A logistics abnormal event closed-loop handling method

Abstract

This invention discloses a closed-loop handling method for logistics anomalies, specifically relating to the field of multi-event collaborative decision-making and resource scheduling in logistics management. It addresses the problems of resource competition and low overall handling efficiency caused by independent handling of multiple concurrent anomalies in existing technologies. The method involves acquiring information on anomalies to be handled and shared resources, identifying groups of anomalies that may compete for the same resources; planning multiple handling paths for each event within a group and constructing a network of dependencies between paths; parallel deducing path combinations to identify mutually exclusive conflicts between solutions; generating and executing a globally optimal collaborative handling plan that coordinates all events; and finally collecting feedback on the handling results to update resource information, forming a closed-loop decision optimization mechanism. This achieves systematic collaborative handling and dynamic resource optimization for concurrent logistics anomalies.

Description

Technical Field

[0001] This invention relates to the field of multi-event collaborative decision-making and resource scheduling technology in logistics management, and more specifically, to a closed-loop handling method for abnormal logistics events. Background Technology

[0002] In the field of logistics management, in order to achieve rapid response to abnormal events in transportation, warehousing and other links, existing technologies usually build integrated information platforms. By connecting various IoT devices and business systems, they can realize automatic detection and alarm of abnormal events, and generate handling suggestions or allocate scheduling resources for individual abnormal events based on preset rules or human experience, aiming to complete the task closed loop from discovery to handling.

[0003] However, when multiple abnormal events occur concurrently in time and space, and the handling actions need to compete for limited shared resources, the existing model of making handling decisions based on individual events as independent units exposes inherent defects. Because each decision-making process is isolated from the others and only aims at its own optimality, it lacks a coordinated consideration of the overall resource status of the system and subsequent potential needs. This can easily lead to resource scheduling conflicts, mutual constraints on handling plans, and even secondary anomalies. This decline in overall operational efficiency and increase in costs caused by local optimization decisions restricts the improvement of automation and intelligence in the handling of logistics abnormal events. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides a closed-loop handling method for logistics anomalies to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A closed-loop handling method for logistics anomalies includes:

[0007] S1. Obtain information on multiple logistics anomalies currently pending handling and available shared handling resources;

[0008] S2. Analyze the resource demand characteristics of multiple logistics anomalies. Based on these characteristics and shared resource information, identify groups of anomalies that may compete for the same shared resources, including:

[0009] Based on the event type of logistics anomalies, determine the typical combination of handling resource requirements corresponding to each logistics anomaly.

[0010] By comparing the typical handling resource demand combinations of all logistics anomalies with the available shared handling resource information, we can identify the event set in which there are the same shared handling resources in the typical handling resource demand combinations and the available quantity of the corresponding shared handling resources cannot meet all the demands. The event set is determined as an anomaly event group that may compete for the same shared handling resources.

[0011] S3. For each logistics anomaly within the anomaly group, determine at least one feasible handling path. Each handling path consists of an ordered sequence of potential handling actions, and construct a dependency network between different handling paths based on shared handling resource information, including:

[0012] For each logistics anomaly within the anomaly event group, multiple potential handling actions suitable for the corresponding logistics anomaly event are matched from the preset handling action library.

[0013] Arrange and combine multiple potential disposal actions according to the sequence of logistics business processes to form at least one feasible disposal path consisting of ordered potential disposal actions;

[0014] Analyze the time and status of resource occupation in the shared disposal resource information for potential disposal actions in each feasible disposal path. Based on the time overlap and state mutual exclusion of resource occupation, construct a dependency network to characterize resource competition and temporal constraints among different disposal paths.

[0015] S4. Parallel simulation of at least one feasible handling path for each logistics anomaly within the anomaly event group, and identification of mutual exclusion conflicts between different handling path combinations based on the dependency relationship network.

[0016] S5. Combining the mutually exclusive conflicts of the solutions with the information on shared disposal resources, generate a collaborative disposal solution for all logistics anomalies within the coordinated anomaly event group.

[0017] S6. Implement the collaborative disposal plan and collect disposal result data to update the shared disposal resource information.

[0018] Furthermore, information on multiple pending logistics anomalies and available shared disposal resources is obtained, including:

[0019] Obtain the event identifiers and event types of multiple logistics anomaly events reported by the logistics monitoring system;

[0020] Obtain shared disposal resource information associated with the event type. The shared disposal resource information includes real-time resource status data and resource inventory data read from the resource management system.

[0021] Furthermore, the event types include at least one of transportation delays, cargo damage, and capacity shortages; real-time resource status data includes vehicle location information and availability status, and resource inventory data includes the quantity and type of warehouse spare parts inventory.

[0022] Furthermore, based on the event type of logistics anomalies, the typical combination of handling resource requirements corresponding to each logistics anomaly is determined, including: according to the event type, querying the resource types and quantities actually used in the historical handling records for successfully handling similar events, and determining the frequently occurring resource types and quantity combinations as the typical combination of handling resource requirements corresponding to the event type.

[0023] Furthermore, at least one feasible handling path for each logistics anomaly within the anomaly event group is simulated in parallel, and mutual exclusion conflicts between different combinations of handling paths are identified based on the dependency network, including:

[0024] Simulate the execution of each feasible handling path for each logistics anomaly within the anomaly event group, and record the time period during which each potential handling action in each feasible handling path occupies resources in the shared handling resource information;

[0025] Based on the dependency network, obtain the resource competition and timing constraints between different processing paths;

[0026] Enumerate all possible handling paths for all logistics anomalies within the anomaly event group to form multiple handling path combinations;

[0027] For each combination of disposal paths, verify whether all feasible disposal paths in the corresponding disposal path combination simultaneously satisfy the resource competition and timing constraints in the dependency network. Disposal path combinations that cannot simultaneously satisfy these constraints are identified as having mutually exclusive conflicts.

[0028] Furthermore, by combining mutually exclusive conflicting solutions with shared resource information, a collaborative handling plan is generated for all logistics anomalies within the anomaly event group, including:

[0029] From the multiple combinations of feasible handling paths for all logistics anomalies within the anomaly event group, the handling path combinations identified as having mutually exclusive conflicts are filtered out, resulting in a set of candidate handling path combinations without conflicts.

[0030] Based on shared disposal resource information, assess the overall resource consumption and disposal timeliness of each candidate disposal path combination in the candidate disposal path combination set;

[0031] Based on the evaluation results, the candidate disposal path combination with the best overall resource consumption and disposal timeliness is selected from the set of candidate disposal path combinations. Each feasible disposal path contained in the corresponding candidate disposal path combination is determined as the final disposal path for the corresponding logistics anomaly. All final disposal paths constitute a collaborative disposal plan.

[0032] Furthermore, the overall resource consumption and disposal timeliness of each candidate disposal path combination in the candidate disposal path combination set are evaluated, including: calculating the estimated total resource occupation time of all final disposal paths and the total disposal time of the critical path within the candidate disposal path combination, and using the estimated total resource occupation time and the total disposal time of the critical path as quantitative evaluation indicators of overall resource consumption and disposal timeliness.

[0033] Furthermore, the collaborative response plan is implemented, and response outcome data is collected to update shared response resource information, including:

[0034] Each final disposal path included in the collaborative disposal plan is broken down into specific operational instructions and distributed to the corresponding executor;

[0035] Monitor and record the actual execution status and completion time of each operation instruction, as well as the amount of resources actually consumed in the shared disposal resource information;

[0036] Based on the recorded actual execution status, completion time, and actual resource consumption, the availability status, available quantity, and expected release time of the corresponding resources in the shared disposal resource information are dynamically adjusted.

[0037] Compared with the prior art, the present invention has the following beneficial effects:

[0038] 1. By constructing a multi-event collaborative handling and global resource optimization framework, the problem of resource competition and decision-making conflict in the handling of concurrent logistics anomalies is effectively solved. Multiple anomalies that were traditionally handled in isolation are dynamically aggregated into event groups that require collaborative handling based on their resource demand characteristics. Multiple feasible handling paths are planned for each event within the group. Potential conflicts are identified by analyzing the dependencies between paths, and finally, a globally optimal collaborative solution that can coordinate all events is generated. This realizes the transformation from discrete and local decision-making to systematic and global collaboration, ensuring the efficient and reasonable allocation of limited resources under competitive demand, and improving the timeliness and economy of logistics anomaly handling as a whole.

[0039] 2. By identifying groups of abnormal events that may compete for the same resources, the complex global resource conflict problem is decomposed into multiple manageable sub-problems, laying the foundation for subsequent refined collaboration. Multiple handling paths are planned for each event, and a dependency network between paths is constructed, enabling the system to predict the mutual exclusion between different combinations of handling strategies in advance, avoiding implicit conflicts during solution execution. Based on the conflict identification results and resource status, a collaborative handling plan is generated, ensuring that the selected plan achieves the comprehensive optimization of resource occupation and handling time among all conflict-free possibilities. By executing the plan and collecting feedback data to update resource information, a complete closed loop from decision-making, execution to learning and optimization is formed, which can continuously adapt to the dynamically changing logistics environment and continuously improve the intelligence level of collaborative handling. Attached Figure Description

[0040] Figure 1 This is a flowchart of a closed-loop handling method for logistics anomalies according to the present invention. Detailed Implementation

[0041] 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.

[0042] Example: Figure 1 This invention provides a closed-loop handling method for logistics anomalies, comprising:

[0043] S1. Obtain information on multiple logistics anomalies currently pending handling and available shared handling resources;

[0044] S2. Analyze the resource demand characteristics of multiple logistics anomalies, and based on the resource demand characteristics and shared resource information, identify anomaly event groups that may compete for the same shared resources.

[0045] S3. For each logistics anomaly in the anomaly group, determine at least one feasible handling path. The handling path consists of an ordered set of potential handling actions, and construct a dependency network between different handling paths based on shared handling resource information.

[0046] S4. Parallel simulation of at least one feasible handling path for each logistics anomaly within the anomaly event group, and identification of mutual exclusion conflicts between different handling path combinations based on the dependency relationship network.

[0047] S5. Combining the mutually exclusive conflicts of the solutions with the information on shared disposal resources, generate a collaborative disposal solution for all logistics anomalies within the coordinated anomaly event group.

[0048] S6. Implement the collaborative disposal plan and collect disposal result data to update the shared disposal resource information.

[0049] In this embodiment of the invention, step S1 acquires information on multiple logistics anomalies currently awaiting disposal and available shared disposal resources. This step is executed by a computing device running the method of the present invention. The event identifiers and event types of multiple logistics anomalies reported by the logistics monitoring system are acquired. The logistics monitoring system continuously receives waybill status updates from the transportation management system, inventory operation records from the warehouse management system, and sensor data streams from vehicle-mounted IoT terminals through a pre-configured data interface. The logistics monitoring system maintains a parameter rule base, in which specific normal operating threshold ranges are set for different monitoring indicators. The normal operating threshold ranges are set based on historical statistical data of logistics operations and business specification requirements. For example, for the temperature of cold chain transport compartments, the lower limit of its normal operating threshold range may be set to 2 degrees Celsius, and the upper limit to 8 degrees Celsius; this range is determined based on the storage requirements of the specific medicines being transported. For the efficiency of sorting operations in the warehouse, its normal operating threshold range may be set to process no less than 500 packages per hour; this value is determined based on 90% of the warehouse's average efficiency over the past three months. The logistics monitoring system compares the received indicator data with the corresponding normal operating threshold ranges in real time. When a certain indicator data continuously exceeds its normal operating threshold for a period of time that reaches a predefined continuous abnormality time condition, the logistics monitoring system determines that an abnormal event has occurred. The continuous abnormality time condition is used to eliminate instantaneous fluctuation interference. This condition itself is also a configurable threshold, and its setting may be based on the characteristics of the indicator. For example, for temperature indicators, the continuous abnormality time condition may be set to 5 minutes; for location offset indicators, the continuous abnormality time condition may be set to 15 minutes.

[0050] The logistics monitoring system generates a globally unique event identifier for each newly identified abnormal event. The generation rule combines a system identifier, the event's date, time (year, month, day, hour, minute, second), and an incrementing serial number. Based on the data source triggering the anomaly, the data deviation pattern, and a pre-defined classification logic tree, the logistics monitoring system assigns an event type to the event. The event type is a classification label indicating the core nature of the anomaly. In the technical solution involved in this embodiment, common event types include transportation delays, cargo damage, and capacity shortages. The logic for determining the transportation delay event type is that when the estimated delivery time calculated by the logistics monitoring system is later than the planned delivery time, and the delay duration exceeds a specifically set transportation delay determination time threshold, it is classified as a transportation delay. The transportation delay determination time threshold is a key business parameter, and its value can be obtained by analyzing the median of the customer-acceptable delay range in historical data, such as 4 hours; or it can be based on the standard specified in the service level agreement, such as 2 hours. The logic for determining cargo damage events is as follows: when the warehouse management system captures the appearance of goods through image acquisition equipment, detects structural damage features through the image analysis module, and the assessed damage level exceeds a preset cargo damage severity threshold, it is classified as cargo damage. The cargo damage severity threshold can be a percentage based on the damaged area, such as outer packaging damage exceeding 10%; or it can be a qualitative classification based on the type of damage, such as reaching a level that severely affects the contents. The logic for determining capacity shortage events is as follows: when the transportation management system confirms that a planned vehicle cannot perform a task, attempts to match alternative resources in the system resource pool fail, and the state of no available resources lasts for more than a capacity shortage confirmation time threshold, it is classified as capacity shortage. The capacity shortage confirmation time threshold is used to confirm that the capacity gap is a real, continuous demand rather than a temporary adjustment. Its setting may be based on the urgency of the task, for example, 30 minutes for ordinary tasks and 10 minutes for urgent tasks.

[0051] Acquire shared disposal resource information associated with identified event types. Shared disposal resource information refers to a data set of various logistics elements whose status can be perceived in real time and used to resolve anomalies. The acquisition process involves reading real-time resource status data and resource inventory data from an independent resource management system. The resource management system aggregates information by connecting to communication links of various resource terminals or synchronizing business databases. Real-time resource status data mainly includes vehicle location information and vehicle availability status. Vehicle location information exists in latitude and longitude coordinate format and is obtained by parsing data packets periodically sent by the vehicle's GPS terminal. Vehicle availability status is an indicator describing whether a vehicle can be dispatched, with values ​​such as idle, in operation, or under maintenance. The resource management system maintains and updates the availability status of each vehicle based on the vehicle's current task assignment, scheduled maintenance plan, and status manually updated by the driver through a mobile terminal application, using a set of status transition rules. Resource inventory data mainly includes warehouse spare parts inventory quantity and spare parts inventory type. Warehouse spare parts inventory quantity refers to the actual physical quantity of goods stored under a specific warehouse code and with a specific material code, obtained by synchronizing inventory snapshots of the warehouse's storage management system. Spare parts inventory type is a standard classification description of materials, such as a cardboard box coded PACK-001 or an ice plate coded COOL-005.

[0052] To establish the association between event types and required resources, this method relies on a pre-configured resource association mapping rule. This rule is stored in a table format, initialized and maintained by the system administrator based on business knowledge. Each row of the table defines the mapping relationship between a given event type and a set of potentially related resource types. For example, one row might specify that the event type "transportation delay" maps to the resource types "idle trucks and backup drivers"; another row might specify that the event type "cargo damage" maps to the resource types "same type of replacement goods and packaging materials". When the process needs to obtain associated resource information for a specific event type, it queries this mapping rule table to obtain a set of target resource types. Subsequently, the process sends a structured query request to the resource management system, which includes these target resource types as filtering conditions. The resource management system searches its full resource information database for all resource records whose resource types match the query conditions and whose current status meets basic availability requirements, and returns the detailed information of these records. For example, for a transportation delay event, the subset of associated resource information obtained after the query might include the current location and load capacity of all idle trucks, and the name, current location, and qualification information of all backup drivers. This targeted query mechanism ensures that the shared resource information provided for subsequent steps is highly relevant and concise. The entire execution of step S1 is automated, using a computer program to call the publicly available data interfaces of various systems to extract, associate, and temporarily store data, preparing for the execution of step S2.

[0053] In this embodiment of the invention, step S2 analyzes the resource demand characteristics of multiple logistics anomalies. Based on these characteristics and shared resource information, it identifies groups of anomalies that may compete for the same shared resources. The processing program executing this method receives output data from step S1. The output data includes the event identifier and event type of each of the multiple logistics anomalies, as well as shared resource information obtained from the resource management system. Based on the event type of each logistics anomaly, a typical combination of resource demands is determined for each anomaly. The determination of the typical combination of resource demands relies on a historical case database. The historical case database is stored in a database, and each record records a completed anomaly handling process. The record includes the event type of the original event, as well as the type and quantity of each resource actually called or consumed during the handling process. The processing program queries the historical case database for all successful handling records with the same event type based on the event type of the current logistics anomaly. The processing program then calculates the frequency of each resource type and its usage quantity in these historical records. For example, querying 100 records with the event type "vehicle malfunction" might show that there were 90 instances of calling for tow truck resources, 85 instances of calling for a repair technician, and 30 instances of calling for a spare tire.

[0054] To filter typical resource demands from the statistical results, the processing program uses a parameter called the high-frequency occurrence threshold. The high-frequency occurrence threshold is a pre-defined percentage value used to determine whether a resource type belongs to typical demand. This threshold value can be configured by the system administrator based on business experience, for example, set to 60%; its setting can be based on the desire to cover most historical scenarios, thus selecting a proportion that represents mainstream practices. The processing program divides the frequency of occurrence of each resource type by the total number of historical records to obtain the occurrence frequency of that resource type. The processing program compares the occurrence frequency with the high-frequency occurrence threshold. For resource types whose occurrence frequency exceeds the high-frequency occurrence threshold, the processing program further determines their typical quantity. The typical quantity can be determined by taking the mode, that is, selecting the usage quantity that occurs most frequently for that resource type in all historical records; for example, if a trailer resource is used 70 times out of 90 times with a quantity of 1, then its typical quantity is determined to be 1. The processing program combines all resource types that meet the high-frequency occurrence threshold condition and their corresponding typical quantities into a dataset; this dataset is the typical handling resource demand combination corresponding to that event type. For example, a typical combination of resources required to handle a vehicle breakdown might be identified as one tow truck and one mechanic.

[0055] By comparing the typical disposal resource demand combinations of all logistics anomaly events with the available shared disposal resource information, the program identifies the set of events where the same shared disposal resource exists in the typical disposal resource demand combinations, and the available quantity of the corresponding shared disposal resource cannot meet all demands. The program iterates through the shared disposal resource information obtained in step S1, which contains the specific quantities currently available for each type of resource. For each resource type, the program performs the following operations: The program checks the typical disposal resource demand combinations of all current logistics anomaly events and calculates the total demand for this specific resource type across all combinations. The total demand is calculated using simple addition. For example, if the typical disposal resource demand combination for event A requires 2 units of packaging material, event B requires 1 unit, and event C requires 2 units, then the total demand for this packaging material is 2 + 1 + 2 = 5 units. The program queries the shared disposal resource information for the current total available quantity of this resource type. The total available quantity is calculated based on the real-time status field of the resource information. For example, for vehicle resources, only the number of vehicles with an idle status is accumulated; for inventory materials, the value of the warehouse spare parts inventory quantity field is directly used.

[0056] The processing procedure performs a resource sufficiency assessment. The rule for this assessment is to compare the total required quantity with the total available quantity. If the total required quantity is greater than the total available quantity, it is determined that the available quantity of that resource type cannot meet the needs of all logistical anomalies. The comparison operation used is a mathematical greater-than comparison. For example, if the total required quantity of packaging materials is 5 units and the total available quantity is 3 units, since 5 is greater than 3, it is determined that the demand cannot be met. When a resource is determined to be unavailable, the processing procedure collects the event identifiers of logistical anomalies that include a demand for that resource in their typical resource demand combinations into a temporary set. This temporary set constitutes a set of events related to that scarce resource.

[0057] The event set is identified as an anomalous event group that may compete for the same shared disposal resources. The process creates a formal group identifier for each event set identified by resource shortages. The group identifier is bound to the specific shared disposal resource type that led to the group's formation. For example, an event set resulting from a shortage of 5-ton truck resources is identified as a separate anomalous event group, marked as competing for 5-ton truck resources. A logistics anomaly may belong to multiple different anomalous event groups simultaneously because its typical disposal resource demand combination includes multiple resources. The process stores and outputs information for each anomalous event group, including the group identifier, the type of resource being competed for, and a list of event identifiers for group members. These anomalous event groups serve as input to step S3, signifying that the system has decomposed the global resource conflict problem into several sub-problems centered on specific scarce resources, requiring internal collaborative solutions. The entire step S2 is executed automatically and sequentially by the process. Its core logic involves determining a demand template through historical statistics, identifying resource gaps through real-time comparison, and clustering related events based on these gaps, thus transforming discrete events into competing event groups.

[0058] In this embodiment of the invention, step S3 determines at least one feasible handling path for each logistics anomaly within the anomaly event group. The handling path consists of ordered potential handling actions, and a dependency network between different handling paths is constructed based on shared handling resource information. The processing program executing this method receives the anomaly event group information output from step S2. The anomaly event group information includes a group identifier and a list of event identifiers for members within the group. For each logistics anomaly within the anomaly event group, multiple potential handling actions applicable to the corresponding logistics anomaly are matched from a preset handling action library. The preset handling action library exists in the form of a database table. Each record in the table describes an atomic handling operation. The record fields include an action identifier, action name, a list of applicable event types, an action precondition expression, and a default estimated time for the action. The list of applicable event types is a string array storing the event type codes that this action can handle. The action precondition expression is a logical judgment used to evaluate whether the current environment allows the execution of this action. The variables in the expression correspond to specific data items in the shared handling resource information. The matching process is executed by the handler. The handler reads the event type of the current logistics anomaly and scans the action database table for all records containing that event type in the applicable event type list field. For each scanned record, the handler parses its action precondition expression, replaces the variables in the expression with the specific values ​​of the latest shared disposal resource information obtained from step S1, and then calculates the result of the logical expression. If the logical expression is true, the precondition for the action is determined to be met, and the action corresponding to that record is selected as a potential disposal action for the current logistics anomaly. For example, a logistics anomaly with the event type of transportation delay might obtain potential disposal actions through this matching process, including the action of dispatching a backup vehicle (action identifier ACT001), the action of replanning the transportation route (action identifier ACT002), and the action of negotiating the delivery time (action identifier ACT003).

[0059] Multiple potential handling actions are arranged and combined according to the sequence of the logistics business process to form at least one feasible handling path consisting of ordered potential handling actions. The sequence of the logistics business process is characterized by a definition called the action partial order rule set. The action partial order rule set is a predefined set of rules, each rule specifying the execution order between two action identifiers. For example, a rule might stipulate that action identifier ACT005, picking up replacement goods, must be executed before action identifier ACT006, repackaging goods. The process obtains the set of action identifiers for all potential handling actions matched for the current logistics anomaly event. Based on the action partial order rule set, the process constructs a directed graph relationship between these action identifiers, where nodes represent actions and directed edges represent constraints that must be executed before... The process uses a depth-first search or topological sorting algorithm to traverse this directed graph and find all linear sequences of nodes that do not violate any directed edge constraints in the graph. Each found linear sequence is a candidate handling path obtained by arranging the potential handling actions. For example, if potential actions are A, B, and C, and the partial order rule requires A to precede B, then the algorithm might generate paths such as A->B->C and A->C->B, but not B->A->C. The process ensures that at least one such candidate action path is generated for each logistical anomaly. These candidate action paths, along with the order of their internal actions, are stored as feasible action paths.

[0060] The analysis examines the time and status of resources in the shared disposal resource information for potential disposal actions within each feasible disposal path. The analysis estimates a time window and resource status transition for each action in each feasible disposal path. The process sets a path baseline start time T0 for the analysis. For the first action in the path, the process reads the default time estimate from its corresponding disposal action library record. The time interval for the first action is calculated from T0 to T0 plus the default time estimate. For subsequent actions in the path, the start time of their time interval is equal to the end time of the previous action's time interval plus a fixed-configuration job switching interval. The job switching interval is a system parameter representing the minimum interval required for action transition, for example, set to 300 seconds. The end time of an action's time interval is equal to its start time plus its own default time estimate. Regarding resource status occupancy, each action record in the disposal action library also defines a status impact description. The status impact description indicates which resources will transition from one state to another when this action is executed. For example, the state impact description of dispatching a standby vehicle might be: changing the available state of the target vehicle from idle to occupied, and maintaining the occupied state during the action's time interval. Based on the state impact description and the calculated action's time interval, the processing program records the state occupancy of the resources associated with the action during its execution.

[0061] Based on the temporal overlap and state mutual exclusion of resource occupancy, a dependency network is constructed to characterize resource competition and temporal constraints among different disposal paths. The dependency network is a graph where nodes represent all feasible disposal paths for all logistics anomalies within all anomaly event groups. The construction process requires establishing two types of edges between nodes. The first type is resource competition edges. The processing program selects two different path nodes, Px and Py. The program checks all actions in path Px and all actions in path Py to determine if there exists a pair of actions Ax and Ay that satisfies the following three conditions: actions Ax and Ay need to occupy the same physical resource instance; the time intervals of action Ax and Ay overlap; and the condition for overlapping time intervals is that the start time of one interval is earlier than the end time of the other interval, and the start time of the other interval is earlier than the end time of this interval. If such a pair of actions is found, an undirected edge is established between nodes Px and Py, and this edge is marked as a resource competition dependency. The second type of edge is state mutual exclusion edges. State mutual exclusion edges originate from state conflicts of shared resources rather than direct instance occupancy. The system predefines a state mutual exclusion rule table. Each rule in the table lists two incompatible states and their associated resource types. For example, a rule might specify that the states of a loading / unloading platform (currently unloading and currently loading) are mutually exclusive. The process selects two path nodes, Px and Py. The process checks the actions in path Px and path Py to determine if there exists a pair of actions Ax and Ay that satisfy the following conditions: Action Ax, in its state impact description, sets a resource to state S1; Action Ay, in its state impact description, requires a resource to be in state S2 or sets a resource to state S2; State S1 and State S2 are defined as a mutually exclusive state pair in the state mutual exclusion rule table; The time intervals of Action Ax and Action Ay overlap. If such a pair of actions is found, an undirected edge is established between nodes Px and Py, and this edge is marked as a state mutual exclusion dependency. By traversing all path node pairs and applying the above rules, the process constructs a complete dependency network. This network reveals the potential conflicts that may occur when different processing paths are executed simultaneously, providing a graphical basis for conflict detection in the parallel deduction of step S4.

[0062] In this embodiment of the invention, step S4 involves parallel simulation of at least one feasible handling path for each logistics anomaly event within the anomaly event group, identifying mutually exclusive conflicts between different handling path combinations based on a dependency network. The set of feasible handling paths and the dependency network from step S3 are received as input. A simulation execution operation creates a virtual timeline for each feasible handling path. The processor sets a common simulation start time for all simulations, typically chosen as the current system time. For each feasible handling path, the processor calculates the simulation time interval for each potential handling action in the order of their arrangement. The simulation start time for the first potential handling action is equal to the simulation start time. The simulation end time for the first potential handling action is equal to its simulation start time plus the estimated time value defined in the handling action library. The simulation start time for the second potential handling action is equal to the simulation end time of the first potential handling action plus a system-configured inter-action buffer time value, which represents the reasonable interval required for action switching, for example, set to 300 seconds. The simulation time intervals for subsequent potential handling actions are calculated recursively according to this rule. The process records the simulation start and end times of each potential action in every feasible disposal path. These two times constitute the resource occupation period of the action. Simultaneously, based on the state impact defined by the potential action, the process marks the specific resource instance associated with it in the simulation context, indicating that it is in a specific occupied state during the corresponding occupation period.

[0063] The dependency acquisition operation reads and parses the dependency network constructed in step S3. The dependency network is stored as graph data, where nodes are identifiers of feasible disposal paths, edges represent relationships between paths, and each edge has an attribute indicating the dependency type. Dependency types include resource contention dependencies and state mutual exclusion dependencies. The process traverses all edges in the dependency network. For each edge, it extracts the identifiers of the two paths connected by the edge and the dependency type attribute of the edge, forming a clear dependency record. All extracted dependency records are organized into a list that fully describes which path pairs have what types of potential conflict constraints, providing a basis for subsequent verification.

[0064] The algorithm performs an enumeration and combination operation, aiming to generate all possible combinations of feasible handling paths for all logistics anomalies within an event group. The process obtains a list of all logistics anomalies within the current event group. For each logistics anomaly in the list, the process obtains the path identifiers of all feasible handling paths corresponding to it, forming a path selection subset. The process calculates the Cartesian product of all path selection subsets using a multi-level iterative loop algorithm. Specifically, if the event group contains n logistics anomalies, the algorithm constructs an n-level nested loop, with each level iterating through all path identifiers in the path selection subset of a logistics anomaly. In the innermost loop, the path identifiers currently selected by each level are combined to form a tuple containing n path identifiers, which represents a handling path combination. The algorithm records all generated handling path combinations. For example, for an event group containing two events, where event one has 2 feasible paths and event two has 3 feasible paths, the algorithm generates 2 × 3 = 6 different handling path combinations through nested loops.

[0065] The conflict verification operation checks for conflicts between all feasible disposal paths within each disposal path combination generated by the enumeration and combination operation. The process selects a disposal path combination sequentially. For the currently selected disposal path combination, the process filters from the dependency record list generated by the dependency acquisition operation those records whose two path identifiers are both contained within the current disposal path combination. The process performs verification on each filtered dependency record. The verification process branches according to the dependency type indicated in the dependency record. If the dependency type is a resource contention dependency type, the process finds the specific resource instance causing the dependency and the two potential disposal actions from the two paths, based on the information from constructing the dependency network in step S3. The process queries the simulation start time and simulation end time of these two potential disposal actions from the occupancy time data of the simulated execution operation record. The process determines whether the two occupancy time periods overlap, using the following logic: checking if the simulation start time of the first action is less than the simulation end time of the second action, and simultaneously checking if the simulation start time of the second action is less than the simulation end time of the first action. If both conditions are met, the time intervals overlap, meaning that resource contention is triggered under the current disposal path combination, constituting a conflict. If the dependency type is a state-mutually exclusive dependency type, the handler also finds the specific action pair causing the mutual exclusion, queries its occupied time period, and uses the same time period overlap judgment logic. If it is determined to be overlapping, the state-mutually exclusive dependency is triggered, constituting a conflict. The handler performs the above verification on all dependency records filtered by the current disposal path combination one by one. If any dependency record is verified to trigger a conflict, the handler marks the current disposal path combination as having a mutually exclusive conflict. The handler performs this conflict verification operation iteratively on all enumerated disposal path combinations. The output of step S4 is a list of identifiers for all disposal path combinations marked as having mutually exclusive conflicts, and a list of identifiers for all disposal path combinations that pass verification and are not marked as conflicting. This output clearly indicates which global parallel disposal solutions are infeasible due to inherent resource or timing contradictions.

[0066] In this embodiment of the invention, step S5 combines mutually exclusive conflict resolution schemes with shared disposal resource information to generate a collaborative disposal scheme for all logistics anomalies within the anomaly event group. Taking the output of step S4 as input, the input includes a list of disposal path combinations with mutually exclusive conflict resolution schemes and a list of non-conflicting disposal path combinations. From all disposal path combinations formed by feasible disposal paths for all logistics anomalies within the anomaly event group, those disposal path combinations identified as having mutually exclusive conflict resolution schemes are removed. The processing program reads the list of disposal path combinations with mutually exclusive conflict resolution schemes and uses it as a filtering criterion. The processing program obtains all disposal path combinations enumerated in step S4. The processing program compares the identifier of each disposal path combination with the identifier in the filtering criterion list. If the identifier of a disposal path combination appears in the filtering criterion list, the processing program discards that combination. After traversal and comparison, all disposal path combinations that have not been discarded constitute a set of conflict-free candidate disposal path combinations.

[0067] Based on shared disposal resource information, the overall resource consumption and disposal timeliness of each candidate disposal path combination in the candidate disposal path combination set are quantitatively evaluated. The evaluation index for overall resource consumption is the estimated total resource occupation time. Calculating the estimated total resource occupation time requires using the data in the simulation execution record from step S4. For a given candidate disposal path combination, the processing program first extracts the specific resource instance identifier occupied by each potential disposal action in all disposal paths within the combination, as well as the simulation start time and simulation end time of each action. The processing program summarizes all unique resource instance identifiers to form a list of resources to be analyzed. For each resource instance in the list, the processing program collects the simulation start time and simulation end time of all potential disposal actions occupying that instance, obtaining a set of time intervals. The processing program sorts these time intervals according to the chronological order of the simulation start time. The process merges time intervals. The merging rule is to sequentially check adjacent intervals. If the simulation end time of the preceding interval is greater than or equal to the simulation start time of the following interval, the two intervals are merged into a new interval. The simulation start time of the new interval is the simulation start time of the original first interval, and the simulation end time of the new interval is the later of the simulation end times of the original two intervals. The process repeats this merging process until no intervals can be merged. After merging, one or more non-overlapping consecutive occupancy periods for the resource instance are obtained. The process calculates the total occupancy duration of the resource instance by subtracting the simulation start time from the simulation end time of all consecutive occupancy periods and summing all the differences. The process repeats the above calculation for all resource instances within the candidate disposal path combination and accumulates the total occupancy duration of all resource instances. The sum obtained is the estimated total resource occupancy duration for the candidate disposal path combination.

[0068] The evaluation metric for handling timeliness is the total handling time of the critical path. Calculating the total handling time of the critical path requires determining the maximum time required to complete the entire candidate handling path combination. The process extracts the simulated completion time of each handling path within the candidate handling path combination from the simulation execution record in step S4. The simulated completion time of a handling path is equal to the simulation end time of the last potential handling action in that path. The process finds the maximum value among all extracted simulated completion times. The process calculates the time difference between this maximum simulated completion time and the simulation start time set in step S4. This time difference is the total handling time of the critical path for that candidate handling path combination. For each combination in the candidate handling path combination set, the process independently performs the above calculations of the estimated total resource consumption time and the total handling time of the critical path, and uses the two calculation results as the evaluation value pair for that combination.

[0069] Based on the numerical results of the evaluation operation, an optimal candidate disposal path combination is selected from the set of candidate disposal path combinations, and a collaborative disposal plan is generated based on this combination. The selection of the optimal combination requires a comprehensive trade-off between resource consumption and disposal timeliness. The processing procedure uses a linear weighted scoring method for comparison. First, the procedure normalizes the estimated total resource consumption time for all combinations within the candidate disposal path combination set. The calculation method involves finding the minimum and maximum values ​​of this indicator among all combinations. For each combination, the minimum value is subtracted, and then divided by the difference between the maximum and minimum values ​​to obtain the normalized score for that combination in the resource consumption dimension. The procedure performs the same normalization process on the total disposal time of the critical path, obtaining a normalized score for each combination in the disposal timeliness dimension. Both normalized scores range from 0 to 1, with lower values ​​indicating better performance in that dimension. Next, the procedure assigns weights to the two dimensions. The weights for resource consumption and disposal timeliness are two pre-configured positive numbers in the system, and their sum is 1. The specific weight values ​​are set by the system administrator based on business strategies. For example, if reducing operating costs is the primary objective, the resource consumption dimension weight can be configured to 0.7, and the handling timeliness dimension weight to 0.3; if rapid response is the primary objective, the resource consumption dimension weight can be configured to 0.3, and the handling timeliness dimension weight to 0.7. Then, the processing program calculates a weighted comprehensive score for each candidate handling path combination. The formula is: the weighted comprehensive score equals the normalized resource consumption score multiplied by the resource consumption dimension weight, plus the normalized handling timeliness score multiplied by the handling timeliness dimension weight. After calculation, the processing program compares the weighted comprehensive scores of all combinations and selects the candidate handling path combination with the lowest weighted comprehensive score as the optimal choice. If two or more combinations have the same lowest weighted comprehensive score, the processing program can set a deciding rule, such as prioritizing the combination with the shorter total handling time for the critical path. After determining the optimal candidate handling path combination, the processing program assigns each handling path within that combination to its corresponding original logistics anomaly event and officially marks these paths as the final handling paths for the corresponding events. The collection of all final disposal paths constitutes a complete collaborative disposal plan. This plan, as the output of step S5, is passed to the subsequent step S6 to drive the actual execution process.

[0070] In this embodiment of the invention, step S6 executes the collaborative disposal plan and collects disposal result data to update the shared disposal resource information. The collaborative disposal plan generated in step S5 is received as input; the collaborative disposal plan consists of a set of final disposal paths. Each final disposal path in the collaborative disposal plan is decomposed into specific operation instructions and distributed to the corresponding executor. The decomposition operation is implemented using a predefined operation instruction template library. The operation instruction template library stores instruction format templates corresponding to different potential disposal action types. For each potential disposal action in each final disposal path, the processor retrieves a matching instruction template from the operation instruction template library based on the action identifier of the potential disposal action. The instruction template is a text structure containing variable placeholders. The processor fills the corresponding variable placeholders in the instruction template with the specific resource instance identifier associated with the current potential disposal action, the event identifier of the associated logistics anomaly event, and the planned time information obtained from the simulation record in step S4, generating a complete and readable operation instruction. For example, for a vehicle dispatch action, the generated instruction may include the specific license plate number, destination address, and planned departure time. After generating all operation instructions, the processor determines the target executor based on the instruction type and the resource category involved. The executors include driver handheld terminals, warehouse manager workstations, or third-party service provider interfaces. The handler sends each operation instruction to its designated executor via integrated communication protocols, such as message queues or application programming interface calls. The handler generates a globally unique instruction trace identifier for each distributed operation instruction and records the instruction trace identifier, the complete instruction content, the target executor identifier, and the distribution timestamp in the instruction execution trace table.

[0071] The system monitors and records the actual execution status and completion time of each operation instruction, as well as the amount of shared disposal resources actually consumed. Monitoring relies on an instruction execution trace table and a status reporting mechanism. The handler provides a status receiving interface for the executor to proactively report instruction progress. The actual execution status is a predefined status enumeration value, including received, executing, completed, canceled, and failed. The handler defines a status transition rule, specifying which states can legally transition to other states. When the handler receives a new status reported by the executor through the status receiving interface, it verifies the legality of the status change according to the status transition rule. If the status change is legal, the handler updates the corresponding instruction trace identifier record in the instruction execution trace table, modifies the actual execution status field to the new status, and records the status update timestamp as the completion time or the latest status time. For operation instructions marked as completed, the handler requires the executor to simultaneously report the actual resource consumption data when reporting the status. The actual resource consumption data indicates how many units of a certain resource were used during the instruction execution. For example, for a maintenance task, feedback data might include the actual model and quantity of parts used. The processing program receives this feedback data and records it in the instruction execution tracking table, associating it with the corresponding instruction.

[0072] Based on the recorded actual execution status, completion time, and actual resource consumption, the available status, available quantity, and expected release time of the corresponding resources in the shared disposal resource information are dynamically adjusted. The processing program continuously checks for record updates in the instruction execution tracking table. When the actual execution status of an operation instruction changes to "completed," the processing program triggers a resource information update process. The update process employs different strategies depending on the type of resource operated by the operation instruction. For consumable resources, the processing program reads the actual resource consumption quantity recorded for that instruction from the instruction execution tracking table. The processing program initiates a data update request to the resource management system, requesting that the value of the corresponding warehouse spare parts inventory quantity field be reduced by the actual resource consumption quantity. This operation directly reduces the available quantity of the resource.

[0073] For reusable, confined resources, such as vehicles or equipment, the update process involves adjustments to status and time. The processor parses the specific resource instance identifier from the instruction content. It retrieves the actual completion time of the instruction from the instruction execution trace table. Based on the resource type and job nature, the processor determines a resource release buffer duration. This buffer duration is a preset empirical value; for example, for transport vehicles, it might be set to 1800 seconds to cover unloading time. The processor calculates the estimated release time, which equals the actual completion time plus the resource release buffer duration. The processor sends an update request to the resource management system, setting the available status field of the corresponding resource instance to idle and the estimated release time field to the calculated estimated release time. For operation instructions whose actual execution status changes to failed or canceled, the processor sends an update request to the resource management system, restoring the available status of the corresponding resource instance to idle or available and clearing any previously set estimated release times. All update requests to the resource management system are completed through its standard data interface. Through the execution of step S6, the shared disposal resource information is corrected in real time to reflect the latest actual situation. The updated shared disposal resource information will serve as the input for subsequent decisions on handling logistics anomalies, thereby achieving a closed loop and continuous optimization of the disposal process.

[0074] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0075] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0076] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0077] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0078] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0079] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0080] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0081] If a function is implemented as a software module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0082] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0083] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A closed-loop handling method for logistics anomalies, characterized in that, include: S1. Obtain information on multiple logistics anomalies currently pending handling and available shared handling resources; S2. Analyze the resource demand characteristics of multiple logistics anomalies. Based on these characteristics and shared resource information, identify groups of anomalies that may compete for the same shared resources, including: Based on the event type of logistics anomalies, determine the typical combination of handling resource requirements corresponding to each logistics anomaly. By comparing the typical handling resource demand combinations of all logistics anomalies with the available shared handling resource information, we can identify the event set in which there are the same shared handling resources in the typical handling resource demand combinations and the available quantity of the corresponding shared handling resources cannot meet all the demands. The event set is determined as an anomaly event group that may compete for the same shared handling resources. S3. For each logistics anomaly within the anomaly group, determine at least one feasible handling path. Each handling path consists of an ordered sequence of potential handling actions, and construct a dependency network between different handling paths based on shared handling resource information, including: For each logistics anomaly within the anomaly event group, multiple potential handling actions suitable for the corresponding logistics anomaly event are matched from the preset handling action library. Arrange and combine multiple potential disposal actions according to the sequence of logistics business processes to form at least one feasible disposal path consisting of ordered potential disposal actions; Analyze the time and status of resource occupation in the shared disposal resource information for potential disposal actions in each feasible disposal path. Based on the time overlap and state mutual exclusion of resource occupation, construct a dependency network to characterize resource competition and temporal constraints among different disposal paths. S4. Parallel simulation of at least one feasible handling path for each logistics anomaly within the anomaly event group, and identification of mutual exclusion conflicts between different handling path combinations based on the dependency relationship network. S5. Combining the mutually exclusive conflicts of the solutions with the information on shared disposal resources, generate a collaborative disposal solution for all logistics anomalies within the coordinated anomaly event group. S6. Implement the collaborative disposal plan and collect disposal result data to update the shared disposal resource information.

2. The closed-loop handling method for logistics anomalies according to claim 1, characterized in that, Obtain information on multiple pending logistics anomalies and available shared handling resources, including: Obtain the event identifiers and event types of multiple logistics anomaly events reported by the logistics monitoring system; Obtain shared disposal resource information associated with the event type. The shared disposal resource information includes real-time resource status data and resource inventory data read from the resource management system.

3. The closed-loop handling method for logistics anomalies according to claim 2, characterized in that, Event types include at least one of transportation delays, cargo damage, and capacity shortages; real-time resource status data includes vehicle location information and availability status, and resource inventory data includes the quantity and type of warehouse spare parts inventory.

4. The closed-loop handling method for logistics anomalies according to claim 1, characterized in that, Based on the event type of logistics anomalies, determine the typical combination of handling resource requirements for each logistics anomaly. This includes: querying the historical handling records of the types and quantities of resources actually used to successfully handle similar events according to the event type, and determining the combination of frequently occurring resource types and quantities as the typical combination of handling resource requirements for the corresponding event type.

5. The closed-loop handling method for logistics anomalies according to claim 1, characterized in that, Parallel simulation of at least one feasible handling path for each logistics anomaly within an anomaly event group; identification of mutual exclusion conflicts between different combinations of handling paths based on a dependency network, including: Simulate the execution of each feasible handling path for each logistics anomaly within the anomaly event group, and record the time period during which each potential handling action in each feasible handling path occupies resources in the shared handling resource information; Based on the dependency network, obtain the resource competition and timing constraints between different processing paths; Enumerate all possible handling paths for all logistics anomalies within the anomaly event group to form multiple handling path combinations; For each combination of disposal paths, verify whether all feasible disposal paths in the corresponding disposal path combination simultaneously satisfy the resource competition and timing constraints in the dependency network. Disposal path combinations that cannot simultaneously satisfy these constraints are identified as having mutually exclusive conflicts.

6. The closed-loop handling method for logistics anomalies according to claim 1, characterized in that, By combining mutually exclusive conflicting solutions with shared resource information, a collaborative handling plan is generated for all logistics anomalies within the anomaly event group, including: From the multiple combinations of feasible handling paths for all logistics anomalies within the anomaly event group, the handling path combinations identified as having mutually exclusive conflicts are filtered out, resulting in a set of candidate handling path combinations without conflicts. Based on shared disposal resource information, assess the overall resource consumption and disposal timeliness of each candidate disposal path combination in the candidate disposal path combination set; Based on the evaluation results, the candidate disposal path combination with the best overall resource consumption and disposal timeliness is selected from the set of candidate disposal path combinations. Each feasible disposal path contained in the corresponding candidate disposal path combination is determined as the final disposal path for the corresponding logistics anomaly. All final disposal paths constitute a collaborative disposal plan.

7. The closed-loop handling method for logistics anomalies according to claim 6, characterized in that, The overall resource consumption and disposal timeliness of each candidate disposal path combination in the candidate disposal path combination set are evaluated, including: calculating the estimated total resource occupation time of all final disposal paths and the total disposal time of the critical path within the candidate disposal path combination, and using the estimated total resource occupation time and the total disposal time of the critical path as quantitative evaluation indicators of overall resource consumption and disposal timeliness.

8. The closed-loop handling method for logistics anomalies according to claim 1, characterized in that, Implement the collaborative response plan and collect response outcome data to update shared response resource information, including: Each final disposal path included in the collaborative disposal plan is broken down into specific operational instructions and distributed to the corresponding executor; Monitor and record the actual execution status and completion time of each operation instruction, as well as the amount of resources actually consumed in the shared disposal resource information; Based on the recorded actual execution status, completion time, and actual resource consumption, the availability status, available quantity, and expected release time of the corresponding resources in the shared disposal resource information are dynamically adjusted.

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