A path planning method for multi-period inspection of curb parking spaces

Abstract

The application discloses a kind of path planning methods of multi-period inspection of in-road parking space, comprising:1 establishes two-level road network, sets inspection period, obtains the length of each parking point in node set The driving time of the road section where each parking point is located;2 obtain the distance between each node in node set, generate driving time matrix;3 establish parking point multi-period inspection path planning model;4 define the set of destruction operators and the set of repair operators;5 solve parking point multi-period inspection path planning model, obtain optimal path planning scheme.The application can combine the actual of current in-road parking space inspection, consider multi-period collaborative arrangement inspection to optimize path planning scheme, so as to reduce energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of on-street parking inspection, specifically a path planning method for multi-cycle inspection of on-street parking spaces. Background Technology

[0002] The Vehicle Routing Problem (VRP) is a common combinatorial optimization problem in transportation and scheduling, aiming to find the optimal route planning scheme while satisfying constraints such as customer point demand. In the scenario of on-street parking space inspection, the service object changes from discrete customer points to continuous road segments. Since VRP cannot directly describe the road segment coverage characteristics, it is usually modeled as the Arc Routing Problem (ARP). However, current ARP models for describing parking inspection problems only consider a single inspection cycle. When parking points in the road network have multiple inspection cycles, existing models cannot adequately handle the situation. Summary of the Invention

[0003] The present invention aims to address the shortcomings of the existing technology by proposing a path planning method for multi-period inspection of on-street parking spaces. This method aims to optimize the path planning scheme by considering multi-period collaborative inspection arrangements, thereby reducing energy consumption, in light of the current on-street parking space inspection practices.

[0004] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:

[0005] The method for path planning of multi-period inspection of on-street parking spaces in this invention is characterized by treating road segments as nodes in a secondary road network, and using the shortest path between the end point of any road segment and the starting point of the next road segment as an arc in the secondary road network, thereby constructing a secondary network. Where N represents a node in the secondary road network, and , Represents the starting set, Represents the final set. Let n represent the set of parking spots, with m being the number of both the starting point and the ending point, and the two points being in one-to-one correspondence. Indicates an arc in a secondary road network. , Let represent the arc between any node i and node j. Based on different inspection cycles, parking points are divided into r types. Let the set of inspection vehicles K contain r×m inspection vehicles. A route is formed by several parking points traversed by each inspection vehicle from the starting point to the ending point. A path planning scheme is formed by the routes of all inspection vehicles. The path planning method is performed according to the following steps:

[0006] Step 1: Set the inspection period E, and obtain the average speed and maximum speed of vehicles traveling at the parking points within the inspection period E;

[0007] Step 2: Obtain the length of the road segment where each parking point is located in the node set N, and calculate the travel time of each parking point road segment based on the average speed of each inspection vehicle.

[0008] Obtain the distance between each node in the node set N, and calculate the travel time between each node based on the maximum speed of each inspection vehicle, thereby generating a travel time matrix;

[0009] Step 3: Establish a multi-cycle inspection route planning model for parking spots;

[0010] Step 4: Define the set of destruction operators and the set of repair operators The execution operation;

[0011] Step 5: Solve the parking spot inspection route planning model based on the operator set to obtain the optimal route planning scheme;

[0012] Step 5.1: Set and initialize the current iteration number The maximum number of iterations is defined as A greedy strategy is used to generate the I-th generation path planning scheme. To make the optimal path planning scheme ; Calculate the temperature of the first generation ,in, These are temperature control parameters;

[0013] Step 5.2: Apply the destruction and repair operators to the pair The updated I-generation path planning scheme is obtained by performing an update. ;

[0014] Step 5.3: Let Assign to Afterwards, if Then the optimal path planning scheme will be output. Otherwise, calculate the temperature of the (I+1)th iteration. ,Will As the I+1 generation path planning scheme Return to step 5.2 and execute sequentially, where c is the cooling coefficient.

[0015] The method for path planning of multi-period inspection of on-street parking spaces described in this invention is also characterized in that step 3 includes:

[0016] Step 3.1: Construct the objective function Z of the parking spot inspection route planning model using equation (1);

[0017] (1)

[0018] In equation (1), This indicates the starting point to which inspection vehicle k is assigned. , ; This indicates that the inspection vehicle k starts from the origin. After departure, should parking point j be inspected immediately? If so, then... Otherwise, let ; This represents the outgoing nodes of node i. Indicates whether the route of inspection vehicle k in the path planning scheme includes an arc. That is, whether the inspection vehicle k immediately inspects parking point j after inspecting parking point i; if so, then... Otherwise, let ; This represents the longest single inspection time for inspection vehicle k. This represents the fixed energy consumption of the inspection vehicle during the inspection period E. This indicates the energy consumption per unit distance of the inspection vehicle. For coefficients; Represents arc Length, Indicates the length of the road segment where parking point i is located;

[0019] Step 3.2: Construct the constraints of the parking spot inspection route planning model using equations (2) to (11):

[0020] (2)

[0021] (3)

[0022] (4)

[0023] (5)

[0024] (6)

[0025] (7)

[0026] (8)

[0027] (9)

[0028] (10)

[0029] (11)

[0030] In equations (2) to (11), This indicates the destination of inspection vehicle k. This indicates the inspection cycle for parking point i; Represents arc The passage time, Indicates the inspection time for parking point i; This indicates the time when inspection vehicle k arrives at parking point j and begins its inspection. M represents the time when inspection vehicle k arrives at parking point i and begins inspection; M is a positive number. Indicates whether the route of inspection vehicle k in the path planning scheme includes an arc. That is, whether the inspection vehicle k immediately inspects parking point i after inspecting parking point j; if so, then... Otherwise, let ; This represents the connected node of node i. Indicates whether the route of inspection vehicle k in the path planning scheme includes an arc. That is, the inspection vehicle K arrives at the destination Had parking spot i been inspected previously? If so, then... Otherwise, let .

[0031] Furthermore, step 4 includes:

[0032] Step 4.1: Define the first destruction operator The operations performed include:

[0033] Step 4.1.1: In the current path planning scheme Randomly select a parking spot i and add it to the set of deleted parking spots D; add all parking spots not included in D to the new set of parking spots L.

[0034] Step 4.1.2: Randomly select a parking point i from D, and calculate the similarity between parking point i and each parking point in L using equation (12). Then, sort all parking points in L in ascending order based on the similarity to obtain the sorted set of parking points. ;

[0035] (12)

[0036] In equation (12), It is the travel time coefficient. It is the demand coefficient; This indicates the inspection time for parking point j in L;

[0037] Step 4.1.3: Generate random numbers and calculate the index. ,in, express The number of parking spots, where h represents a coefficient;

[0038] Step 4.1.4: Obtain The index is parking spot and parking spot After adding it to D, the parking point from Remove from the middle;

[0039] Step 4.1.5: If the number of parking spots in D reaches q, then from Delete all parking spots in D and update The inspection cycle of all lines is used to obtain a route planning scheme after damage. as well as Check the inspection cycle of all lines; otherwise, return to step 4.1.2 and execute sequentially.

[0040] Step 4.2: Define the second type of destruction operator The execution operation is performed in the current path planning scheme. Randomly select a parking spot i from the list, and from... After deletion, add it to the set D of deleted parking spots, and update. The inspection cycle for all routes is repeated until the number of deleted parking points reaches q, thus obtaining the route planning scheme after the disruption. as well as The inspection cycle for all lines in the system;

[0041] Step 4.3: Define the first repair operator The operations performed include:

[0042] Step 4.3.1: Assume any parking point insert At each position along each line, the increment of the objective function value at different positions is obtained;

[0043] Step 4.3.2: Find the one that makes After finding the optimal position with the smallest increment of the objective function value, the parking point will be... Insert into path planning scheme At the optimal position of the corresponding line, and Delete from D until Thus, candidate path planning schemes are obtained. ;

[0044] Step 4.4: Define the second type of repair operator The operations performed include:

[0045] Step 4.4.1: Assume any parking point Insert into At the optimal position of each line, the optimal position makes The objective function value increment is minimized, and the increment of the objective function value of each line after insertion is added to the increment set. middle;

[0046] Step 4.4.2: For After sorting the increments in ascending order, we get the sorted increment set. Calculate the regret value at parking point i. ;in, , They represent The first and second increments;

[0047] Step 4.4.3: Select the parking point corresponding to the maximum regret value in D. And insert into Among all the optimal positions of the lines, the optimal position is one that makes To minimize the increment of the objective function value, Delete from D until So far, candidate path planning schemes have been obtained. .

[0048] Furthermore, step 5.2 includes:

[0049] Step 5.2.1: Use the roulette wheel selection method to select from... and Select the destruction operator in the I-th iteration. and repair operator And form the operator pair in the I-th iteration. ;

[0050] Step 5.2.2: Utilize the destruction operator in the I-th iteration right After the destruction is performed, the path scheme after destruction in the I-th iteration is obtained. and the set of nodes removed in the I-th iteration ;

[0051] Step 5.2.3: According to Using the repair operator in the I-th iteration right After repair, the candidate path planning scheme for the I-th iteration is obtained. ;

[0052] Step 5.2.4: Calculate using equation (1) respectively. objective function value and objective function value Therefore, the calculation of the I-th iteration is performed according to equation (14). Acceptance probability :

[0053] (14)

[0054] Step 5.2.5: Generate pseudo-random numbers for the I-th iteration ;like or Then As an updated first-generation path planning scheme and order objective function value Otherwise, As an updated first-generation path planning scheme and order ;

[0055] Step 5.2.6: If Then let ;otherwise, It remains unchanged.

[0056] The present invention provides an electronic device, including a memory and a processor, wherein the memory is used to store a program that supports the processor in executing the path planning method, and the processor is configured to execute the program stored in the memory.

[0057] The present invention discloses a computer-readable storage medium on which a computer program is stored, wherein the computer program is executed by a processor to perform the steps of the path planning method.

[0058] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0059] 1. This invention establishes a secondary road network, considers parking spots with multiple inspection cycles within the road network, uses the total energy consumption during the inspection period as the objective function, and utilizes the VRP model to construct a mixed integer linear programming model that minimizes the total energy consumption. This overcomes the shortcomings of the existing ARP model, which cannot describe the multi-cycle inspection path planning problem of on-street parking spaces, thereby significantly reducing inspection energy consumption under complex road networks and providing a scientific decision-making solution that balances efficiency and sustainability for city-level smart parking management.

[0060] 2. This invention employs a large-scale neighborhood search algorithm with a hybrid simulated annealing mechanism to solve the mixed integer linear programming model. By utilizing the advantage of the simulated annealing mechanism in escaping local optima, it compensates for the premature convergence of general large neighborhood search algorithms, thereby obtaining the inspection path planning scheme with the lowest total energy consumption. Attached Figure Description

[0061] Figure 1 This is a flowchart of the large-scale neighborhood search algorithm based on the hybrid simulated annealing mechanism of this invention;

[0062] Figure 2 This is a berth diagram for one side of the one-way lane in this invention;

[0063] Figure 3 This is a diagram showing the berths on both sides of the one-way lane in this invention;

[0064] Figure 4 This is a diagram showing the parking spaces on both sides and one side of the two-way lane in this invention;

[0065] Figure 5 This is an example diagram of the inspection path planning scheme of the present invention. Detailed Implementation

[0066] In this embodiment, a path planning method for multi-period inspection of on-street parking spaces uses road segments as nodes in a secondary road network and the shortest path between the end point of any road segment and the starting point of the next road segment as an arc in the secondary road network, thereby constructing a secondary network. Where N represents a node in the secondary road network, and , Represents the starting set, Represents the final set. Let n represent the set of parking spots, with m being the number of both the starting point and the ending point, and the two points being in one-to-one correspondence. Indicates an arc in a secondary road network. , Represents the arc between any node i and node j; Figure 2 , Figure 3 , Figure 4 The diagram illustrates the relationship between parking spaces, traffic flow, and the direction of patrol vehicle travel. Solid arrows indicate traffic flow direction, while dashed arrows indicate the direction of patrol vehicle travel. Figure 2 Parts (a1) and (b1) in the text indicate that parking spaces are set up on the right side of the one-way traffic flow, respectively. Figure 2 Parts (a2) and (b2) in the text indicate that parking spaces are set up on the left side of the one-way traffic flow, respectively. Figure 3 Parts (a) and (b) in the text indicate that berths are set up on both sides of the one-way street, respectively. Figure 4 Parts (a1) and (b1) in the text indicate that parking spaces are set up on both sides of the two-way lane, respectively. Figure 4 Parts (a2) and (b2) in the diagram represent parking spaces set up on one side of a two-way lane, respectively. Based on different inspection cycles, parking points are divided into r types. Let the inspection vehicle set K contain r×m inspection vehicles. A route is formed by several parking points passed by each inspection vehicle from its starting point to its ending point. All the routes of the inspection vehicles constitute a path planning scheme. A path planning scheme contains multiple routes consisting of the vehicle's starting point, ending point, and the inspection parking points, such as... Figure 5 As shown, Figure 5 The numbers on the middle section indicate the section and parking spot numbers, and the numbers in the circles indicate the intersection numbers. Two routes are marked in blue and red respectively. Solid arrows indicate that the inspection vehicle is inspecting parking spots on the section, and dashed arrows indicate that it is passing directly through the section. This route planning method is carried out in the following steps:

[0067] Step 1: Set the inspection period E, which is usually several consecutive hours in a day, and obtain the average speed and maximum speed of vehicles traveling at the parking points within the inspection period E; vehicles use the average speed to pass through the road section where the parking point is located, and use the maximum speed to reach the next parking point from the current parking point.

[0068] Step 2: Obtain the length of the road segment where each parking point is located in the node set N, and calculate the travel time of each parking point road segment based on the average speed of each inspection vehicle.

[0069] Obtain the distance between each node in the node set N, and calculate the travel time between each node based on the maximum speed of each inspection vehicle, thereby generating a travel time matrix.

[0070] Step 3: Establish a multi-cycle inspection route planning model for parking spots;

[0071] Step 3.1: Construct the objective function Z of the parking spot inspection route planning model using equation (1);

[0072] (1)

[0073] In equation (1), This indicates the starting point to which inspection vehicle k is assigned. , ; This indicates that the inspection vehicle k starts from the origin. After departure, should parking point j be inspected immediately? If so, then... Otherwise, let ; Let i be the set of outgoing nodes of node i, for any The set of connected nodes includes the starting point and all other parking points except for i; for any , The set of nodes connected to the destination includes the destination and all parking points; Indicates whether the route of inspection vehicle k in the path planning scheme includes an arc. That is, whether the inspection vehicle k immediately inspects parking point j after inspecting parking point i; if so, then... Otherwise, let ; This represents the longest single inspection time for inspection vehicle k. This represents the fixed energy consumption of the inspection vehicle during the inspection period E. This indicates the energy consumption per unit distance of the inspection vehicle. For coefficients; Represents arc Length, This indicates the length of the road segment where parking point i is located.

[0074] Step 3.2: Construct the constraints of the parking spot inspection route planning model using equations (2) to (11):

[0075] (2)

[0076] (3)

[0077] (4)

[0078] (5)

[0079] (6)

[0080] (7)

[0081] (8)

[0082] (9)

[0083] (10)

[0084] (11)

[0085] Equation (2) indicates that a vehicle is assigned to inspect any parking point; Equation (3) ensures that any vehicle must start from its starting point; Equation (4) ensures that the route of any vehicle from its starting point to its destination is continuous, that is, for each vehicle, it must leave from parking point i after arriving at parking point i; Equation (5) indicates that any vehicle must eventually reach its destination; Equation (6) indicates that the number of vehicles used for inspection does not exceed a given number; Equation (7) indicates that the travel time of any vehicle cannot exceed the minimum cycle of the parking points visited, so as to meet the inspection time interval requirements of each parking point; Equation (8) indicates that the travel time of any vehicle does not exceed the maximum duration; Equation (9) indicates the relationship between the start service times of two nodes in the sequential service; Equation (10) indicates that the access time meets the time window, that is, it ensures that the access time is within the inspection cycle of the parking point; Equation (11) indicates the range of values ​​for the decision variable.

[0086] In equations (2) to (11), This indicates the destination of inspection vehicle k. This indicates the inspection cycle for parking point i; Represents arc The passage time, Indicates the inspection time for parking point i; This indicates the time when inspection vehicle k arrives at parking point j and begins its inspection. M represents the time when inspection vehicle k arrives at parking point i and begins inspection; M is a positive number. Indicates whether the route of inspection vehicle k in the path planning scheme includes an arc. That is, whether the inspection vehicle k immediately inspects parking point i after inspecting parking point j; if so, then... Otherwise, let ; Denotes the set of connected nodes of node i, for any The set of connected nodes includes the starting point and all other parking points except i; Indicates whether the route of inspection vehicle k in the path planning scheme includes an arc. That is, the inspection vehicle K arrives at the destination Had parking spot i been inspected previously? If so, then... Otherwise, let .

[0087] Step 4: Define the set of destruction operators and the set of repair operators The execution operation;

[0088] Step 4.1: Define the first destruction operator The operations performed include:

[0089] Step 4.1.1: In the current path planning scheme Randomly select a parking spot i and add it to the set of deleted parking spots D; add all parking spots not included in D to the new set of parking spots L.

[0090] Step 4.1.2: Randomly select a parking point i from D, and calculate the similarity between parking point i and each parking point in L using equation (12). Then, sort all parking points in L in ascending order based on the similarity to obtain the sorted set of parking points. ;

[0091] (12)

[0092] In equation (12), It is the travel time coefficient. It is the demand coefficient; This indicates the inspection time for parking point j in L;

[0093] Step 4.1.3: Generate random numbers and calculate the index. ,in, express The number of parking spots, where h represents a coefficient;

[0094] Step 4.1.4: Obtain The index is parking spot and parking spot After adding it to D, the parking point from Remove from the middle;

[0095] Step 4.1.5: If the number of parking spots in D reaches q, then from Delete all parking spots in D and update The inspection cycle of all lines is used to obtain a route planning scheme after damage. as well as Check the inspection cycle of all lines; otherwise, return to step 4.1.2 and execute sequentially.

[0096] Step 4.2: Define the second type of destruction operator The execution operation is performed in the current path planning scheme. Randomly select a parking spot i from the list, and from... After deletion, add it to the set D of deleted parking spots, and update. The inspection cycle for all routes is repeated until the number of deleted parking points reaches q, thus obtaining the route planning scheme after the disruption. as well as The inspection cycle for all lines in the system;

[0097] Step 4.3: Define the first repair operator The execution operation is to insert the parking point in D into the disrupted path planning scheme. The optimal position is determined until all deleted parking points in D are reinserted into the disrupted path planning scheme. Thus, candidate path planning schemes are obtained. ,include:

[0098] Step 4.3.1: Assume any parking point insert At each position along each line, the increment of the objective function value at different positions is obtained;

[0099] Step 4.3.2: Find the one that makes After finding the optimal position with the smallest increment of the objective function value, the parking point will be... Insert into path planning scheme At the optimal position of the corresponding line, and Delete from D until Thus, candidate path planning schemes are obtained. .

[0100] Step 4.4: Define the second type of repair operator The execution operation involves selecting the parking point with the highest regret value at each step and inserting it into the optimal position of the disrupted path planning scheme. This process continues until all deleted parking points are reinserted into the disrupted path planning scheme, thus obtaining candidate path planning schemes. ,include:

[0101] Step 4.4.1: Assume any parking point Insert into At the optimal position of each line, the optimal position makes The objective function value increment is minimized, and the increment of the objective function value of each line after insertion is added to the increment set. middle;

[0102] Step 4.4.2: For After sorting the increments in ascending order, we get the sorted increment set. Calculate the regret value at parking point i. ;in, , They represent The first and second increments;

[0103] Step 4.4.3: Select the parking point corresponding to the maximum regret value in D. And insert into Among all the optimal positions of the lines, the optimal position is one that makes To minimize the increment of the objective function value, Delete from D until So far, candidate path planning schemes have been obtained. .

[0104] Step 5: Based on the operator set, solve the parking spot inspection path planning model to obtain the optimal path planning scheme. The algorithm flow is as follows: Figure 1 As shown;

[0105] Step 5.1: Set and initialize the current iteration number The maximum number of iterations is defined as A greedy strategy is used to generate the I-th generation path planning scheme. To make the optimal path planning scheme ; Calculate the temperature of the first generation ,in, These are temperature control parameters;

[0106] Step 5.2: Apply the destruction and repair operators to the pair The updated I-generation path planning scheme is obtained by performing an update. ;

[0107] Step 5.2.1: Use the roulette wheel selection method to select from... and Select the destruction operator in the I-th iteration. and repair operator And form the operator pair in the I-th iteration. Among them, the destruction operator The number q of parking spots to be deleted satisfies , It is a coefficient.

[0108] Step 5.2.2: Utilize the destruction operator in the I-th iteration right After the destruction is performed, the path scheme after destruction in the I-th iteration is obtained. and the set of nodes removed in the I-th iteration ;

[0109] Step 5.2.3: According to Using the repair operator in the I-th iteration right After repair, the candidate path planning scheme for the I-th iteration is obtained. ;

[0110] Step 5.2.4: Calculate using equation (1) respectively. objective function value and objective function value Therefore, the calculation of the I-th iteration is performed according to equation (14). Acceptance probability :

[0111] (14)

[0112] Step 5.2.5: Generate pseudo-random numbers for the I-th iteration ;like or Then As an updated first-generation path planning scheme and order objective function value Otherwise, As an updated first-generation path planning scheme and order .

[0113] Step 5.2.6: If Then let ;otherwise, It remains unchanged.

[0114] Step 5.3: Let Assign to Afterwards, if Then the optimal path planning scheme will be output. Otherwise, calculate the temperature of the (I+1)th iteration. ,Will As the I+1 generation path planning scheme Return to step 5.2 and execute sequentially, where c is the cooling coefficient.

[0115] In this embodiment, an electronic device includes a memory and a processor. The memory stores a program that supports the processor in executing the above-described method, and the processor is configured to execute the program stored in the memory.

[0116] In this embodiment, a computer-readable storage medium stores a computer program, which is executed by a processor to perform the steps of the above method.

Claims

1. A path planning method for multi-period inspection of curb parking spaces, characterized in that, is to take the road section as the node in the secondary road network, and take the shortest path between the end point of any road section and the start point of the next road section as the arc in the secondary road network, thereby constructing the secondary network wherein N represents the node in the secondary road network, and represents the start point set, represents the end point set, represents the set of parking points, let the number of start points and end points be m, and be one-to-one corresponding, and the number of parking points be n; represents the arc in the secondary road network, represents the arc between any node i and node j, according to different inspection periods, the parking points are divided into r types; let the set K of inspection vehicles contain r x m inspection vehicles, a route is formed by the several parking points passed through by each inspection vehicle from the start point to the end point, and a path planning scheme is formed by the routes of all inspection vehicles, and the path planning method is performed according to the following steps:​​ Step 1: set the inspection period E, and obtain the average vehicle speed and the highest vehicle speed of the vehicle running on the parking point in the inspection period E; Step 2: obtain the length of the road section where each parking point in the node set N is located, and calculate the running time of the road section where each parking point is located according to the average speed of each inspection vehicle; Obtain the distance between each node in the node set N, and calculate the running time between each node according to the highest speed of each inspection vehicle, thereby generating a running time matrix; Step 3: establish a multi-period inspection path planning model of the parking point; Step 3.1: use formula (1) to construct the objective function Z of the parking point inspection path planning model; (1) in formula (1), denotes the starting point assigned to the inspection vehicle k, , ; denotes whether the inspection vehicle k inspects the parking point j immediately after the starting point , if yes, then , otherwise, ; denotes the out-coming node of the node i, denotes whether the route of the inspection vehicle k in the path planning scheme contains the arc , i.e., whether the inspection vehicle k inspects the parking point j immediately after the parking point i, if yes, then , otherwise, ; denotes the single longest inspection time of the inspection vehicle k, denotes the fixed energy consumption of the inspection vehicle during the inspection period E, denotes the unit distance energy consumption of the inspection vehicle, is a coefficient; denotes the length of the arc , denotes the length of the road section where the parking point i is located; Step 3.2: use formula (2) to formula (11) to construct the constraint condition of the parking point inspection path planning model: (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) in formulas (2)-(11), denotes the end point of the inspection vehicle k, denotes the inspection cycle of the parking point i; denotes the passing time of the arc , denotes the inspection time of the parking point i; denotes the time when the inspection vehicle k arrives at the parking point j and starts inspection, denotes the time when the inspection vehicle k arrives at the parking point i and starts inspection, M is a positive number; denotes whether the route of the inspection vehicle k in the path planning scheme contains the arc , i.e. whether the inspection vehicle k inspects the parking point i immediately after inspecting the parking point j, if yes, then , otherwise, ; denotes the incoming node of the node i, denotes whether the route of the inspection vehicle k in the path planning scheme contains the arc , i.e. whether the inspection vehicle k inspects the parking point i before arriving at the end point , if yes, then , otherwise, ; Step 4: Define the set of destroy operators and the set of repair operators performing operations; Step 5: based on the operator set, the parking point inspection path planning model is solved to obtain the optimal path planning scheme; Step 5.1: Set and initialize the current iteration number , define the maximum iteration number as , generate the Ith generation path planning scheme using a greedy strategy , let the optimal path planning scheme ; calculate the Ith generation temperature , where is the temperature control parameter; Step 5.2: update the first generation path planning scheme by using the destruction operator and the repair operator to obtain an updated first generation path planning scheme Step 5.2: update the first generation path planning scheme by using the destruction operator and the repair operator to obtain an updated first generation path planning scheme ; Step 5.3: Let be assigned to After that, if , the optimal path planning scheme is output , otherwise, the temperature of the I+1th iteration is calculated , and is taken as the I+1th path planning scheme , and the step 5.2 is returned for sequential execution, wherein c is the cooling coefficient.

2. The path planning method for multi-cycle inspection of curb parking spaces according to claim 1, characterized in that, Step 4 comprises: Step 4.1 : Defining the first kind of destruction operator The execution operation includes: Step 4.1.1: Randomly select one parking point i in the current path planning scheme and add it to the deleted parking point set D; add all parking points not contained in D to the new parking point set L; Step 4.1.2: Randomly select a parking spot i from D, and calculate the similarity between parking spot i and each parking spot in L using formula (12), then sort all parking spots in L in ascending order according to the similarity, and obtain a sorted parking spot set ; (12) In formula (12), is a travel time coefficient, is a demand coefficient; denotes the inspection time for a stop j in L. Step 4.1.3: Generating a random number and calculating the index wherein, denotes the number of parking points in h, h denotes a coefficient; Step 4.1.4: Obtain The index is parking spot and parking spot After adding it to D, the parking point from Remove from the middle; Step 4.1.5: If the number of parking points in D reaches q, remove all parking points in D from and update the inspection period of all lines in to obtain the damaged path planning scheme and the inspection period of all lines in ; otherwise, return to Step 4.1.2 for sequential execution; Step 4.2: define the second destruction operator The execution operation is to randomly select a parking point i in the current path planning scheme , and delete it from , and add it to the deleted parking point set D, update the inspection period of all lines in , until the number of deleted parking points reaches q, thereby obtaining the destroyed path planning scheme and The inspection period of all lines in ; Step 4.3: Defining the first repair operator The execution operation includes: Step 4.3.1: Assume any stopping point insertion at each position on each route and obtain the increment of the objective function value at different positions; Step 4.3.2: Find the one that makes After finding the optimal position with the smallest increment of the objective function value, the parking point will be... Insert into path planning scheme At the optimal position of the corresponding line, and Delete from D until Thus, candidate path planning schemes are obtained. ; Step 4.4: Defining the second repair operator The execution operation includes: Step 4.4.1: Assume any stop inserted into the optimal position of each line, which makes the increment of the objective function value of the minimum, and add the increment of the objective function value of each line after insertion to the increment set ; Step 4.4.2: Sort the increments in in ascending order to obtain a sorted increment set , calculate the regret value of the parking point i ; wherein, , respectively represent the first and second increments in . Step 4.4.3: Select the stop point corresponding to the maximum regret value in D and inserted into the optimal position of all lines in , which is the position that minimizes the increment of the objective function value of , and is deleted from D until , obtaining the candidate path planning scheme .

3. The path planning method for multi-cycle inspection of curb parking spaces according to claim 2, wherein, Step 5.2 comprises: Step 5.2.1 : Select the disruptor and the repairer for the Ith iteration from the sets and respectively using the roulette wheel selection method and form the pair of operators for the Ith iteration ; Step 5.2.2: Utilize the destruction operator under the Ith iteration to destroy the path scheme under the Ith iteration After destruction, the path scheme under the Ith iteration after destruction is obtained and the set of removed nodes under the Ith iteration ; Step 5.2.3: According to , the repair operator under the Ith iteration is used to repair , and the candidate path planning scheme under the Ith iteration is obtained. Step 5.2.4: Calculate the objective function value of formula (1) respectively :​​​​​ (14) Step 5.2.5: generate pseudo-random number under the first iteration ; if or , then is the updated path planning scheme under the Ith iteration , and let be the objective function value of ; otherwise, is the updated path planning scheme under the Ith iteration , and let ; Step 5.2.6: If then let ; otherwise, remain unchanged.

4. An electronic device comprising a memory and a processor, characterized in that The memory is used to store a program supporting the processor to execute the path planning method in any one of claims 1-3, and the processor is configured to execute the program stored in the memory.

5. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to execute the steps of the path planning method in any one of claims 1-3.

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