Vehicle path planning method and device, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a vehicle path planning method and device, electronic equipment and a storage medium, and belongs to the technical field of intelligent logistics scheduling optimization. The method comprises the steps that a vehicle path planning model comprises a path cost function and an initial constraint condition, the initial constraint condition is converted into integer knapsack constraint, candidate constraint conditions are obtained, the candidate constraint conditions comprise constraint variables, constraint coefficients, constraint symbols and constraint limiting values, a constraint range is determined according to the value range of the constraint variables and the constraint coefficients, and the constraint range is determined according to the constraint coefficients; determining a candidate constraint limit value according to the constraint range and the constraint limit value, calculating a Floribenius number according to the constraint coefficient, tightening the candidate constraint limit value according to the Floribenius number and the constraint symbol to obtain a target constraint limit value, and updating the initial constraint condition according to the target constraint limit value to obtain a target constraint condition. And solving the path cost function according to the target constraint condition to obtain a vehicle planning path. The efficiency of vehicle path planning can be improved.

Description

Technical Field

[0001] This application relates to the technical field of intelligent logistics scheduling optimization, and particularly to a vehicle path planning method and device, an electronic device, and a storage medium. Background Art

[0002] In the logistics transportation industry, a vehicle path planning model is usually used for vehicle path planning to improve product distribution efficiency and reduce logistics transportation costs. The vehicle path planning problem is essentially a Mixed Integer Programming (MIP) problem. The mixed integer programming problem has the NP-hard property, which makes the computational complexity of the vehicle path planning model increase exponentially with the increase of the problem scale, resulting in low efficiency of vehicle path planning. Summary of the Invention

[0003] The main purpose of the embodiments of this application is to propose a vehicle path planning method and device, an electronic device, and a storage medium, aiming to improve the efficiency of vehicle path planning.

[0004] To achieve the above object, a first aspect of the embodiments of this application proposes a vehicle path planning method, and the method includes:

[0005] Obtain a vehicle path planning model, where the vehicle path planning model includes a path cost function and initial constraint conditions;

[0006] Convert the initial constraint conditions into integer knapsack constraints to obtain candidate constraint conditions; wherein, the candidate constraint conditions include constraint variables, constraint coefficients, constraint signs, and constraint limits, and the constraint signs are used to indicate the relationship between the linear combination constructed based on the constraint variables and the constraint coefficients and the constraint limits;

[0007] Determine a constraint range according to the value range of the constraint variables and the constraint coefficients;

[0008] Determine candidate constraint limits according to the constraint range and the constraint limits;

[0009] Calculate the Frobenius number according to the constraint coefficients;

[0010] Tighten the candidate constraint limits according to the Frobenius number and the constraint signs to obtain target constraint limits;

[0011] Update the initial constraint conditions according to the target constraint limits to obtain target constraint conditions;

[0012] Solve the path cost function according to the target constraint conditions to obtain a vehicle planning path.

[0013] In some embodiments, calculating the Frobenius number according to the constraint coefficients includes:

[0014] Obtaining the number of coefficients of the constraint coefficients in the candidate constraint conditions;

[0015] If the number of coefficients is equal to two, multiplying the constraint coefficients to obtain an intermediate coefficient value, and performing a subtraction operation on the intermediate coefficient value and the constraint coefficients to obtain the Frobenius number;

[0016] If the number of coefficients is greater than two, constructing a graph model according to the constraint coefficients, and calculating the Frobenius number according to the graph model.

[0017] In some embodiments, constructing a graph model according to the constraint coefficients and calculating the Frobenius number according to the graph model includes:

[0018] Performing node construction according to the constraint coefficients to obtain coefficient nodes and node identifiers of the coefficient nodes;

[0019] Constructing edges between the coefficient nodes according to the constraint coefficients and the node identifiers, and obtaining initial edge weights of the edges;

[0020] Constructing the graph model according to the coefficient nodes, the node identifiers, the edges, and the initial edge weights; the graph model includes a starting coefficient node and a plurality of non-starting coefficient nodes;

[0021] For each non-starting coefficient node, obtaining the reference edge weight from the starting coefficient node to the non-starting coefficient node in the graph model; the reference edge weight is the sum of the initial edge weights of the edges between the starting coefficient node and the non-starting coefficient node;

[0022] For each non-starting coefficient node, selecting the smallest reference edge weight as the target edge weight;

[0023] Calculating the Frobenius number according to each of the target edge weights.

[0024] In some embodiments, calculating the Frobenius number according to each of the target edge weights includes:

[0025] Selecting the largest target edge weight from each of the target edge weights to obtain the maximum weight value;

[0026] Obtaining the number of nodes of the coefficient nodes in the graph model;

[0027] Subtracting the maximum weight value from the number of nodes to obtain the Frobenius number.

[0028] In some embodiments, calculating the Frobenius number according to each of the target edge weights includes:

[0029] Calculating an unreachable set of the candidate constraint limit according to each of the target edge weights;

[0030] Calculating a reachable set of the candidate constraint limit according to the unreachable set and each of the target edge weights;

[0031] Selecting the maximum value in the reachable set to obtain the Frobenius number.

[0032] In some embodiments, after calculating the reachable set of the candidate constraint limit according to the unreachable set and each of the target edge weights, the vehicle path planning method further includes:

[0033] Calculating a non - negative integer solution of the constraint variable according to the reachable set and the graph model to obtain a deterministic variable;

[0034] Solving the path cost function according to the deterministic variable and the target constraint condition to obtain a vehicle planning path.

[0035] In some embodiments, tightening the candidate constraint limit according to the Frobenius number and the constraint symbol to obtain a target constraint limit includes:

[0036] If the constraint symbol indicates that a linear combination constructed based on the constraint variable and the constraint coefficient is less than or equal to the constraint limit, then decreasing the candidate constraint limit until the decreased candidate constraint limit is less than the Frobenius number, and taking the decreased candidate constraint limit as the target constraint limit;

[0037] If the constraint symbol indicates that a linear combination constructed based on the constraint variable and the constraint coefficient is greater than or equal to the constraint limit, then increasing the candidate constraint limit until the increased candidate constraint limit is greater than the Frobenius number, and taking the increased candidate constraint limit as the target constraint limit.

[0038] To achieve the above object, a second aspect of the embodiments of the present application proposes a vehicle path planning device, and the device includes:

[0039] An acquisition module, configured to acquire a vehicle path planning model, where the vehicle path planning model includes a path cost function and an initial constraint condition;

[0040] A conversion module, configured to convert the initial constraint conditions into integer knapsack constraints to obtain candidate constraint conditions; wherein, the candidate constraint conditions include constraint variables, constraint coefficients, constraint signs, and constraint limit values, and the constraint signs are used to indicate the relationship between the linear combination constructed based on the constraint variables and the constraint coefficients and the constraint limit values;

[0041] A first determination module, configured to determine a constraint range according to the value range of the constraint variables and the constraint coefficients;

[0042] A second determination module, configured to determine candidate constraint limit values according to the constraint range and the constraint limit values;

[0043] A calculation module, configured to calculate the Frobenius number according to the constraint coefficients;

[0044] A tightening module, configured to tighten the candidate constraint limit values according to the Frobenius number and the constraint signs to obtain target constraint limit values;

[0045] An update module, configured to update the initial constraint conditions according to the target constraint limit values to obtain target constraint conditions;

[0046] A solving module, configured to solve the path cost function according to the target constraint conditions to obtain a vehicle planning path.

[0047] To achieve the above object, a third aspect of the embodiments of the present application proposes an electronic device, where the electronic device includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect above is implemented.

[0048] To achieve the above object, a fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect above is implemented.

[0049] The vehicle path planning method, vehicle path planning device, electronic device, and computer-readable storage medium according to the embodiments of the present application obtain a vehicle path planning model to perform vehicle path planning based on the vehicle path planning model. The vehicle path planning model includes a path cost function and initial constraints. The initial constraints are converted into integer knapsack constraints to simplify the complex initial constraints and obtain candidate constraints. The candidate constraints include constraint variables, constraint coefficients, constraint signs, and constraint limits. To determine whether the constraint limits are out of range, the constraint range is determined according to the value range of the constraint variables and the constraint coefficients. According to the constraint range and the constraint limits, candidate constraint limits are determined to adjust the out-of-range constraint limits according to the constraint range, ensuring the accuracy of the candidate constraint limits. To simplify the solution of the vehicle path planning model, the Frobenius number is calculated according to the constraint coefficients to perform constraint reduction according to the Frobenius number. The candidate constraint limits are tightened according to the Frobenius number and the constraint signs to obtain target constraint limits. By tightening the constraint limits, the search space of the model solution values and the number of feasible solutions can be reduced to reduce the problem scale and further reduce the computational complexity. The initial constraints are updated according to the target constraint limits to simplify the initial constraints and obtain target constraints. The path cost function is solved according to the target constraints to obtain the vehicle planned path. Before solving the vehicle path planning model, the initial constraints are tightened using the Frobenius number to determine the target constraints, simplify the vehicle path planning model, reduce the problem scale, and improve the efficiency of vehicle path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a flowchart of the vehicle path planning method provided by the embodiments of the present application;

[0051] Figure 2 is Figure 1 a flowchart of step S150 in

[0052] Figure 3 is Figure 2 a flowchart of step S230 in

[0053] Figure 4 is Figure 3 a flowchart of step S360 in

[0054] Figure 5 is Figure 3 another flowchart of step S360 in

[0055] Figure 6 is another flowchart of the vehicle path planning method provided by the embodiments of the present application;

[0056] Figure 7 is Figure 1Flowchart of step S160 in

[0057] Figure 8 is a schematic structural diagram of a vehicle route planning device provided by an embodiment of the present application;

[0058] Figure 9 is a schematic hardware structure diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] It should be noted that although functional module division is performed in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order from the module division in the device or the order in the flowchart. Terms such as "first" and "second" in the description and claims and the above accompanying drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing embodiments of this application and are not intended to limit this application.

[0062] In the logistics and transportation industry, vehicle route planning models are usually used for vehicle route planning to improve product distribution efficiency and reduce logistics and transportation costs. The vehicle route planning problem is essentially a Mixed Integer Programming (MIP) problem, and the mixed integer programming problem has the NP-hard property, which makes the computational complexity of the vehicle route planning model increase exponentially with the increase of the problem scale, resulting in low efficiency of vehicle route planning.

[0063] Based on this, embodiments of the present application provide a vehicle route planning method, a vehicle route planning device, an electronic device and a computer-readable storage medium, aiming to improve the efficiency of vehicle route planning.

[0064] The vehicle route planning method, vehicle route planning device, electronic device and computer-readable storage medium provided by the embodiments of the present application will be specifically described through the following embodiments. First, the vehicle route planning method in the embodiments of the present application will be described.

[0065] The vehicle path planning method provided by the embodiments of this application relates to the technical field of intelligent logistics scheduling optimization. The vehicle path planning method provided by the embodiments of this application can be applied to a terminal, or to a server side, or can be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the vehicle path planning method, etc., but is not limited to the above forms.

[0066] This application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0067] Figure 1 is an optional flowchart of the vehicle path planning method provided by the embodiments of this application, which may include but is not limited to steps S110 to S180.

[0068] Step S110, obtain a vehicle path planning model, where the vehicle path planning model includes a path cost function and initial constraint conditions;

[0069] Step S120, convert the initial constraint conditions into integer knapsack constraints to obtain candidate constraint conditions; among them, the candidate constraint conditions include constraint variables, constraint coefficients, constraint signs, and constraint limits, and the constraint signs are used to indicate the relationship between the linear combination constructed based on the constraint variables and the constraint coefficients and the constraint limits;

[0070] Step S130, determine the constraint range according to the value range of the constraint variables and the constraint coefficients;

[0071] Step S140: Determine candidate constraint limits according to the constraint range and constraint limits.

[0072] Step S150: Calculate the Frobenius number according to the constraint coefficient.

[0073] Step S160: Tighten the candidate constraint limits according to the Frobenius number and the constraint symbol to obtain the target constraint limits.

[0074] Step S170: Update the initial constraint conditions according to the target constraint limits to obtain the target constraint conditions.

[0075] Step S180: Solve the path cost function according to the target constraint conditions to obtain the vehicle planned path.

[0076] In step S110 of some embodiments, a vehicle path planning model is constructed. The vehicle path planning model includes a path cost function and initial constraint conditions, and is used to plan the vehicle driving route. The path cost function is the objective function and is used to minimize the path cost of the vehicle. The initial constraint conditions are the limiting conditions that must be satisfied when planning the vehicle path.

[0077] The path cost function is expressed as:

[0078]

[0079] where C is the path cost; N is the set of path nodes, N = {0, 1, 2, 3,.., h}, 0 is the starting path node, and 1 to h are all cargo demand nodes; V is the set of vehicle numbers, V = {1, 2, 3,.., m}, and m is the number of vehicles; c ij is the path cost for the vehicle from path node i to path node j; is a binary decision variable, if vehicle k passes through the path between path node i and path node j, then is 1, otherwise it is 0.

[0080] The initial constraint conditions are Diophantine equations, and their general form is expressed as:

[0081]

[0082] where a i is the constraint coefficient, and the value of the constraint coefficient is an integer; is the constraint variable of vehicle k, the feasible region of is the intersection of the feasible solutions of all vehicles for the constraint variable; the value of the constraint variable is an integer greater than or equal to 0; is the constraint symbol, which can be >, <, =, ≥, ≤; b k is the constraint limit of vehicle k.

[0083] Consider the vehicle routing problem with capacity constraints. The initial constraint is used to ensure that the total volume of goods loaded on the vehicle meets the vehicle's capacity loading requirement, which is expressed as:

[0084]

[0085] where Q is the category number of goods, Q = {1, 2, 3.., n}, and n is the number of categories of goods; a i is the unit volume of the i-th category of goods; is the quantity of the i-th category of goods loaded on vehicle k; b k is the maximum capacity of vehicle k.

[0086] The initial constraint with capacity constraints can be expressed as The feasible region P of this initial constraint can be expressed as:

[0087]

[0088] where, is a non-negative integer solution and belongs to the intersection of the solution sets constructed for each vehicle.

[0089] The vehicle routing model also includes other constraints, which are expressed as:

[0090]

[0091] Among them, the first term is used to ensure that the demand for each type of goods at each goods demand node is met, is the quantity of the q-th category of goods delivered by vehicle k to the i-th goods demand node, is the demand for the q-th category of goods at the i-th goods demand node; the second term is used to ensure that goods can only be delivered to a goods demand node when the vehicle visits that node, and K is a very large positive integer; the third term is used to ensure that the vehicle starts from the starting position; the fourth term is used to ensure the balance of the inflow and outflow of the path nodes.

[0092] In step S120 of some embodiments, in order to simplify the problem structure of the path planning problem and improve the efficiency of path planning, the initial constraint conditions are converted into integer knapsack constraints to obtain candidate constraint conditions. The candidate constraint conditions include constraint variables, constraint coefficients, constraint signs, and constraint limits. The linear combination constructed by the constraint variables and constraint coefficients is located on the left side of the candidate constraint condition, the constraint limit is located on the right side of the candidate constraint condition, and the constraint sign is used to connect the left and right sides of the candidate constraint condition, which can indicate the relationship between the linear combination constructed based on the constraint variables and constraint coefficients and the constraint limit. The integer knapsack constraint includes the classical knapsack constraint where both the constraint variables and constraint coefficients are non-negative integers and implicit constraints with similar properties.

[0093] For the initial constraint conditions x 1 is a single continuous variable, and other constraint variables are all integers. To reduce the computational complexity of the path planning model, it is necessary to eliminate the influence of the slack variable on the knapsack constraint. There is usually only one slack variable in the initial constraint conditions. If x 1 is the slack variable, then directly remove the constraint variable x 1 and its corresponding constraint coefficient a 1 from the initial constraint conditions to avoid the interference of the slack variable and its coefficient on the identification of integer knapsack constraints. Considering that the initial constraint condition is a standard integer knapsack constraint condition, it is expressed as If x 1 is the slack variable with a 1 = 1, then replace the initial constraint condition with the inequality

[0094] Given a set of numbers a 1 , …, a n , then the extended greatest common divisor is d = gcd(a 1 , …, a n ). The greatest common divisor refers to the largest d such that a j / d ∈ Z holds for all a j . For an equation assuming x 1 is a single continuous variable and other variables are all integers, then x 1 must also be a multiple of the extended greatest common divisor. If d′ := gcd(a 2 / a 1 , …, a n / a 1 , b k / a 1 ), and d′ ∈ Z, then x 1 becomes an implicit integer variable, and the initial constraint condition can be effectively converted into an integer knapsack constraint, and the integer knapsack constraint is expressed as:

[0095]

[0096] Among them, o is a constraint symbol; n is the number of categories of goods; represents the rounding operation.

[0097] Considering the initial constraint conditions with capacity limits, after eliminating the slack variables, given that the unit volume of each different category of goods is a 1 ,…,a n ∈Q, referring to the above formula, the embodiments of the present application use the extended greatest common divisor to convert the initial constraint conditions with fractional constraint coefficients into constraint conditions with integer constraint coefficients, and process non-relatively prime coefficients, check whether b k is a decimal and integerize it to accurately obtain the integer knapsack constraint in the vehicle routing problem with capacity limits, and obtain candidate constraint conditions, thereby allowing subsequent reduction of the constraint conditions using the Frobenius number.

[0098] In step S130 of some embodiments, in order to ensure the feasibility of the solution values of the constraint variables and improve the accuracy of path planning, it is necessary to ensure that the constraint limits in the candidate constraint conditions are within a reasonable range. The value ranges of each constraint variable in the candidate constraint conditions are predefined, and weighted calculations are performed based on the value ranges of each constraint variable and the constraint coefficients corresponding to the constraint variables to obtain the constraint range. The formula for the weighted calculation is expressed as:

[0099]

[0100] Among them, is the i-th constraint variable; a i is the constraint coefficient of the i-th constraint variable.

[0101] In step S140 of some embodiments, if the constraint limit b k is within the constraint range, it means that the constraint limit is a reasonable value, and the constraint limit is used as the candidate constraint limit. If the constraint limit b k does not fall within the constraint range, that is, the constraint limit exceeds the constraint range, the constraint limit is adjusted to make it fall within the constraint range to obtain the candidate constraint limit.

[0102] Given several pairwise relatively prime positive integers, the Frobenius number is the largest integer that cannot be expressed as a non - negative integer linear combination of these integers. Candidate constraint conditions such as capacity constraints are Diophantine equations, involving integer coefficients, right - hand sides, and variables. To improve the efficiency of path planning, in the embodiments of the present application, before solving the path planning model, the Frobenius number is used to pre - process the constraint conditions to simplify the path planning problem and reduce the problem scale, thereby reducing the solving duration of the path planning problem and accelerating the solving speed of the path planning problem.

[0103] Considering the capacity constraint, given the volumes of different categories of goods \(a=(a 1 ,…,a n )\in X n >0\), \(\gcd(a 1 ,…,a n ) = 1\), the set is the non - representable set of \(a\), which can be defined as For all constraint variables in the candidate constraint conditions The elements in the set are non - negative integer combinations that cannot be represented by the constraint coefficient \(a i That is, contains all the integer elements that the knapsack polyhedron does not have. The Frobenius number \(F(a)\) is expressed as the largest number in the set .

[0104] According to the given theorem, calculate the set The theorem is given \(a=(a 1 ,…,a n ) T \in Z n >0\), \(\gcd(a 1 ,…,a n ) = 1\), and \(L\in Z ≥0 , it can be determined that:

[0105]

[0106] where \(\varepsilon(L)\) represents the minimum value of the sum of all terms under the condition that the cumulative value starting from the second term has a remainder of 1 when divided by the coefficient of the first term.

[0107] That is, there exist integers \(x j \geq0\) such that if and only if \(L\geq\varepsilon(L)\). For each and \(L\in\{1,\ldots,a 1 - 1\}\), there do not exist non - negative integers \(x such that j, and is such that the non - negative integer solution x j has no solution, and the Frobenius number F(a)=max L (ε(L)) - a 1 .

[0108] Please refer to Figure 2 , in some embodiments, step S150 may include but is not limited to steps S210 to S230:

[0109] Step S210, obtain the number of coefficients of the constraint coefficients in the candidate constraint conditions;

[0110] Step S220, if the number of coefficients is equal to two, multiply the constraint coefficients to obtain an intermediate coefficient value, and perform a subtraction operation on the intermediate coefficient value and the constraint coefficients to obtain the Frobenius number;

[0111] Step S230, if the number of coefficients is greater than two, construct a graph model based on the constraint coefficients, and calculate the Frobenius number according to the graph model.

[0112] In step S210 of some embodiments, obtain the number of constraint coefficients in the candidate constraint conditions to obtain the number of coefficients, so as to distinguish the calculation method of the Frobenius number according to the number of coefficients.

[0113] In step S220 of some embodiments, if the number of coefficients is equal to two, the constraint coefficients are represented as a 1 and a 2 , then multiply the constraint coefficients a 1 and a 2 to obtain an intermediate coefficient value, and perform a subtraction operation on the constraint coefficients a 1 and a 2 according to the intermediate coefficient value to obtain the Frobenius number. That is:

[0114] F(a 1 , a 2 ) = a 1 a 2 - a 1 - a 2 .

[0115] In step S230 of some embodiments, if the number of coefficients is greater than two, based on the Dijkstra quotient queue decreasing algorithm, construct a Frobenius cycle graph according to the constraint coefficients to obtain a graph model, and calculate the Frobenius number according to the graph model. Among them, the graph model is a directed graph, denoted as G=(N,A), N is the set of nodes constituting the directed graph, A ={(i,j)|i,j∈N,i≠j}, and A represents the set of edges between nodes i and j constituting the directed graph.

[0116] In the above steps S210 to S230, the calculation method of the Frobenius number is distinguished by the number of coefficients, so as to improve the calculation efficiency of the Frobenius number, thereby performing constraint reduction according to the Frobenius number, and further improving the efficiency of vehicle path planning.

[0117] Please refer to Figure 3 , in some embodiments, step S230 may include but is not limited to steps S310 to S360:

[0118] Step S310, construct nodes according to the constraint coefficients to obtain coefficient nodes and the node identifiers of the coefficient nodes;

[0119] Step S320, construct edges between the coefficient nodes according to the constraint coefficients and the node identifiers, and obtain the initial edge weights of the edges;

[0120] Step S330, construct a graph model according to the coefficient nodes, the node identifiers, the edges and the initial edge weights; the graph model includes a starting coefficient node and a plurality of non-starting coefficient nodes;

[0121] Step S340, for each non-starting coefficient node, obtain the reference edge weight from the starting coefficient node to the non-starting coefficient node in the graph model; the reference edge weight is the sum of the initial edge weights of the edges between the starting coefficient node and the non-starting coefficient node;

[0122] Step S350, for each non-starting coefficient node, select the smallest reference edge weight as the target edge weight;

[0123] Step S360, calculate the Frobenius number according to each target edge weight.

[0124] In step S310 of some embodiments, a 1 is the first constraint coefficient in the candidate constraint conditions. According to the constraint coefficient a 1 construct a 1 nodes to obtain a 1 coefficient nodes. The coefficient nodes represent the equivalence classes a 1 of the remaining residue classes, and mark each coefficient node as {0,…,a 1 -1} to obtain the node identifier.

[0125] In step S320 of some embodiments, for any two coefficient nodes, construct an edge between the two coefficient nodes according to the node identifiers of the two coefficient nodes and the constraint coefficients other than a 1 and calculate the weight of the edge to obtain the initial edge weight.

[0126] Specifically, the node identifiers of the two coefficient nodes are respectively represented as u and v, 0≤u,v≤a 1-1, if there is any other constraint coefficient a 1 except a k ∈ a \ a 1 in the candidate constraint conditions, such that the congruence u + a k = v mod a 1 , then there is a directed edge between the coefficient node u and the coefficient node v, and the weight of this edge is a k .

[0127] In step S330 of some embodiments, the congruence is repeatedly applied, and a Frobenius cycle graph is constructed according to the coefficient nodes, node identifiers, edges, and initial edge weights to obtain a graph model. The graph model is a weighted directed graph with a 1 coefficient nodes, including a starting coefficient node and multiple non-starting coefficient nodes. The starting coefficient node refers to the coefficient node with a node identifier of 0, and the non-starting coefficient nodes refer to the coefficient nodes with any value between 1 and a 1 -1.

[0128] In step S340 of some embodiments, for each non-starting coefficient node, the path from the starting coefficient node to the non-starting coefficient node is obtained from the graph model, and the number e i of the edges with the initial edge weight a i in the path is obtained. The number e i of the edges and the initial edge weight a i are weighted and calculated to obtain a reference edge weight. The reference edge weight is expressed as:

[0129] ∑ i＞1 e i a i = w,

[0130] where w is the reference edge weight.

[0131] The reference edge weight w of the path determines the non-starting coefficient node v of the path, and v is expressed as:

[0132] v = w mod a 1 .

[0133] In step S350 of some embodiments, for each path starting from the starting coefficient node with the same weight for each reference edge, the same end point will be reached. Therefore, the order of traversing the edges does not affect the calculation result of the Frobenius number. The inherent symmetry of the Frobenius cycle graph allows the use of decreasing paths, that is, as the path progresses, the weight of the edge decreases or remains unchanged. Identifying all the decreasing paths starting from the starting coefficient node with the minimum reference edge weight to determine all the minimum weight paths significantly reduces the number of edge weight combinations that need to be considered, thereby improving the calculation efficiency of the Frobenius number and the efficiency of vehicle path planning. For each non-starting coefficient node, the minimum reference edge weight is selected as the target edge weight of the edge between the starting coefficient node and the non-starting coefficient node.

[0134] In step S360 of some embodiments, when the number of coefficients is greater than two, the Frobenius number is calculated according to each target edge weight.

[0135] Through the above steps S310 to S360, the Frobenius number with the number of coefficients greater than two can be obtained, so as to simplify the constraint conditions based on the Frobenius number and improve the efficiency of vehicle path planning.

[0136] Please refer to Figure 4 , in some embodiments, step S360 may include but is not limited to steps S410 to S430:

[0137] Step S410, select the maximum target edge weight from each target edge weight to obtain the maximum weight value;

[0138] Step S420, obtain the number of nodes of the coefficient nodes in the graph model;

[0139] Step S430, subtract the number of nodes from the maximum weight value to obtain the Frobenius number.

[0140] In step S410 of some embodiments, obtain the minimum path weight in the graph model from the starting coefficient node to each non-starting coefficient node v to obtain each target edge weight ε(L). Select the maximum target edge weight from each target edge weight to obtain the maximum weight value max L (ε(L)).

[0141] In step S420 of some embodiments, determine that the number of nodes of the coefficient nodes in the graph model is a q .

[0142] In step S430 of some embodiments, subtract the number of nodes from the maximum weight value to obtain the Frobenius number. The Frobenius number is expressed as max L (ε(L)) - a 1 .

[0143] In the above steps S410 to S430, the Frobenius number can be obtained through simple mathematical calculations, which improves the calculation efficiency of the Frobenius number and further improves the efficiency of vehicle path planning.

[0144] Please refer to Figure 5 , in some embodiments, step S360 may further include but is not limited to steps S510 to S530:

[0145] Step S510, calculating the non-representable set of candidate constraint limits according to each target edge weight;

[0146] Step S520, calculating the representable set of candidate constraint limits according to the non-representable set and each target edge weight;

[0147] Step S530, selecting the maximum value in the representable set to obtain the Frobenius number.

[0148] In step S510 of some embodiments, let S v represent the respective target edge weights in the graph model from the starting coefficient node to each non-starting coefficient node v, M = v moda q , and only when M ≥ S v , M can be represented by the basis a that constitutes the target edge weight. The Dijkstra quotient queue decreasing algorithm efficiently solves the shortest path problem using a label-setting procedure. The algorithm utilizes decreasing paths and combines two strategies: one is to represent edge and path weights using an ordered pair of quotient and remainder (moda 1 ), and the other is to maintain a priority queue of coefficient nodes according to the sorting of the weight quotients of the ordered pairs. This method achieves significant computational savings based on a hashing technique of the quotient structure inherent in the Frobenius problem. Once all shortest paths are determined, the non-representable set of the right-side value can be obtained by subtracting multiples of a q until the result of the subtraction calculation becomes negative.

[0149] The embodiments of the present application utilize the classical Frobenius number to determine the right-side set where the equation is infeasible. Specifically, for each target edge weight, subtract multiples of the constraint coefficient from the target edge weight and judge the result of the subtraction calculation. If the result of the subtraction calculation is greater than or equal to 0, include the result of the subtraction calculation in the non-representable set of candidate constraint limits, and increment the multiple, repeating the subtraction operation of the target edge weight and the multiple of the constraint coefficient until the result of the subtraction calculation is less than 0.

[0150] In step S520 of some embodiments, given a = (a 1 , …, a n ), and s ∈ Z ≥0, then the s-representable set can be defined as P(a, b) is the corresponding knapsack polyhedron, which contains all the right-hand values that can be represented by a in s different cases. To calculate the 1-representable set Given a = (a 1 , …, a n ) T ∈Z n >0, gcd(a q , …, a n ) = 1, L∈Z ≥0 , define:

[0151]

[0152] Let a′ = (a 1 , …, a n ) T , define to represent the set of non-negative integers that cannot be represented as non-negative linear combinations of a 1 , a 2 , …, a n . For each L∈{0, 1, …, a 1 - 1}, there exists a unique non-negative integer x 1 , x 2 , …, x n such that For all s∈{1, …, k}, and if and only if

[0153] The representable set of candidate constraint limits is the right-hand values (candidate constraint limits) in s different cases represented by the constraint coefficient a. To obtain the representable set of the right-hand value in 1 case, for each non-start coefficient node v, check whether there is a unique minimum decreasing path from the start coefficient node 0 to the non-start coefficient node v, that is, determine whether the target edge weight between 0 and v is unique. If the minimum decreasing path is unique, include the target edge weight S v in the 1-representable set Add multiples of a 1 to S v and include this value in the 1-representable set until for all s∈Z ≥0 , there is I′ represents the non-representable set, and the representable set of candidate constraint limits is obtained.

[0154] In step S530 of some embodiments, select the 1-representable set The maximum value in it determines the Frobenius number as the 1 - Frobenius number.

[0155] The above steps S510 to S530 can obtain the Frobenius number, based on which the complexity of the constraint conditions is reduced, thereby improving the solution efficiency of the vehicle path planning model.

[0156] Please refer to Figure 6 , in some embodiments, after step S520, the vehicle path planning method may further include but is not limited to steps S610 to S620:

[0157] Step S610, calculate the non - negative integer solution of the constraint variables according to the representable set and the graph model to obtain the deterministic variables;

[0158] Step S620, solve the path cost function according to the deterministic variables and the target constraint conditions to obtain the vehicle planning path.

[0159] In step S610 of some embodiments, for S in the 1 - representable set v perform modulo calculation with a 1 , construct a set according to the remainder v of the modulo calculation Let M = v mod a 1 . According to S in the representable set v and v, retrieve the number e of edges with the initial edge weight a i from the graph model i , obtain S v = ∑ i＞i e 1 a i and e i ≥ 0, then is the non - negative integer solution of the equation , and take the non - negative integer solution as the deterministic variable.

[0160] In step S620 of some embodiments, if the constraint limit value of the target constraint condition belongs to the 1 - representable set, that is then the target constraint condition has a unique solution Each constraint variable in the target constraint condition Each has its own domain of definition. Compare the domain of definition of the deterministic variable with that of the constraint variable. If there is a deterministic variable outside the domain of definition, update the deterministic variable to the maximum value under the domain of definition of the corresponding constraint variable, and fix the remaining constraint variables according to the unique solution (deterministic variable) of the equation. If there is no deterministic variable outside the domain of definition, do not update the deterministic variable. Solve the path cost function according to the deterministic variable, the target constraint condition and other constraint conditions to obtain the vehicle planning path. It should be noted that according to the deterministic variable, the target constraint condition can be removed from the vehicle path planning model, thereby improving the efficiency of vehicle path planning.

[0161] Consider the capacity constraint in the vehicle path planning problem The vehicle has a demand domain for each category of goods All have a demand domain of definition. Compare the deterministic variable with the demand domain of definition of the corresponding constraint variable. If the deterministic variable is outside the demand domain of definition, set the value of the deterministic variable to the upper limit of the demand domain of definition; if the deterministic variable falls within the demand domain of definition, fix the constraint variable according to the deterministic variable. By eliminating the infeasible right-hand side values based on the domain of definition of the deterministic variable and the constraint variable, the constraint Is removed from the model.

[0162] Through the above steps S610 to S620, use the 1-Frobenius number to fix the latent variable in the constraint, thereby removing the constraint condition from the vehicle path planning model, and then reducing the solution complexity of the vehicle path planning model and improving the efficiency of vehicle path planning.

[0163] Please refer to Figure 7 , in some embodiments, step S160 may include but is not limited to step S710 or step S720:

[0164] Step S710, if the constraint symbol indicates that the linear combination constructed based on the constraint variable and the constraint coefficient is less than or equal to the constraint limit value, decrement the candidate constraint limit value until the decremented candidate constraint limit value is less than the Frobenius number, and use the decremented candidate constraint limit value as the target constraint limit value;

[0165] Step S720, if the constraint symbol indicates that the linear combination constructed based on the constraint variable and the constraint coefficient is greater than or equal to the constraint limit value, increment the candidate constraint limit value until the incremented candidate constraint limit value is greater than the Frobenius number, and use the incremented candidate constraint limit value as the target constraint limit value.

[0166] In step S710 of some embodiments, if the constraint symbol indicates that the linear combination constructed based on the constraint variable and the constraint coefficient is less than or equal to the constraint limit value, that is, the constraint symbol o is "≤", "<", "=", then continuously decrement the candidate constraint limit value by 1, that is, b k -1, to tighten the candidate constraint limit value downward until the decremented candidate constraint limit value is less than the Frobenius number, and take the decremented candidate constraint limit value as the target constraint limit value.

[0167] Considering the capacity constraint in the vehicle path planning problem Can be tightened to Repeat tightening until b k -1 is not in The first value of the non-representable set, that is, the Frobenius number.

[0168] In step S720 of some embodiments, if the constraint symbol indicates that the linear combination constructed based on the constraint variable and the constraint coefficient is greater than or equal to the constraint limit value, that is, the constraint symbol o is "≥", ">", "=", then continuously increment the candidate constraint limit value by 1, that is, b k +1, to perform upward tightening of the right-side value until the incremented candidate constraint limit value is greater than the Frobenius number, and take the incremented candidate constraint limit value as the target constraint limit value.

[0169] Considering the capacity constraint Can be tightened to Repeat tightening until b i +1 is not in The first value of the non-representable set.

[0170] Through the above steps S710 to S720, the infeasible right-side value can be adjusted to the feasible range to reduce the problem scale, thereby improving the efficiency of vehicle path planning.

[0171] In step S170 of some embodiments, update the constraint limit value in the initial constraint condition to the target constraint limit value to obtain the target constraint condition.

[0172] In step S180 of some embodiments, solve the path cost function according to the target constraint condition and other constraint conditions to perform the optimality solution of the vehicle path planning model and obtain the vehicle planning path. In the embodiments of the present application, the Frobenius number can be used to determine the non-representable set of the left-side value of the equation and the possible unique representable number in the preprocessing stage, so as to tighten the right-side value or detect the infeasibility of the constraint in advance, so as to reduce the scale of the vehicle path planning problem by eliminating redundancy, tightening variable bounds, and detecting infeasibility in advance, thereby improving the efficiency of vehicle path planning.

[0173] Please refer toFigure 8 , an embodiment of the present application further provides a vehicle path planning device, which can implement the above vehicle path planning method. The vehicle path planning device includes:

[0174] An acquisition module 810, configured to acquire a vehicle path planning model, where the vehicle path planning model includes a path cost function and initial constraint conditions;

[0175] A conversion module 820, configured to convert the initial constraint conditions into integer knapsack constraints to obtain candidate constraint conditions; wherein, the candidate constraint conditions include constraint variables, constraint coefficients, constraint symbols, and constraint limits, and the constraint symbols are used to indicate the relationship between the linear combination constructed based on the constraint variables and the constraint coefficients and the constraint limits;

[0176] A first determination module 830, configured to determine a constraint range according to the value range of the constraint variables and the constraint coefficients;

[0177] A second determination module 840, configured to determine candidate constraint limits according to the constraint range and the constraint limits;

[0178] A calculation module 850, configured to calculate the Frobenius number according to the constraint coefficients;

[0179] A tightening module 860, configured to tighten the candidate constraint limits according to the Frobenius number and the constraint symbols to obtain target constraint limits;

[0180] An update module 870, configured to update the initial constraint conditions according to the target constraint limits to obtain target constraint conditions;

[0181] A solution module 880, configured to solve the path cost function according to the target constraint conditions to obtain a vehicle planning path.

[0182] The specific implementation manner of this vehicle path planning device is basically the same as the specific embodiment of the above vehicle path planning method, and will not be elaborated here.

[0183] An embodiment of the present application further provides an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the above vehicle path planning method. The electronic device can be any intelligent terminal including a tablet computer, an in-vehicle computer, etc.

[0184] Please refer to Figure 9 , Figure 9 which schematically shows the hardware structure of an electronic device in another embodiment. The electronic device includes:

[0185] The processor 910 can be implemented in the form of a general - purpose central processing unit (CPU), a microprocessor, an application - specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0186] The memory 920 can be implemented in the form of a read - only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 920 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 920 and are called by the processor 910 to execute the vehicle path planning method of the embodiments of the present application;

[0187] The input / output interface 930 is used to implement information input and output;

[0188] The communication interface 940 is used to implement communication interaction between this device and other devices. It can implement communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.);

[0189] The bus 950 transmits information between the various components of the device (such as the processor 910, the memory 920, the input / output interface 930, and the communication interface 940);

[0190] Among them, the processor 910, the memory 920, the input / output interface 930, and the communication interface 940 are communicatively connected to each other inside the device through the bus 950.

[0191] The embodiments of the present application also provide a computer - readable storage medium. The computer - readable storage medium stores a computer program, and when the computer program is executed by a processor, the above - mentioned vehicle path planning method is implemented.

[0192] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory may optionally include memories that are remotely located relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0193] The embodiments described in the embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.

[0194] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or combine certain steps, or different steps.

[0195] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0196] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and appropriate combinations thereof.

[0197] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above figures are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0198] It should be understood that in this application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one (item) of the following" or its similar expressions refer to any combination of these items, including any combination of single items (items) or plural items (items). For example, at least one (item) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0199] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in electrical, mechanical or other forms.

[0200] The units described above as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0201] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0202] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of this application. The aforementioned storage medium includes: various media that can store programs such as USB flash drives, mobile hard disks, read-only memories (ROM for short), random access memories (RAM for short), magnetic disks, or optical discs.

[0203] The preferred embodiments of the embodiments of this application have been described above with reference to the accompanying drawings, and thus do not limit the scope of the rights of the embodiments of this application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of this application shall be within the scope of the rights of the embodiments of this application.

Claims

1. A vehicle path planning method, characterized in that: The method comprises: Acquire a vehicle path planning model, wherein the vehicle path planning model includes a path cost function and initial constraints; Convert the initial constraint condition into an integer knapsack constraint to obtain a candidate constraint condition; wherein the candidate constraint condition includes a constraint variable, a constraint coefficient, a constraint symbol and a constraint limit value, and the constraint symbol is used to indicate the relationship between a linear combination constructed based on the constraint variable and the constraint coefficient and the constraint limit value; Determining a constraint range according to the value range of the constraint variable and the constraint coefficient; Determining candidate constraint limits according to the constraint range and the constraint limit; calculating a Frobenius number based on the constraint coefficient; Tightening the candidate constraint limit value according to the Frobenius number and the constraint sign to obtain a target constraint limit value; Update the initial constraint condition according to the target constraint limit to obtain the target constraint condition; The path cost function is solved according to the target constraint condition to obtain a vehicle planning path.

2. The method according to claim 1, characterized in that: The calculating of the Frobenius number according to the constraint coefficient comprises: Obtain the coefficient number of the constraint coefficient in the candidate constraint condition; If the number of coefficients is equal to two, the constraint coefficients are multiplied to obtain an intermediate coefficient value, and the intermediate coefficient value is subtracted from the constraint coefficient to obtain the Frobenius number; If the number of the coefficients is greater than two, a graphical model is constructed according to the constraint coefficients, and the Frobenius number is calculated according to the graphical model.

3. The method according to claim 2, characterized in that The step of constructing a graphical model according to the constraint coefficients and calculating the Frobenius number according to the graphical model includes: Perform node construction according to the constraint coefficient to obtain coefficient nodes and node identifiers of the coefficient nodes; constructing edges between coefficient nodes according to the constraint coefficients and the node identifiers, and obtaining initial edge weights of the edges; Constructing the graph model according to the coefficient node, the node identifier, the edge and the initial edge weight; the graph model includes a starting coefficient node and a plurality of non-starting coefficient nodes; For each non-starting coefficient node, a reference edge weight from the starting coefficient node to the non-starting coefficient node is obtained from the graph model; the reference edge weight is the sum of the initial edge weights of the edges from the starting coefficient node to the non-starting coefficient node; For each non-starting coefficient node, select the smallest reference edge weight as the target edge weight; The Frobenius number is calculated according to each of the target edge weights.

4. The method according to claim 3, characterized in that The calculating the Frobenius number according to each of the target edge weights comprises: Selecting the largest target edge weight from each of the target edge weights to obtain the maximum weight; Obtaining the number of nodes of the coefficient nodes in the graph model; The maximum weight value is subtracted from the number of nodes to obtain the Frobenius number.

5. The method according to claim 3, characterized in that: The calculating the Frobenius number according to each of the target edge weights comprises: Calculate the unrepresentable set of the candidate constraint limits according to each of the target edge weights; Calculate a representable set of the candidate constraint limits according to the unrepresentable set and each of the target edge weights; The maximum value in the representable set is selected to obtain the Frobenius number.

6. The method according to claim 5, characterized in that After calculating the representable set of the candidate constraint limits according to the unrepresentable set and each of the target edge weights, the vehicle path planning method further includes: Calculating non-negative integer solutions of the constraint variables according to the representable set and the graphical model to obtain deterministic variables; The path cost function is solved according to the deterministic variables and the target constraint conditions to obtain a vehicle planning path.

7. The method according to any one of claims 1 to 6, characterized in that: The step of tightening the candidate constraint limit value according to the Frobenius number and the constraint sign to obtain a target constraint limit value includes: If the constraint sign indicates that the linear combination constructed based on the constraint variable and the constraint coefficient is less than or equal to the constraint limit, the candidate constraint limit is decreased until the decreased candidate constraint limit is less than the Frobenius number, and the decreased candidate constraint limit is used as the target constraint limit; If the constraint symbol indicates that the linear combination constructed based on the constraint variable and the constraint coefficient is greater than or equal to the constraint limit, the candidate constraint limit is incremented until the incremented candidate constraint limit is greater than the Frobenius number, and the incremented candidate constraint limit is used as the target constraint limit.

8. A vehicle path planning device, characterized in that: The device comprises: An acquisition module, used to acquire a vehicle path planning model, wherein the vehicle path planning model includes a path cost function and initial constraints; A conversion module, used for converting the initial constraint condition into an integer knapsack constraint to obtain a candidate constraint condition; wherein the candidate constraint condition includes a constraint variable, a constraint coefficient, a constraint symbol and a constraint limit value, and the constraint symbol is used to indicate the relationship between a linear combination constructed based on the constraint variable and the constraint coefficient and the constraint limit value; A first determination module, used to determine a constraint range according to a value range of the constraint variable and the constraint coefficient; A second determination module, configured to determine a candidate constraint limit value according to the constraint range and the constraint limit value; A calculation module, used for calculating the Frobenius number according to the constraint coefficient; A tightening module, used for tightening the candidate constraint limit value according to the Frobenius number and the constraint sign to obtain a target constraint limit value; An updating module, used for updating the initial constraint condition according to the target constraint limit to obtain the target constraint condition; The solution module is used to solve the path cost function according to the target constraint condition to obtain the vehicle planning path.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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