Intelligent vehicle scheduling method based on multi-factor dynamic optimization

Abstract

The embodiment of the invention provides an intelligent vehicle scheduling method based on multi-factor dynamic optimization, and the method comprises the steps: a data collection step, which is used for collecting orders, vehicles, drivers and external environment time, and carrying out the cleaning and standardization processing of the data; a scheduling algorithm determination step: according to the data output in the data collection step, raising a preliminary scheduling and route planning scheme; initial constraints and targets are set, and optimization targets are set; operating an optimization algorithm to iteratively solve an optimal scheme; wherein the optimization targets include but are not limited to reduction of transportation time, reduction of cost and improvement of service quality; the scheme evaluation step is used for evaluating the candidate schemes output by the scheduling algorithm determination step in a multi-dimensional mode, selecting an optimal scheduling scheme and issuing a scheduling instruction; and a monitoring and feedback step: monitoring the data of the vehicle and the transportation process through the Internet of Things equipment and the sensor, and dynamically adjusting the optimal scheduling scheme in real time according to the data.

Description

Technical Field

[0001] The present application relates to the field of information security technology, and in particular to an intelligent vehicle scheduling method based on multi-factor dynamic optimization. Background Art

[0002] Existing vehicle dispatching systems usually face many limitations in optimizing transportation efficiency and reducing costs. Traditional dispatching schemes often rely on static data and lack consideration of real-time changing factors, such as traffic conditions, weather, road restrictions, etc., resulting in inflexible dispatching. At the same time, with the development of the logistics industry, the diversification and personalized needs of orders have also put forward higher requirements on the dispatching system, such as order priority, delivery time limit, special handling requirements, etc. In addition, it is difficult for existing dispatching algorithms to balance transportation time, operating costs and service quality, and they cannot fully adapt to the volatility and complexity of transportation demand. Therefore, there is an urgent need for an intelligent dispatching algorithm that can comprehensively consider multiple factors and dynamically adjust the dispatching scheme to improve transportation efficiency, reduce operating costs, and provide efficient service quality. Summary of the invention

[0003] In order to improve the efficiency of vehicle scheduling, the present application provides an intelligent vehicle scheduling method based on multi-factor dynamic optimization.

[0004] In order to achieve the above objectives, the present application embodiment proposes an intelligent vehicle scheduling method based on multi-factor dynamic optimization, including:

[0005] Data collection step, used to collect orders, vehicles, drivers, external environment time, and clean and standardize the data;

[0006] Scheduling algorithm determination step: used to generate preliminary scheduling and route planning schemes based on the data output from the data collection step; set preliminary constraints and goals, and set optimization goals; run the optimization algorithm to iteratively solve the optimal solution; the optimization goals include but are not limited to: reducing transportation time, reducing costs, and improving service quality;

[0007] Scheme evaluation step: used to evaluate the candidate schemes output by the scheduling algorithm determination step in multiple dimensions, select the optimal scheduling scheme and issue scheduling instructions;

[0008] Monitoring and feedback step: used to monitor the data of vehicles and transportation processes through IoT devices and sensors, and dynamically adjust the optimal scheduling plan in real time according to the data;

[0009] Storage and learning step: used to store the execution results of the optimal scheduling solution in the database for subsequent machine learning model training and optimization.

[0010] Furthermore, the data collection step is used to obtain parameters that affect the intelligent dispatch of vehicles, including order parameters, vehicle parameters, driver parameters, and environmental parameters;

[0011] Order parameters include: delivery deadline, priority, and special requirements of the order, evaluate and prioritize the orders to ensure that high-priority orders are processed in a timely manner;

[0012] Vehicle parameters include: vehicle type, capacity, location, availability and maintenance status, and reasonable allocation of vehicles to ensure that there is no overloading or overtime during transportation;

[0013] Driver parameters include: driver's working hours, skill level and qualifications, assigning the most suitable driver to the corresponding task to ensure safety and efficiency;

[0014] Environmental parameters include: real-time traffic conditions, weather changes and road construction information, which are combined with scheduling algorithms to make adjustments to cope with uncertain external environmental changes.

[0015] Furthermore, the monitoring and feedback step is used to collect vehicle location, traffic flow, and road condition information in real time through sensors installed on vehicles and infrastructure; it is also used to obtain traffic information in real time through integration with the data interface of the traffic management department to ensure that the scheduling plan is closely integrated with the actual situation.

[0016] Furthermore, the machine learning prediction step is used to predict future traffic congestion trends and changes in transportation demand based on historical transportation data using machine learning algorithms, so as to adjust the scheduling strategy in advance and improve the accuracy of decision-making and the intelligence of scheduling solutions.

[0017] The beneficial effects of the above technical solution of the present application are as follows: The intelligent scheduling algorithm based on dynamic multi-factor optimization proposed in the present application is applicable to scenarios involving vehicle scheduling, route planning and transportation resource optimization in transportation systems. The algorithm integrates meta-heuristic algorithms, machine learning predictions, real-time data collection and other technologies, and realizes the automatic generation and adjustment of scheduling plans through multi-objective optimization, thereby improving transportation efficiency and reducing operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the algorithm flow of the embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the technical problems, technical solutions and advantages to be solved by the present application clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0020] The disclosed embodiments combine artificial intelligence and machine learning technologies, comprehensively consider order management, vehicle management, driver management, environmental factors, etc., dynamically optimize vehicle scheduling and route planning, and solve the problems of insufficient scheduling flexibility and low efficiency in the prior art. The core of the present invention is to use meta-heuristic algorithms (such as genetic algorithms, ant colony algorithms, particle swarm optimization, etc.) to solve complex scheduling problems, achieve multi-objective optimization based on the acquisition and prediction of real-time data, and finally dynamically generate the optimal scheduling plan. Through this algorithm, it is possible to significantly reduce operating costs and improve service quality while ensuring transportation efficiency.

[0021] The embodiment of the present disclosure proposes an intelligent scheduling algorithm based on dynamic multi-factor optimization. The algorithm flow is as follows: Figure 1 As shown, including:

[0022] Data collection step, used to collect orders, vehicles, drivers, external environment time, and clean and standardize the data;

[0023] Scheduling algorithm determination step: used to generate preliminary scheduling and route planning schemes based on the data output from the data collection step; set preliminary constraints and goals, and set optimization goals; run the optimization algorithm to iteratively solve the optimal solution; the optimization goals include but are not limited to: reducing transportation time, reducing costs, and improving service quality;

[0024] Scheme evaluation step: used to evaluate the candidate schemes output by the scheduling algorithm determination step in multiple dimensions, select the optimal scheduling scheme and issue scheduling instructions;

[0025] Monitoring and feedback step: used to monitor the data of vehicles and transportation processes through IoT devices and sensors, and dynamically adjust the optimal scheduling plan in real time according to the data;

[0026] Storage and learning step: used to store the execution results of the optimal scheduling solution in the database for subsequent machine learning model training and optimization.

[0027] Among them, the data collection step is used to obtain parameters that affect vehicle intelligent scheduling, including order parameters, vehicle parameters, driver parameters, and environmental parameters;

[0028] Order parameters include: delivery deadline, priority, and special requirements of the order, evaluate and prioritize the orders to ensure that high-priority orders are processed in a timely manner;

[0029] Vehicle parameters include: vehicle type, capacity, location, availability and maintenance status, and reasonable allocation of vehicles to ensure that there is no overloading or overtime during transportation;

[0030] Driver parameters include: driver's working hours, skill level and qualifications, assigning the most suitable driver to the corresponding task to ensure safety and efficiency;

[0031] Environmental parameters include: real-time traffic conditions, weather changes and road construction information, which are combined with scheduling algorithms to make adjustments to cope with uncertain external environmental changes.

[0032] The monitoring and feedback step is used to collect vehicle location, traffic flow, and road condition information in real time through sensors installed on vehicles and infrastructure. It is also used to obtain traffic information in real time through integration with the data interface of the traffic management department to ensure that the dispatch plan is closely integrated with the actual situation.

[0033] Among them, the machine learning prediction step is used to predict future traffic congestion trends and changes in transportation demand based on historical transportation data using machine learning algorithms, so as to adjust the scheduling strategy in advance and improve the accuracy of decision-making and the intelligence of the scheduling plan.

[0034] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer programs. When the computer program is loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage medium such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0035] A person skilled in the art may understand that the various numerical numbers such as first and second involved in the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application, and also indicate the order of precedence.

[0036] At least one in the present application can also be described as one or more, and a plurality can be two, three, four or more, which is not limited in the present application. In the embodiments of the present application, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no order of precedence or size between the technical features described by the "first", "second", "third", "A", "B", "C" and "D".

[0037] The corresponding relationships shown in each table in the present application can be configured or predefined. The values ​​of the information in each table are only examples and can be configured as other values, which are not limited by the present application. When configuring the corresponding relationship between the information and each parameter, it is not necessarily required to configure all the corresponding relationships illustrated in each table. For example, in the table in the present application, the corresponding relationships shown in some rows may not be configured. For another example, appropriate deformation adjustments can be made based on the above table, such as splitting, merging, etc. The names of the parameters shown in the titles of the above tables can also use other names that can be understood by the communication device, and the values ​​or representations of the parameters can also be other values ​​or representations that can be understood by the communication device. When implementing the above tables, other data structures can also be used, such as arrays, queues, containers, stacks, linear lists, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables or hash tables.

[0038] The predefined in the present application may be understood as defined, predefined, stored, pre-stored, pre-negotiated, pre-configured, solidified, or pre-burned.

[0039] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0040] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0041] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent vehicle dispatching method based on multi-factor dynamic optimization, comprising: Data collection step, used to collect orders, vehicles, drivers, external environment time, and clean and standardize the data; Scheduling algorithm determination step: used to generate preliminary scheduling and route planning schemes based on the data output from the data collection step; set preliminary constraints and goals, and set optimization goals; run the optimization algorithm to iteratively solve the optimal solution; the optimization goals include but are not limited to: reducing transportation time, reducing costs, and improving service quality; Scheme evaluation step: used to evaluate the candidate schemes output by the scheduling algorithm determination step in multiple dimensions, select the optimal scheduling scheme and issue scheduling instructions; Monitoring and feedback step: used to monitor the data of vehicles and transportation processes through IoT devices and sensors, and dynamically adjust the optimal scheduling plan in real time according to the data; Storage and learning step: used to store the execution results of the optimal scheduling solution in the database for subsequent machine learning model training and optimization.

2. The intelligent vehicle dispatching method based on multi-factor dynamic optimization according to claim 1, wherein: The data collection step is used to obtain parameters that affect vehicle intelligent scheduling, including order parameters, vehicle parameters, driver parameters, and environmental parameters; Order parameters include: delivery deadline, priority, and special requirements of the order, evaluate and prioritize the orders to ensure that high-priority orders are processed in a timely manner; Vehicle parameters include: vehicle type, capacity, location, availability and maintenance status, and reasonable allocation of vehicles to ensure that there is no overloading or overtime during transportation; Driver parameters include: driver's working hours, skill level and qualifications, assigning the most suitable driver to the corresponding task to ensure safety and efficiency; Environmental parameters include: real-time traffic conditions, weather changes and road construction information, which are combined with scheduling algorithms to make adjustments to cope with uncertain external environmental changes.

3. The intelligent vehicle dispatching method based on multi-factor dynamic optimization according to claim 1, wherein: The monitoring and feedback step is used to collect vehicle location, traffic flow, and road condition information in real time through sensors installed on vehicles and infrastructure. It is also used to obtain traffic information in real time through integration with the data interface of the traffic management department to ensure that the scheduling plan is closely integrated with the actual situation.

4. According to the intelligent vehicle scheduling method based on multi-factor dynamic optimization as described in claim 1, the machine learning prediction step is used to predict future traffic congestion trends and changes in transportation demand based on historical transportation data using machine learning algorithms, so as to adjust the scheduling strategy in advance and improve the accuracy of decision-making and the intelligence of the scheduling plan.

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