Big data-based vehicle scheduling method

Abstract

The invention relates to the technical field of intelligent traffic, and provides a big data-based vehicle scheduling method, which comprises the following steps of: establishing a vehicle scheduling system model and acquiring a performance function of the vehicle scheduling system model; deriving a system performance parameter expression, a load balancing first theoretical threshold expression and a relationship between an idle rate and a load rate based on the performance function; a second theoretical threshold expression of load balancing is obtained by combining the maximum load capacity of the vehicle and the response time design parameters; according to the vehicle design parameters and the two load balancing threshold expressions, calculating a divergence threshold under output load balancing; and simulating a load response curve and a corresponding performance parameter value within the threshold value through a simulation system. The system model comprises a demand input feedback module which comprises a demand processing sub-module, a task distribution sub-module, a coordinate conversion sub-module and a position feedback sub-module, and vehicle scheduling adjustment is achieved through a multi-stage feedback mechanism. The system response efficiency and the resource utilization rate can be improved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent transportation technology, and more specifically, to a vehicle scheduling method based on big data. Background Art

[0002] In today's society, with the rapid development of the transportation industry, vehicle demand is becoming increasingly diverse and complex. Traditional vehicle scheduling methods rely primarily on manual experience or simple algorithms, which often suffer from numerous shortcomings when faced with large-scale, dynamically changing vehicle demand. For example, manual scheduling is susceptible to subjective factors, resulting in low scheduling efficiency, long response times, and difficulty in achieving optimal resource allocation. While some simple algorithms can improve scheduling efficiency to a certain extent, they often fail to fully consider factors such as the vehicle's actual load capacity, response time, and dynamic changes during task execution. This can lead to inaccurate scheduling results, low resource utilization, and even the possibility of overloaded or idle vehicles.

[0003] Existing vehicle dispatch systems, in terms of technical principles, mostly allocate tasks and schedule vehicles based on fixed rules or simple mathematical models. These systems often assume that vehicle performance and task requirements are constant, while ignoring the variability of vehicle load capacity, the uncertainty of task execution time, and the impact of external interference factors in real-world scenarios. As a result, these systems often struggle to meet user demands for efficient and accurate dispatch in complex real-world vehicle environments.

[0004] In implementing the embodiments of the present invention, the inventors discovered that the existing technology has at least the following problems or defects: First, traditional scheduling methods cannot effectively handle dynamically changing vehicle demand, resulting in delayed scheduling results and an inability to promptly respond to users' real-time needs; second, existing scheduling systems fail to fully consider the actual load capacity and task execution efficiency of vehicles when allocating resources, which can easily lead to resource waste or vehicle overload; finally, existing technologies lack an effective mechanism for handling external interference factors during vehicle scheduling, which significantly affects the accuracy and stability of scheduling results. These problems seriously affect the performance of vehicle scheduling systems and user experience, and urgently need to be addressed through technological innovation. Summary of the Invention

[0005] The present invention provides a vehicle scheduling method based on big data, comprising:

[0006] Establish a vehicle dispatching system model based on the vehicle dispatching system mechanism;

[0007] Based on the vehicle dispatching system model, the vehicle dispatching system performance function is obtained;

[0008] Based on the performance function of the vehicle dispatching system, the performance parameter expression of the vehicle dispatching system, the first theoretical threshold expression of the load balancing of the vehicle dispatching system, and the relationship between the idle rate and load rate of the vehicle dispatching system are obtained;

[0009] Based on the performance function of the vehicle dispatching system, combined with the vehicle's designed maximum load capacity and designed maximum response time, the second theoretical threshold expression for load balancing of the vehicle dispatching system is obtained;

[0010] Calculate and output the divergence threshold under load balancing of the vehicle to be dispatched based on the design parameter value of the vehicle to be dispatched, the first theoretical threshold expression of the load balancing of the vehicle dispatching system, and the second theoretical threshold expression of the load balancing of the vehicle dispatching system;

[0011] A simulation system with a vehicle scheduling system model is used to simulate and output a load response curve of the vehicle to be scheduled within a divergence threshold, and output corresponding performance parameter values ​​under load balancing of the vehicle to be scheduled.

[0012] Furthermore, the vehicle dispatching system model specifically includes:

[0013] The vehicle demand input feedback module is used to process the input demand to obtain the vehicle scheduling adjustment parameters. The vehicle demand input feedback loop includes a demand processing module, an inner loop task allocation feedback module, a position feedback module, and a coordinate conversion module.

[0014] The demand processing module is used to output the processed demand after sorting the input priorities, and then input the processed demand into the inner loop task allocation feedback module;

[0015] The inner loop task allocation feedback module outputs vehicle scheduling adjustment tasks based on the processed demand;

[0016] A coordinate conversion module is used to convert the vehicle scheduling adjustment task into the vehicle scheduling adjustment position, and then input the vehicle scheduling adjustment position into the position feedback module;

[0017] The position feedback module is used to multiply the vehicle scheduling adjustment position by the feedback output sensor proportional coefficient and output the position feedback. The difference between the position feedback and the input vehicle instruction is then used as the input of the demand processing module.

[0018] Furthermore, the inner loop task allocation feedback module includes a vehicle load calculation module, a task execution time calculation module, an inner loop resource allocation feedback module, and a real-time data feedback module, wherein:

[0019] The vehicle load calculation module is used to calculate and obtain the current vehicle load, and then output the vehicle load to the task execution time calculation module;

[0020] The task execution time calculation module is used to calculate and obtain the task execution time based on the vehicle load, and then output the task execution time to the inner loop resource allocation feedback module;

[0021] The inner loop resource allocation feedback module outputs vehicle scheduling adjustment tasks based on task execution time;

[0022] The real-time data feedback module calculates the real-time data feedback of the vehicle based on the vehicle scheduling adjustment task, and uses the difference between the real-time data feedback and the processed demand as the input of the vehicle load calculation module.

[0023] Furthermore, the inner loop resource allocation feedback module includes a task allocation priority calculation module, a vehicle scheduling calculation module, and an external interference feedback module, wherein:

[0024] The task allocation priority calculation module is used to calculate the task allocation priority and output it to the vehicle scheduling calculation module;

[0025] The vehicle dispatch calculation module calculates and obtains the vehicle dispatch position based on the task allocation priority, and outputs the vehicle dispatch position to the coordinate conversion module and the external interference feedback module;

[0026] The external interference feedback module calculates the external interference feedback based on the vehicle scheduling position, and uses the difference between the external interference feedback and the task execution time as the input of the task allocation priority calculation module.

[0027] Furthermore, the performance function of the vehicle dispatching system is specifically expressed as the following expression:

[0028]

[0029] Where s is the transfer function operator of the vehicle dispatching system, K p is the vehicle dispatch instruction proportional gain, K t is the vehicle task execution coefficient, η is the vehicle resource allocation efficiency, J is the vehicle task execution inertia, and D is the vehicle scheduling damping coefficient.

[0030] Furthermore, considering that the vehicle task execution inertia is much smaller than the vehicle scheduling damping coefficient, the vehicle task execution inertia is simplified to 0, and the vehicle scheduling system performance function expression is simplified to the following expression:

[0031]

[0032] Where G(s) is the vehicle dispatching system gain, which can be expressed as:

[0033]

[0034] S is the natural frequency of the vehicle dispatching system, which can be expressed as:

[0035]

[0036] D is the inherent damping coefficient of the vehicle dispatching system, which can be expressed as:

[0037]

[0038] Among them, the vehicle dispatching system gain, the vehicle dispatching system natural frequency, and the vehicle dispatching system natural damping coefficient are the performance parameters of the vehicle dispatching system.

[0039] Furthermore, the first theoretical threshold expression for load balancing of the vehicle dispatching system is obtained based on the Routh criterion and the performance function of the vehicle dispatching system, and is specifically expressed as follows:

[0040]

[0041] Furthermore, the relationship between the idle rate and load rate of the vehicle dispatching system specifically includes:

[0042] The load rate of the vehicle dispatch system increases to times the idle rate of the vehicle dispatch system;

[0043] The response time of the vehicle dispatch system is reduced to the idle rate of the vehicle dispatch system;

[0044] The resource utilization rate of the vehicle dispatching system increases to the idle rate of the vehicle dispatching system;

[0045] The task delay difference of the vehicle dispatching system increases, which can be expressed as:

[0046]

[0047] Where ω0 is the natural frequency corresponding to the idle rate of the vehicle dispatching system, ω is the natural frequency corresponding to the load rate of the vehicle dispatching system, and ξ0 is the natural damping coefficient corresponding to the idle rate of the vehicle dispatching system.

[0048] Furthermore, the second theoretical threshold expression of the load balancing of the vehicle dispatching system specifically includes:

[0049]

[0050] Where, I max is the vehicle's maximum design load capacity, T max is the maximum design response time of the vehicle, P req is the input vehicle request power, and θ is the load balancing coefficient of the vehicle scheduling system.

[0051] Furthermore, based on the design parameter values ​​of the vehicles to be dispatched, the first theoretical threshold expression of the load balancing of the vehicle dispatching system, and the second theoretical threshold expression of the load balancing of the vehicle dispatching system, the divergence threshold under the load balancing of the vehicles to be dispatched is calculated and output, specifically including:

[0052] Substituting the design parameter values ​​of the vehicle to be dispatched into the first theoretical threshold expression of the load balance of the vehicle dispatching system, the first theoretical threshold of the load rate of the vehicle dispatching system is calculated;

[0053] Substitute the design parameter values ​​of the vehicle to be dispatched, the vehicle's designed maximum load capacity value, and the vehicle's designed maximum response time value into the second theoretical threshold expression of the vehicle dispatch system's load balance to calculate the second theoretical threshold of the vehicle dispatch system's load rate;

[0054] The larger of the first theoretical threshold value of the vehicle dispatching system load rate and the second theoretical threshold value of the vehicle dispatching system load rate is used as the vehicle dispatching system load rate divergence threshold value.

[0055] According to the above-mentioned embodiment of the present invention, there are at least the following beneficial effects: the vehicle scheduling method of the present invention can improve the efficiency and accuracy of the vehicle scheduling system. By establishing a vehicle scheduling system model based on big data, combined with vehicle design parameters and system performance functions, the divergence threshold under load balancing can be accurately calculated, thereby providing a scientific basis for vehicle scheduling. This method can effectively avoid vehicle overload or idleness, optimize resource utilization, and ensure that the vehicle operates efficiently within a reasonable load range. At the same time, the method simulates the load response curve of the vehicle within the divergence threshold through a simulation system, which can predict the performance of the vehicle in advance, provide strong support for scheduling decisions, and further improve the reliability and stability of the scheduling system.

[0056] In addition, the present invention can also dynamically adapt to complex vehicle usage environments and changing needs. Its vehicle demand input feedback module and inner-loop task allocation feedback module can process user needs in real time, and flexibly adjust scheduling tasks according to task priorities and vehicle load conditions to ensure the timeliness and accuracy of scheduling results. By simplifying the system performance function and introducing load balancing theoretical thresholds, the present invention can more efficiently allocate resources and schedule tasks, reduce system response time, and improve the overall performance of the system. These improvements enable the present invention to better meet users' real-time needs and enhance user experience when faced with large-scale, dynamically changing vehicle demand. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily apparent by reading the following detailed description with reference to the accompanying drawings, in which several embodiments of the present invention are shown by way of example and not limitation, in which:

[0058] Figure 1 A flowchart of a vehicle scheduling method based on big data provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0059] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided solely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Rather, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0060] Those skilled in the art will appreciate that the embodiments of the present invention may be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.

[0061] It should be noted that any number of elements in the drawings is for illustration only and not for limitation, and any naming is only for distinction and does not have any limiting meaning.

[0062] Reference below Figure 1 , Figure 1 This is a flow chart of a vehicle scheduling method based on big data provided by an embodiment of the present invention. Figure 1 As shown in FIG, a vehicle scheduling method based on big data includes:

[0063] S1. Establish a vehicle dispatching system model based on the vehicle dispatching system mechanism;

[0064] S2. Based on the vehicle scheduling system model, obtain the vehicle scheduling system performance function;

[0065] S3. Based on the performance function of the vehicle scheduling system, obtain the vehicle scheduling system performance parameter expression, the first theoretical threshold expression of the vehicle scheduling system load balancing, and the relationship between the idle rate and load rate of the vehicle scheduling system;

[0066] S4. Based on the vehicle dispatch system performance function, combined with the vehicle's designed maximum load capacity and designed maximum response time, obtain the second theoretical threshold expression for load balancing of the vehicle dispatch system;

[0067] S5. Calculate and output a divergence threshold value under load balancing of the vehicle to be dispatched based on the design parameter value of the vehicle to be dispatched, the first theoretical threshold expression of the vehicle dispatching system load balancing, and the second theoretical threshold expression of the vehicle dispatching system load balancing;

[0068] S6. Apply a simulation system having a vehicle scheduling system model to simulate and output a load response curve of the vehicle to be scheduled within a divergence threshold, and output corresponding performance parameter values ​​of the vehicle to be scheduled under load balancing.

[0069] It should be noted that this invention proposes a big data-based vehicle scheduling method, the core of which is to optimize the vehicle scheduling process by establishing a vehicle scheduling system model. The vehicle scheduling system model is a mathematical model that reflects the system's operational characteristics, formed by analyzing and modeling the mechanisms of the vehicle scheduling system. The purpose of this model is to better understand and predict system performance, thereby providing a scientific basis for scheduling decisions. The performance function is a function derived from this model that quantifies system performance indicators, such as response time and load balance. Performance parameter expressions are further derived from the performance function and are parameter formulas that specifically describe system performance. The first and second theoretical load balance threshold expressions are derived through theoretical analysis and are used to determine whether the system load is balanced. The divergence threshold is the maximum load value that the system can withstand under load balance conditions. By calculating the divergence threshold, system overload can be avoided and stable operation can be ensured. The simulation system is used to simulate the response of vehicles under different load conditions. Through simulation, the effectiveness of scheduling strategies can be verified in advance and performance parameters can be optimized.

[0070] Specifically, the vehicle scheduling system model consists of multiple modules, with the vehicle demand input and feedback module being one of the core components. This module receives user vehicle demand requests, prioritizes them through the demand processing module, and then passes the prioritized results to the inner-loop task allocation feedback module. The inner-loop task allocation feedback module further generates vehicle scheduling adjustment tasks based on the prioritized vehicle demand requests, combined with vehicle load capacity and task execution time. The vehicle load calculation module calculates the current vehicle load in real time, while the task execution time calculation module estimates task execution time based on vehicle load. The inner-loop resource allocation feedback module allocates vehicle resources based on task execution time and converts tasks into specific vehicle scheduling locations through the coordinate transformation module. The position feedback module compares the scheduling locations with actual demand, forming a feedback loop to optimize scheduling results. The performance function is derived through the interaction and data flow of these modules, reflecting the system's performance under different parameter settings. For example, performance parameters such as system gain, natural frequency, and damping coefficient are calculated through the performance function, which directly influences the system's response speed and stability.

[0071] Preferably, the vehicle scheduling system model can be constructed through the following steps: First, a large amount of vehicle scheduling data is collected, including information such as vehicle load capacity, response time, and task execution time. This data is then analyzed and processed to extract key features, such as the average vehicle load factor and the distribution of task response times. Next, a mathematical model is established based on these features. The model's input parameters include the vehicle's designed maximum load capacity and maximum response time, and its output is the system's performance parameters and load balancing threshold. During data processing, data mining techniques, such as cluster analysis, can be employed to classify vehicles according to load capacity, enabling more accurate scheduling. To simplify the performance function, when the vehicle's task execution inertia is significantly smaller than the scheduling damping coefficient, the effect of inertia can be ignored, thereby simplifying the calculation process and improving the model's efficiency. Through these detailed steps, a vehicle scheduling system model can be constructed more effectively and the vehicle scheduling process can be optimized.

[0072] In some embodiments, the vehicle dispatching system model specifically includes:

[0073] The vehicle demand input feedback module is used to process the input demand to obtain the vehicle scheduling adjustment parameters. The vehicle demand input feedback loop includes a demand processing module, an inner loop task allocation feedback module, a position feedback module, and a coordinate conversion module.

[0074] The demand processing module is used to output the processed demand after sorting the input priorities, and then input the processed demand into the inner loop task allocation feedback module;

[0075] The inner loop task allocation feedback module outputs vehicle scheduling adjustment tasks based on the processed demand;

[0076] A coordinate conversion module is used to convert the vehicle scheduling adjustment task into the vehicle scheduling adjustment position, and then input the vehicle scheduling adjustment position into the position feedback module;

[0077] The position feedback module is used to multiply the vehicle scheduling adjustment position by the feedback output sensor proportional coefficient and output the position feedback. The difference between the position feedback and the input vehicle instruction is then used as the input of the demand processing module.

[0078] It should be noted that the vehicle demand input and feedback module in the vehicle scheduling system model mentioned in the present invention is the core part of the entire scheduling system. It is responsible for receiving and processing the user's vehicle demand and converting it into vehicle scheduling adjustment parameters. This module works together through multiple sub-modules to achieve efficient processing and response to vehicle demand. Among them, the function of the demand processing module is to prioritize the input vehicle demand according to certain rules to ensure that high-priority demands can be processed first. The inner-loop task allocation feedback module generates specific vehicle scheduling tasks based on the sorted vehicle demand. The position feedback module and the coordinate conversion module work together to convert the vehicle scheduling task into a specific vehicle scheduling position, and continuously optimize the scheduling results through the feedback mechanism. This modular structure enables the system to flexibly respond to complex vehicle demand and improve the accuracy and efficiency of scheduling.

[0079] Specifically, after the demand processing module in the vehicle demand input feedback module receives the user's vehicle request, it will prioritize the demands according to the urgency of the request, user level, or other preset standards. For example, emergency rescue tasks may be given the highest priority, while ordinary commuting needs may be given a lower priority. After receiving the sorted vehicle demand, the inner-loop task allocation feedback module will generate vehicle scheduling adjustment tasks based on the current status of the vehicle and the task execution capability. The coordinate conversion module converts these tasks into specific vehicle scheduling location information, such as determining the vehicle's driving path and target location through a geographic coordinate system. The position feedback module compares the actual scheduling position of the vehicle with the target position, and feeds back the deviation information to the demand processing module to adjust subsequent scheduling tasks. This closed-loop feedback mechanism can ensure the accuracy and timeliness of scheduling tasks, while also effectively responding to dynamically changing vehicle demand.

[0080] Preferably, the construction of the vehicle demand input feedback module can be further refined. For example, in the demand processing module, a multi-dimensional priority sorting algorithm can be used to comprehensively consider factors such as the urgency of the task, user reputation, and task distance to more accurately determine the task priority. The inner-loop task allocation feedback module can dynamically adjust the task allocation strategy based on the vehicle's real-time data (such as current load, remaining power, etc.). The coordinate conversion module can combine high-precision map data to optimize the vehicle's driving path planning and reduce unnecessary detours. The position feedback module can use advanced sensor technology to obtain the vehicle's precise location information in real time, and improve the accuracy of the feedback data through data fusion technology. Through these refined steps and optimization measures, the vehicle demand input feedback module can handle complex vehicle demands more efficiently and improve the performance and user experience of the entire vehicle scheduling system.

[0081] In some embodiments, the inner loop task allocation feedback module includes a vehicle load calculation module, a task execution time calculation module, an inner loop resource allocation feedback module, and a real-time data feedback module, wherein:

[0082] The vehicle load calculation module is used to calculate and obtain the current vehicle load, and then output the vehicle load to the task execution time calculation module;

[0083] The task execution time calculation module is used to calculate and obtain the task execution time based on the vehicle load, and then output the task execution time to the inner loop resource allocation feedback module;

[0084] The inner loop resource allocation feedback module outputs vehicle scheduling adjustment tasks based on task execution time;

[0085] The real-time data feedback module calculates the real-time data feedback of the vehicle based on the vehicle scheduling adjustment task, and uses the difference between the real-time data feedback and the processed demand as the input of the vehicle load calculation module.

[0086] It should be noted that the inner-loop task allocation feedback module mentioned in the present invention is a key part of the vehicle scheduling system model, and its main function is to further refine and optimize the vehicle scheduling tasks based on the processed vehicle demand. This module realizes the precise allocation and dynamic adjustment of vehicle scheduling tasks through the collaborative work of the vehicle load calculation module, the task execution time calculation module, the inner-loop resource allocation feedback module and the real-time data feedback module. The vehicle load calculation module is used to monitor the current load of the vehicle in real time, the task execution time calculation module estimates the execution time of the task based on the vehicle load, the inner-loop resource allocation feedback module allocates vehicle resources based on the task execution time, and the real-time data feedback module compares the actual operating data of the vehicle with the scheduling requirements to form a feedback loop to optimize the scheduling results. This modular structure enables the system to flexibly respond to complex vehicle demand while ensuring the rational allocation and efficient utilization of vehicle resources.

[0087] Specifically, the vehicle load calculation module within the inner-loop task allocation feedback module is responsible for real-time monitoring of the vehicle's current load, including information such as the number of passengers and cargo weight. The task execution time calculation module estimates the time required to complete the task based on the vehicle's current load and performance parameters (such as maximum load and driving speed). The inner-loop resource allocation feedback module rationally allocates vehicle resources based on task execution time, for example, selecting the appropriate vehicle to perform the task and determining the vehicle's dispatch location. The real-time data feedback module compares the vehicle's actual operating data (such as location, speed, and remaining battery life) with the dispatch requirements, generating feedback information to dynamically adjust the dispatch tasks. For example, if a vehicle's actual load approaches its maximum load capacity or if the task execution time exceeds expectations, the system can reallocate tasks or adjust the vehicle dispatch plan based on this feedback information. This dynamic feedback mechanism effectively improves the system's flexibility and adaptability, ensuring efficient vehicle operation within a reasonable load range.

[0088] Preferably, the construction of the inner-loop task allocation feedback module can be further refined. For example, the vehicle load calculation module can collect the vehicle's load data in real time through sensors installed on the vehicle, and send the data to the dispatch center through the data transmission module. The task execution time calculation module can combine historical data and real-time road condition information, and use machine learning algorithms to make more accurate predictions on task execution time. The inner-loop resource allocation feedback module can dynamically adjust the vehicle's scheduling tasks based on the vehicle's real-time status (such as location, remaining power, current task completion status, etc.), for example, giving priority to assigning tasks to vehicles that are closer and have lighter loads. The real-time data feedback module can use advanced sensor technology and data processing algorithms to monitor the vehicle's operating status in real time, and adjust the scheduling strategy in a timely manner through a feedback mechanism. Through these refined steps and optimization measures, the inner-loop task allocation feedback module can handle complex vehicle needs more efficiently and improve the performance and user experience of the entire vehicle scheduling system.

[0089] In some embodiments, the inner loop resource allocation feedback module includes a task allocation priority calculation module, a vehicle scheduling calculation module, and an external interference feedback module, wherein:

[0090] The task allocation priority calculation module is used to calculate the task allocation priority and output it to the vehicle scheduling calculation module;

[0091] The vehicle dispatch calculation module calculates and obtains the vehicle dispatch position based on the task allocation priority, and outputs the vehicle dispatch position to the coordinate conversion module and the external interference feedback module;

[0092] The external interference feedback module calculates the external interference feedback based on the vehicle scheduling position, and uses the difference between the external interference feedback and the task execution time as the input of the task allocation priority calculation module.

[0093] It should be noted that the inner loop resource allocation feedback module mentioned in the present invention is a key part for optimizing vehicle resource allocation and task scheduling in the vehicle scheduling system. This module realizes the priority sorting of vehicle scheduling tasks, scheduling position calculation and dynamic compensation of external interference through the collaborative work of the task allocation priority calculation module, the vehicle scheduling calculation module and the external interference feedback module. The function of the task allocation priority calculation module is to calculate the priority of the task based on factors such as the urgency of the task and the vehicle status; the vehicle scheduling calculation module generates the scheduling position of the vehicle based on the priority; and the external interference feedback module is used to deal with the impact of external environmental changes on scheduling tasks. Through this modular design, the system can more flexibly respond to complex and changeable vehicle usage scenarios and ensure the efficiency and stability of vehicle scheduling.

[0094] Specifically, the task allocation priority calculation module in the inner-loop resource allocation feedback module will comprehensively consider factors such as the urgency of the task, the current load of the vehicle, and the special needs of the user to calculate the priority of each task. The vehicle scheduling calculation module calculates the optimal vehicle scheduling position based on the task priority and the real-time location and status of the vehicle. The external interference feedback module monitors changes in the external environment, such as traffic congestion and weather conditions, and dynamically adjusts the vehicle scheduling position. For example, if traffic congestion occurs in a certain area, the external interference feedback module will feed this information back to the task allocation priority calculation module, recalculate the task priority, and adjust the vehicle scheduling path and position through the vehicle scheduling calculation module. This dynamic feedback mechanism can effectively improve the adaptability and stability of the system, ensuring the efficient operation of vehicles in complex environments.

[0095] Preferably, the construction of the inner-loop resource allocation feedback module can be further refined. The task allocation priority calculation module can quantify different factors by setting weights. For example, the weight of emergency tasks can be set to the highest, while the weight of vehicle load is adjusted according to the maximum design load capacity of the vehicle. The vehicle scheduling calculation module can combine geographic information system (GIS) data to calculate the optimal driving path of the vehicle in real time. The external interference feedback module can obtain real-time road condition information through sensors and traffic monitoring systems installed on the vehicle, and analyze and process this information through data processing algorithms. For example, when a sudden increase in traffic flow in a certain area is detected, the system can adjust the vehicle's scheduling path to avoid congested areas, thereby improving scheduling efficiency. Through these refined steps and optimization measures, the inner-loop resource allocation feedback module can handle complex vehicle needs more efficiently and improve the performance and user experience of the entire vehicle scheduling system.

[0096] In some embodiments, the vehicle dispatch system performance function is specifically expressed as the following expression:

[0097]

[0098] Where S is the transfer function operator of the vehicle dispatching system, K p is the vehicle dispatch instruction proportional gain, K t is the vehicle task execution coefficient, η is the vehicle resource allocation efficiency, J is the vehicle task execution inertia, and D is the vehicle scheduling damping coefficient.

[0099] It should be noted that the vehicle dispatch system performance function mentioned in this invention is a mathematical expression established through in-depth analysis of the vehicle dispatch system model, which is used to describe the dynamic performance characteristics of the system. This performance function is based on key system parameters, such as the vehicle dispatch instruction proportional gain K p , vehicle mission execution coefficient K t , vehicle resource allocation efficiency η, vehicle task execution inertia J, and vehicle scheduling damping coefficient D. This performance function allows for quantitative analysis of key performance indicators such as system response speed, stability, and load balancing, providing a theoretical basis for optimizing scheduling strategies. Establishing this performance function is a key step in achieving precise scheduling and optimal resource allocation, helping the scheduling system better adapt to dynamically changing vehicle demand.

[0100] Specifically, each parameter in the vehicle dispatch system performance function has a clear physical meaning and function. p It reflects the response strength of the system to the dispatch instruction. A higher gain value means that the system responds to the instruction more quickly, but it may also lead to a decrease in the stability of the system. Vehicle mission execution coefficient K t The vehicle's ability to complete a task is closely related to its performance and load capacity. The vehicle resource allocation efficiency η measures the system's efficiency in allocating resources. Efficient resource allocation improves overall system performance. The vehicle's task execution inertia J reflects the vehicle's inertial characteristics when performing a task. A larger inertia means the vehicle takes longer to adjust. The vehicle dispatch damping coefficient D describes the system's damping characteristics during dispatch. A higher damping coefficient helps improve system stability but may reduce response speed. By properly setting the values ​​of these parameters, the system's performance function can be optimized to achieve optimal performance in different application scenarios.

[0101] Preferably, the construction of the vehicle scheduling system performance function can be achieved through the following steps. First, a large amount of actual operation data is collected, including the vehicle load, task execution time, scheduling instruction response time, etc., to determine the reasonable value range of each parameter. Then, through data analysis and modeling techniques, such as regression analysis or machine learning algorithms, a relationship model between the performance function and the system performance indicators is established. For example, the relationship between parameters such as system gain, natural frequency and damping coefficient and vehicle load and task execution time can be fitted through historical data. In actual applications, the values ​​of these parameters can be dynamically adjusted according to the real-time status of the vehicle and task requirements to optimize the performance of the system. For example, when the vehicle load is heavy, the task execution coefficient K can be appropriately reduced. t To avoid vehicle overload; when the system needs to respond quickly, the dispatch instruction proportional gain K can be appropriately increased p , but at the same time, attention should be paid to the stability of the system. Through these detailed steps and optimization measures, the performance function of the vehicle scheduling system can be constructed more effectively, improving the system's scheduling efficiency and resource utilization.

[0102] In some embodiments, considering that the vehicle task execution inertia is much smaller than the vehicle scheduling damping coefficient, the vehicle task execution inertia is simplified to 0, and the vehicle scheduling system performance function expression is simplified to the following expression:

[0103]

[0104] Where G(s) is the vehicle dispatching system gain, which can be expressed as:

[0105]

[0106] S is the natural frequency of the vehicle dispatching system, which can be expressed as:

[0107]

[0108] D is the inherent damping coefficient of the vehicle dispatching system, which can be expressed as:

[0109]

[0110] Among them, the vehicle dispatching system gain, the vehicle dispatching system natural frequency, and the vehicle dispatching system natural damping coefficient are the performance parameters of the vehicle dispatching system.

[0111] It should be noted that in the present invention, the vehicle dispatch system performance function has been simplified to more efficiently describe the dynamic characteristics of the system. This simplification is based on the assumption that the vehicle task execution inertia is much smaller than the vehicle dispatch damping coefficient. In this case, the system performance function can be simplified into a form that is easier to calculate and analyze. The simplified performance function not only retains the main dynamic characteristics of the system, but also more intuitively reflects the relationship between key performance parameters such as system gain, natural frequency, and natural damping coefficient. This simplification allows for faster evaluation and optimization of system performance in practical applications, while reducing computational complexity and improving the real-time and adaptability of the system.

[0112] Specifically, the vehicle task execution inertia J refers to the inertia characteristics exhibited by the vehicle during the task execution process, which reflects the difficulty of the vehicle state change. When the vehicle task execution inertia is much smaller than the vehicle scheduling damping coefficient D, it means that the vehicle state change is relatively easy and the system responds to the scheduling instruction more quickly. In this case, the vehicle task execution inertia can be simplified to zero, thereby simplifying the performance function. In the simplified performance function, the system gain G(s) represents the system's amplification factor for the input signal, which is determined by the vehicle scheduling instruction proportional gain K p , vehicle mission execution coefficient K t The product of the vehicle resource allocation efficiency η and the vehicle scheduling damping coefficient D is obtained. The natural frequency S and the natural damping coefficient D are key parameters describing system stability and response speed. This simplification allows the system performance function to more intuitively reflect the system's key dynamic characteristics, facilitating rapid evaluation and optimization in practical applications.

[0113] Preferably, when constructing the simplified vehicle dispatch system performance function, the following steps can be followed. First, determine through experiments or data analysis whether the condition that the vehicle task execution inertia is much smaller than the vehicle dispatch damping coefficient is met. If the condition is met, the vehicle task execution inertia can be simplified to zero. Next, based on the actual operating data of the system, determine the vehicle dispatch instruction proportional gain K p , vehicle mission execution coefficient K t and the specific values ​​of parameters such as vehicle resource allocation efficiency η. These parameters can be determined by analyzing historical scheduling data, vehicle performance testing, and evaluation of the actual operating environment. For example, the vehicle scheduling instruction proportional gain K p The vehicle task execution coefficient K can be determined by analyzing the relationship between the dispatch instruction and the vehicle response; tThis can be set based on the actual vehicle performance and load capacity; the vehicle resource allocation efficiency η can be determined by evaluating the efficiency loss during the resource allocation process. Finally, these parameters are substituted into the simplified performance function to calculate key performance parameters such as system gain, natural frequency, and natural damping coefficient. Through these detailed steps, a simplified vehicle scheduling system performance function can be more accurately constructed, providing strong support for system optimization and scheduling.

[0114] In some embodiments, the first theoretical threshold expression for load balancing of the vehicle dispatching system is obtained based on the Routh criterion and the performance function of the vehicle dispatching system, and is specifically expressed as the following expression:

[0115]

[0116] It should be noted that the first theoretical threshold expression for load balancing of the vehicle dispatching system mentioned in the present invention is derived based on the system performance function and stability theory. This threshold expression is used to determine under what conditions the system can maintain stable operation, avoiding system instability or performance degradation due to excessive load. Through this threshold, theoretical guidance can be provided for vehicle dispatching to ensure that the system operates efficiently within a reasonable load range. System gain is an important parameter in the performance function, which reflects the response strength of the system to the dispatching instructions, while the damping coefficient is closely related to the stability of the system. By reasonably setting these parameters, it can be ensured that the system operates stably under a load-balanced state.

[0117] Specifically, the system gain is determined by multiple parameters such as the vehicle dispatch instruction proportional gain, the vehicle task execution coefficient and the vehicle resource allocation efficiency. The vehicle dispatch instruction proportional gain reflects the system's response speed and intensity to the dispatch instruction; the vehicle task execution coefficient is related to the actual performance and load capacity of the vehicle, and reflects the vehicle's ability to complete the task; the vehicle resource allocation efficiency measures the efficiency of the system in allocating resources. The damping coefficient is related to the stability of the system. A higher damping coefficient helps to improve the stability of the system, but may reduce the response speed. Through the Routh criterion, the relationship between these parameters can be analyzed to derive the conditions for system stability. In the present invention, the condition for system stability is that the ratio of the system gain to the damping coefficient is greater than 1. This means that when the ratio of the system gain to the damping coefficient is greater than 1, the system can maintain stable operation and load balance.

[0118] Preferably, in practical applications, the following steps can be used to determine the first theoretical threshold for load balancing in a vehicle dispatch system. First, actual system operating data, including vehicle load, task execution time, and dispatch instruction response time, is collected to determine the specific values ​​of parameters such as the vehicle dispatch instruction proportional gain, vehicle task execution coefficient, and vehicle resource allocation efficiency. These parameters can be determined through analysis of historical dispatch data, vehicle performance testing, and evaluation of the actual operating environment. For example, the vehicle dispatch instruction proportional gain can be determined by analyzing the relationship between dispatch instructions and vehicle responses; the vehicle task execution coefficient can be set based on the vehicle's actual performance and load capacity; and the vehicle resource allocation efficiency can be determined by evaluating the efficiency loss during the resource allocation process. Next, the value of the vehicle dispatch damping coefficient is determined based on the system's design parameters. Finally, these parameters are substituted into the threshold expression to calculate the first theoretical threshold for system load balancing. This threshold can be used to determine whether the system is in a stable operating state and adjust the vehicle dispatch strategy accordingly to ensure efficient operation within a reasonable load range.

[0119] In some embodiments, the relationship between the idle rate and load rate of the vehicle dispatch system specifically includes:

[0120] The load rate of the vehicle dispatch system increases to times the idle rate of the vehicle dispatch system;

[0121] The response time of the vehicle dispatch system is reduced to the idle rate of the vehicle dispatch system;

[0122] The resource utilization rate of the vehicle dispatching system increases to the idle rate of the vehicle dispatching system;

[0123] The task delay difference of the vehicle dispatching system increases, which can be expressed as:

[0124]

[0125] Where ω0 is the natural frequency corresponding to the idle rate of the vehicle dispatching system, ω is the natural frequency corresponding to the load rate of the vehicle dispatching system, and ξ0 is the natural damping coefficient corresponding to the idle rate of the vehicle dispatching system.

[0126] It should be noted that the relationship between the idle rate and the load rate of the vehicle scheduling system mentioned in the present invention is obtained through an in-depth analysis of the system performance. The idle rate refers to the proportion of unoccupied resources in the system, while the load rate refers to the proportion of occupied resources in the system. The relationship between the two reflects the efficiency of system resource utilization and the efficiency of task execution. When the load rate increases, the idle rate decreases accordingly, which usually means that system resources are more fully utilized, but it may also bring about an increase in response time and the risk of task delays. Therefore, it is crucial to reasonably control the balance between load rate and idle rate for optimizing system performance. In addition, the calculation of task delay difference further quantifies the delay in task execution under different load conditions of the system, providing a basis for the optimization of scheduling strategies.

[0127] Specifically, the relationship between the idle rate and load rate in a vehicle dispatch system can be explained from the following perspectives. The idle rate refers to the proportion of unoccupied resources in the system and generally reflects the system's idle capacity. The load rate, on the other hand, refers to the proportion of occupied resources in the system and reflects the system's level of activity. As the load rate increases, the system's resource utilization also increases, meaning more resources are used to complete tasks, thereby improving overall system efficiency. However, an increase in the load rate may also lead to longer system response times as the system must process more tasks. Furthermore, task latency difference, calculated by comparing task latencies under different load conditions, reflects the performance differences of the system under different load conditions. For example, when the load rate is high, task latency may increase, while when the idle rate is high, task latency may decrease. This relationship can be further quantified and optimized through analysis and modeling of system performance data.

[0128] Preferably, in practical applications, the following steps can be used to optimize the relationship between the idle rate and load rate of a vehicle scheduling system. First, data analysis is performed to determine the system's performance under different load conditions, including key metrics such as response time and task latency. Then, a system performance model is established based on this data. The model's input parameters may include vehicle load capacity, task execution time, and dispatch instruction response time, and its output is the system's load rate and idle rate. This model analysis can determine the system's optimal operating state under different load conditions. For example, by adjusting the scheduling strategy and rationally allocating tasks, the system can maintain a short response time even under high load. Furthermore, by monitoring system performance in real time, the balance between load rate and idle rate can be dynamically adjusted to accommodate changing vehicle demand. For example, if the system load rate is detected to be excessively high, the load rate can be reduced by adding vehicle resources or optimizing task allocation, thereby improving system stability and responsiveness. Through these detailed steps and optimization measures, the system's load rate and idle rate can be more effectively managed, improving overall system performance and user experience.

[0129] In some embodiments, the second theoretical threshold expression for load balancing of the vehicle dispatching system specifically includes:

[0130]

[0131] Where, I max is the vehicle's maximum design load capacity, T max is the maximum design response time of the vehicle, P req is the input vehicle request power, and θ is the load balancing coefficient of the vehicle scheduling system.

[0132] It should be noted that the second theoretical threshold expression for load balancing of the vehicle scheduling system mentioned in the present invention is determined based on the vehicle design parameters and the input vehicle request power. This threshold expression is used to evaluate whether the system can maintain load balance under the design conditions, thereby ensuring the efficient operation of the vehicle scheduling system. The maximum load capacity of the vehicle design refers to the maximum load that the vehicle can withstand during design, and the maximum response time of the vehicle design refers to the maximum time required for the vehicle to complete the task from receiving the scheduling instruction. The input vehicle request power reflects the intensity of the user's demand for vehicle resources. Through these parameters, a theoretical load balancing coefficient can be calculated to determine whether the system can reasonably allocate tasks within the design range to avoid overload or idle resources.

[0133] Specifically, the vehicle is designed to have a maximum load capacity of I max It refers to the maximum load that the vehicle can bear when it is designed, which reflects the vehicle's carrying capacity. Vehicle design maximum response time T max It refers to the maximum time required for a vehicle to complete a task from receiving a dispatch instruction, which reflects the vehicle's response speed. req The load balancing coefficient θ refers to the intensity of user demand for vehicle resources and reflects real-time user needs. The vehicle dispatch system's load balancing coefficient, θ, is a parameter used to assess the system's load balancing status. It is determined by comparing the maximum load capacity and response time of a vehicle with the user's requested power. When the ratio of the vehicle's designed maximum load capacity and maximum response time to the input requested power is greater than the load balancing coefficient, the system is considered to be able to reasonably distribute tasks within the design range and maintain load balance.

[0134] Preferably, in practical applications, the following steps can be used to determine the second theoretical threshold of load balancing in the vehicle scheduling system. First, according to the design parameters of the vehicle, the maximum load capacity of the vehicle is determined. max and the vehicle's maximum design response time T max These parameters are usually provided by the vehicle manufacturer and clearly marked in the vehicle design documents. Secondly, according to the actual needs of the user, the input vehicle request power P is determined.req . This parameter can be obtained by analyzing the user's vehicle request data, for example, by counting the number of vehicles and task types requested by users within a certain period of time. Finally, the load balancing coefficient θ of the vehicle scheduling system is calculated based on these parameters, and compared with the ratio of the vehicle's designed maximum load capacity and maximum response time to the input vehicle request power. If the ratio is greater than the load balancing coefficient, it means that the system is able to reasonably allocate tasks within the design range and maintain load balance. In this way, the load balancing status of the system can be effectively evaluated, and the vehicle scheduling strategy can be optimized accordingly to ensure that the system operates efficiently within a reasonable load range.

[0135] In some embodiments, the divergence threshold under load balancing of the vehicles to be dispatched is calculated and output based on the design parameter values ​​of the vehicles to be dispatched, the first theoretical threshold expression of the load balancing of the vehicle dispatching system, and the second theoretical threshold expression of the load balancing of the vehicle dispatching system, specifically including:

[0136] Substituting the design parameter values ​​of the vehicle to be dispatched into the first theoretical threshold expression of the load balance of the vehicle dispatching system, the first theoretical threshold of the load rate of the vehicle dispatching system is calculated;

[0137] Substitute the design parameter values ​​of the vehicle to be dispatched, the vehicle's designed maximum load capacity value, and the vehicle's designed maximum response time value into the second theoretical threshold expression of the vehicle dispatch system's load balance to calculate the second theoretical threshold of the vehicle dispatch system's load rate;

[0138] The larger of the first theoretical threshold value of the vehicle dispatching system load rate and the second theoretical threshold value of the vehicle dispatching system load rate is used as the vehicle dispatching system load rate divergence threshold value.

[0139] It should be noted that the calculation of the divergence threshold under load balancing of vehicles to be dispatched mentioned in the present invention is derived based on the first and second theoretical threshold expressions of load balancing of the vehicle dispatching system. The divergence threshold refers to the maximum load value that the system can withstand under load balancing conditions. By calculating the divergence threshold, system overload can be avoided and the stable operation of the system can be ensured. In practical applications, the calculation of the divergence threshold needs to comprehensively consider factors such as vehicle design parameters, vehicle design maximum load capacity, vehicle design maximum response time, and input vehicle request power. By comparing the first theoretical threshold of load rate and the second theoretical threshold of load rate, the larger value is selected as the divergence threshold, thereby providing a scientific basis for vehicle dispatching and ensuring that the vehicle operates efficiently within a reasonable load range.

[0140] Specifically, vehicle design parameters refer to various performance indicators determined during vehicle design, including the vehicle's maximum load capacity, maximum response time, etc. maxIt refers to the maximum load that the vehicle can bear when it is designed, reflecting the vehicle's carrying capacity. Vehicle design maximum response time T max It refers to the maximum time required for a vehicle to complete a task from receiving a dispatch instruction, reflecting the vehicle's response speed. req It refers to the intensity of user demand for vehicle resources and reflects their real-time needs. The first theoretical load rate threshold is calculated based on the ratio of the system gain to the damping coefficient and is used to determine the conditions under which the system can maintain stable operation. The second theoretical load rate threshold is calculated based on the ratio of the vehicle's designed maximum load capacity and maximum response time to the input vehicle request power and is used to assess whether the system can maintain load balance under the designed conditions. By comparing these two thresholds, the larger value is selected as the divergence threshold, ensuring that the system operates efficiently within a reasonable load range.

[0141] Preferably, in practical applications, the divergence threshold under load balancing of the scheduled vehicles can be calculated by the following steps. First, according to the design parameters of the vehicle, the maximum load capacity of the vehicle is determined. max and the vehicle's maximum design response time T max These parameters are usually provided by the vehicle manufacturer and clearly marked in the vehicle design documents. Secondly, according to the actual needs of the user, the input vehicle request power P is determined. req . This parameter can be obtained by analyzing the user's vehicle request data. For example, it can be estimated by counting the number of vehicles and task types requested by users within a certain period of time. Then, the vehicle design parameters are substituted into the first theoretical threshold expression of load balancing to calculate the first theoretical threshold of load rate. Next, the vehicle's designed maximum load capacity, maximum response time, and input vehicle request power are substituted into the second theoretical threshold expression of load balancing to calculate the second theoretical threshold of load rate. Finally, compare the two thresholds and select the larger value as the divergence threshold. Through this method, the load balancing status of the system can be effectively evaluated, and the vehicle scheduling strategy can be optimized accordingly to ensure that the system operates efficiently within a reasonable load range.

[0142] The aforementioned embodiments of the present invention have the following beneficial effects: By constructing a multi-level feedback vehicle scheduling system model, the present invention accurately establishes the mathematical relationship between performance functions and load balancing thresholds, providing a quantitative basis for vehicle resource allocation. The first theoretical threshold derived from the Routh criterion ensures system stability, while the second theoretical threshold, combined with vehicle design parameters, takes into account actual operational requirements. This dual-threshold mechanism dynamically optimizes scheduling strategies. The synergistic effect of the coordinate transformation module and real-time data feedback allows for precise control of vehicle scheduling positions, effectively improving system response accuracy.

[0143] By establishing a system architecture encompassing modules such as task priority calculation and resource allocation feedback, intelligent processing and efficient allocation of vehicle demand can be achieved. A simplified performance function model reduces computational complexity while accurately describing the system's dynamic characteristics. The quantitative relationship between load and idle rates guides optimal resource allocation, and simulation verification ensures the feasibility of the scheduling solution. This solution significantly improves vehicle utilization, reduces response latency, and enhances the system's robustness through external disturbance feedback.

[0144] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a terminal device such as a computer, a server, a mobile phone, or a tablet.

[0145] The above descriptions are merely some preferred embodiments of the present invention and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also encompass other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned inventive concept. For example, a technical solution formed by mutually replacing the above-mentioned features with (but not limited to) technical features having similar functions disclosed in the embodiments of the present invention.

Claims

1. A vehicle dispatching method based on big data, characterized in that the method include: Establish a vehicle dispatching system model based on the vehicle dispatching system mechanism; Based on the vehicle dispatching system model, the vehicle dispatching system performance function is obtained; Based on the performance function of the vehicle dispatching system, the performance parameter expression of the vehicle dispatching system, the first theoretical threshold expression of the load balancing of the vehicle dispatching system, and the relationship between the idle rate and load rate of the vehicle dispatching system are obtained; Based on the performance function of the vehicle dispatching system, combined with the vehicle's designed maximum load capacity and designed maximum response time, the second theoretical threshold expression for load balancing of the vehicle dispatching system is obtained; Calculate and output the divergence threshold under load balancing of the vehicle to be dispatched based on the design parameter value of the vehicle to be dispatched, the first theoretical threshold expression of the load balancing of the vehicle dispatching system, and the second theoretical threshold expression of the load balancing of the vehicle dispatching system; A simulation system with a vehicle scheduling system model is used to simulate and output a load response curve of the vehicle to be scheduled within a divergence threshold, and output corresponding performance parameter values ​​under load balancing of the vehicle to be scheduled.

2. A vehicle dispatching method based on big data according to claim 1, characterized in that: The car dispatching system model specifically includes: The vehicle demand input feedback module is used to process the input demand to obtain the vehicle scheduling adjustment parameters. The vehicle demand input feedback loop includes a demand processing module, an inner loop task allocation feedback module, a position feedback module, and a coordinate conversion module. The demand processing module is used to output the processed demand after sorting the input priorities, and then input the processed demand into the inner loop task allocation feedback module; The inner loop task allocation feedback module outputs vehicle scheduling adjustment tasks based on the processed demand; A coordinate conversion module is used to convert the vehicle scheduling adjustment task into the vehicle scheduling adjustment position, and then input the vehicle scheduling adjustment position into the position feedback module; The position feedback module is used to multiply the vehicle scheduling adjustment position by the feedback output sensor proportional coefficient and output the position feedback. The difference between the position feedback and the input vehicle instruction is then used as the input of the demand processing module.

3. A vehicle dispatching method based on big data according to claim 2, characterized in that: The inner loop task allocation feedback module includes a vehicle load calculation module, a task execution time calculation module, an inner loop resource allocation feedback module, and a real-time data feedback module. The vehicle load calculation module is used to calculate and obtain the current vehicle load, and then output the vehicle load to the task execution time calculation module; The task execution time calculation module is used to calculate and obtain the task execution time based on the vehicle load, and then output the task execution time to the inner loop resource allocation feedback module; The inner loop resource allocation feedback module outputs vehicle scheduling adjustment tasks based on task execution time; The real-time data feedback module calculates the real-time data feedback of the vehicle based on the vehicle scheduling adjustment task, and uses the difference between the real-time data feedback and the processed demand as the input of the vehicle load calculation module.

4. A vehicle dispatching method based on big data according to claim 3, characterized in that: The inner loop resource allocation feedback module includes a task allocation priority calculation module, a vehicle scheduling calculation module, and an external interference feedback module, among which, The task allocation priority calculation module is used to calculate the task allocation priority and output it to the vehicle scheduling calculation module; The vehicle dispatch calculation module calculates and obtains the vehicle dispatch position based on the task allocation priority, and outputs the vehicle dispatch position to the coordinate conversion module and the external interference feedback module; The external interference feedback module calculates the external interference feedback based on the vehicle scheduling position, and uses the difference between the external interference feedback and the task execution time as the input of the task allocation priority calculation module.

5. A vehicle dispatching method based on big data according to claim 1, characterized in that: The performance function of the vehicle dispatching system is specifically expressed as the following expression: Where s is the transfer function operator of the vehicle dispatching system, K p is the vehicle dispatch instruction proportional gain, K t is the vehicle task execution coefficient, η is the vehicle resource allocation efficiency, J is the vehicle task execution inertia, and D is the vehicle scheduling damping coefficient.

6. A vehicle dispatching method based on big data according to claim 5, characterized in that: Considering that the vehicle task execution inertia is much smaller than the vehicle scheduling damping coefficient, the vehicle task execution inertia is simplified to 0, and the vehicle scheduling system performance function expression is simplified to the following expression: Where G(s) is the vehicle dispatching system gain, s is the natural frequency of the vehicle dispatching system, which can be expressed as: D is the inherent damping coefficient of the vehicle dispatching system, which can be expressed as: Among them, the vehicle dispatching system gain, the vehicle dispatching system natural frequency, and the vehicle dispatching system natural damping coefficient are the performance parameters of the vehicle dispatching system.

7. A vehicle dispatching method based on big data according to claim 6, characterized in that: The first theoretical threshold expression for load balancing in the vehicle dispatching system is obtained based on the Routh criterion and the performance function of the vehicle dispatching system, and is specifically expressed as follows:

8. A vehicle dispatching method based on big data according to claim 7, characterized in that: The relationship between the idle rate and load rate of the vehicle dispatch system includes: The load rate of the vehicle dispatch system increases to times the idle rate of the vehicle dispatch system; The response time of the vehicle dispatch system is reduced to the idle rate of the vehicle dispatch system; The resource utilization rate of the vehicle dispatching system increases to the idle rate of the vehicle dispatching system; The task delay difference of the vehicle dispatching system increases, which can be expressed as: Where ω0 is the natural frequency corresponding to the idle rate of the vehicle dispatching system, ω is the natural frequency corresponding to the load rate of the vehicle dispatching system, ξ0 is the natural damping coefficient corresponding to the idle rate of the vehicle dispatching system, and ξ is the natural damping coefficient corresponding to the load rate of the vehicle dispatching system.

9. A vehicle dispatching method based on big data according to claim 8, characterized in that: The second theoretical threshold expression of load balancing in the vehicle dispatching system specifically includes: Where, I max is the vehicle's maximum design load capacity, T max is the maximum design response time of the vehicle, P req is the input vehicle request power, and θ is the load balancing coefficient of the vehicle scheduling system.

10. A vehicle dispatching method based on big data according to claim 9, characterized in that: Based on the design parameter values ​​of the vehicles to be dispatched, the first theoretical threshold expression of the load balancing of the vehicle dispatching system, and the second theoretical threshold expression of the load balancing of the vehicle dispatching system, the divergence threshold under the load balancing of the vehicles to be dispatched is calculated and output, specifically including: Substituting the design parameter values ​​of the vehicle to be dispatched into the first theoretical threshold expression of the load balance of the vehicle dispatching system, the first theoretical threshold of the load rate of the vehicle dispatching system is calculated; Substitute the design parameter values ​​of the vehicle to be dispatched, the vehicle's designed maximum load capacity value, and the vehicle's designed maximum response time value into the second theoretical threshold expression of the vehicle dispatch system's load balance to calculate the second theoretical threshold of the vehicle dispatch system's load rate; The larger of the first theoretical threshold value of the vehicle dispatching system load rate and the second theoretical threshold value of the vehicle dispatching system load rate is used as the vehicle dispatching system load rate divergence threshold value.

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