CN116843135A

CN116843135A - System, architecture and method for cluster management and scheduling of short-range vehicles

Info

Publication number: CN116843135A
Application number: CN202310743629.4A
Authority: CN
Inventors: 闫健; 吴沁霖; 解飞
Original assignee: Jiangsu Sunong Xintong Digital Technology Co ltd
Current assignee: Jiangsu Sunong Xintong Digital Technology Co ltd
Priority date: 2023-06-22
Filing date: 2023-06-22
Publication date: 2023-10-03

Abstract

The invention discloses a system, a framework and a method for managing and dispatching short vehicles, which relate to the technical field of vehicle cluster management and dispatching and comprise a data acquisition layer, a data processing layer, a data execution layer, a user interface layer and a data feedback layer.

Description

System, architecture and method for cluster management and scheduling of short-range vehicles

Technical Field

The invention belongs to the technical field of vehicle cluster management and scheduling, and particularly relates to a system, a framework and a method for managing and scheduling short-range vehicles.

Background

The agricultural product wholesale market is an important node for agricultural product circulation and is also an important infrastructure for guaranteeing and improving folk life. At present, the agricultural product wholesale market in China has the problems of high circulation cost, low circulation efficiency, disordered circulation order, hidden danger of circulation and the like, and the market system construction and standard management are required to be enhanced.

The main components of the prior art scheme are as follows:

(1) Vehicle positioning technology: the position and status of the short-range vehicle is tracked and monitored in real time using Global Positioning System (GPS) and wireless communication technology. This technique can provide accurate location information of the vehicle, providing basic data for cluster management and scheduling.

(2) Data processing and analysis technology: by integrating and analyzing vehicle positioning data with other related data, such as traffic conditions, cargo information, vehicle loads, etc., the system may generate a real-time vehicle scheduling scheme. These techniques include data mining, machine learning, and optimization algorithms, among others.

(3) Communication technology: real-time communication is performed between the short-range vehicle and the system by utilizing a wireless communication technology, so as to receive a scheduling instruction, transmit data and report the vehicle state. This technique ensures efficient collaboration and information exchange between the vehicle and the system.

(4) Cluster management algorithm: by adopting a distributed algorithm and a collaborative optimization method, the system can automatically manage the whole short-range vehicle cluster. The algorithms can formulate an optimized dispatch plan according to factors such as cargo demands, vehicle availability, routes and the like, thereby improving overall efficiency and reducing transportation cost.

(5) A vehicle identification device: the method is used for identifying short vehicles in the market, such as license plate identification, two-dimensional code identification, RFID identification and the like;

(6) Vehicle positioning device: the method is used for acquiring real-time position information of the short-distance vehicles, such as GPS positioning, base station positioning, beidou positioning and the like;

(7) Vehicle scheduling apparatus: the intelligent dispatching system is used for intelligently distributing and dispatching short vehicles according to information such as cargo demands, vehicle positions, vehicle states and the like, such as a central control console, a mobile terminal, voice prompts and the like;

(8) A vehicle monitoring device: for monitoring the running condition of short-range vehicles, such as running speed, running route, running time, etc., and the state of goods, such as temperature, humidity, weight, etc.

The defects are that:

(1) Data accuracy and real-time: the short-range vehicle cluster management system has higher requirements on the accuracy and the real-time property of data. If the information such as positioning data, traffic conditions and the like is inaccurate or not updated timely, inaccuracy and efficiency of the scheduling scheme may be reduced.

(2) System complexity and reliability: the system needs to process a large amount of data and make complex algorithm calculations and decisions. This may lead to increased complexity of the system, as well as higher demands on computing and communication resources. At the same time, system reliability is also a challenge, as the effects of signal interference, network failure, equipment damage, etc., may lead to scheduling delays or errors.

(3) Personnel training and acceptance: the introduction of such a new system and device requires training of the personnel involved to ensure that they are able to properly use and understand the function and manner of operation of the system. In addition, some individuals may have varying acceptability to automated systems, requiring proper training and communication to mitigate conflicting moods.

(4) Privacy protection and network security: the short-range vehicle cluster management system needs to collect and process sensitive data such as vehicle positioning data, cargo information and the like. In designing a system, it is necessary to ensure secure storage and transmission of such data and to take appropriate privacy protection measures to prevent unauthorized access and data disclosure. The system involves real-time communication between the vehicle and the backend system, and thus needs to be protected against the risk of network attacks and data tampering. Appropriate security measures, such as the use of encryption techniques, authentication, access control, etc., must be taken to ensure confidentiality, integrity, and reliability of the communications.

(5) Data backup and recovery: for important data in the system, such as vehicle dispatch records, cargo information, etc., periodic data backup and restoration policies must be implemented. This prevents data loss or corruption and allows for quick recovery of data in the event of a system failure or disaster.

Disclosure of Invention

The invention aims to solve the technical problem of providing a system, a framework and a method for managing and dispatching short vehicles in clusters, which utilize an informatization means to improve the running stability of the system, simplify the use flow of the system, enhance the adaptability of the system, uniformly dispatch and manage the short vehicles in the market, realize the rapid transfer and distribution of goods, and further improve the circulation efficiency and the service level of the agricultural product wholesale market.

The invention adopts the following technical scheme for solving the technical problems:

short-range vehicle cluster management and scheduling system for realizing agricultural batch market by constructing the following layers

Intelligent management of short-cut scheduling, specifically as follows:

(1) The data acquisition layer is used for acquiring the goods demand, the vehicle position and the vehicle state data in the market by utilizing a vehicle positioning technology and sensor equipment and transmitting the data to the data processing layer in a wireless transmission mode;

(2) The data processing layer is used for receiving the data sent by the data acquisition layer, carrying out data integration, data cleaning and data analysis processing, extracting useful information by utilizing data mining and machine learning technologies, and carrying out vehicle dispatching optimization and cargo batch management, so that an optimized short-distance vehicle dispatching scheme is obtained, decision support such as transportation route optimization and vehicle dispatching scheme is provided, and the decision support is sent to the data execution layer in a wireless transmission mode;

(3) Data execution layer: the system is used for executing a scheme of sending a data processing layer, such as actual operation of dispatching of short-range vehicles and batched goods, sending instructions to related personnel of drivers of the short-range vehicles and warehouse administrators in a voice or graphical interface mode, guiding the drivers of the short-range vehicles and the related personnel of the warehouse administrators to transfer and distribute the goods according to the scheme, and ensuring accurate execution of a dispatching plan;

(4) The user interface layer is used for providing a user-friendly interface, so that a user can conveniently view and manage vehicle dispatching, cargo information and market demand related information, and the user can perform order management, inquire transportation state and adjust dispatching plan operation through the interface;

(5) The data feedback layer is used for collecting feedback information of the dispatching scheme executed by the driver of the short-range vehicle, and sending the feedback information to the data processing layer in a wireless transmission mode to serve as a basis for optimizing the dispatching scheme by the data processing layer.

As a further preferable scheme of the cluster management and scheduling system of the short-range vehicles, the data processing layer adopts

The reinforcement learning algorithm, the optimization algorithm and the real-time monitoring and predicting algorithm receive data sent by the data acquisition layer, perform data integration, data cleaning and data analysis processing, and extract useful information by utilizing data mining and machine learning technologies.

As a further preferred embodiment of the cluster management and scheduling system for short-range vehicles of the present invention, the reinforcement learning algorithm,

the method comprises the following steps:

continuously updating the strategy network parameters of the self according to the reward signals provided by the data feedback layer, so as to learn an optimal or nearly optimal short-distance vehicle scheduling scheme; specifically, the reinforcement learning algorithm includes the following elements:

(1) state space: the state space comprises the characteristics of the current problem, the current solution and the solving process; specifically, the state space is composed of the following parts:

cargo demand vector: representing that each cargo demand point includes a number of cargo required by the warehouse;

vehicle position vector: representing the current position of each short-cut vehicle;

vehicle state vector: representing the number of cargo currently carried by each short-range vehicle;

route length vector: representing a current traveled route length of each short vehicle;

route sequence vector: representing a sequence of cargo demand points that each short-range vehicle has currently visited;

residual demand vector: representing that each cargo demand point comprises the number of unsatisfied cargoes remaining in the warehouse;

residual capacity vector: representing the number of cargo remaining per short-range vehicle;

(2) Action space: the action space includes all possible short-barge vehicle scheduling schemes; specifically, the action space is composed of the following parts:

vehicle selection actions: selecting a currently available short-range vehicle which is not completed in tasks and is not overloaded for scheduling;

demand point selection action: selecting a cargo demand point which is not accessed currently and has residual demand, namely non-zero, as a next access target;

and (3) cargo transferring action: after reaching the target demand point, determining how much goods are unloaded or loaded from the point according to the demand condition of the point and the state condition of the vehicle;

(3) bonus function: the reward function is used to evaluate the contribution of each action to the overall objective function, i.e. minimizing the total route length; specifically, the reward function consists of the following parts:

fixed rewards: indicating that a fixed value of rewards are obtained every time a goods demand point is accessed except a warehouse;

penalty factor: deducting a proportional value of the reward every time the route length of one unit length is increased;

terminating the bonus: indicating that when all cargo demand points are accessed and satisfied, an extra value is awarded;

(4) policy network: the strategy network is used for outputting optimal or near optimal action probability distribution according to the current state; specifically, the policy network consists of the following parts:

Input layer: the input layer receives each vector in the state space as input and splices the vectors into a one-dimensional vector;

hidden layer: the hidden layer is composed of a plurality of full-connection layers or convolution layers, and nonlinear characteristics are added by using an activation function;

output layer: the output layer consists of three sub-output layers, which respectively correspond to three partial action spaces, namely a vehicle selection action, a demand point selection action and a goods transfer action; specifically:

A. vehicle selection sub-output layer: the sub-output layer consists of a full connection layer and a softmax layer, outputs a vector with the length of K, and represents the probability of selecting each short vehicle;

B. the demand point selection sub-output layer: the sub-output layer consists of a full connection layer and a softmax layer, outputs a vector with the length of n+1, and represents the probability of selecting each goods demand point to comprise a warehouse as the next access target;

C. cargo transfer sub-output layer: the sub-output layer consists of a full connection layer and a softmax layer, and outputs a vector with the length of C+1, which represents the probability of unloading or loading 0 to C units of goods from a target demand point;

according to the output of the three sub-output layers, a complete action is obtained, namely which vehicle is selected, which demand point is accessed and how much goods are transferred; and calculating the rewarding value of the action according to the rewarding function, and updating the parameters of the strategy network according to the strategy gradient algorithm.

As a further preferable scheme of the short-range vehicle cluster management and scheduling system, the vehicle scheduling algorithm comprises the following steps: according to the vehicle position, market demand and cargo information data, an optimal vehicle dispatching scheme is determined by utilizing an optimization algorithm, wherein the optimal vehicle dispatching scheme comprises route planning and vehicle distribution;

cargo batching algorithm: based on market demand and vehicle availability data, an optimization algorithm is adopted to determine a reasonable batching scheme of goods so as to improve distribution efficiency and meet market demand;

real-time monitoring and prediction algorithm: the method comprises the steps of monitoring data such as vehicle positions and traffic conditions in real time, carrying out prediction analysis by combining historical data, finding potential problems in time, and carrying out corresponding adjustment such as route re-planning and vehicle replacement.

An architecture based on a short-range vehicle cluster management and dispatch system comprising

Front-rear end section architecture:

the front end portion includes a user interface that can be provided for use by administrators, short-range vehicle drivers, and other related personnel through a web application or mobile application; the user interface provides the functions of user registration, task viewing, data query and statistics, so that the user can interact with the system;

the back end part comprises main modules such as cluster management, a single robbing function, a single dispatching function and the like; the cluster management module is responsible for managing registration, state monitoring and task allocation optimization of the short-range vehicle cluster; the order-robbing functional module processes order-robbing requests of drivers, screening matching and task allocation; the dispatch function module is responsible for task release, vehicle screening matching and task scheduling.

Cluster management server architecture:

the cluster management server is a core component of the system and is responsible for cluster management, a scheduling algorithm and a data analysis function of the whole system; the architecture adopts a distributed architecture and comprises the following modules:

the cluster management module is used for monitoring and managing the state, the position and the running condition of the short-distance vehicle cluster and providing vehicle management and task allocation functions.

The scheduling algorithm module is used for realizing a batch scheduling algorithm, generating an optimal scheduling scheme according to real-time vehicle and cargo information, optimizing the vehicle utilization rate and reducing the transportation cost;

the data analysis module is used for analyzing the vehicle and cargo data stored in the database in real time and providing decision support such as scheduling optimization and route planning;

the communication module is responsible for carrying out data exchange and instruction issuing with the short-distance vehicle terminal equipment and the dispatching center terminal equipment;

short-range vehicle terminal equipment architecture:

the vehicle-mounted terminal equipment is equipment arranged on each short-range vehicle and is used for data acquisition, communication, scheduling instruction execution and other functions; the framework comprises the following components:

a sensor module: the system comprises a GPS positioning sensor, a speed sensor, a load sensor and the like, and is used for acquiring the position, state and operation data of the vehicle;

And a communication module: the system is used for transmitting real-time data and issuing instructions with the cluster management server by utilizing a wireless network technology;

the control module is used for controlling operations such as loading, unloading, transportation and the like of the vehicle according to the received scheduling instruction;

the data storage module is used for temporarily storing the acquired data and ensuring the real-time performance and reliability of the data;

scheduling center terminal equipment architecture:

the dispatching center terminal equipment is used for interacting a dispatcher with the cluster management server to realize the functions of issuing dispatching instructions and data query; the framework comprises the following components:

the user interface is used for providing an intuitively friendly user interface, and a dispatcher can interact with the cluster management server through the interface;

the data exchange module is used for carrying out data exchange with the cluster management server and comprises the issuing of a scheduling instruction and the return of a data query result;

the communication module is used for carrying out real-time communication with the cluster management server by utilizing network connection;

third party logistics platform:

the platform is an external partner of the system and is responsible for providing service requirements, including goods names, goods picking addresses, delivery addresses, goods picking time and time information of arrival requirements; the platform is in the form of a cargo owner enterprise management system, a third party logistics management system or an open business module; the platform is connected with the dispatching platform through an open interface to realize the functions of sending and receiving service requests, inquiring and updating order states and acquiring and analyzing transportation data.

As a further preferable scheme of the framework of the short-range vehicle cluster management and scheduling system, the invention further comprises a database, and the method specifically comprises the following steps:

vehicle information database:

the database is used for storing and managing information related to short-range vehicles, including but not limited to the following:

vehicle number: a number uniquely identifying each short-range vehicle;

position information: recording real-time position coordinates of the vehicle so as to monitor and schedule in real time;

status information: the method comprises the steps of judging the availability of the vehicle and carrying out scheduling decision, wherein the running state, the loading state and the like of the vehicle are used for judging the availability of the vehicle;

cargo information database:

the database is used to store and manage information about the goods, including but not limited to the following:

cargo number: a number uniquely identifying each good;

the cargo category: recording the type of goods so as to schedule and match proper vehicles;

quantity information: recording the quantity of goods for scheduling and delivery plans;

scheduling rules database:

the database is used to store and manage scheduling rules including, but not limited to, the following:

vehicle dispatch priority: according to different conditions, setting the scheduling priority of the vehicle to ensure the optimal scheduling decision;

Batch scheduling algorithm: storing relevant parameters and rules of the batch scheduling algorithm for generating an optimal batch scheduling scheme;

scheduling policy: recording scheduling rules, such as vehicle loading rules and delivery route planning, so as to ensure the rationality and the high efficiency of scheduling;

historical data database:

the database is used to store and manage historical data for subsequent data analysis and decision support, including but not limited to the following:

vehicle transportation record: recording a transportation history of each vehicle, including a start point, a destination, a transportation time, etc., for analyzing transportation efficiency and quality of the vehicle;

and (5) cargo delivery time record: recording the delivery time of each cargo so as to predict and optimize the delivery time;

the databases can be stored and managed by adopting a relational database or a distributed database so as to meet the requirements of the system on the real-time property, the reliability and the expansibility of the data;

the database design of the short-refuted vehicle cluster management and batch dispatching system comprises a vehicle information database, a cargo information database, a dispatching rule database and a historical data database, and is used for storing and managing relevant real-time and historical data so as to support the dispatching decision and analysis functions of the system.

A method based on a short-range vehicle cluster management and scheduling system specifically comprises the following steps:

the cluster management algorithm specifically comprises the following steps:

vehicle registration: the short-range vehicles are registered in the system and provide relevant information such as vehicle numbers and loading capacity;

vehicle condition monitoring: the system monitors the position, availability and running state of the vehicle in real time; acquiring real-time data of a vehicle through a vehicle-mounted sensor and GPS equipment, and feeding the real-time data back to cluster management software;

task allocation and optimization: assigning tasks to the appropriate vehicles using task assignment and optimization algorithms based on the location, availability, and real-time data of the vehicles; the algorithm considers the load capacity, the driving distance and the time window factors of the vehicle so as to realize efficient task allocation;

task release is specifically as follows:

the system distributes the task to the most suitable vehicle driver according to the information such as the position of the vehicle, the task type, real-time traffic and the like;

in the dispatch process, the system needs to consider the factors of the current task condition, task priority, working time of the vehicle and the like of the vehicle so as to ensure that the task can be completed in time;

after dispatch, the system updates the task state and related information assigned to the vehicle for subsequent scheduling and monitoring;

The robbery single function algorithm specifically comprises the following steps: :

and (5) receiving a robbery order request: when a task needs to be executed, the system issues task information, including a task type, a starting place and a destination; the driver of the short-range vehicle can receive a task request through the robbery-to-order function software;

driver screening and matching: the system screens and matches the robbery request according to the task requirement and the capability and availability of the driver; evaluating according to the position, the loading capacity, the available time and other factors of the driver to find the most suitable driver;

task allocation: the system distributes the task to the most suitable driver to ensure that the task can be executed in time; the task allocation algorithm can consider factors such as distance of a driver, traffic conditions, task priority and the like so as to realize the optimal task allocation effect;

the dispatch function algorithm specifically comprises the following steps:

task release: the system issues task information according to task demands and priorities, wherein the task information comprises task types, starting places and destinations;

vehicle screening and matching: the system screens and matches the vehicles for the tasks according to the positions, the availability and the real-time data of the vehicles; the algorithm considers the load capacity, the driving distance and the time window factors of the vehicle so as to find the most suitable vehicle;

Task scheduling: the system dispatches the task to the most suitable vehicle and generates a corresponding dispatch plan; the scheduling algorithm can consider the route, time window and priority factors of the vehicle so as to realize the optimal task scheduling effect;

the scheduling optimization is specifically as follows:

the system collects and analyzes historical data of the vehicle, including running track, task completion time and waiting time information;

based on the historical data and the real-time information, the system can optimize the scheduling strategy, and the scheduling efficiency and the task completion rate of the vehicle are improved;

scheduling optimization may include route planning, task batch scheduling, task priority adjustment techniques to minimize overall cost and time;

the system is monitored and updated as follows:

the system monitors information such as the position, task progress, driver state and the like of the vehicle in real time so as to perform real-time scheduling and task tracking;

the system dynamically updates the task state and allocation according to the actual condition of the vehicle and the task progress;

the system can also provide real-time data statistics and reports so that management personnel can make decisions and optimize the data statistics and reports;

the evaluation algorithm specifically comprises the following steps:

receiving operation data of the short-distance vehicle, such as speed, oil consumption, mileage and load information;

Receiving satisfaction feedback of the clients, such as scoring and comment information;

receiving the completion condition of the order, if yes, arriving on time or not, and if not, obtaining information on whether the order is intact;

according to the running data of the short-distance vehicle, the satisfaction feedback of the customer, the completion condition of the order and other indexes, the service quality of the short-distance vehicle is evaluated, and corresponding rewards and punishment measures such as adding points, subtracting points, rewards and fines are given;

the method comprises the steps of sending an evaluation result and rewarding and punishing measures to a short barge through a wireless communication module;

the steps of the optimization algorithm are as follows:

receiving indexes such as service quality, transport efficiency, operation cost and the like of the short-distance vehicle;

optimizing and updating the partitioned scheduling algorithm and the robbery algorithm according to the change trend and the target value of the index, such as parameter adjustment, constraint increase and strategy improvement;

and deploying the optimized batch scheduling algorithm and the optimized robbery scheduling algorithm to the central server and the mobile terminal. Compared with the prior art, the technical scheme provided by the invention has the following technical effects:

the invention improves the efficiency: the intelligent management and scheduling of the vehicle clusters are realized through the functions of robbing and dispatching; the vehicle can actively rob or send the bill according to the actual demand, so that unnecessary waiting time and resource waste in the traditional scheduling mode are avoided, and the transportation efficiency is improved;

2. The invention optimizes the resource utilization: the system distributes cargoes to the most suitable vehicles in batches for transportation through an intelligent scheduling algorithm, avoids no-load or low-load operation of the vehicles, and optimizes the resource utilization efficiency to the maximum extent. Thus, the transportation cost can be reduced, and the operation benefit can be improved;

the invention improves the service quality: the dispatch function in the system can rapidly and accurately distribute tasks according to the position, the state and the goods requirements of the vehicle; the method is beneficial to improving the response speed and accuracy of the transportation service, meeting the requirements of customers and improving the satisfaction of the customers;

4. the invention monitors and manages in real time: the system has a real-time monitoring function, and can monitor and manage the position, the transportation progress, the running track and the like of the vehicle in real time; the method is beneficial to enterprises to timely track and adjust the vehicle operation condition, and improves the transportation safety and controllability;

5. the invention is data analysis and decision support: the system collects and analyzes vehicle operation data and provides data analysis reports on transportation efficiency, cost, customer requirements and the like; the method provides data support for enterprises, helps the enterprises make reasonable decisions, optimizes operation strategies, and further improves transportation efficiency and profitability;

6. The invention has the advantages of user friendliness: the system design considers the demands and the use habits of users, and provides an intuitive and concise user interface, so that the operation is more convenient and easier to understand; the user can easily perform operations such as order robbing, order dispatching, monitoring, data analysis and the like, and can use the mobile phone without tedious training;

7. the invention communicates and cooperates in real time: the communication module in the system realizes real-time communication and cooperation between the vehicle and the dispatching center and between the driver and the dispatcher; through the functions of instant messaging, voice communication or video conference, the problems can be quickly solved, the transportation plan can be adjusted, and the information can be kept updated in time;

8. the powerful algorithm of the invention supports: the system adopts an advanced scheduling algorithm, and can quickly generate an optimal scheduling scheme under the condition of considering various constraint conditions; this includes consideration of vehicle capacity, distance, cargo urgency, etc., to minimize total transportation costs or maximize resource utilization;

9. the invention is highly automated: the automatic function in the system greatly reduces the requirement of manual intervention, and improves the efficiency and accuracy of operation; for example, the system can automatically generate scheduling tasks, send notifications and alarms, record transportation data and the like, so that human errors and tedious manual operations are reduced;

10. The invention protects data security and privacy: the system adopts a strict data encryption and access control mechanism, so that the safety and privacy protection of transportation data and user information are ensured; at the same time, we follow relevant regulations and standards to ensure compliance and confidentiality of the data.

Detailed Description

The technical scheme of the invention is further described in detail as follows:

in order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention are clearly and completely described below. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The invention aims to provide a short-distance vehicle cluster management and batch scheduling system and device applied to an intelligent logistics park, which utilize an informatization means to improve the running stability of a system, simplify the use flow of the system, enhance the adaptability of the system, uniformly schedule and manage short-distance vehicles in the market, realize rapid transfer and distribution of goods, and further improve the circulation efficiency and service level of agricultural product wholesale markets.

Through the invention, the following layers are constructed to realize intelligent management of short-cut scheduling in the agricultural batch market:

(1) Data acquisition layer: the method comprises the steps of collecting data such as cargo demands, vehicle positions, vehicle states and the like in the market by utilizing equipment such as a vehicle positioning technology and a sensor, and sending the data to a data processing layer in a wireless transmission mode;

(2) Data processing layer: the layer is responsible for receiving data sent by the data acquisition layer, carrying out data integration, data cleaning, data analysis and other processes, extracting useful information such as vehicle dispatching optimization, cargo batch management and the like by utilizing technologies such as data mining, machine learning and the like, so as to obtain an optimized short-distance vehicle dispatching scheme, provide decision support such as transportation route optimization, vehicle dispatching scheme and the like, and send the decision support to the data execution layer in a wireless transmission mode;

(3) Data execution layer: the layer is responsible for executing a scheme sent by a data processing layer, such as actual operation of dispatching of short-range vehicles and batched goods, and sending instructions to related personnel such as drivers of the short-range vehicles, warehouse administrators and the like in a voice or graphical interface mode, so as to guide the short-range vehicles to transfer and distribute the goods according to the scheme, and ensure accurate execution of a dispatching plan;

(4) User interface layer: a user-friendly interface is provided, so that a user (such as an agricultural product wholesaler, a dispatcher and the like) can conveniently view and manage relevant information such as vehicle dispatching, cargo information, market demands and the like. The user can perform operations such as order management, inquiring transportation state, adjusting scheduling plan and the like through the interface.

(5) Data feedback layer: the layer is responsible for collecting feedback information of the dispatching scheme executed by the driver of the short-range vehicle, and sending the feedback information to the data processing layer in a wireless transmission mode, and the feedback information is used as a basis for optimizing the dispatching scheme by the data processing layer.

The invention mainly utilizes reinforcement learning algorithm, optimization algorithm and real-time monitoring and prediction algorithm to realize the core function of the data processing layer.

(1) Reinforcement learning algorithm: according to the rewarding signal provided by the data feedback layer, the strategy network parameters of the rewarding system can be continuously updated, so that an optimal or nearly optimal short-distance vehicle dispatching scheme is learned. Specifically, the reinforcement learning algorithm includes the following elements:

(1) state space: the state space includes features of the current problem, the current solution, and the solution process. Specifically, the state space is composed of the following parts:

Cargo demand vector: representing the number of goods required for each goods requiring point (including warehouse);

residual demand vector: indicating the number of unsatisfied goods remaining at each goods demand point (including warehouse);

residual capacity vector: indicating the number of cargo remaining per short vehicle.

(2) Action space: the action space includes all possible short-range vehicle scheduling schemes. Specifically, the action space is composed of the following parts:

vehicle selection actions: selecting a currently available (i.e., unfinished task and not overloaded) short-range vehicle for scheduling;

demand point selection action: selecting a cargo demand point which is not accessed currently and has residual demands (i.e. non-zero) as a next access target;

and (3) cargo transferring action: after reaching the target demand point, determining how much cargo to unload or load from the point according to the demand condition of the point and the state condition of the vehicle.

(3) Bonus function: the reward function is used to evaluate the contribution of each action to the overall objective function (i.e., minimizing the overall route length). Specifically, the reward function consists of the following parts:

fixed rewards: indicating that a fixed value (e.g., 1) of rewards are obtained for each complete visit to a cargo demand point (other than the warehouse);

penalty factor: indicating that a prize of a proportional value (e.g., 0.01) is deducted for each increase in the length of the route per unit length;

terminating the bonus: indicating that when all cargo demand points are accessed and satisfied, an extra value (e.g., 10) is awarded.

(4) Policy network: the policy network is used for outputting an optimal or near optimal action probability distribution according to the current state. Specifically, the policy network consists of the following parts:

hidden layer: the hidden layer is composed of a plurality of full connection layers or convolution layers, and an activation function (such as ReLU) is used for adding nonlinear characteristics;

output layer: the output layer consists of three sub-output layers, which respectively correspond to three partial action spaces, namely a vehicle selection action, a demand point selection action and a goods transfer action. Specifically:

B. the demand point selection sub-output layer: the sub-output layer consists of a full connection layer and a softmax layer, outputs a vector with the length of n+1, and represents the probability of selecting each cargo demand point (including a warehouse) as the next access target;

C. cargo transfer sub-output layer: the sub-output layer consists of a full connection layer and a softmax layer, outputs a vector with a length of C+1, and represents the probability of unloading or loading 0 to C units of goods from the target demand point.

Based on the outputs of the three sub-output layers, a complete action can be obtained, i.e. which vehicle is selected, which demand point is visited, and how much cargo is transferred. And then calculating the rewarding value of the action according to the rewarding function, and updating the parameters of the strategy network according to the strategy gradient algorithm.

(2) Vehicle scheduling algorithm: and determining an optimal vehicle dispatching scheme, including route planning, vehicle allocation and the like, by utilizing an optimization algorithm (such as a genetic algorithm, a simulated annealing algorithm and the like) according to the vehicle position, the market demand, the cargo information and the like.

(3) Cargo batching algorithm: based on market demand, vehicle availability and other data, an optimization algorithm (such as dynamic planning, greedy algorithm and the like) is adopted to determine a reasonable batching scheme of goods so as to improve distribution efficiency and meet market demand.

(4) Real-time monitoring and prediction algorithm: the method comprises the steps of monitoring data such as vehicle positions and traffic conditions in real time, carrying out prediction analysis by combining historical data, finding potential problems in time, and carrying out corresponding adjustment such as route re-planning, vehicle replacement and the like.

The intelligent management of short-connection scheduling in the agricultural batch market is realized through an algorithm:

(1) Vehicle dispatch optimization: there are a large number of short-range vehicles in the traditional agricultural wholesale market, and scheduling and managing these vehicles becomes complex and difficult. The system utilizes a cluster management algorithm to automatically formulate an optimized dispatch plan in combination with vehicle positioning and cargo information. Through optimization of the algorithm, the empty rate of the vehicle can be reduced, the waiting time is shortened, and the transportation efficiency and the drop are improved. Low cost.

(2) And (3) cargo batch management: the wholesale market for agricultural products typically has a large number of goods that need to be batched to different buyers. The system combines the cargo demand and the vehicle availability through the data processing and analysis technology, and realizes intelligent cargo batch management. Through optimization of the algorithm, the sequence and time of the goods batch can be reasonably arranged, the distribution efficiency is improved, and the market demand is met.

(3) Real-time vehicle monitoring: through vehicle positioning technology and communication technology, the system can monitor the position, state and driving condition of the short-distance vehicle in real time. By integrating the real-time data, the system can timely find and solve potential problems such as traffic jams, vehicle faults and the like, and improves the reliability and safety of transportation.

(4) Data analysis and decision support: the system collects and analyzes a large amount of data including vehicle positioning data, cargo information, market demand, etc. Through data mining, machine learning and optimization algorithms, the system can provide decision support, such as predicting market demands, optimizing transportation routes, allocating vehicles in advance and the like, and the accuracy and efficiency of decision making are improved.

The technical scheme comprises the following technical structures:

(1) The system purchase comprises front and rear end parts

(1) The front end portion includes a user interface that may be provided for use by an administrator, short vehicle driver, and other related personnel through a web application or mobile application. The user interface provides user registration, task viewing, data querying, statistics, etc., enabling the user to interact with the system.

(2) The back end part comprises main modules such as cluster management, a single robbing function, a single dispatching function and the like. The cluster management module is responsible for managing registration, state monitoring and task allocation optimization of the short-range vehicle cluster. The order-robbing functional module processes the order-robbing request of the driver, screening matching and task allocation. The dispatch function module is responsible for task release, vehicle screening matching and task scheduling.

(2) Cluster management server architecture:

the cluster management server is a core component of the system and is responsible for the functions of cluster management, scheduling algorithm, data analysis and the like of the whole system. The architecture can adopt a distributed architecture, and comprises the following modules:

(1) cluster management module: the system is used for monitoring and managing the state, the position and the running condition of the short-distance vehicle cluster, and providing functions of vehicle management, task allocation and the like.

(2) A scheduling algorithm module: the batch scheduling algorithm is realized, an optimal scheduling scheme is generated according to real-time vehicle and cargo information, the vehicle utilization rate is optimized, and the transportation cost is reduced.

(3) And a data analysis module: the vehicle and cargo data stored in the database are analyzed in real time, providing decision support such as scheduling optimization, route planning, etc.

(4) And a communication module: and the terminal equipment is responsible for data exchange and instruction issuing with the short-distance vehicle terminal equipment and the dispatching center terminal equipment.

(3) Short-range vehicle terminal equipment architecture:

the vehicle-mounted terminal equipment is equipment installed on each short vehicle and is used for data acquisition, communication, scheduling instruction execution and other functions. The framework comprises the following components:

(1) a sensor module: the system comprises a GPS positioning sensor, a speed sensor, a load sensor and the like, and is used for collecting the position, state and operation data of the vehicle.

(2) And a communication module: and real-time data transmission and instruction issuing are carried out with the cluster management server by utilizing wireless network technology (such as 4G and 5G).

(3) And the control module is used for: and controlling the loading, unloading, transportation and other operations of the vehicle according to the received dispatching instruction.

(4) And a data storage module: and the collected data is temporarily stored, so that the real-time performance and reliability of the data are ensured.

(4) Scheduling center terminal equipment architecture:

the dispatching center terminal equipment is used for interaction between a dispatcher and the cluster management server, and achieves the functions of dispatching instruction issuing, data query and the like. The framework comprises the following components:

(1) user interface: an intuitively friendly user interface is provided through which a dispatcher can interact with the cluster management server.

(2) And a data exchange module: is responsible for data exchange with the cluster management server, including dispatch instruction issuing and data

And returning the result of the query.

(3) And a communication module: and carrying out real-time communication with the cluster management server by utilizing network connection.

(5) Third party logistics platform:

the platform is an external partner of the system and is responsible for providing service requirements, including information such as goods names, goods picking addresses, delivery addresses, goods picking time, required arrival time and the like. The platform may be in the form of a shipper enterprise management system, a third party logistics management system, or an open business module, among others. The platform is connected with the dispatching platform through an open interface, and achieves the functions of sending and receiving service requests, inquiring and updating order states, acquiring and analyzing transportation data and the like.

The system in the technical scheme comprises software and hardware. Their composition will be discussed separately below.

The software comprises the following components:

(1) Cluster management software: this is the core software module of the system for managing the entire cluster of short-range vehicles. The system comprises functions of vehicle registration, vehicle state monitoring, vehicle dispatching, task allocation and optimization, dispatching algorithm, data analysis and the like. The software is realized based on a cloud computing platform or a distributed system, is responsible for coordinating the operation of vehicles, optimizing a scheduling scheme and providing decision support.

(2) Robbery-to-order function software: the system has the function of robbing a bill, namely a vehicle driver can check the task of the bill to be dispatched through the mobile terminal application and voluntarily select and accept the task. The robbery function can realize real-time response and flexible scheduling, and improve the resource utilization rate. This software module should be able to receive the request for the robbery, screen and match the driver, and assign the task to the appropriate driver.

(3) And the dispatch function software: this software module is responsible for assigning tasks to the appropriate short-range vehicle driver. The dispatching method should be used for dispatching in an optimal mode based on a certain dispatching algorithm by considering the factors of the position, availability, real-time data, idle time, distance and the like of the vehicle. The software module may also provide real-time task state monitoring and feedback functionality.

(4) Data processing and analysis software: the system is used for processing and analyzing the acquired data, including monitoring of the position and the state of the vehicle, execution of a scheduling algorithm and the like. The software can analyze and support decision-making for data by utilizing techniques such as data mining, machine learning, artificial intelligence and the like.

(5) Scheduling algorithm software: and a software module for realizing a batch scheduling algorithm generates an optimal scheduling scheme according to the scheduling rules and the vehicle state. The software module may consider a number of factors, such as vehicle priority, cargo category, route planning, etc., to achieve efficient scheduling decisions.

The hardware comprises the following components:

(1) Short-range vehicle terminal equipment: each short vehicle is provided with an on-board terminal device for real-time communication with the system, and for data interaction and instruction execution with the cluster management server. The vehicle-mounted terminal equipment can receive task orders, send position information and the like, and achieve seamless connection with the system.

(2) Cluster management server: a server device for centralized management of short-range vehicles stores and processes data of vehicles and goods, performs scheduling algorithms and decision support. Servers are required to have high performance computing and storage capabilities to handle the processing of large-scale vehicle and cargo data.

(3) GPS positioning system: the short-range vehicle is equipped with a GPS positioning system, and can acquire the position information of the vehicle in real time and transmit the position information to the system. The GPS positioning system can provide accurate data of vehicle position, speed, mileage and the like, and provides basic data support for task scheduling.

(4) Scheduling center terminal equipment: the terminal equipment for issuing and monitoring the scheduling instruction comprises a computer, a tablet computer or a smart phone and the like. Through the device, a dispatcher can send a dispatching instruction to the terminal device of the short-distance vehicle and monitor the position and the state of the vehicle in real time.

(5) Communication network: the system requires the establishment of a reliable communication network for real-time data transmission and communication between the vehicle and the system. This may be a wireless network, a mobile communication network or other suitable communication technology.

The technical scheme comprises the following database design:

(1) Vehicle information database:

(1) vehicle number: a number uniquely identifying each short-range vehicle.

(2) Position information: and recording the real-time position coordinates of the vehicle so as to monitor and schedule in real time.

(3) Status information: including the running status, load status, etc. of the vehicle for determining vehicle availability and making scheduling decisions.

(2) Cargo information database:

(1) cargo number: a number uniquely identifying each item.

(2) The cargo category: the type of cargo is recorded for scheduling and matching to the appropriate vehicle.

(3) Quantity information: the quantity of the goods is recorded for scheduling and delivery plans.

(3) Scheduling rules database:

(1) vehicle dispatch priority: the scheduling priority of the vehicle is set according to different conditions (such as vehicle type, idle time, etc.) to ensure optimal scheduling decisions.

(2) Batch scheduling algorithm: relevant parameters and rules of the batch scheduling algorithm are stored for generating an optimal batch scheduling scheme.

(3) Scheduling policy: scheduling rules, such as vehicle loading rules, delivery route planning, etc., are recorded to ensure the rationality and efficiency of scheduling.

(4) Historical data database:

(1) Vehicle transportation record: the transportation history of each vehicle, including the start point, destination, transportation time, etc., is recorded for analyzing the transportation efficiency and quality of the vehicle.

(2) And (5) cargo delivery time record: the delivery time of each item is recorded for prediction and optimization of delivery time.

The databases can be stored and managed by adopting a relational database (such as MySQL) or a distributed database (such as Hadoop) so as to meet the requirements of the system on the real-time property, the reliability and the expansibility of the data.

In summary, the database design of the short-vehicle cluster management and batch scheduling system includes a vehicle information database, a cargo information database, a scheduling rule database, and a history database for storing and managing related real-time and history data to support the scheduling decision and analysis functions of the system.

Comprising a series of algorithm steps. The following is a detailed description of the algorithm steps thereof:

(1) Cluster management algorithm step:

a. vehicle registration: the short-range vehicles are registered in the system and provide relevant information such as vehicle number, load carrying capacity, etc.

b. Vehicle condition monitoring: the system monitors the position, availability and running status of the vehicle in real time. The real-time data of the vehicle can be acquired through the vehicle-mounted sensor, the GPS and other devices, and fed back to the cluster management software.

c. Task allocation and optimization: based on the location, availability, and real-time data of the vehicle, tasks are assigned to the appropriate vehicle using task assignment and optimization algorithms. The algorithm considers the factors of the loading capacity, the driving distance, the time window and the like of the vehicle so as to realize efficient task allocation.

(2) Task release:

a. the system distributes the task to the most suitable vehicle driver according to the information such as the position of the vehicle, the task type, real-time traffic and the like.

b. In the dispatch process, the system needs to consider the factors of the current task condition, the task priority, the working time of the vehicle and the like of the vehicle so as to ensure that the task can be completed in time.

c. After dispatch, the system updates the task status and related information assigned to the vehicle for subsequent scheduling and monitoring.

(3) The step of robbing single function algorithm:

a. and (5) receiving a robbery order request: when a task needs to be executed, the system issues task information, including task type, starting location, destination, etc. The short-range vehicle driver can receive the task request through the robbery-to-single function software.

b. Driver screening and matching: the system screens and matches the robbery requests according to the task requirements and the capability and availability of the driver. This may be evaluated based on the driver's location, load carrying capacity, time available, etc., to find the most appropriate driver.

c. Task allocation: the system assigns the task to the most appropriate driver to ensure that the task can be performed in a timely manner. The task allocation algorithm can consider factors such as distance of drivers, traffic conditions, task priorities and the like so as to achieve the optimal task allocation effect.

(4) The dispatch function algorithm step:

a. task release: the system issues task information according to task demands and priorities, including task types, starting places, destinations and the like.

b. Vehicle screening and matching: the system screens and matches the vehicles for the tasks according to the position, availability and real-time data of the vehicles. The algorithm considers the load carrying capacity, distance travelled, time window, etc. of the vehicle to find the most appropriate vehicle.

c. Task scheduling: the system schedules the tasks to the most appropriate vehicles and generates a corresponding scheduling plan. The scheduling algorithm can consider factors such as routes, time windows, priorities and the like of the vehicles so as to achieve the optimal task scheduling effect.

(5) Scheduling and optimizing:

a. the system collects and analyzes historical data of the vehicle, including information such as running tracks, task completion time, waiting time and the like.

b. Based on the historical data and the real-time information, the system can optimize the scheduling strategy, and the scheduling efficiency and the task completion rate of the vehicle are improved.

c. Scheduling optimization may include technical means such as route planning, task batch scheduling, task priority adjustment, etc., to minimize overall cost and time.

(6) System monitoring and updating:

a. the system monitors information such as the position of the vehicle, task progress, driver state and the like in real time so as to perform real-time scheduling and task tracking.

b. The system dynamically updates the task state and allocation according to the actual condition of the vehicle and the task progress.

c. The system may also provide real-time data statistics and reporting for the manager to make decisions and optimizations.

(7) The evaluation algorithm comprises the following steps:

a. and receiving operation data of the short-distance vehicle, such as speed, oil consumption, mileage, load and the like.

And b, receiving satisfaction feedback of the clients, such as scoring, commenting and the like.

c. And receiving the completion condition of the order, such as whether the order arrives on time, whether the order is intact, and the like.

d. And evaluating the service quality of the short-distance vehicle according to the running data of the short-distance vehicle, the satisfaction feedback of the customer, the completion condition of the order and other indexes, and giving corresponding rewards and punishment measures such as adding points, subtracting points, rewards, fines and the like.

e. And sending the evaluation result and the rewarding and punishing measures to the short-distance vehicle through the wireless communication module.

(5) The step of optimizing algorithm:

a. and receiving indexes such as service quality, transportation efficiency, operation cost and the like of the short-distance vehicle.

And b, optimizing and updating the partitioned scheduling algorithm and the robbery algorithm according to the change trend and the target value of the index, such as parameter adjustment, constraint increase, strategy improvement and the like.

And c, deploying the optimized batch scheduling algorithm and the optimized robbery scheduling algorithm to a central server and the mobile terminal.

The invention improves the efficiency: the intelligent management and scheduling of the vehicle clusters are realized through the functions of robbing and dispatching; the vehicle can actively rob or send the bill according to the actual demand, so that unnecessary waiting time and resource waste in the traditional scheduling mode are avoided, and the transportation efficiency is improved; the invention optimizes the resource utilization: the system distributes cargoes to the most suitable vehicles in batches for transportation through an intelligent scheduling algorithm, avoids no-load or low-load operation of the vehicles, and optimizes the resource utilization efficiency to the maximum extent. Thus, the transportation cost can be reduced, and the operation benefit can be improved; the invention improves the service quality: the dispatch function in the system can rapidly and accurately distribute tasks according to the position, the state and the goods requirements of the vehicle; the method is beneficial to improving the response speed and accuracy of the transportation service, meeting the requirements of customers and improving the satisfaction of the customers; the invention monitors and manages in real time: the system has a real-time monitoring function, and can monitor and manage the position, the transportation progress, the running track and the like of the vehicle in real time; the method is beneficial to enterprises to timely track and adjust the vehicle operation condition, and improves the transportation safety and controllability; the invention is data analysis and decision support: the system collects and analyzes vehicle operation data and provides data analysis reports on transportation efficiency, cost, customer requirements and the like; the method provides data support for enterprises, helps the enterprises make reasonable decisions, optimizes operation strategies, and further improves transportation efficiency and profitability; the invention has the advantages of user friendliness: the system design considers the demands and the use habits of users, and provides an intuitive and concise user interface, so that the operation is more convenient and easier to understand; the user can easily perform operations such as order robbing, order dispatching, monitoring, data analysis and the like, and can use the mobile phone without tedious training; the invention communicates and cooperates in real time: the communication module in the system realizes real-time communication and cooperation between the vehicle and the dispatching center and between the driver and the dispatcher; through the functions of instant messaging, voice communication or video conference, the problems can be quickly solved, the transportation plan can be adjusted, and the information can be kept updated in time; the powerful algorithm of the invention supports: the system adopts an advanced scheduling algorithm, and can quickly generate an optimal scheduling scheme under the condition of considering various constraint conditions; this includes consideration of vehicle capacity, distance, cargo urgency, etc., to minimize total transportation costs or maximize resource utilization; the invention is highly automated: the automatic function in the system greatly reduces the requirement of manual intervention, and improves the efficiency and accuracy of operation; for example, the system can automatically generate scheduling tasks, send notifications and alarms, record transportation data and the like, so that human errors and tedious manual operations are reduced; the invention protects data security and privacy: the system adopts a strict data encryption and access control mechanism, so that the safety and privacy protection of transportation data and user information are ensured; at the same time, we follow relevant regulations and standards to ensure compliance and confidentiality of the data.

Claims

1. A cluster management and dispatch system for short-range vehicles, characterized by: the following layers are constructed to realize the agricultural market

Intelligent management of short-cut scheduling, specifically as follows:

(3) Data execution layer: the system is used for executing a scheme sent by a data processing layer, such as actual operation of dispatching of short-range vehicles and batched goods, sending instructions to related personnel of drivers and warehouse administrators of the short-range vehicles in a voice or graphical interface mode, guiding the drivers and the warehouse administrators to transfer and distribute the goods according to the scheme, and ensuring accurate execution of a dispatching plan;

2. The short-range vehicle cluster management and dispatch system of claim 1, wherein: the data place

The processing layer receives the data sent by the data acquisition layer by adopting a reinforcement learning algorithm, an optimization algorithm and a real-time monitoring and predicting algorithm, integrates the data, cleans the data and analyzes the data, and extracts useful information by utilizing data mining and machine learning technologies.

3. The short-range vehicle cluster management and dispatch system of claim 2, wherein: the chemical enhancement is

Xi Suanfa, specifically the following:

4. The short-range vehicle cluster management and dispatch system of claim 2, wherein:

vehicle scheduling algorithm: according to the vehicle position, market demand and cargo information data, an optimal vehicle dispatching scheme is determined by utilizing an optimization algorithm, wherein the optimal vehicle dispatching scheme comprises route planning and vehicle distribution;

real-time monitoring and prediction algorithm: the method comprises the steps of monitoring vehicle position and traffic condition data in real time, carrying out prediction analysis by combining historical data, finding potential problems in time and carrying out corresponding adjustment, such as route re-planning and vehicle replacement.

5. An architecture based on the short-barge cluster management and scheduling system according to any one of claims 1 to 4, characterized in that: comprises

Front-rear end section architecture:

the back end part comprises a main module of cluster management, a single robbing function and a single dispatching function; the cluster management module is responsible for managing registration, state monitoring and task allocation optimization of the short-range vehicle cluster; the order-robbing functional module processes order-robbing requests of drivers, screening matching and task allocation; the dispatch function module is responsible for task release, vehicle screening matching and task scheduling;

Cluster management server architecture:

the cluster management module is used for monitoring and managing the state, the position and the running condition of the short-distance vehicle cluster and providing vehicle management and task allocation functions;

short-range vehicle terminal equipment architecture:

the vehicle-mounted terminal equipment is equipment installed on each short-range vehicle and is used for data acquisition, communication and execution of scheduling instruction functions; the framework comprises the following components:

a sensor module: the system comprises a GPS positioning sensor, a speed sensor and a load sensor, wherein the GPS positioning sensor, the speed sensor and the load sensor are used for acquiring the position, the state and the operation data of a vehicle;

the control module is used for controlling loading, unloading and transportation operations of the vehicle according to the received scheduling instruction;

scheduling center terminal equipment architecture:

third party logistics platform:

6. The architecture of a short-range vehicle cluster management and dispatch system of claim 5, wherein: the system also comprises a database, and specifically comprises the following steps:

vehicle information database:

vehicle number: a number uniquely identifying each short-range vehicle;

status information: the method comprises the steps of judging the availability of the vehicle and carrying out scheduling decision, wherein the running state and the loading state of the vehicle are used for judging the availability of the vehicle;

cargo information database:

cargo number: a number uniquely identifying each good;

scheduling rules database:

historical data database:

vehicle transportation record: recording a transportation history of each vehicle, including a start place, a destination and a transportation time, for analyzing the transportation efficiency and quality of the vehicle;

7. A method based on the short-barge cluster management and scheduling system according to any one of claims 1 to 6, characterized in that: the method specifically comprises the following steps:

the cluster management algorithm specifically comprises the following steps:

task release is specifically as follows:

the system distributes the task to the most suitable vehicle driver according to the position of the vehicle, the task type and the real-time traffic information;

in the dispatch process, the system needs to consider the current task condition, task priority and working time factors of the vehicle so as to ensure that the task can be completed in time;

driver screening and matching: the system screens and matches the robbery request according to the task requirement and the capability and availability of the driver; evaluating according to the position, the loading capacity and the available time factors of the driver to find the most suitable driver;

task allocation: the system distributes the task to the most suitable driver to ensure that the task can be executed in time; the task allocation algorithm can consider the distance of a driver, traffic conditions and task priority factors so as to realize the optimal task allocation effect;

the dispatch function algorithm specifically comprises the following steps:

the scheduling optimization is specifically as follows:

the system is monitored and updated as follows:

the system monitors the position, task progress and driver state information of the vehicle in real time so as to perform real-time scheduling and task tracking;

the evaluation algorithm specifically comprises the following steps:

according to the running data of the short-distance vehicle, the satisfaction feedback of the customer and the completion condition index of the order, the service quality of the short-distance vehicle is evaluated, and corresponding rewarding and punishing measures such as adding, subtracting, rewarding and punishing are given;

the steps of the optimization algorithm are as follows:

receiving service quality, transport efficiency and operation cost indexes of the short barge;

and deploying the optimized batch scheduling algorithm and the optimized robbery scheduling algorithm to the central server and the mobile terminal.