deviation of the causal reasoning accuracy is obtained, and the inference confidence threshold is set to the average accuracy minus an exemplary 2 times the standard deviation, which is an exemplary value of 85%.

[0071] The visualization module overlays and analyzes the delay risk prediction results with the pre-processed order logistics full-link data to generate a delay risk heat map.

[0072] The delay risk prediction results are integrated with the pre-processed order logistics full-link data through spatial coordinate system conversion and attribute field association to generate spatiotemporal benchmark fusion data, and the risk intensity is quantified to generate a risk space matrix.

[0073] Specifically, the logistics node identifier in the delay risk prediction result is precisely matched with the logistics node code in the pre-processed order logistics full-link data; the Gauss-Krüger projection formula is used to convert the longitude and latitude coordinates (B, L) of the logistics node into plane rectangular coordinates (x, y) through the ellipsoid to plane conformal projection transformation, where x is the vertical coordinate, y is the horizontal coordinate, and the central meridian longitude L0 takes the standard value of the exemplary 6-degree zone; the attribute field association is achieved by establishing a mapping relationship between the logistics node identifier and the risk level field, the real-time capacity status field, and the historical timeliness field; the spatiotemporal reference fusion data generation process integrates the projection coordinates and the associated attribute fields to form a plane coordinate x value, plane coordinate y value, and a plane coordinate x value. The structured data set includes risk value, risk level value, real-time capacity utilization rate and historical average delay time. The risk intensity quantification process defines the risk level value corresponding to the exemplary risk weight coefficient of low risk 0.2, medium risk 0.5 and high risk 0.8, the real-time capacity utilization rate is scalar-producted with the exemplary conversion coefficient 0.01, and the historical average delay time is normalized with the exemplary benchmark value of 60 minutes. The risk space matrix is ​​generated by using a regular grid discretization method to divide the plane coordinate area into exemplary 500m×500m risk grid units. The risk intensity value of each risk grid unit is the sum of the weighted risk intensity coefficients of all logistics nodes in the risk unit, and finally the complete risk space matrix is ​​output.

[0074] The risk space matrix is ​​subjected to color mapping and spatial interpolation to obtain a three-level delay risk heat map.

[0075] Specifically, the risk intensity value risk grading in the risk space matrix adopts a fixed division, with 0-30% being low risk, 30-70% being medium risk, and 70-100% being high risk. Chroma mapping adopts HSL color space conversion, with the low-risk interval mapped to an exemplary light green (H=120°), the medium-risk interval mapped to an exemplary yellow (H=60°), and the high-risk interval mapped to an exemplary red (H=0°), with the saturation and brightness maintained at exemplary fixed values ​​of 80% and 70% respectively. Spatial interpolation processing adopts an inverse distance weighted algorithm, with the search radius set to an exemplary 500 meters and the number of neighborhood points set to an exemplary 8, and interpolation calculations are performed on the center point of each risk grid unit. Heat map rendering fills the interpolated risk grid units with corresponding colors according to the chroma mapping results, with the transparency set to an exemplary 60%, to generate a three-level delay risk heat map with a gradient effect.

[0076] The expression for interpolation calculation of the center point of each risk grid unit should be stated:

[0077]

[0078] Among them, Q is the center point to be interpolated, n is the total number of neighboring points, a is the index variable of the neighboring points, R i is the observation value of the ith neighboring point, is the square of the distance from the ath neighboring point to the center point.

[0079] Through the spatial clustering algorithm, the key risk areas and main transmission paths of the three-level delay risk heat map are identified to generate a delay risk heat map.

[0080] Specifically, the three-level delay risk heat map is input into the DBSCAN spatial clustering algorithm, and the neighborhood radius and minimum number of neighborhood points parameters are set to perform density clustering; high-risk areas are screened from the density clustering results, the area of ​​each area is obtained, and continuous areas are retained; the paths connecting high-risk areas are extracted based on the logistics topology network, the path length of high-risk areas is calculated, and effective transmission paths are screened; the risk transmission intensity of the effective transmission paths is calculated, and the weighted average of the risk intensity values ​​of the effective transmission paths passing through the risk grid cells is used; the final output is a delay risk heat map containing the original three-level color map, key risk area boundary identification, main transmission path markings, and corresponding intensity annotations;

[0081] It should be noted that the expression for calculating the path length of the high-risk area is:

[0082]

[0083] Among them, L cb is the length of the path from high-risk area c to b, m is the number of line segments contained in the path of the high-risk area, k is the index variable of the number of line segments in the path of the high-risk area, xk is the x-coordinate of the kth line segment of the path in the high-risk area, y k is the y-coordinate of the kth segment of the path in the high-risk area;

[0084] It should be noted that the expression for calculating the risk transmission intensity of the effective transmission path is:

[0085]

[0086] Among them, S o is the risk transmission intensity of the effective transmission path o, q is the number of risk grid units, u is the index variable of the number of risk grid units, D u is the distance weight coefficient of the u-th risk grid cell, ∝ u is the risk intensity value of the u-th risk grid unit.

[0087] The anti-destruction module conducts vulnerability assessment on the delay risk heat map, generates a vulnerability analysis report, and performs multi-objective optimization evaluation through the k-shortest path algorithm to generate an anti-destruction strategy table.

[0088] Based on the delay risk heat map, the delay risk thermal value is extracted. Through spatial cluster analysis and time series trend prediction, a delay risk thermal value map is generated. Logistics node connectivity analysis and load pressure assessment are carried out to generate logistics node vulnerability indicators.

[0089] Specifically, the risk thermal value of the center point of the risk grid unit is obtained from the delay risk heat map to form a risk intensity distribution; a density-based spatial clustering analysis is performed on the risk intensity distribution to identify spatially continuous high-density risk areas; combined with historical delay record data, a time series method is used to extract the daily risk thermal value of each high-density risk area from the historical delay data, and an exemplary 30-day moving average is obtained to determine the basic trend line. Then, an exemplary 7-day sliding window is used to detect short-term fluctuations. When the thermal value exceeds the trend line by an exemplary 20% for three consecutive exemplary days, it is marked as a risk-rising area, and when it is lower than an exemplary 15%, it is marked as a mitigation area; adjacent grid units with a daily change rate exceeding an exemplary 5% are merged into evolution blocks, and the number of days, change amplitude and diffusion direction of each block are counted to generate a spatiotemporal evolution map marked with risk escalation areas (red), stable areas (yellow) and mitigation areas (green);

[0090] The results of spatial cluster analysis are integrated with those of time series analysis to form a delay risk thermal value map that includes spatial distribution and time characteristics. Based on the logistics topology network, the number of connection edges and the length of the main transmission path of each logistics node in a specific time window are counted to obtain the connectivity measure. The real-time load rate is obtained by collecting the current number of pending orders at the logistics node and the rated processing capacity of the current logistics node, and a division operation is performed. When the load rate is continuously high, it is determined to be a high-load state, and the load pressure measure of the node is generated. The connectivity measure and the load pressure measure are combined according to the exemplary value of 0.6 for the connectivity weight and 0.4 for the load pressure weight. After normalization, a logistics node vulnerability index in the range of 0 to 1 is formed.

[0091] Conduct invulnerability tests on the logistics topology network based on the vulnerability indicators of logistics nodes, generate a resilience assessment report, conduct cascading failure analysis on the logistics topology network and locate key logistics nodes, and generate recommendations on delay risk transmission paths and optimal blocking points.

[0092] Specifically, the Monte Carlo simulation method is adopted to randomly select logistics nodes in the logistics topology network for failure simulation. The proportion of the number of logistics nodes selected for each simulation to the total number of logistics nodes in the logistics topology network is set to an exemplary 10%; the decline in the overall connectivity efficiency of the logistics topology network and the increase in the average length of the logistics transportation critical path after each simulation are recorded; after repeating the simulation process for an exemplary 1,000 times, the frequency and degree of the impact of the failure of each logistics node on the performance of the logistics topology network are counted; the resilience assessment report includes three core indicators: the logistics topology network connectivity efficiency retention rate, the average logistics transportation critical path length growth rate and the distribution of the impact of logistics node failure; the delay risk transmission path identification is based on the logistics node vulnerability index and the topological connection relationship, and the shortest delay risk transmission path between logistics nodes with a logistics node vulnerability index exceeding an exemplary 0.7 is extracted; the optimal blocking point recommendation selects a logistics node that is located at the intersection of multiple delay risk transmission paths and has its own logistics node vulnerability index lower than an exemplary 0.3, to ensure that the blocking measures will not cause secondary risks; the final output generates a delay risk transmission path and an optimal blocking point recommendation.

[0093] Integrate the resilience assessment report, delay risk transmission path and optimal blocking point recommendations to generate a vulnerability analysis report.

[0094] Specifically, the logistics topology network connectivity efficiency retention rate and the average growth rate of the length of the logistics transport key path in the resilience assessment report are extracted and classified according to the type of logistics transport key path; the delay risk transmission path is converted into a topological graph structure, the sorting center and distribution station nodes that the delay risk transmission path passes through are marked, and the risk intensity value of the delay risk transmission path is marked; the optimal blocking point suggestion is associated with the specific logistics node coordinates in the logistics topology network, and the logistics node vulnerability index and the recommended priority are marked; the report integration adopts a hierarchical structured method, the first part shows the logistics topology network-level resilience indicators, including the connectivity efficiency retention rate distribution histogram and the delay risk transmission path length growth trend curve; the second part presents the delay risk transmission path-level analysis results, superimposing the delay risk transmission path heat map and the optimal blocking point position mark; the third part outputs the logistics node-level recommended measures, and lists the sorting centers and distribution stations that need to be reinforced in order of priority; and finally generates a report including text analysis, data charts, and network topology. Figure 3 Vulnerability analysis report of the elements.

[0095] Based on the vulnerability analysis report, multi-dimensional path evaluation and optimization screening are performed through the k-shortest path algorithm to form a path optimization plan.

[0096] Specifically, the coordinates of the starting and ending points of the logistics transport critical paths are extracted from the vulnerability analysis report; the k-shortest path algorithm is used in the logistics topology network, and the exemplary k value is set to 5 to calculate 5 candidate logistics transport critical paths between each pair of starting points and ending points; the multi-dimensional path evaluation includes three dimensions: the length of the logistics transport critical path, the risk intensity value, and the logistics node vulnerability index. The length of the logistics transport critical path takes the actual mileage, the risk intensity value takes the average value of the logistics transport critical path passing through the risk grid units, and the logistics node vulnerability index takes the maximum value of the logistics transport critical path involving logistics nodes; the optimization screening sets an exemplary weight distribution: the weight of the logistics transport critical path length is 0.5, the risk intensity value weight is 0.3, and the logistics node vulnerability index weight is 0.2; a weighted score is obtained for each candidate logistics transport critical path, and logistics transport critical paths with a vulnerability index exceeding the exemplary 0.7 passing through logistics nodes are excluded; the top 3 logistics transport critical paths are retained to form a preliminary plan, and the overlap rate between logistics transport critical paths is checked to ensure that the overlap rate does not exceed the exemplary threshold of 30%; the final output includes the optimal logistics transport critical path selection for each starting and ending point, a list of alternative logistics transport critical paths, and a path optimization plan with risk avoidance recommendations.

[0097] The non-inferior solution set of the path optimization scheme is screened through the Pareto front to generate an anti-destruction strategy table.

[0098] Specifically, an evaluation matrix is ​​established for candidate logistics transport critical paths in the path optimization scheme based on three target dimensions: logistics transport critical path length, risk intensity value, and logistics node vulnerability index; a Pareto optimal solution set that is not dominated by other solutions in all three target dimensions is identified; exemplary screening conditions are set: the logistics transport critical path length does not exceed an exemplary 120% of the shortest logistics transport critical path, the risk intensity value does not exceed an exemplary 0.5, and the logistics node vulnerability index does not exceed an exemplary 0.6; a congestion analysis is performed on the Pareto optimal solutions that meet the conditions, and solutions with sparse distribution in the solution space are retained; the screened non-inferior solutions are classified according to the type of logistics transport critical path, with two exemplary optimal solutions being retained for trunk paths between sorting centers and one exemplary optimal solution being retained for branch paths at distribution stations; the structured output of the anti-destruction strategy table includes four core fields: logistics transport critical path number, start and end point coordinates, multi-dimensional evaluation index value, and applicable scenario description, with the logistics transport critical path numbers sorted in ascending order of risk intensity value; the resulting anti-destruction strategy table covers an exemplary 95% or more of the critical transportation needs in the logistics topology network, ensuring that an alternative path can be immediately activated when any logistics node fails.

[0099] The scheduling module, based on the anti-destruction strategy table, uses the multi-objective resource optimization model to match transportation resources and select scheduling solutions to obtain the scheduling instruction set.

[0100] Based on the historical anti-destruction strategy table, the multi-objective resource optimization model is incrementally trained through the deep reinforcement learning framework to obtain the trained multi-objective resource optimization model, and the capacity resource matching and scheduling plan selection are carried out to obtain the resource matching plan.

[0101] Specifically, the logistics transportation key path number, starting point and end point coordinates, and multi-dimensional evaluation index values ​​in the historical anti-destruction strategy table are converted into state feature vectors, and the vehicle type, load capacity, and current position data in the capacity resource data are extracted to construct the action space; a dual-depth Q network structure is used to initialize the deep reinforcement learning framework, and an exemplary learning rate of 0.001 and a discount factor of 0.9 are set; an incremental training process is performed, and the latest anti-destruction strategy table and real-time capacity status are input every 24 hours. The network parameters are updated through time-series difference error back propagation. The training is completed when the loss function change rate of three consecutive trainings is less than the exemplary 1%, and the trained multi-objective resource optimization model is obtained. ; The trained multi-objective resource optimization model receives the current order and capacity status input, and outputs three-dimensional reward signals of vehicle scheduling plan score, logistics transportation critical path risk score, and resource utilization score for each candidate plan; the capacity resource matching process matches the starting and ending coordinates of the current order to be delivered with the logistics transportation critical path number in the anti-destruction strategy table, and screens available vehicles with a load matching degree exceeding 90%; the scheduling plan is selected according to the Q value of each plan output by the deep Q network, and the plan with a Q value exceeding 0.8 is given priority; the resource matching plan finally generated records four core data: vehicle number, assigned path number, estimated arrival time, and risk warning level.

[0102] Perform resource conflict detection and timeliness verification on the resource matching plan, generate a resource conflict detection report, adjust the resource matching plan to generate an optimized scheduling plan, and generate a scheduling instruction set through instruction encoding and protocol conversion.

[0103] Specifically, a resource conflict check is performed on the vehicle number and the assigned route number in the resource matching plan to identify the situation where the same vehicle is assigned multiple transport routes in overlapping time windows, and the time window overlap judgment standard is 15 minutes for example; the time difference between the estimated arrival time and the promised delivery time of each transport route is verified, and a difference exceeding 30 minutes for example is considered to be not up to the time limit; a resource conflict record is formed, which includes four items: conflict vehicle number, conflict route number, overlapping time period and time limit deviation value; the adjustment process gives priority to retaining high Q value plans, and the logistics transportation key path in the anti-destruction strategy table is used to update the conflicting path. The logistics transportation key path needs to control the time difference within 20 minutes for example; the optimized scheduling plan is processed through instruction coding, and the vehicle number, logistics transportation key path number, and time node are converted into a 9-digit instruction code, and then converted into a communication format suitable for various types of on-board terminals; the final output scheduling instruction set includes four parts: instruction sequence number, execution time mark, instruction content and verification code. The instruction sequence number is arranged in order of execution time, and the verification code adopts the exemplary CRC-16 standard product.

[0104] The optimization module, based on the scheduling instruction set, dynamically adjusts multimodal operating parameters through the intelligent collaborative control engine, reconstructs the logistics topology network, forms a highly fault-tolerant network, and obtains the optimal three-dimensional decision-making time management solution through real-time operation data monitoring and dynamic optimization decision-making mechanism.

[0105] Based on the scheduling instruction set, the multimodal operating parameters are dynamically adjusted through the intelligent collaborative control engine to form a coordinated operating parameter set.

[0106] Specifically, the scheduling instruction set is parsed to obtain the instruction sequence number, execution time stamp, instruction content and verification code, and the processing speed and accuracy indicators in the associated sorting equipment parameters are extracted; the real-time position, load status and fuel consumption data in the transport vehicle parameters are extracted; the transport path planning accuracy and obstacle avoidance sensitivity settings in the path guidance parameters are read; the environmental adaptation parameters including temperature, humidity, visibility and traffic conditions are monitored in real time; the intelligent collaborative control engine dynamically adjusts the sorting equipment processing speed to an exemplary peak of 90% according to the transport path priority, and optimizes the fuel consumption parameters to an exemplary optimal range according to the transport vehicle load status; the path guidance parameters automatically improve the transport path planning accuracy to an exemplary centimeter level according to the changes in the environmental adaptation parameters; when the environmental adaptation parameters trigger the safety mode activation conditions in the transport vehicle parameters, the speed is automatically reduced to an exemplary standard value of 80%; the adjusted sorting equipment parameters, transport vehicle parameters, path guidance parameters and environmental adaptation parameters are integrated to form a coordinated operation parameter set containing the optimal configuration of each parameter.

[0107] The topological relationship of the coordinated operation parameter set is analyzed and the service association features are extracted to obtain the logistics node association, and the service mapping relationship between the sorting center and the distribution station is reconstructed to generate a new logistics topology network.

[0108] Specifically, the high-frequency transport route data in the transport vehicle parameters is used to identify strong connections between logistics nodes with a transport frequency exceeding an exemplary 5 times per day; the optimal path data in the path guidance parameters is used to identify cross-regional hub logistics nodes; service association feature extraction is based on historical order flow data to obtain the service dependency relationship between the sorting center and the distribution station, and logistics node pairs with a dependency index exceeding an exemplary 0.7 are marked as priority associations; the logistics node association strength is dynamically adjusted according to the real-time transport task volume, and the connection priority is increased when the task volume increases by an exemplary 20%, ultimately obtaining a logistics node association relationship that includes spatial location relationships, transport frequency associations, and service dependency levels;

[0109] Based on the extracted logistics node association relationships, at least two backup connections to sorting centers are established for each distribution station to ensure service continuity; the logistics node connection weights are dynamically adjusted according to real-time transportation demand changes, with a focus on ensuring redundant connections on key logistics transportation paths; the generated new logistics topology network fully records the three core architectural elements of the updated logistics node longitude and latitude coordinates, connection edge service priorities, and sorting-distribution mapping relationships. The network topology depth is strictly controlled within 6 levels, and ultimately a new logistics topology network with optimized structure is output.

[0110] Perform stress testing on the new logistics topology network, evaluate the connectivity retention rate and time attenuation under logistics node failure scenarios, and output a highly fault-tolerant network.

[0111] Specifically, the new logistics topology network input stress test environment simulates the order volume peak reaching an exemplary workload of 3 times the daily workload; the logistics node failure test randomly selects an exemplary 10% of the sorting center logistics nodes to be forced offline, and continuously observes the operation status of the new logistics topology network; the connectivity retention rate statistics the proportion of distribution stations that can still provide normal services, and an alarm is triggered if the proportion is lower than the exemplary 85%; the time decay amplitude records the change in the average transportation time of the key logistics transportation paths before and after the failure of the logistics node, and the change amplitude exceeds the exemplary 25% and is judged to be abnormal; the backup path is dynamically enabled during the test to verify that the fault switching time is controlled within the exemplary 5 minutes; the high fault tolerance network output standard requires that the three conditions of connectivity retention rate exceeding the exemplary 90%, time decay amplitude lower than the exemplary 20%, and fault switching time shorter than the exemplary 3 minutes are met at the same time; the new logistics topology network that finally passes the test is marked as a high fault tolerance network.

[0112] Multimodal operation parameters include sorting equipment parameters, transport vehicle parameters, path guidance parameters and environmental adaptation parameters.

[0113] Based on a high fault-tolerant network, outlier detection is used to identify abnormal readings, path deviation events, and time deviation signals, and output abnormal event reports.

[0114] Specifically, the real-time operating data in the high-fault-tolerance network is input into the outlier detection process, and the interquartile range method is used to identify abnormal readings that exceed the exemplary 1.5 times interquartile range; the path deviation event detection compares the deviation distance between the actual trajectory of the transport vehicle and the planned path, and triggers an alarm if the deviation exceeds the exemplary 500 meters; the time deviation signal monitoring records the difference between the actual processing time and the expected time of each logistics node, and the difference exceeding the exemplary 30 minutes is marked as an abnormality; the abnormal event report integrates three types of detection results, including the timestamp and value of the abnormal reading, the vehicle number and position coordinates of the path deviation, and the logistics node number and delay duration of the time deviation; the report generation frequency is set to an exemplary time of once per hour, covering all key logistics nodes and transportation paths in the high-fault-tolerance network; and the final output is the abnormal event report.

[0115] Conduct Pareto frontier analysis on abnormal event reports in the key dimensions of timeliness, cost efficiency, and reliability to form the optimal three-dimensional decision-making timeliness management plan.

[0116] Specifically, the abnormal readings, path deviation events, and timeliness deviation signals in the abnormal event report are mapped to the three evaluation dimensions of timeliness, cost efficiency, and reliability respectively; the timeliness dimension quantifies the processing time deviation, and those exceeding the exemplary 30 minutes are marked as high priority; the cost efficiency dimension counts the extra mileage caused by the deviation of the transportation path, and exceeding the exemplary 5 kilometers triggers the optimization demand; the reliability dimension records the frequency of abnormal readings, and it is determined to be an equipment failure if it exceeds the exemplary 3 times per hour; a three-dimensional decision space is constructed through a multi-objective trade-off algorithm, and the timeliness weight is set to an exemplary 0.5, cost efficiency 0.3, and reliability 0.2; Pareto frontier analysis selects a non-inferior solution set that simultaneously meets the requirements of timeliness deviation control within an exemplary 15 minutes, cost increase less than an exemplary 10%, and reliability compliance rate exceeding an exemplary 95%; the final optimal three-dimensional decision timeliness management plan includes three core strategies: processing timeliness standards for each logistics node, transportation path optimization tolerance range, and equipment maintenance cycle.

[0117] In summary, the present invention achieves causal tracing and probabilistic quantification of logistics delay risks by: constructing a collaborative prediction mechanism of virtual intervention scenarios and causal graph models, solving the problem of insufficient explanatory power of statistical predictions in complex scenarios; at the same time, through the dynamic parameter adjustment and topology reconstruction of the intelligent collaborative control engine, it realizes multimodal collaborative optimization and online self-healing capabilities of the logistics network, overcoming the rigid defects of the static resource allocation model. This two-way enhancement mechanism can not only proactively identify potential delay risks, but also actively build a defense system through dynamic network reconstruction, and ultimately achieve the global optimization of the three-dimensional management indicators of timeliness, cost efficiency and reliability in complex scenarios such as business peak periods, significantly improving the overall effectiveness of the system in responding to abnormal events.

[0118] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An order logistics time management system, characterized by: include, The risk prediction module collects and preprocesses data from the entire order logistics chain, calculates the probability of delay risk at logistics nodes through virtual intervention scenarios and causal graph models, and obtains delay risk prediction results; The visualization module overlays and analyzes the delay risk prediction results with the pre-processed order logistics data to generate a delay risk heat map. The invulnerability module performs vulnerability assessment on the delay risk heat map, generates a vulnerability analysis report, and uses the k-shortest path algorithm to perform multi-objective optimization assessment and generate an invulnerability strategy table. The scheduling module uses a multi-objective resource optimization model to match transport resources and select scheduling solutions based on the invulnerability strategy table to obtain a scheduling instruction set. The optimization module, based on the scheduling instruction set, dynamically adjusts multimodal operating parameters through the intelligent collaborative control engine, reconstructs the logistics topology network, forms a highly fault-tolerant network, and obtains the optimal three-dimensional decision-making time management solution through real-time operation data monitoring and dynamic optimization decision-making mechanism.

2. The order logistics time management system according to claim 1, characterized in that: The order logistics full-link data includes product feature data, transportation environment data, timeliness history data and logistics topology network; The preprocessing includes outlier cleaning, missing value filling, feature normalization and spatiotemporal alignment.

3. The order logistics time management system according to claim 1, characterized in that: The delay risk probability of logistics nodes is calculated through virtual intervention scenarios and causal graph models to obtain delay risk prediction results. The specific steps are as follows: Based on the pre-processed order logistics data, we define transportation acceleration intervention measures, route change intervention measures, and resource addition intervention measures through intervention variables, and then combine them to construct a virtual intervention scenario. Based on historical virtual intervention scenarios, the causal graph model is incrementally trained using machine learning methods to obtain a trained causal graph model. The virtual intervention scenario is input into the trained causal graph model, forward reasoning is performed, the delay risk probability matrix is ​​output, and risk classification and reasoning confidence verification are performed to obtain the delay risk prediction results.

4. The order logistics time management system according to claim 3, characterized in that: The delay risk prediction results are superimposed and analyzed with the pre-processed order logistics full-link data to generate a delay risk heat map. The specific steps are as follows: The delay risk prediction results are integrated with the pre-processed order logistics data through spatial coordinate system conversion and attribute field association to generate spatiotemporal benchmark fusion data. The risk intensity is then quantified to generate a risk space matrix. Perform color mapping and spatial interpolation on the risk space matrix to obtain a three-level delay risk heat map; Through the spatial clustering algorithm, the key risk areas and main transmission paths of the three-level delay risk heat map are identified to generate a delay risk heat map.

5. The order logistics time management system according to claim 4, characterized in that: The vulnerability assessment of the delay risk heat map is performed to generate a vulnerability analysis report. The specific steps are as follows: Extract delay risk thermal values ​​based on the delay risk heat map. Generate a delay risk thermal value map through spatial cluster analysis and time series trend prediction. Also, conduct logistics node connectivity analysis and load pressure assessment to generate logistics node vulnerability indicators. Conducting survivability tests on logistics topology networks based on vulnerability indicators of logistics nodes, generating resilience assessment reports, and conducting cascading failure analysis of logistics topology networks and locating key logistics nodes, generating recommendations on delay risk transmission paths and optimal blocking points. Integrate the resilience assessment report, delay risk transmission path and optimal blocking point recommendations to generate a vulnerability analysis report.

6. The order logistics time management system according to claim 5, characterized in that: The multi-objective optimization evaluation is performed by the k-shortest path algorithm to generate an anti-destruction strategy table. The specific steps are as follows: Based on the vulnerability analysis report, multi-dimensional path evaluation and optimization screening are performed using the k-shortest path algorithm to form a path optimization plan; The non-inferior solution set of the path optimization scheme is screened through the Pareto front to generate an anti-destruction strategy table.

7. The order logistics time efficiency management system according to claim 6, characterized in that: Based on the invulnerability strategy table, the multi-objective resource optimization model is used to match transport resources and select scheduling solutions to obtain a scheduling instruction set. The specific steps are as follows: Based on the historical invulnerability strategy table, the multi-objective resource optimization model is incrementally trained through a deep reinforcement learning framework to obtain a trained multi-objective resource optimization model. The model then performs capacity resource matching and scheduling scheme selection to obtain a resource matching solution. Perform resource conflict detection and timeliness verification on the resource matching plan, generate a resource conflict detection report, adjust the resource matching plan to generate an optimized scheduling plan, and generate a scheduling instruction set through instruction encoding and protocol conversion.

8. The order logistics time management system according to claim 7, characterized in that: Based on the scheduling instruction set, the intelligent collaborative control engine dynamically adjusts the multimodal operation parameters, reconstructs the logistics topology network, and forms a high fault-tolerant network. The specific steps are as follows: Based on the scheduling instruction set, the intelligent collaborative control engine dynamically adjusts the multi-modal operating parameters to form a coordinated operating parameter set; Perform topological relationship analysis and service association feature extraction on the coordinated operation parameter set to obtain logistics node associations, reconstruct the service mapping relationship between the sorting center and the distribution station, and generate a new logistics topology network; Perform stress testing on the new logistics topology network, evaluate the connectivity retention rate and time attenuation under logistics node failure scenarios, and output a highly fault-tolerant network.

9. The order logistics time management system according to claim 8, characterized in that: The multimodal operation parameters include sorting equipment parameters, transport vehicle parameters, path guidance parameters and environmental adaptation parameters.

10. The order logistics time management system according to claim 8, characterized in that: The optimal three-dimensional decision-making time management plan is obtained through real-time operation data monitoring and dynamic optimization decision-making mechanism. The specific steps are as follows: Based on a highly fault-tolerant network, it uses outlier detection to identify abnormal readings, path deviation events, and time deviation signals, and outputs abnormal event reports. Conduct Pareto frontier analysis on abnormal event reports in the key dimensions of timeliness, cost efficiency, and reliability to form the optimal three-dimensional decision-making timeliness management plan.

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