Real-time distribution method and system based on path planning

Abstract

The invention discloses a timely delivery method and system based on path planning, and relates to the field of logistics and supply chain management, and the method comprises the steps: receiving order information, and inputting the order information into a delivery management center; based on the order information, obtaining a standard path by using a simulated annealing algorithm; based on a standard path, connecting an urban intelligent traffic center to collect real-time traffic data, and analyzing the real-time traffic data by using the traffic flow prediction model to obtain traffic condition changes; adjusting a standard path by using a multi-objective evolutionary algorithm through traffic condition changes to obtain an optimal path; continuously tracking the optimal path distribution progress; and calling a multi-objective evolutionary algorithm in real time to adjust the optimal path based on tracking the optimal path distribution progress. According to the method, the standard path and the real-time traffic condition change are obtained through the simulated annealing algorithm, the path is dynamically adjusted through the multi-objective evolutionary algorithm, the global search capability and robustness are enhanced, and the distribution efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] The present invention relates to the field of logistics and supply chain management, and in particular to a timely delivery method and system based on path planning. Background Art

[0002] With the rapid development of e-commerce and logistics industries, timely delivery has become a key factor in improving customer satisfaction and service quality. Path planning technology plays a vital role in this process. Traditional path planning methods mainly rely on static map data and predefined routes. Although these methods can optimize the delivery path to a certain extent, they are unable to cope with the changes in real-time traffic conditions. In recent years, with the development of intelligent transportation systems (ITS), dynamic path planning methods based on real-time traffic data have gradually become a research hotspot. By combining real-time traffic information, historical data and prediction models, these methods can more accurately predict traffic conditions and adjust the delivery path accordingly, thereby improving delivery efficiency and punctuality.

[0003] However, existing dynamic path planning methods still have some shortcomings. First, many methods have low computational efficiency when dealing with large-scale orders and complex traffic conditions, resulting in long response times and unable to meet the needs of instant delivery. Second, existing methods often lack support for multi-objective optimization, that is, considering multiple objectives such as minimizing total driving time and total driving distance at the same time. This makes the generated path perform well in one aspect, but not ideal in other aspects, affecting the overall delivery effect. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a timely delivery method and system based on path planning to solve the problems of low computational efficiency and insufficient multi-objective optimization.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a timely delivery method based on path planning, which comprises:

[0008] Receive order information and enter it into the distribution management center; based on the order information, use the simulated annealing algorithm to obtain the standard path; based on the standard path, connect to the city's intelligent transportation center to collect real-time traffic data, use the traffic flow prediction model to analyze the real-time traffic data, and obtain changes in traffic conditions; according to changes in traffic conditions, use the multi-objective evolutionary algorithm to adjust the standard path and obtain the optimal path; continuously track the delivery progress of the optimal path; based on tracking the delivery progress of the optimal path, call the multi-objective evolutionary algorithm in real time to adjust the optimal path.

[0009] As a preferred solution of the timely delivery method based on path planning described in the present invention, the received order information is entered into the delivery management center, and the specific steps are as follows:

[0010] Receive customer order information, automatically verify inventory and payment status, and confirm that the order is valid;

[0011] For valid orders, real-time order information is automatically entered into the distribution management center through an automated interface.

[0012] As a preferred solution of the timely delivery method based on path planning of the present invention, wherein: based on the order information, the standard path is obtained by using the simulated annealing algorithm, and the specific steps are as follows:

[0013] Extract vehicle information through the distribution management center;

[0014] Based on the order information and vehicle information, random initialization is used to generate a random initial route combination;

[0015] Through the simulated annealing algorithm, the inversion method is used to select two points in the initial path combination and exchange the path segments between them to generate a new solution. The Metropolis criterion is used to decide whether to accept the new solution. The expression is:

[0016]

[0017] Among them, P is the probability value, ΔF is the fitness difference between the new solution and the current solution, T is the current temperature, and ΔF max is the maximum value of the fitness difference, T a is the actual travel time, T e is the estimated travel time, t is the time step of the iteration, λ is the fitness difference adjustment coefficient, μ is the time deviation adjustment coefficient, and k is the time decay coefficient;

[0018] Generate a random number r through random.random();

[0019] When P>r, it means accepting the new solution;

[0020] When P≤r, do not accept new solutions and keep the current solution unchanged;

[0021] The unacceptable new solutions are treated through the exponential cooling method, gradually reducing the temperature parameters, and repeating the inversion method and Metropolis criterion to generate the optimized path combination;

[0022] Define constraints such as vehicle capacity, order delivery time, and single travel distance through problem requirements and business rules;

[0023] The optimized path combination is filtered and adjusted through defined constraints to obtain a standard path.

[0024] As a preferred solution of the timely delivery method based on path planning described in the present invention, wherein: based on the standard path, connecting to the city intelligent transportation center to collect real-time traffic data, using the traffic flow prediction model to analyze the real-time traffic data, and obtaining changes in traffic conditions, the specific steps are as follows:

[0025] Based on the standard route, the distribution management center connects to the city's intelligent transportation center through the API interface, sends data requests, obtains real-time traffic data, and stores the collected real-time traffic data in the database;

[0026] The traffic flow prediction model is constructed using the LSTM model as the basic model;

[0027] Use time series to convert real-time traffic data into serialized data;

[0028] By setting the input_shape parameter of the LSTM model input layer to match the shape of the serialized data;

[0029] Add an LSTM layer by using the SequentialAPI method and add a fully connected layer after the LSTM layer;

[0030] A linear activation function is used in the output layer to predict the traffic flow index;

[0031] Train and validate the traffic flow prediction model and test the final performance;

[0032] The preprocessed real-time data is input into the traffic flow prediction model to obtain the traffic flow index, which is expressed as:

[0033]

[0034] in, is the traffic flow index, X is the preprocessed real-time traffic data, θ is the parameter of the LSTM model, α is the data deviation adjustment coefficient, and X avg is the mean value of the input data, σ X is the standard deviation of the input data, β is the time decay adjustment coefficient, t' is the time variable, t0 is the reference time point, and τ is the time decay constant;

[0035] Divide traffic conditions into different levels according to the traffic flow index;

[0036] Continuously update traffic flow prediction models;

[0037] By predicting and classifying traffic conditions in real time, combined with visual display and dynamic route adjustment, the traffic change status can be finally obtained.

[0038] As a preferred solution of the timely delivery method based on path planning described in the present invention, wherein: the standard path is adjusted by using a multi-objective evolutionary algorithm according to changes in traffic conditions to obtain the optimal path. The specific steps are as follows:

[0039] Define the constraints of path planning based on real-time traffic condition changes;

[0040] The constraints include dynamic time window constraints, dynamic driving time constraints, dynamic driving distance constraints, weather impact constraints and congested road section avoidance constraints;

[0041] Using a multi-objective evolutionary algorithm, the initial population consists of a set of randomly generated path combinations, and the multi-objective problem is decomposed into multiple single-objective sub-problems. Each sub-problem corresponds to a weight vector, and the weight vectors are evenly distributed on the unit hyperplane.

[0042] Assign neighbors to each subproblem based on the Euclidean distance between weight vectors, and set an initial reference point for each subproblem, set to the maximum value of each objective;

[0043] For each subproblem, randomly select individuals from the neighborhood as parents, perform crossover and mutation operations on the parent individuals to generate new offspring individuals, update the reference point of each subproblem to the minimum value of the objective function of all individuals in the current population, and retain better individuals by comparing the individuals in the neighborhood with the newly generated offspring individuals;

[0044] When the stopping condition is met, the algorithm terminates, and the population eventually converges to a set of non-dominated solutions;

[0045] Based on the constraints, the matching path combinations are screened out from the solution set of non-dominated solutions, and the optimal route is determined from the matching path combinations using the lexicographic order method.

[0046] As a preferred solution of the timely delivery method based on path planning of the present invention, the specific steps of continuously tracking the delivery progress of the optimal path are as follows:

[0047] Continuously track vehicle delivery progress through on-board GPS devices and cellular networks, and continuously receive the latest traffic data from the city's intelligent transportation center.

[0048] As a preferred solution of the timely delivery method based on path planning described in the present invention, wherein: based on tracking the delivery progress of the optimal path, a multi-objective evolutionary algorithm is called in real time to adjust the optimal path. The specific steps are as follows:

[0049] According to the vehicle delivery progress and the latest traffic data, if the actual driving time exceeds the expected time, serious traffic jams and unexpected events occur, the route re-planning process will be automatically initiated;

[0050] A multi-objective evolutionary algorithm is used to adjust the optimal path for the existing path combination.

[0051] In the second aspect, the present invention provides a timely delivery system based on path planning, including an order management module, a path planning module, a real-time traffic analysis module, a dynamic path adjustment module, a delivery tracking and adjustment module and a customer feedback analysis module; an order management module, which is used to receive order information and enter it into a delivery management center; a path planning module, which is used to obtain a standard path based on the order information using a simulated annealing algorithm; a real-time traffic analysis module, which is used to connect to the city's intelligent transportation center to collect real-time traffic data based on the standard path, and use a traffic flow prediction model to analyze the real-time traffic data to obtain changes in traffic conditions; a dynamic path adjustment module, which is used to adjust the standard path according to changes in traffic conditions and use a multi-objective evolutionary algorithm to obtain an optimal path; a delivery tracking module, which is used to continuously track the delivery progress of the optimal path; a real-time adjustment module, which is used to call the multi-objective evolutionary algorithm in real time to adjust the optimal path based on tracking the delivery progress of the optimal path.

[0052] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the timely delivery method based on path planning as described in the first aspect of the present invention is implemented.

[0053] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the timely delivery method based on path planning as described in the first aspect of the present invention.

[0054] The beneficial effects of the present invention are as follows: by utilizing the simulated annealing algorithm to obtain the standard path, the present invention realizes high-quality path planning, enhances the global search capability and robustness of the system, and ensures the generation of the optimal path under complex constraints; at the same time, by using a multi-objective evolutionary algorithm to dynamically adjust the path according to real-time traffic conditions, multi-objective optimization of the total driving time and total driving distance is achieved, thereby improving the distribution efficiency and resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0056] Figure 1 This is a flow chart of the timely delivery method based on path planning in Example 1.

[0057] Figure 2 This is a system diagram of the timely delivery system based on path planning in Example 1. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0061] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, and provides a timely delivery method based on path planning, comprising the following steps:

[0062] S1. Receive order information and enter it into the distribution management center.

[0063] S1.1. Receive customer order information, automatically verify inventory status and payment status, and confirm that the order is valid.

[0064] It should be noted that when customers place orders through the platform, the platform receives customer order information including the customer’s detailed address, type of goods and weight.

[0065] The above information is obtained with the user's consent and is used for legal purposes.

[0066] S1.2. For valid orders, real-time order information is automatically entered into the distribution management center through an automated interface.

[0067] It should be noted that the automation interface (API) is developed and configured by the technical teams of the e-commerce platform and the distribution management center to achieve data exchange between the two.

[0068] S2. Based on the order information, the simulated annealing algorithm is used to obtain the standard path.

[0069] S2.1. Extract vehicle information through the distribution management center.

[0070] It should be noted that vehicle information is extracted through the internal management system of the distribution management center.

[0071] S2.2. Based on the order information and vehicle information, random initialization is used to generate a random initial path combination.

[0072] Specifically, the vehicle information includes vehicle location information, vehicle dispatch information, and vehicle capacity information.

[0073] S2.3, through the simulated annealing algorithm, the inversion method is used to select two points in the initial path combination and exchange the path segments between them to generate a new solution. The Metropolis criterion is used to decide whether to accept the new solution. The expression is:

[0074]

[0075] Among them, P is the probability value, ΔF is the fitness difference between the new solution and the current solution, T is the current temperature, and ΔF max is the maximum value of the fitness difference, T a is the actual driving time, T e is the estimated travel time, t is the time step of the iteration, λ is the fitness difference adjustment coefficient, μ is the time deviation adjustment coefficient, and K is the time decay coefficient;

[0076] Generate a random number r through random.random();

[0077] When P>r, it means accepting the new solution;

[0078] When P≤r, do not accept new solutions and keep the current solution unchanged.

[0079] It should be noted that the fitness difference between the new solution and the current solution is calculated as:

[0080] ΔF=F new -F current ;

[0081] Among them, ΔF is the fitness difference between the new solution and the current solution, F new is the fitness value of the newly generated solution, F current is the fitness value of the current solution;

[0082] If ΔF<0, that is, the new solution is better, then directly accept the new solution;

[0083] If ΔF>0, that is, the new solution is worse, you need to decide whether to accept the new solution based on P.

[0084] S2.4. The unacceptable new solutions are treated through the exponential cooling method, the temperature parameters are gradually reduced, and the inversion method and Metropolis criterion are repeated to generate the optimized path combination.

[0085] It should be noted that the exponential cooling method is a temperature reduction strategy commonly used in simulated annealing algorithms. By simulating the gradual decrease in temperature during metal annealing, the optimization algorithm can extensively explore the solution space in the initial stage and perform fine optimization in the later stage, thereby balancing global search and local search and increasing the possibility of finding the global optimal solution.

[0086] S2.5. Define constraints such as vehicle capacity limitation, order delivery time and single driving distance limitation through problem requirements and business rules.

[0087] It should be noted that the business rules include the maximum load capacity of the vehicle, the delivery time window of the order, and the maximum allowed driving distance of each vehicle.

[0088] S2.6. Filter and adjust the optimized path combination through defined constraints to obtain a standard path.

[0089] S3. Based on standard routes, connect to the city's intelligent transportation center to collect real-time traffic data, use traffic flow prediction models to analyze real-time traffic data, and obtain changes in traffic conditions.

[0090] S3.1. Based on the standard path, the distribution management center connects to the city intelligent transportation center through the API interface, sends data requests, obtains real-time traffic data, and stores the collected real-time traffic data in the database.

[0091] Specifically, real-time traffic data includes traffic flow, average vehicle speed, traffic incidents, weather conditions and traffic lights.

[0092] S3.2. Construct a traffic flow prediction model using the LSTM model as the basic model.

[0093] Use time series to convert real-time traffic data into serialized data;

[0094] By setting the input_shape parameter of the LSTM model input layer to match the shape of the serialized data;

[0095] Add an LSTM layer by using the SequentialAPI method and add a fully connected layer after the LSTM layer;

[0096] A linear activation function is used in the output layer to predict the traffic flow index.

[0097] It should be noted that the traffic flow prediction model is constructed based on the LSTM model because LSTM can effectively capture the long-term dependencies and complex timing patterns in time series data, thereby more accurately predicting future traffic flow; LSTM can handle trends, seasonality, and cyclical changes in traffic data through its unique memory units and gating mechanisms, and can maintain good prediction performance even when there are long time intervals in the data.

[0098] S3.3. Train and validate the traffic flow prediction model, and test the final performance.

[0099] Specifically, use the train_test_split function to divide the historical traffic data into training set, test set and validation set;

[0100] Use the training set data to set the appropriate batch size and number of training rounds to train the traffic flow prediction model. During the training process, use the validation set to monitor the performance of the traffic flow prediction model. Use the trained traffic flow prediction model to evaluate the final performance of the traffic flow prediction model based on the test set data.

[0101] S3.4. Input the preprocessed real-time data into the traffic flow prediction model to obtain the traffic flow index, which is expressed as:

[0102]

[0103] in, is the traffic flow index, X is the preprocessed real-time traffic data, θ is the parameter of the LSTM model, α is the data deviation adjustment coefficient, and X avg is the mean value of the input data, σ X is the standard deviation of the input data, β is the time decay adjustment factor, t' is the time variable, t0 is the reference time point, and τ is the time decay constant.

[0104] It should be noted that the calculation process of the expression is as follows:

[0105] Input the preprocessed real-time traffic data X into the LSTM model to obtain the basic prediction result f LSTM (X; θ), calculate the input data X and the average value X avg The Euclidean distance is divided by the standard deviation σ of the input data. X , take the sine function and multiply it by the data deviation adjustment coefficient α. Next, calculate the difference between the time variable t' and the reference time point t0, divide it by the time decay constant τ, take the hyperbolic tangent function, and multiply it by the time decay adjustment coefficient β. Add the above two results, add 1, and then multiply it by the basic prediction result f of the LSTM model. LSTM(X; θ), and finally get the adjusted traffic flow prediction result

[0106] S3.5. Divide traffic conditions into different levels according to the traffic flow index.

[0107] The traffic is divided into different levels: unblocked, slightly congested, moderately congested and severely congested.

[0108] S3.6. Continuously update traffic flow prediction models to maintain an accurate understanding of current and upcoming traffic conditions.

[0109] S3.7. By real-time prediction and classification of traffic conditions, combined with visualization and dynamic path adjustment, the traffic change status can be finally obtained.

[0110] S4. According to the changes in traffic conditions, a multi-objective evolutionary algorithm is used to adjust the standard path to obtain the optimal path.

[0111] It should be noted that the multi-objective evolutionary algorithm originates from the field of evolutionary computation. It is used to solve optimization problems with multiple conflicting objectives by simulating natural selection and genetic mechanisms and combining the needs of multi-objective optimization. For example, in path planning, it may be necessary to optimize multiple objectives at the same time, such as minimizing the total driving time, minimizing the total driving distance, minimizing fuel consumption, maximizing customer satisfaction, etc. These objectives are often conflicting, that is, the performance of another objective may be sacrificed when optimizing one objective. The multi-objective evolutionary algorithm can effectively handle this multi-objective optimization problem, find a set of non-dominated solutions (Pareto optimal solutions), and thus find the best solution among multiple objectives.

[0112] S4.1. Define the constraints of path planning based on real-time traffic condition changes;

[0113] The constraints include dynamic time window constraints, dynamic driving time constraints, dynamic driving distance constraints, weather impact constraints and congested road section avoidance constraints.

[0114] It should be noted that the dynamic time window constraint: constrains the delivery time of each order; the dynamic driving time constraint: constrains the actual driving time of each route; the dynamic driving distance constraint: constrains the single driving distance of each vehicle; the weather impact constraint: constrains the weather factors in route planning; the congested section avoidance constraint: constrains the traffic congestion in the route.

[0115] S4.2. Using a multi-objective evolutionary algorithm, the initial population consists of a set of randomly generated path combinations, and the multi-objective problem is decomposed into multiple single-objective sub-problems. Each sub-problem corresponds to a weight vector, and the weight vectors are uniformly distributed on the unit hyperplane.

[0116] It needs to be explained that the multi-objective problem refers to minimizing the total driving time and minimizing the total driving distance.

[0117] S4.3. Assign neighbors to each subproblem based on the Euclidean distance between weight vectors, set an initialization reference point for each subproblem, and set it to the maximum value of each objective.

[0118] S4.4. For each subproblem, randomly select individuals from the neighbors as parents, perform crossover and mutation operations on the parent individuals to generate new offspring individuals, update the reference point of each subproblem to the minimum value of the objective function of all individuals in the current population, and retain better individuals by comparing the individuals in the neighbors with the newly generated offspring individuals.

[0119] It should be noted that the crossover operation: using partial mapping crossover PMX; mutation operation: randomly exchanging the positions of two nodes in the path;

[0120] S4.5. When the stopping condition is met, the algorithm terminates and eventually the population converges to a set of non-dominated solutions.

[0121] It needs to be explained that the stopping conditions are the maximum number of iterations, fitness convergence, time limit and solution set stability.

[0122] S4.6. Based on the constraint conditions, select the path combination that meets the requirements from the solution set of non-dominated solutions, and use the lexicographic order method to determine the optimal route from the path combination that meets the requirements.

[0123] It should be noted that the lexicographic order method determines the optimal route from the matching path combinations by comparing the objective function values ​​of the solutions in the multi-objective optimization problem one by one, and usually sorts them according to a preset priority order.

[0124] S5. Continuously track the delivery progress of the optimal route.

[0125] S5.1. Continuously track vehicle delivery progress through vehicle-mounted GPS devices and cellular networks, and continuously receive the latest traffic data from the city's intelligent transportation center.

[0126] It should be noted that the latest traffic data from the city’s intelligent transportation center is continuously received through the automation interface (API).

[0127] S6. Based on tracking the delivery progress of the optimal path, a multi-objective evolutionary algorithm is called in real time to adjust the optimal path.

[0128] S6.1. Based on the vehicle delivery progress and the latest traffic data, if the actual driving time exceeds expectations, serious traffic jams or unexpected events occur, the route replanning process will be automatically initiated.

[0129] It should be noted that when the path replanning process is automatically started, the system will set a threshold. Once it detects that a certain indicator exceeds the threshold, the system will automatically call the path replanning module to recalculate the optimal path.

[0130] S6.2. Use a multi-objective evolutionary algorithm to adjust the optimal path for the existing path combination.

[0131] When the vehicle arrives at the designated address, the arrival is confirmed through the mobile device, the customer signs on the mobile device to confirm receipt, and the mobile device automatically pops up a customer satisfaction questionnaire, through which customer feedback is obtained.

[0132] Based on the vehicle-mounted GPS device and cellular network, the vehicle delivery progress is continuously tracked to obtain delivery data.

[0133] The collected customer feedback and delivery data are saved in the data center, and the data is analyzed regularly to continuously optimize the delivery algorithm and customer service process.

[0134] This embodiment also provides a timely delivery system based on route planning, including: an order management module, a route planning module, a real-time traffic analysis module, a dynamic route adjustment module, a delivery tracking and adjustment module, and a customer feedback analysis module;

[0135] The order management module is used to receive order information and enter it into the distribution management center;

[0136] The path planning module is used to obtain the standard path based on the order information using the simulated annealing algorithm;

[0137] The real-time traffic analysis module is used to connect to the city's intelligent traffic center to collect real-time traffic data based on standard routes, analyze the real-time traffic data using the traffic flow prediction model, and obtain changes in traffic conditions;

[0138] Dynamic path adjustment module, which is used to adjust the standard path according to traffic conditions and obtain the optimal path using a multi-objective evolutionary algorithm;

[0139] Delivery tracking module, used to continuously track the progress of delivery along the optimal route;

[0140] The real-time adjustment module is used to adjust the optimal path based on tracking the delivery progress of the optimal path and calling the multi-objective evolutionary algorithm in real time.

[0141] This embodiment also provides a computer device, which is suitable for the timely delivery method based on path planning, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the timely delivery method based on path planning proposed in the above embodiment.

[0142] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0143] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the timely delivery method based on path planning as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0144] In summary, the present invention achieves high-quality path planning by using a simulated annealing algorithm to obtain a standard path, enhances the global search capability and robustness of the system, and ensures the generation of an optimal path under complex constraints; at the same time, the path is dynamically adjusted using a multi-objective evolutionary algorithm according to real-time traffic conditions, thereby achieving multi-objective optimization of the total driving time and total driving distance, and improving delivery efficiency and resource utilization.

[0145] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a timely delivery method based on path planning are provided.

[0146] In order to verify the effectiveness and advantages of the timely delivery method based on path planning, a specific implementation example was designed. This implementation example was carried out in a logistics distribution center in a certain city, and the peak period of the day (9:00 to 11:00 am) was selected as the test time period. The test objects were 50 randomly generated orders, each of which contained the customer's detailed address, cargo type and weight. During the experiment, simulated annealing algorithm, multi-objective evolutionary algorithm and traffic flow prediction model were used, and compared with the existing traditional path planning method.

[0147] First, the order information is entered into the distribution management center through an automated interface, and a standard route is generated using a simulated annealing algorithm. Second, the city’s intelligent transportation center is connected to collect real-time traffic data, and the LSTM model is used to predict changes in traffic conditions. Next, based on changes in traffic conditions, a multi-objective evolutionary algorithm is used to adjust the standard route to obtain the optimal route, and the delivery progress is continuously tracked and adjusted. Finally, delivery is completed and customer feedback and delivery data are recorded to optimize future delivery algorithms and service processes.

[0148] The details are shown in Table 1 below:

[0149] Table 1 Comparative analysis of delivery efficiency and customer satisfaction

[0150]

[0151] Through the data analysis of the above table, it can be clearly seen that the method of the present invention is superior to the traditional path planning method in multiple key indicators. Compared with the traditional method, the present invention achieves more efficient and accurate path planning by combining simulated annealing algorithm, multi-objective evolutionary algorithm and real-time traffic data. For example, in terms of actual driving time, the ratio of actual driving time to expected driving time of the method of the present invention is 1.08 hours, which is 13.6% less than the 1.25 hours of the traditional method. This shows that the method of the present invention can more accurately predict and respond to traffic conditions and improve the on-time rate of distribution. In addition, in terms of actual driving distance, the ratio of actual driving distance to expected driving distance of the method of the present invention is 1.05 kilometers, which is 11% less than the 1.18 kilometers of the traditional method, effectively reducing fuel costs and maintenance costs. In terms of the number of path adjustments, the method of the present invention is only 1.2 times, which is 65.7% less than the 3.5 times of the traditional method, and improves the overall distribution efficiency. In terms of total distribution cost, the ratio of the total distribution cost of the method of the present invention to the benchmark cost is 11,000 yuan, which is 21.4% less than the traditional method of 14,000 yuan, significantly improving the economic benefits.

[0152] The present invention significantly improves the on-time rate of delivery, reduces operating costs, reduces the number of route adjustments, and greatly improves customer satisfaction by combining simulated annealing algorithm, multi-objective evolutionary algorithm and real-time traffic data. It is superior to traditional methods in multiple key indicators such as actual driving time, driving distance, route planning efficiency and total delivery cost, providing a more efficient, reliable and customer-friendly solution for the logistics and distribution industry.

[0153] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A timely delivery method based on path planning, characterized by: include, Receive order information and enter it into the distribution management center; Based on the order information, the simulated annealing algorithm is used to obtain the standard path; Based on standard routes, connect to the city's intelligent transportation center to collect real-time traffic data, use traffic flow prediction models to analyze real-time traffic data, and obtain changes in traffic conditions; According to the changes in traffic conditions, the standard path is adjusted using a multi-objective evolutionary algorithm to obtain the optimal path; Continuously track the optimal route delivery progress; Based on tracking the delivery progress of the optimal path, the multi-objective evolutionary algorithm is called in real time to adjust the optimal path.

2. The timely delivery method based on path planning as claimed in claim 1, characterized in that: The specific steps of receiving order information and entering it into the distribution management center are as follows: Receive customer order information, automatically verify inventory and payment status, and confirm that the order is valid; For valid orders, real-time order information is automatically entered into the distribution management center through an automated interface.

3. The timely delivery method based on path planning as claimed in claim 2, characterized in that: Based on the order information, the simulated annealing algorithm is used to obtain the standard path. The specific steps are as follows: Extract vehicle information through the distribution management center; Based on the order information and vehicle information, random initialization is used to generate a random initial route combination; Through the simulated annealing algorithm, the inversion method is used to select two points in the initial path combination and exchange the path segments between them to generate a new solution. The Metropolis criterion is used to decide whether to accept the new solution. The expression is: Among them, P is the probability value, ΔF is the fitness difference between the new solution and the current solution, T is the current temperature, and ΔF max is the maximum value of the fitness difference, T a is the actual travel time, T e is the estimated travel time, t is the time step of iteration, λ is the fitness difference adjustment coefficient, μ is the time deviation adjustment coefficient, and κ is the time decay coefficient; Generate a random number r through random.random(); When P>r, it means accepting the new solution; When P≤r, do not accept new solutions and keep the current solution unchanged; The unacceptable new solutions are treated through the exponential cooling method, gradually reducing the temperature parameters, and repeating the inversion method and Metropolis criterion to generate the optimized path combination; Define constraints such as vehicle capacity, order delivery time, and single travel distance through problem requirements and business rules; The optimized path combination is filtered and adjusted through defined constraints to obtain a standard path.

4. The timely delivery method based on path planning as claimed in claim 3, characterized in that: The standard route is used to connect to the city intelligent transportation center to collect real-time traffic data, and the traffic flow prediction model is used to analyze the real-time traffic data to obtain changes in traffic conditions. The specific steps are as follows: Based on the standard route, the distribution management center connects to the city intelligent transportation center through the API interface, sends data requests, obtains real-time traffic data, and stores the collected real-time traffic data in the database; The traffic flow prediction model is constructed using the LSTM model as the basic model; Use time series to convert real-time traffic data into serialized data; By setting the input_shape parameter of the LSTM model input layer to match the shape of the serialized data; Add an LSTM layer by using the SequentialAPI method and add a fully connected layer after the LSTM layer; A linear activation function is used in the output layer to predict the traffic flow index; Train and validate the traffic flow prediction model and test the final performance; The preprocessed real-time data is input into the traffic flow prediction model to obtain the traffic flow index, which is expressed as: in, is the traffic flow index, X is the preprocessed real-time traffic data, θ is the parameter of the LSTM model, α is the data deviation adjustment coefficient, and X avg is the mean value of the input data, σ X is the standard deviation of the input data, β is the time decay adjustment coefficient, t' is the time variable, t0 is the reference time point, and τ is the time decay constant; Divide traffic conditions into different levels according to the traffic flow index; Continuously update traffic flow prediction models; By predicting and classifying traffic conditions in real time, combined with visual display and dynamic route adjustment, the traffic change status can be finally obtained.

5. The timely delivery method based on path planning as claimed in claim 4, characterized in that: The multi-objective evolutionary algorithm is used to adjust the standard path according to the change of traffic conditions to obtain the optimal path. The specific steps are as follows: Define the constraints of path planning based on real-time traffic condition changes; The constraints include dynamic time window constraints, dynamic driving time constraints, dynamic driving distance constraints, weather impact constraints and congested road section avoidance constraints; Using a multi-objective evolutionary algorithm, the initial population consists of a set of randomly generated path combinations, and the multi-objective problem is decomposed into multiple single-objective sub-problems. Each sub-problem corresponds to a weight vector, and the weight vectors are evenly distributed on the unit hyperplane. Assign neighbors to each subproblem based on the Euclidean distance between weight vectors, and set an initial reference point for each subproblem, set to the maximum value of each objective; For each subproblem, randomly select individuals from the neighborhood as parents, perform crossover and mutation operations on the parent individuals to generate new offspring individuals, update the reference point of each subproblem to the minimum value of the objective function of all individuals in the current population, and retain better individuals by comparing the individuals in the neighborhood with the newly generated offspring individuals; When the stopping condition is met, the algorithm terminates, and the population eventually converges to a set of non-dominated solutions; Based on the constraints, the matching path combinations are screened out from the solution set of non-dominated solutions, and the optimal route is determined from the matching path combinations using the lexicographic order method.

6. The timely delivery method based on path planning as claimed in claim 5, characterized in that: The specific steps of continuously tracking the optimal path delivery progress are as follows: Continuously track vehicle delivery progress through on-board GPS devices and cellular networks, and continuously receive the latest traffic data from the city's intelligent transportation center.

7. The timely delivery method based on path planning as claimed in claim 6, characterized in that: Based on tracking the optimal path delivery progress, the multi-objective evolutionary algorithm is called in real time to adjust the optimal path. The specific steps are as follows: According to the vehicle delivery progress and the latest traffic data, if the actual driving time exceeds the expected time, serious traffic jams and unexpected events occur, the route re-planning process will be automatically initiated; A multi-objective evolutionary algorithm is used to adjust the optimal path for the existing path combination.

8. A timely delivery system based on path planning, based on the timely delivery method based on path planning according to any one of claims 1 to 7, characterized in that: Including order management module, route planning module, real-time traffic analysis module, dynamic route adjustment module, delivery tracking and adjustment module and customer feedback analysis module; The order management module is used to receive order information and enter it into the distribution management center; The path planning module is used to obtain the standard path based on the order information using the simulated annealing algorithm; The real-time traffic analysis module is used to connect to the city's intelligent traffic center to collect real-time traffic data based on standard routes, analyze the real-time traffic data using the traffic flow prediction model, and obtain changes in traffic conditions; Dynamic path adjustment module, which is used to adjust the standard path according to traffic conditions and obtain the optimal path using a multi-objective evolutionary algorithm; Delivery tracking module, used to continuously track the progress of delivery along the optimal route; The real-time adjustment module is used to adjust the optimal path based on tracking the delivery progress of the optimal path and calling the multi-objective evolutionary algorithm in real time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the timely delivery method based on path planning described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the timely delivery method based on path planning described in any one of claims 1 to 7 are implemented.

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