Dynamic scheduling optimization method for government and enterprise official vehicles based on multi-source data fusion

Abstract

The invention relates to the technical field of vehicle scheduling, in particular to a government and enterprise official vehicle dynamic scheduling optimization method based on multi-source data fusion, which comprises the following steps: acquiring multi-source state data related to government and enterprise official vehicle scheduling, modeling a time association relationship among different data sources, and performing unified state structure representation, forming a current scheduling state; constructing a performance state space for official vehicle tasks which are distributed and are in an execution process, determining a scheduling intervention action which can influence the official vehicle tasks in combination with a current scheduling state, and constructing a scheduling intervention action space; based on the performance state space and the scheduling intervention action space, constructing an anti-fact performance state transition relation, and performing reachability analysis on the anti-fact performance state transition relation; and generating a corresponding official vehicle dynamic scheduling decision according to a reachability analysis result, and updating and executing a government and enterprise official vehicle scheduling scheme based on the dynamic scheduling decision.

Description

A Dynamic Dispatch Optimization Method for Government and Enterprise Vehicles Based on Multi-Source Data Fusion Technical Field

[0001] This invention relates to the field of vehicle dispatching technology, and in particular to a dynamic dispatching optimization method for government and enterprise official vehicles based on multi-source data fusion. Background Technology

[0002] As the scale of government and enterprise official activities expands, the frequency of use of official vehicles in scenarios such as cross-departmental collaboration, official travel support, and emergency response is constantly increasing. The scheduling and management of official vehicles is gradually showing characteristics of parallel tasks, dynamic status changes, and complex scheduling conditions. Existing technologies usually use vehicle status, task plans, and scheduling rules to uniformly schedule and dynamically adjust official vehicles to meet the execution needs of official tasks. In such solutions, the scheduling system generally assigns, adjusts, or re-plans vehicle tasks based on currently available vehicle locations, task arrangements, and environmental information, thereby achieving management of the operation process of official vehicles.

[0003] When the status of a dispatched and executed official vehicle task changes, existing technologies typically generate a scheduling adjustment plan directly based on the current status. They lack a calculation and judgment mechanism to determine whether the task still exists and can be successfully fulfilled through scheduling intervention. This can easily lead to scheduling decisions being based on an unrecoverable fulfillment status. Summary of the Invention

[0004] To address the above shortcomings, this invention provides a dynamic scheduling optimization method for government and enterprise official vehicles based on multi-source data fusion, aiming to improve the existing technology's lack of information on whether the task can still be successfully fulfilled through scheduling intervention during the execution of official vehicle tasks.

[0005] This invention provides the following technical solution: a dynamic scheduling optimization method for government and enterprise official vehicles based on multi-source data fusion, comprising the following steps:

[0006] S1. Obtain multi-source status data related to the dispatch of government and enterprise vehicles, model the temporal correlation between different data sources, and represent them with a unified status structure to form the current dispatch status;

[0007] S2. Based on the current scheduling status, construct the performance status space for official vehicle tasks that have been dispatched and are in the process of execution;

[0008] S3. Based on the current scheduling state and the performance state space, determine the scheduling intervention actions that can affect the official vehicle task under the current scheduling conditions, and construct the scheduling intervention action space using the scheduling intervention actions as the state transition conditions.

[0009] S4. Based on the performance state space and the scheduling intervention action space, construct the counterfactual performance state transition relationship and perform reachability analysis on the counterfactual performance state transition relationship;

[0010] S5. Based on the reachability analysis results, determine whether there is a reachable path from the current performance status to the successful performance status for official vehicle tasks, and generate corresponding dynamic dispatch decisions for official vehicles based on the determination results.

[0011] S6. Based on the dynamic dispatch decision of official vehicles, update and execute the dispatch plan for government and enterprise official vehicles.

[0012] By adopting the above technical solution, the dispatching system can calculate and determine whether there is still a reachable path to a successful fulfillment state through dispatching intervention during the execution of official vehicle tasks. This allows the feasibility of task fulfillment to be identified before generating dispatching decisions, thus avoiding dispatching decisions based on an unrecoverable fulfillment state.

[0013] Preferably, in step S1, the step of acquiring multi-source status data related to the dispatching of government and enterprise vehicles includes:

[0014] Acquire vehicle operation data that characterizes the operating status of official vehicles, including vehicle location, driving status, or vehicle availability status;

[0015] Acquire task execution data that characterizes the task execution status of official vehicles, including task start and end times, task progress, or task constraint information;

[0016] Acquire scheduling environment data that characterizes the scheduling environment, including traffic conditions, road conditions, or temporary scheduling constraints;

[0017] Based on the vehicle operation data, task execution data, and scheduling environment data, the temporal correlation between different data sources is modeled to form time-consistent multi-source state data.

[0018] Preferably, in step S1, the step of modeling the temporal correlation between different data sources includes:

[0019] Obtain the time stamp information corresponding to the vehicle operation data, task execution data, and scheduling environment data;

[0020] Based on the time identification information, time alignment processing is performed on data from different data sources to form a multi-source data sequence under a unified time reference.

[0021] For data sources with time offsets or sampling frequency differences, the corresponding data is mapped based on a preset time window;

[0022] The multi-source data, after time alignment and mapping, is used as multi-source state data with time correlation.

[0023] Preferably, in step S2, the step of constructing the performance state space for dispatched and executed official vehicle tasks includes:

[0024] Based on the current scheduling status, obtain task execution progress information, remaining time information, and current vehicle location information related to official vehicle tasks;

[0025] Based on the task execution progress information, remaining time information, and current vehicle location information, determine the set of status parameters for the official vehicle task during the performance of the task;

[0026] Based on the set of state parameters, the performance process of official vehicle tasks is divided into states, forming a performance state space composed of multiple performance states.

[0027] Preferably, in step S3, the step of determining the scheduling intervention action that can affect the official vehicle mission under the current scheduling conditions includes:

[0028] Based on the current scheduling status, obtain the vehicle availability status, task constraint information, and scheduling environment constraint information related to the official vehicle task;

[0029] Based on vehicle availability, task constraints, and scheduling environment constraints, determine the set of scheduling operations that can be executed under the current scheduling conditions;

[0030] From the set of executable scheduling operations, select those scheduling operations that can affect the performance status of official vehicle tasks, and use them as scheduling intervention actions.

[0031] Preferably, in step S3, the step of constructing the scheduling intervention action space using scheduling intervention actions as state transition conditions includes:

[0032] Based on the performance state space, determine the set of state nodes that are allowed to undergo state transitions under different performance states of official vehicle tasks;

[0033] The scheduling intervention actions are associated with the corresponding performance status in the set of status nodes, forming a mapping relationship between performance status and scheduling intervention actions;

[0034] Using mapping relationships as state transition conditions, a scheduling intervention action space containing multiple state nodes and their corresponding scheduling intervention actions is constructed.

[0035] Preferably, in step S4, the step of constructing the counterfactual performance state transition relationship includes:

[0036] Based on the performance state space, determine the current performance status of official vehicle tasks;

[0037] Based on the scheduling intervention action space, obtain the set of scheduling intervention actions that can be executed in the current performance state;

[0038] Apply the set of scheduling intervention actions to the current performance status to determine the subsequent performance status that can be reached under the corresponding scheduling intervention actions;

[0039] Based on the current performance status, scheduling intervention actions, and subsequent performance status, a state transition relationship between performance statuses is constructed, forming a counterfactual performance state transition relationship.

[0040] Preferably, in step S4, the step of performing reachability analysis on the counterfactual performance state transition relationship includes:

[0041] Based on the counterfactual performance state transition relationship, a state node is determined with the current performance state of the official vehicle task as the starting state;

[0042] Based on the counterfactual performance state transition relationship, determine the state node with the successful performance state as the target state;

[0043] Starting from the initial state, traverse the state transition paths in the performance state space along the counterfactual performance state transition relations;

[0044] During the traversal, it is determined whether there is at least one state transition path from the initial state to the target state, which is used as the result of the reachability analysis.

[0045] Preferably, in step S5, the step of generating the corresponding dynamic dispatch decision for official vehicles based on the determination result includes:

[0046] Based on the reachability analysis results, determine whether there is a reachable path from the current performance status to the successful performance status for official vehicle tasks;

[0047] When a reachable path exists, a combination of scheduling operations is determined based on the reachable path to adjust the execution process of official vehicle tasks, serving as a dynamic scheduling decision for official vehicles.

[0048] When no reachable path exists, the corresponding task handling and scheduling decision is determined based on the reachability analysis results.

[0049] Preferably, in step S6, the step of updating and executing the government and enterprise official vehicle dispatch plan includes:

[0050] Based on dynamic dispatching decisions for official vehicles, the vehicle allocation information, task execution sequence, or driving arrangement in the existing official vehicle dispatching scheme are updated to form an updated official vehicle dispatching scheme.

[0051] The updated official vehicle dispatch plan will be distributed to the corresponding vehicle execution unit or dispatch execution unit.

[0052] According to the updated official vehicle dispatch plan, the corresponding official vehicle dispatch operations shall be carried out.

[0053] The present invention has the following beneficial effects:

[0054] 1. In this invention, by modeling the problem of dispatching official vehicles as an accessibility determination problem based on the counterfactual performance state transition relationship, the dispatching system can determine at the current dispatching time whether there is still at least one set of actually executable dispatching intervention action sequences that can enable the official vehicle task to reach the successful performance state. Thus, without actually executing dispatching intervention, a structured determination of the feasibility of task performance is completed, avoiding the establishment of dispatching decisions on an unrecoverable performance state.

[0055] 2. In this invention, by continuously modeling the connectivity structure of the solution space for official vehicle scheduling and mapping external confirmation, feedback and other interactive behaviors to structural cutting operations of the solution space, the scheduling system can automatically identify the moment when the scheduling variable enters an irreversible frozen state when the solution space undergoes irreversible topological collapse. This prevents the continued searching or adjustment of scheduling variables that no longer have feasible solutions at the computational level, thus avoiding structural conflicts between scheduling calculations and established administrative commitments.

[0056] 3. In this invention, when a government vehicle task is determined to be unreachable through scheduling calculation, the scheduling system can identify the key causal path that leads to irreversible failure of performance by structurally recording the constraint triggering sequence during the scheduling process and tracing the causal path of the failure state in reverse. Based on the causal path, the system can output a scheduling failure result with clear direction, so that the scheduling failure is no longer just a termination state, but forms a failure structure information that can be analyzed and distinguished. Attached Figure Description

[0057] Figure 1 is a flowchart of the method for optimizing the dynamic scheduling of government and enterprise official vehicles based on multi-source data fusion proposed in this invention. Detailed Implementation

[0058] The technical solutions in 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.

[0059] In the first embodiment of the present invention, the present invention provides a method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion, as shown in Figure 1, including the following steps:

[0060] S1. Obtain multi-source status data related to the dispatch of government and enterprise vehicles, model the temporal correlation between different data sources, and represent them with a unified status structure to form the current dispatch status;

[0061] Furthermore, in step S1, the steps for obtaining multi-source status data related to the dispatching of government and enterprise vehicles include:

[0062] Obtain vehicle operation data that characterizes the operational status of official vehicles. Vehicle operation data includes vehicle location, driving status, or vehicle availability status.

[0063] Acquire task execution data that characterizes the performance of official vehicle tasks. The task execution data includes task start and end times, task progress, or task constraint information.

[0064] Acquire scheduling environment data that characterizes the scheduling environment, including traffic conditions, road conditions, or temporary scheduling constraints;

[0065] Based on vehicle operation data, task execution data, and scheduling environment data, the temporal correlation between different data sources is modeled to form time-consistent multi-source state data.

[0066] Furthermore, in step S1, the step of modeling the temporal correlation between different data sources includes:

[0067] Obtain the time stamp information corresponding to vehicle operation data, task execution data, and scheduling environment data;

[0068] Based on time stamp information, time alignment processing is performed on data from different data sources to form a multi-source data sequence under a unified time reference.

[0069] For data sources with time offsets or sampling frequency differences, the corresponding data is mapped based on a preset time window;

[0070] The multi-source data, after time alignment and mapping, is used as multi-source state data with time correlation.

[0071] Specifically, the dynamic dispatch system for government and enterprise vehicles first performs multi-source status data acquisition and unified modeling processing during operation to form the current dispatch status for subsequent dispatch calculations;

[0072] Multi-source status data includes vehicle operation data, task execution data, and dispatching environment data, which are used to characterize the operating status of official vehicles, task execution status, and external dispatching environment, respectively.

[0073] Vehicle operation data is used to reflect the real-time operation status of official vehicles, including information such as vehicle location, driving status and vehicle availability. Task execution data is used to reflect the execution of official vehicle tasks, including task start and end time, task progress and task constraint information. Dispatch environment data is used to reflect the external environment status that affects the operation of official vehicles, including traffic conditions, road conditions and temporary dispatch constraint information.

[0074] Since the above-mentioned multi-source state data come from different sources, their collection frequency and time identification methods may differ. Therefore, before representing the multi-source state data in a unified state structure, it is necessary to model the temporal correlation between different data sources.

[0075] In the specific implementation process, the system first extracts the corresponding time identifier information from various types of multi-source state data, and performs time alignment processing on the multi-source state data using a unified time reference as a benchmark.

[0076] Let the time point under the unified time reference be . The status data from different data sources are denoted as follows:

[0077] ;

[0078] in, Indicates the first Data source class Indicates the first Data source in time The corresponding state values, Indicates the number of data sources;

[0079] For the target time point The system in the From the class data source, select state data that meets the following conditions as the time-aligned state representation:

[0080] ;

[0081] in, Indicates the time point relative to the target time. The closest time marker, This indicates the state value corresponding to that point in time;

[0082] In one alternative implementation, to avoid using data with excessively large time offsets in the modeling, the system can pre-set a time window. Only when the following conditions are met Only when the time is right will the corresponding state value be obtained. Incorporate time alignment results; after completing time alignment, the system combines state values ​​from different data sources to form a multi-source state vector under a unified time reference: ,in, Indicates a point in time Corresponding multi-source state data;

[0083] Based on a pre-defined state structure model, the system performs unified state structure representation processing on multi-source state vectors, mapping state information from different sources to a unified scheduling state structure to form the current scheduling state.

[0084] S2. Based on the current scheduling status, construct the performance status space for official vehicle tasks that have been dispatched and are in the process of execution;

[0085] Furthermore, in step S2, the steps of constructing the performance state space for dispatched and ongoing official vehicle tasks include:

[0086] Based on the current scheduling status, obtain task execution progress information, remaining time information, and current vehicle location information related to official vehicle tasks;

[0087] Based on the task execution progress information, remaining time information, and current vehicle location information, determine the set of status parameters for the official vehicle task during the performance of the task;

[0088] Based on the set of state parameters, the performance process of official vehicle tasks is divided into states, forming a performance state space composed of multiple performance states.

[0089] Specifically, after obtaining the current dispatch status, the government and enterprise official vehicle dynamic dispatch system constructs and processes the performance status space of the dispatched official vehicle tasks that are in the process of execution, in order to depict the status changes of the official vehicle tasks during the execution process and provide a status basis for subsequent dispatch intervention analysis.

[0090] In this process, the construction of the performance state space takes the current scheduling state as the input premise. By extracting and modeling the relevant state information of the task execution process, the performance process of official vehicle tasks can be represented in the form of state space.

[0091] During actual operation, the system first obtains multi-dimensional status information related to the official vehicle task based on the current scheduling status. Among them, the task execution progress information is used to reflect the current execution ratio or execution stage of the official vehicle task, the remaining time information is used to reflect the remaining time length of the official vehicle task until the planned completion time or constraint time, and the vehicle current location information is used to reflect the current spatial location of the vehicle undertaking the official vehicle task.

[0092] The aforementioned task execution progress information, remaining time information, and vehicle current location information can be obtained from the task management module, vehicle positioning module, and scheduling status maintenance module, and are kept updated synchronously in the current scheduling status;

[0093] After obtaining the above status information, the system parameterizes the status of official vehicle tasks during the performance process, taking the task execution progress, remaining time and the current vehicle location as the basic status parameters of the performance status, thereby forming a set of status parameters to characterize the task performance status.

[0094] In one possible implementation, the set of state parameters can be represented as ,in, This parameter represents the task execution progress and describes the degree of completion of official vehicle tasks. The remaining time parameter describes the remaining execution time of the official vehicle task in terms of time constraints. This represents the vehicle location parameter, used to describe the current spatial location of the official vehicle undertaking the task;

[0095] Based on the set of state parameters, the system further performs state division processing on the performance process of official vehicle tasks, so as to map the continuously changing task execution process into discrete performance states.

[0096] In the specific implementation process, the system determines the status of official vehicle tasks at different points in time based on the value range or combination relationship of each parameter in the status parameter set, so that when the status parameter set changes, it can correspond to different performance statuses.

[0097] In one possible implementation, the performance status can be represented as: ,in, This indicates the performance status of official vehicle duties during the contract fulfillment process. This represents a state mapping function that determines the performance status based on a set of state parameters. This state mapping function is used to map different combinations of state parameters to corresponding performance states.

[0098] By continuously updating the set of state parameters during the execution of official vehicle tasks and determining the performance status based on the state mapping function, the system can form a performance state space consisting of multiple performance statuses throughout the entire execution cycle of official vehicle tasks.

[0099] S3. Based on the current scheduling state and the performance state space, determine the scheduling intervention actions that can affect the official vehicle task under the current scheduling conditions, and construct the scheduling intervention action space using the scheduling intervention actions as the state transition conditions.

[0100] Furthermore, in step S3, the steps for determining the dispatch intervention actions that can affect official vehicle tasks under the current dispatch conditions include:

[0101] Based on the current scheduling status, obtain the vehicle availability status, task constraint information, and scheduling environment constraint information related to the official vehicle task;

[0102] Based on vehicle availability, task constraints, and scheduling environment constraints, determine the set of scheduling operations that can be executed under the current scheduling conditions;

[0103] From the set of executable scheduling operations, select those scheduling operations that can affect the performance status of official vehicle tasks, and use them as scheduling intervention actions.

[0104] Furthermore, in step S3, the step of constructing the scheduling intervention action space using the scheduling intervention action as the state transition condition includes:

[0105] Based on the performance state space, determine the set of state nodes that are allowed to undergo state transitions under different performance states of official vehicle tasks;

[0106] The scheduling intervention actions are associated with the corresponding performance status in the set of status nodes, forming a mapping relationship between performance status and scheduling intervention actions;

[0107] Using mapping relationships as state transition conditions, a scheduling intervention action space containing multiple state nodes and their corresponding scheduling intervention actions is constructed.

[0108] Specifically, after the dynamic dispatch system for government and enterprise official vehicles establishes the current dispatch status and constructs the performance status space, it further executes the determination of dispatch intervention actions and the construction of the dispatch intervention action space to clarify the executable dispatch behaviors that can affect the performance status of official vehicle tasks under the current dispatch conditions, and to provide an action basis for subsequent performance status transfer analysis.

[0109] In this process, the determination of scheduling intervention actions is not based on fixed rules or manual decision-making, but on the current scheduling state and performance state space, and the executable scheduling behaviors are systematically screened and modeled.

[0110] During actual operation, the system first obtains multi-dimensional constraint information related to official vehicle tasks based on the current scheduling status. Among them, the vehicle availability status is used to reflect whether the vehicle undertaking the official vehicle task is in a scheduling or adjustable state, the task constraint information is used to reflect the restrictions on the official vehicle task in terms of time, sequence or administrative requirements, and the scheduling environment constraint information is used to reflect the restrictions of the external environment on scheduling behavior.

[0111] The aforementioned vehicle availability status, task constraint information, and scheduling environment constraint information can be obtained from the vehicle management module, task management module, and scheduling environment perception module, and are kept updated synchronously in the current scheduling status;

[0112] After obtaining the above constraint information, the system determines the scheduling operations that are allowed to be executed under the current scheduling conditions based on the vehicle availability status, task constraint information, and scheduling environment constraint information, so as to form a set of executable scheduling operations.

[0113] In this process, the set of executable scheduling operations is used to represent the set of scheduling behaviors that can be theoretically implemented under the current scheduling conditions and constraints. This set of scheduling operations is not limited to specific scheduling methods, but is only used as a candidate set for screening subsequent scheduling intervention actions.

[0114] Based on this, the system further filters out scheduling operations that can affect the performance status of official vehicle tasks from the set of executable scheduling operations, and determines the selected scheduling operations as scheduling intervention actions.

[0115] Dispatch intervention actions are used to represent dispatch behaviors that, under the current dispatch conditions, may cause changes in the performance status of official vehicles once applied, thereby establishing a clear technical link between dispatch behaviors and performance status;

[0116] After determining the scheduling intervention actions, the system further constructs the scheduling intervention action space using the scheduling intervention actions as state transition conditions.

[0117] In the specific implementation process, the system determines the set of state nodes that are allowed to undergo state transitions under different performance states of official vehicle tasks based on the performance state space, so that each state node in the performance state space can establish a corresponding relationship with the scheduling intervention action.

[0118] Subsequently, the system associates the determined scheduling intervention actions with the corresponding performance status in the set of status nodes, forming a mapping relationship between performance status and scheduling intervention actions;

[0119] In one possible implementation, this mapping relationship can be represented as:

[0120] ;

[0121] in, Represents the first in the performance state space A contract fulfillment status, Indicates the first Each scheduling intervention action, the mapping relationship is used to represent the performance status. Allow scheduling intervention actions ;

[0122] Based on the above mapping relationship, the system uses the correspondence between the performance status and the scheduling intervention action as the state transition condition to construct a scheduling intervention action space containing multiple state nodes and their corresponding scheduling intervention actions.

[0123] S4. Based on the performance state space and the scheduling intervention action space, construct the counterfactual performance state transition relationship and perform reachability analysis on the counterfactual performance state transition relationship;

[0124] Furthermore, in step S4, the steps for constructing the counterfactual performance state transition relationship include:

[0125] Based on the performance state space, determine the current performance status of official vehicle tasks;

[0126] Based on the scheduling intervention action space, obtain the set of scheduling intervention actions that can be executed in the current performance state;

[0127] Apply the set of scheduling intervention actions to the current performance status to determine the subsequent performance status that can be reached under the corresponding scheduling intervention actions;

[0128] Based on the current performance status, scheduling intervention actions, and subsequent performance status, a state transition relationship between performance statuses is constructed, forming a counterfactual performance state transition relationship.

[0129] Furthermore, in step S4, the steps of performing reachability analysis on the counterfactual performance state transition relationship include:

[0130] Based on the counterfactual performance state transition relationship, a state node is determined with the current performance state of the official vehicle task as the starting state;

[0131] Based on the counterfactual performance state transition relationship, determine the state node with the successful performance state as the target state;

[0132] Starting from the initial state, traverse the state transition paths in the performance state space along the counterfactual performance state transition relations;

[0133] During the traversal, it is determined whether there is at least one state transition path from the initial state to the target state, which is used as the result of the reachability analysis.

[0134] Specifically, after constructing the performance status space and the dispatch intervention action space, the dynamic dispatch system for government and enterprise official vehicles constructs the counterfactual performance status transition relationship of official vehicle task execution and performs accessibility analysis based on the performance status space and the dispatch intervention action space, so as to characterize the possible evolution path of the performance status of official vehicle tasks under different dispatch intervention conditions.

[0135] In this process, the system first determines the current performance status of the official vehicle task under the current scheduling state based on the performance state space. The current performance status is used to characterize the starting state node of the official vehicle task in the performance state space.

[0136] Subsequently, the system obtains the set of scheduling intervention actions that are allowed to be executed in the current performance state based on the scheduling intervention action space, so that the subsequent state transition analysis is limited to the range of executable scheduling intervention actions;

[0137] During actual operation, the system will apply each scheduling intervention action in the set of scheduling intervention actions to the current performance status in order to determine the subsequent performance status that the official vehicle task may reach under the corresponding scheduling intervention action.

[0138] In one possible implementation, the state transition relationship of the performance state under the action of scheduling intervention can be represented as:

[0139] ;

[0140] in, Indicates the current performance status. Indicates dispatching intervention actions, This indicates the subsequent performance status that may be achieved under the action of scheduling intervention. This represents the state transition function used to describe the change in performance status under the action of scheduling intervention.

[0141] By summarizing the state transition results corresponding to each scheduling intervention action in the current performance state, the system constructs a counterfactual performance state transition relationship consisting of the current performance state, scheduling intervention actions, and subsequent performance states. This relationship is used to describe the performance state changes that may be caused by different scheduling intervention actions when no actual scheduling intervention is performed.

[0142] After completing the construction of the counterfactual performance state transition relationship, the system further performs reachability analysis on the counterfactual performance state transition relationship to determine whether there is a feasible state transition path for the official vehicle task to reach the successful performance state from the current performance state.

[0143] In the specific implementation process, reachability analysis is based on a state transition diagram formed by counterfactual performance state transition relationships, where the set of states can be represented as:

[0144] ;

[0145] The set of state transition relations can be represented as:

[0146]

[0147] in, and This represents a performance state node in the performance state space. The state transition relationship represents the transition relationship between performance states under the action of the scheduling intervention action.

[0148] Starting from the initial state node corresponding to the current performance status, the system searches for state transition paths in the performance status space along the state transition relationship, and determines whether there is at least one state transition path from the initial state node to the target state node corresponding to the successful performance status. This result of the reachability analysis is used to generate subsequent dynamic scheduling decisions for official vehicles.

[0149] S5. Based on the reachability analysis results, determine whether there is a reachable path from the current performance status to the successful performance status for official vehicle tasks, and generate corresponding dynamic dispatch decisions for official vehicles based on the determination results.

[0150] Furthermore, in step S5, the step of generating the corresponding dynamic dispatch decision for official vehicles based on the determined result includes:

[0151] Based on the reachability analysis results, determine whether there is a reachable path from the current performance status to the successful performance status for official vehicle tasks;

[0152] When a reachable path exists, a combination of scheduling operations is determined based on the reachable path to adjust the execution process of official vehicle tasks, serving as a dynamic scheduling decision for official vehicles.

[0153] When no reachable path exists, the corresponding task handling and scheduling decision is determined based on the reachability analysis results.

[0154] Specifically, after completing the construction of the counterfactual performance status transition relationship and obtaining the reachability analysis results, the government and enterprise official vehicle dynamic dispatch system further generates dynamic dispatch decisions based on the reachability analysis results to guide the execution of official vehicle tasks, so as to realize the transformation of dispatch calculation results into dispatch execution plans;

[0155] In this process, the system first determines whether there is a reachable path from the current performance status to the successful performance status of the official vehicle task based on the reachability analysis results. The judgment result is used to reflect whether the official vehicle task still has the possibility of completing the performance through scheduling adjustment under the current scheduling conditions and scheduling intervention action constraints.

[0156] ;

[0157] in, This represents the reachability analysis result. A value of 1 indicates that a reachable path exists, while a value of 0 indicates that no reachable path exists.

[0158] When the reachability analysis results indicate that a reachable path exists, the system determines the combination of scheduling operations to adjust the execution process of official vehicle tasks based on the state transition sequence contained in the reachable path, and determines the combination of scheduling operations as the dynamic scheduling decision for official vehicles.

[0159] In this case, the scheduling operation combination is used to represent a set of scheduling intervention actions that can guide the official vehicle mission to gradually transfer to the successful performance state under the current performance state conditions, so that the scheduling decision is consistent with the performance state evolution path;

[0160] When the reachability analysis results show that there is no reachable path, the system determines the corresponding task handling and scheduling decision based on the reachability analysis results. This task handling and scheduling decision is used to characterize the handling strategy to be executed when the official vehicle task cannot reach the successful performance state through scheduling intervention under the current scheduling conditions.

[0161] In one possible implementation, task handling and scheduling decisions can be used to trigger scheduling processes such as task adjustment, task termination, or task replanning, and serve as input for subsequent updates and execution of government and enterprise vehicle scheduling plans.

[0162] Through the above processing, the system can generate dynamic dispatch decisions for official vehicles based on the reachability analysis results, enabling the counterfactual state analysis results to be transformed into decision outputs with clear dispatch implications, and providing a decision basis for the subsequent updating and execution of government and enterprise official vehicle dispatch schemes.

[0163] S6. Based on the dynamic scheduling decision of official vehicles, update and implement the scheduling plan for government and enterprise official vehicles.

[0164] Furthermore, in step S6, the steps of updating and executing the government and enterprise official vehicle dispatch plan include:

[0165] Based on dynamic dispatching decisions for official vehicles, the vehicle allocation information, task execution sequence, or driving arrangement in the existing official vehicle dispatching scheme are updated to form an updated official vehicle dispatching scheme.

[0166] The updated official vehicle dispatch plan will be distributed to the corresponding vehicle execution unit or dispatch execution unit.

[0167] According to the updated official vehicle dispatch plan, the corresponding official vehicle dispatch operations shall be carried out.

[0168] Specifically, after generating a dynamic dispatch decision for government and enterprise official vehicles, the dynamic dispatch system further updates and executes the dispatch plan based on the dynamic dispatch decision, so as to realize the transformation of dispatch decision into dispatch execution and keep the dispatch plan consistent with the current dispatch status.

[0169] In this process, the system first obtains the existing official vehicle dispatching plan, which may include vehicle allocation information, task execution order and driving arrangement. Among them, vehicle allocation information is used to represent the correspondence between tasks and vehicles, task execution order is used to represent the execution sequence of the same vehicle or the same set of tasks, and driving arrangement is used to represent the route or station sequence arrangement of vehicles in the process of executing tasks.

[0170] The system further acquires dynamic dispatch decisions for official vehicles, which may include combinations of dispatch operations or task handling dispatch decisions used to adjust the task execution process, and serve as input conditions for updating the dispatch plan.

[0171] In specific implementation, the system updates the existing vehicle dispatching scheme based on the dynamic dispatching decision of official vehicles. In one possible implementation, the update process can be represented as replacing or rewriting the updatable fields in the dispatching scheme, so that at least one of the vehicle allocation information, task execution order and driving arrangement is adjusted, thereby forming the updated vehicle dispatching scheme.

[0172] To facilitate a structured representation of the update process, the updated official vehicle dispatching scheme can be represented as follows:

[0173] ;

[0174] in, This indicates the existing official vehicle dispatching plan. This indicates the dynamic dispatching decision-making of official vehicles. This represents the scheduling scheme update function. This indicates the updated official vehicle dispatch plan;

[0175] The scheduling scheme update function is used to map dynamic scheduling decisions to updatable fields of the scheduling scheme, so that the updated scheduling scheme can reflect the vehicle allocation adjustment, task order adjustment or driving arrangement adjustment corresponding to the dynamic scheduling decision.

[0176] After the updated official vehicle dispatch plan is formed, the system will distribute the updated official vehicle dispatch plan to the corresponding vehicle execution unit or dispatch execution unit. The vehicle execution unit can be one or more of the following: vehicle terminal, vehicle communication module or vehicle management terminal. The dispatch execution unit can be one or more of the following: dispatch command platform, task assignment module or dispatch control module.

[0177] During the distribution process, the updated official vehicle dispatch plan can be transmitted in the form of message instructions, task lists, or plan table data structures, and parsing and queuing are completed on the receiving side to drive subsequent dispatch execution.

[0178] After the plan is issued, the vehicle execution unit or the dispatch execution unit will perform the corresponding vehicle dispatch operation according to the updated official vehicle dispatch plan. The vehicle dispatch operation may include vehicle task switching, task execution order adjustment, driving route or station sequence update, etc., and the execution status will be sent back to the dispatch system during the execution process for subsequent dispatch status updates.

[0179] Through the above update and execution process, the dynamic dispatch decision of official vehicles can be transformed into an executable dispatch plan and implemented on the execution side, thereby forming a continuous closed loop in the dynamic dispatch optimization process of government and enterprise official vehicles.

[0180] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. 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 method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion, characterized in that, Includes the following steps: S1. Obtain multi-source status data related to the dispatch of government and enterprise vehicles, model the temporal correlation between different data sources, and represent them with a unified status structure to form the current dispatch status; S2. Based on the current scheduling state, construct the performance state space for the dispatched official vehicle tasks that are in the process of execution; S3. Based on the current scheduling state and the performance state space, determine the scheduling intervention actions that can affect the official vehicle tasks under the current scheduling conditions, and construct the scheduling intervention action space using the scheduling intervention actions as the state transition conditions. S4. Based on the performance state space and the scheduling intervention action space, construct the counterfactual performance state transition relationship and perform reachability analysis on the counterfactual performance state transition relationship; S5. Based on the reachability analysis results, determine whether there is a reachable path from the current performance state to the successful performance state for official vehicle tasks, and generate the corresponding dynamic scheduling decision for official vehicles based on the determination results; S6. Based on the dynamic scheduling decision for official vehicles, update and execute the government and enterprise official vehicle scheduling plan.

2. The method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion according to claim 1, characterized in that, In step S1, the step of acquiring multi-source status data related to the dispatching of government and enterprise vehicles includes: acquiring vehicle operation data characterizing the operating status of government vehicles, the vehicle operation data including vehicle location, driving status, or vehicle availability status; acquiring task execution data characterizing the task execution status of government vehicles, the task execution data including task start and end time, task progress, or task constraint information; acquiring dispatch environment data characterizing the dispatching environment, the dispatch environment data including traffic status, road conditions, or temporary dispatch constraints; and modeling the temporal correlation between different data sources based on the vehicle operation data, task execution data, and dispatch environment data to form time-consistent multi-source status data.

3. The method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion according to claim 1, characterized in that, In step S1, the step of modeling the temporal correlation between different data sources includes: obtaining time identifier information corresponding to the vehicle operation data, task execution data, and scheduling environment data; performing time alignment processing on the data from different data sources based on the time identifier information to form a multi-source data sequence under a unified time reference; mapping the corresponding data based on a preset time window for data sources with time offsets or sampling frequency differences; and using the multi-source data after time alignment and mapping processing as multi-source state data with temporal correlation.

4. The method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion according to claim 1, characterized in that, In step S2, the step of constructing a performance state space for dispatched and executed official vehicle tasks includes: obtaining task execution progress information, remaining time information, and vehicle current location information related to the official vehicle task based on the current scheduling state; determining the set of state parameters for the official vehicle task in the performance process based on the task execution progress information, remaining time information, and vehicle current location information; and dividing the performance process of the official vehicle task into states based on the set of state parameters to form a performance state space composed of multiple performance states.

5. The method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion according to claim 1, characterized in that, In step S3, the step of determining the scheduling intervention action that can affect the official vehicle task under the current scheduling conditions includes: based on the current scheduling state, obtaining the vehicle availability status, task constraint information, and scheduling environment constraint information related to the official vehicle task; determining the set of scheduling operations that can be executed under the current scheduling conditions based on the vehicle availability status, task constraint information, and scheduling environment constraint information; and selecting scheduling operations that can affect the performance status of the official vehicle task from the set of executable scheduling operations as scheduling intervention actions.

6. The method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion according to claim 1, characterized in that, In step S3, the step of constructing a scheduling intervention action space using scheduling intervention actions as state transition conditions includes: determining a set of state nodes that are allowed to undergo state transitions under different performance states of official vehicle tasks based on the performance state space; associating scheduling intervention actions with the corresponding performance states in the set of state nodes to form a mapping relationship between performance states and scheduling intervention actions; and constructing a scheduling intervention action space containing multiple state nodes and their corresponding scheduling intervention actions using the mapping relationship as a state transition condition.

7. The method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion according to claim 1, characterized in that, In step S4, the step of constructing the counterfactual performance state transition relationship includes: determining the current performance state of the official vehicle task based on the performance state space; obtaining the set of scheduling intervention actions that can be executed in the current performance state based on the scheduling intervention action space; applying the set of scheduling intervention actions to the current performance state respectively, and determining the subsequent performance state that can be reached under the corresponding scheduling intervention action; constructing the state transition relationship between performance states based on the current performance state, scheduling intervention actions, and subsequent performance states, thus forming the counterfactual performance state transition relationship.

8. The method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion according to claim 1, characterized in that, In step S4, the step of performing reachability analysis on the counterfactual performance state transition relationship includes: determining a state node with the current performance state of the official vehicle task as the starting state based on the counterfactual performance state transition relationship; determining a state node with the successful performance state as the target state based on the counterfactual performance state transition relationship; traversing the state transition path in the performance state space along the counterfactual performance state transition relationship starting from the starting state; and determining whether there is at least one state transition path from the starting state to the target state during the traversal process, as the result of the reachability analysis.

9. The method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion according to claim 1, characterized in that, In step S5, the step of generating the corresponding dynamic dispatch decision for official vehicles based on the determination result includes: determining whether there is a reachable path from the current performance state to the successful performance state for the official vehicle task based on the reachability analysis result; when there is a reachable path, determining the combination of dispatch operations to adjust the execution process of the official vehicle task based on the reachable path as the dynamic dispatch decision for the official vehicle; when there is no reachable path, determining the corresponding task disposal dispatch decision based on the reachability analysis result.

10. The method for dynamic scheduling optimization of government and enterprise official vehicles based on multi-source data fusion according to claim 1, characterized in that, In step S6, the step of updating and executing the government and enterprise official vehicle dispatching plan includes: updating the vehicle allocation information, task execution order or driving arrangement in the existing official vehicle dispatching plan based on the dynamic dispatching decision of official vehicles, forming an updated official vehicle dispatching plan; distributing the updated official vehicle dispatching plan to the corresponding vehicle execution unit or dispatching execution unit; and executing the corresponding official vehicle dispatching operation according to the updated official vehicle dispatching plan.

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