A city roadside parking space inspection path planning method

Abstract

The present application relates to the technical field of urban intelligent parking and traffic engineering, and discloses a kind of urban roadside parking space inspection path planning method and system.The method constructs the secondary road network into the first-level road network, and the inspection section is abstracted as the node of secondary road network, and the non-inspection section is abstracted as arc, the driving time of inspection vehicle in inspection section and non-inspection section is calculated, and the road network driving time matrix is generated;The length of inspection section and the length of non-inspection section are respectively converted to generate the corresponding energy consumption cost;Based on the driving time matrix and the energy consumption cost, the inspection path planning model containing exclusive constraint conditions is established;Define the field operator set and the local search operator set;Based on the operator set, the variable field algorithm is used to solve the inspection path planning model containing exclusive constraint conditions, and the optimal inspection path planning scheme is obtained.

Description

Technical Field

[0001] This invention relates to the field of urban smart parking and traffic engineering technology, and in particular to a method for planning inspection routes for urban roadside parking spaces. Background Technology

[0002] Roadside parking space inspection is a core component of a smart parking management system. Electric inspection vehicles must complete inspection tasks such as checking the status of roadside parking spaces, obtaining evidence of illegal parking, and calculating parking space utilization rates according to planned routes. The rationality of the inspection route directly determines the inspection efficiency and energy consumption costs. Roadside parking space inspection route planning is essentially an Arc Routing Problem (ARP), the core of which is to optimize the inspection vehicle's travel route while meeting constraints such as inspection cycle, vehicle range, and travel time, thereby minimizing total energy consumption.

[0003] Existing technologies for urban roadside parking space inspection route planning still have several shortcomings: First, existing planning models do not incorporate dedicated charging constraints based on the range characteristics of electric inspection vehicles, only considering conventional constraints such as inspection cycle and driving time. When the cumulative mileage of the inspection vehicle exceeds the full-charge range threshold, power outages are likely to occur midway, leading to interruptions in the inspection task and failing to guarantee the continuity of inspection operations. Second, existing road network modeling methods do not closely match the actual scenario of roadside parking space inspection, failing to abstract the inspection segments in the urban primary road network into... The abstraction of the passageway between nodes and inspection sections into arcs results in low modeling accuracy, leading to poor matching between path planning results and actual inspection operations. Third, there are significant differences in the driving speed and energy consumption characteristics of electric inspection vehicles on inspection sections and non-inspection sections. Existing models do not perform differentiated calculations on the energy consumption of the two types of sections, resulting in a large deviation between energy consumption assessment and reality. Fourth, existing solution algorithms mostly adopt a single neighborhood search strategy, which has a limited search space and is prone to getting trapped in local optima, making it difficult to obtain a globally optimal inspection path solution under complex urban road networks. Summary of the Invention

[0004] To address the technical problems of existing urban roadside parking space inspection route planning methods, such as the modeling method being out of touch with the actual scenario, the lack of specific constraints for electric inspection vehicles, the failure to perform differentiated calculations on energy consumption for two types of road sections, the large deviation between energy consumption assessment and reality, and the difficulty in obtaining globally optimal inspection route solutions under complex urban road networks, this invention provides an urban roadside parking space inspection route planning method.

[0005] To achieve the above objectives, the present invention adopts the following technical solution, including: A method for planning inspection routes for roadside parking spaces in urban areas includes the following steps: S1, Construct a secondary road network: Abstract the inspected road segments in the primary urban road network into nodes of the secondary road network, and the non-inspected road segments into arcs, and obtain the length of the inspected road segment corresponding to the node and the length of the non-inspected road segment corresponding to the arc. S2, calculate the travel time of the inspection vehicle on the inspected road segment and the non-inspected road segment, and generate the road network travel time matrix; S3 converts the length of the inspected road section and the length of the non-inspected road section into corresponding energy consumption costs; S4, based on the driving time matrix and energy consumption cost, establishes an inspection path planning model with specific constraints; S5 defines the set of domain operators and the set of local search operators; S6, based on the operator set, uses a variable domain algorithm to solve the inspection path planning model with specific constraints, and obtains the optimal inspection path planning scheme.

[0006] Preferably, the specific process of step S1 is as follows: S11 abstracts the roadside parking spaces that need to be inspected in the city's primary road network into nodes of the secondary road network, and the roadside parking spaces that need to be inspected are the inspection sections. S12 defines the shortest travel path between the end point of the previous inspection segment and the start point of the next inspection segment as a non-inspection segment, and abstracts the non-inspection segment as an arc of the secondary road network. S13, Construct a secondary network ,in, N For a set of points, , The starting set, For the final set, For the set of inspection nodes, For charging stations Indicates an arc in a secondary road network. , Represents any node arrive The arc between; S14, obtain the length of the inspected road segment corresponding to the node and the length of the non-inspected road segment corresponding to the arc.

[0007] Preferably, the specific process of step S2 is as follows: S21, Set inspection periods ; S22, Obtain Inspection Period Within the inspection area, the average speed of the inspection vehicles meets the speed limit and inspection requirements. S23, Calculate the travel time of the inspection vehicle in each inspection section based on the length of each inspection section and the average vehicle speed. S24, Obtain Inspection Period The maximum speed of the internal inspection vehicle between inspection nodes is used to calculate the travel time of the inspection vehicle on each non-inspection section based on the maximum speed. S25. Based on the travel time of the inspection vehicle on the inspection section and the non-inspection section, a road network travel time matrix is ​​generated.

[0008] Preferably, the specific process of step S3 is as follows: S31, based on the same type of inspection vehicle, calibrates its energy consumption per unit mileage in inspection sections and non-inspection sections respectively. S32, separately count the mileage of inspected road sections and the mileage of non-inspected road sections; S33, the total energy consumption is calculated by weighting the mileage of the inspected road section and the mileage of the non-inspected road section and the corresponding energy consumption per kilometer.

[0009] Preferably, the objective function of the multi-objective urban roadside parking space inspection path planning model constructed in step S4 is: ; in, For inspection vehicles during inspection periods E Fixed energy consumption within; Energy consumption per unit mileage of the inspection vehicle; This is the percentage coefficient; For inspection vehicle The starting point , , This represents the total number of inspection vehicles. For inspection vehicle Starting from the beginning Drive directly to the inspection point , If yes, then it is 1; otherwise, it is 0. From node The set of connected nodes; This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? That is, to arrive Is the next point inspected by the patrol vehicle a... If yes, the value is 1; otherwise, it is 0. Represents arc Length; Indicates the inspection section Length; This is the inspection period; Indicates patrol vehicle The longest single service time.

[0010] Preferably, the constraints of the objective function of the multi-objective urban roadside parking space inspection path planning model are as follows: S401, at any parking spot, a vehicle is assigned to patrol it: ; S402, all vehicles must depart from its starting point: ; S403, the route for any vehicle from its starting point to its destination is continuous: ; in, This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? ; From node The set of connected nodes; S404, every vehicle must eventually reach the finish line: ; in, Represents a node i Belongs to node j Connect one point in the set of outgoing nodes; This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? ; S405, the inspection vehicle may pass by charging stations: ; in, This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? ; S406, the number of vehicles used for inspection does not exceed a given number. m : ; S407, the travel time of any vehicle must not exceed the minimum cycle time of the visited parking points to meet the inspection interval requirements of each parking point: ; in, Indicates inspection nodes The inspection cycle; S408, the travel time of any vehicle shall not exceed the maximum duration: ; in, Represents arc The passage time, Represents a node Inspection time; S409, the relationship between the start times of two nodes in sequential service: ; in, Indicates patrol vehicle Reaching the node And the start time of the inspection, Indicates patrol vehicle Reaching the node And the start time of the inspection, It is a pre-defined positive number; S410, the access time meets the time window, that is, the access time is guaranteed to be within the inspection cycle of the parking point: ; S411, The range of values ​​for the decision variable: ; S412, if the inspection vehicle's mileage exceeds the threshold, it must proceed to a charging station during this inspection mission: ; ; ; ; in, Indicates patrol vehicle The cumulative mileage driven to complete a single inspection task. This indicates the mileage threshold for a single inspection by the patrol vehicle; Indicates patrol vehicle Whether the cumulative mileage exceeds the preset threshold.

[0011] Preferably, step S5 defines the neighborhood operator. Define local search operators ; S51, Define the first type of neighborhood operator The operations include: S511, from the current path planning scheme Randomly select a non-empty path Let its inspection point sequence be denoted as ; S512, Determine the length of the random sub-segment and in Randomly select continuous segments ; S513, for the selected sub-segment Perform a reverse operation to obtain the reversed subarray. ;Will sub-segments in Replace with A new path is obtained. The new inspection point sequence is as follows: ; S514, if new path If feasible, then use replace Update the path planning scheme for If not feasible, return to step S511 to reselect the path and sub-segment until a feasible neighborhood solution is obtained. S52, defining the second type of neighborhood operator The operations include: S521, from the current route planning scheme Two non-empty paths are randomly selected. and The inspection point sequences are as follows: and ; S522, Determine the length of the random sub-segment and in Randomly select continuous segments ,exist Randomly select continuous segments ; S523, from Delete And randomly select a position to insert Candidate paths are obtained. ;from Delete And randomly select a position to insert Candidate paths are obtained. ; S524, if candidate path and If feasible, then use and replace and Update the path planning scheme for If not feasible, return to step S521 to reselect paths and sub-segments until a feasible neighborhood solution is obtained. S53, select the scheme with the smallest incremental value obtained in steps S51 and S52. ; S54, defining the first type of local search operator. The operations include: S541, from the current path planning scheme Two non-empty paths are randomly selected. and The inspection point sequences are as follows: and ; S542, Determine the length of the random sub-segment and in Randomly select continuous segments ,exist Randomly select continuous segments and sub-segment and Swap to the other's position in the path, and get and ; S543, if feasible, then use and replace and Update the path planning scheme for Calculate the increment value of the objective function at different locations. ;like , If the minimum increment is the current value, then record the combination of segments and update the current minimum increment to the current value. ; S544, after traversing all paths and all possible sub-segments, select the one that makes The minimum segment swap operation updates the path planning scheme to If all operations are not feasible, then keep constant; S55 defines the second type of local search operator. The operations include: S551, from the current path planning scheme Traverse all non-empty paths Record the sequence of inspection points for each path. Determine the length of the random sub-segment Select all possible continuous sub-segments; S552, reverse the selected sub-segment, inserting the original field positions from the sequence into the reversed sub-segment, resulting in... ; S553, if feasible, then use replace Update the path planning scheme for Calculate the increment of the objective function of the new path planning scheme after performing the reverse operation. ;like If so, record the reverse operation of that sub-segment and update the current minimum increment to . ; S554, after traversing all paths and all possible sub-segments, select the one that makes Reverse the smallest subarray and update. for If all operations are not feasible, then keep constant; S56, Select the scheme with the smallest incremental value obtained in steps S54 and S55 as... .

[0012] Preferably, the specific process of step S6 is as follows: S61, Set and initialize the current iteration number. The maximum number of iterations is defined as A greedy algorithm is used to generate the initial feasible solution. Path planning scheme To make the optimal path planning scheme The algorithm accepts the simulated annealing rule and defines a temperature. To control the probability of accepting a new solution, calculate the first... Temperature ,in These are temperature control parameters; S62, Application Domain Operator Pairs The solution is obtained by perturbation. Applying the local search operator to perform a neighborhood search, we obtain the updated [number]th [unit]. Path planning scheme of the generation ; S63, order ,like Then the optimal path planning scheme will be output. ;like Then calculate the first Next iteration temperature ,Will As the first Path planning scheme Return to step S62 and continue execution, where It is the cooling coefficient. .

[0013] Preferably, the specific process of step S62 is as follows: S621, Setting the set of domain operators Local search operator set Initialize the current solution to be optimized as the first... Initial path planning scheme Neighborhood perturbations are marked as feasible, and the optimal increment is searched locally. ; S622, Random selection of neighborhood operator For the current solution Execute the corresponding disturbance operation: If selected : Perform the operation according to step S51, for Randomly select a non-empty path and perform a segment reversal operation to generate a perturbed intermediate solution; if selected : Perform the operation according to step S52, for Perform a segment cross-insertion operation on two random non-empty paths to generate a perturbed intermediate solution; If feasible, the intermediate solution of the perturbation is defined as the neighborhood perturbation solution. Proceed to step S63; If this is not feasible, select another neighborhood operator to perform the perturbation operation. If perturbation with both neighborhood operators is not feasible, then let... Proceed directly to step S623; S623, resolve neighborhood perturbation As the initial solution for the local search, the local search operators are called sequentially. Perform iterative optimization to gradually find local optima: Let the local search for the current solution traversal markers , This indicates that not all local search operators have been traversed. This indicates that the traversal is complete; Call the first local search operator Perform the operation according to step S54. Explore all possible combinations of two non-empty paths by performing segment swapping, and select the one that makes... The minimum feasible operation, if the minimum Then update For the corresponding optimal exchange solution, and The minimum ; Call the second local search operator : Perform the operation according to step S55, for Perform segment reverse probing on all non-empty paths to filter out those that make... The minimum feasible operation, if the minimum Then update For the corresponding optimal reverse order solution, and The minimum ; Finish , After traversal, let The final local search optimal solution Defined as the updated first Path planning scheme ; S624, obtained from local search based on simulated annealing rules. Perform an acceptance test to ensure the algorithm escapes local optima and calculate the original solution. With updated solution objective function value , And solve for the difference in the objective function. ; like :illustrate For a better solution, we directly accept this solution and determine it. For the first The final updated solution; like Generate uniformly distributed pseudo-random numbers Calculate the simulated annealing acceptance probability ; like : based on probability Accept inferior solutions It was determined to be the first The final updated solution; like Reject inferior solutions, The original solution remains unchanged.

[0014] An electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described urban roadside parking space inspection path planning method.

[0015] The advantages of this invention are: 1. This invention constructs a secondary road network that fits the actual situation of urban roadside parking space inspection. It abstracts the inspection road segments as nodes and the passage road segments between inspection road segments as arcs, realizing accurate modeling of the road network. This greatly improves the matching degree between the path planning results and the actual inspection operation, and solves the problem of the existing modeling method being out of touch with the actual scenario. 2. This invention sets up charging-specific constraints and completes the formula definition for the range characteristics of electric two-wheeled inspection vehicles, realizing the hard constraint of "charging is required when the driving range exceeds the threshold", ensuring the continuity of the inspection vehicle's inspection tasks, avoiding the occurrence of power outages midway, and filling the gap in the lack of specific constraints for electric inspection vehicles in the existing models. 3. This invention performs differentiated calculations on the energy consumption of inspection vehicles on and off inspection routes, accurately converting mileage into energy costs, improving the accuracy of energy consumption assessment, and providing reliable data support for the optimization goal of minimizing total energy consumption during inspections. 4. This invention designs a variable neighborhood algorithm that includes two neighborhood operators and two local search operators, and integrates the simulated annealing acceptance criterion for model solving. The search space is expanded through neighborhood perturbation, local search achieves fine optimization, and the simulated annealing criterion avoids the algorithm from getting trapped in local optima. Finally, it can obtain the globally optimal inspection path planning scheme under complex urban road networks, significantly reducing the total energy consumption of inspection. Attached Figure Description

[0016] Figure 1 This is a flowchart of a method for planning inspection routes for urban roadside parking spaces provided in this embodiment.

[0017] Figure 2 This is an example diagram of the inspection path planning scheme provided in this embodiment.

[0018] Figure 3 The flowchart of the variable-neighborhood algorithm for the hybrid simulated annealing mechanism provided in this embodiment is shown. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1

[0021] Please see Figure 1 and Figure 2 , Figure 1 This is a flowchart of a method for planning inspection routes for urban roadside parking spaces provided in this embodiment. Figure 2 This is an example diagram of the inspection path planning scheme provided in this embodiment. The inspection path planning method includes the following steps: S1, Construct a secondary road network: Abstract the inspected road segments in the primary urban road network into nodes of the secondary road network, and the non-inspected road segments into arcs, and obtain the length of the inspected road segment corresponding to the node and the length of the non-inspected road segment corresponding to the arc. In this embodiment, the specific process of step S1 is as follows: S11 abstracts the roadside parking spaces that need to be inspected in the city's primary road network into nodes of the secondary road network, and the roadside parking spaces that need to be inspected are the inspection sections. S12 defines the shortest travel path between the end point of the previous inspection segment and the start point of the next inspection segment as a non-inspection segment, and abstracts the non-inspection segment as an arc of the secondary road network. S13, Construct a secondary network ,in, N For a set of points, , The starting set, For the final set, For the set of inspection nodes, For charging stations Indicates an arc in a secondary road network. , Represents any node arrive The arc between; S14, obtain the length of the inspected road segment corresponding to the node and the length of the non-inspected road segment corresponding to the arc.

[0022] S2, calculate the travel time of the inspection vehicle on the inspected road segment and the non-inspected road segment, and generate the road network travel time matrix; In this embodiment, the specific process of step S2 is as follows: S21, Set inspection periods ; S22, Obtain Inspection Period Within the inspection area, the average speed of the inspection vehicles meets the speed limit and inspection requirements. S23, Calculate the travel time of the inspection vehicle in each inspection section based on the length of each inspection section and the average vehicle speed. S24, Obtain Inspection Period The maximum speed of the internal inspection vehicle between inspection nodes is used to calculate the travel time of the inspection vehicle on each non-inspection section based on the maximum speed. S25. Based on the travel time of the inspection vehicle on the inspection section and the non-inspection section, a road network travel time matrix is ​​generated.

[0023] S3 converts the length of the inspection section corresponding to each inspection node and the distance between inspection nodes into corresponding energy consumption costs. S31, based on the same type of inspection vehicle, calibrates its energy consumption per unit mileage in inspection sections and non-inspection sections respectively. S32, separately count the mileage of inspected road sections and the mileage of non-inspected road sections; S33, the total energy consumption is calculated by weighting the mileage of the inspected road section and the mileage of the non-inspected road section and the corresponding energy consumption per kilometer.

[0024] S4, based on the driving time matrix and energy consumption cost, establishes an inspection path planning model with specific constraints; In this embodiment, the objective function of the multi-objective urban roadside parking space inspection path planning model constructed in step S4 is: ; in, For inspection vehicles during inspection periods E Fixed energy consumption within; Energy consumption per unit mileage of the inspection vehicle; This is the percentage coefficient; For inspection vehicle The starting point , , This represents the total number of inspection vehicles. For inspection vehicle Starting from the beginning Drive directly to the inspection point , If yes, then it is 1; otherwise, it is 0. From node The set of connected nodes; This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? That is, to arrive Is the next point inspected by the patrol vehicle a... If yes, the value is 1; otherwise, it is 0. Represents arc Length; Indicates the inspection section Length; This is the inspection period; Indicates patrol vehicle The longest single service time.

[0025] Specifically, the constraints of the objective function of the multi-objective urban roadside parking space inspection path planning model are as follows: S401, at any parking spot, a vehicle is assigned to patrol it: ; S402, all vehicles must depart from its starting point: ; S403, the route for any vehicle from its starting point to its destination is continuous; that is, for each vehicle, the route to the parking point is continuous. You must then proceed from the parking spot leave: ; in, This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? That is, to arrive Is the next point inspected by the patrol vehicle a... If yes, the value is 1; otherwise, it is 0. From node The set of connected nodes; S404, every vehicle must eventually reach the finish line: ; in, Represents a node i Belongs to node j Connect one point in the set of outgoing nodes; This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? That is, patrol vehicle Upon reaching the destination Before inspection nodes If so, then take Otherwise, take ; Indicates patrol vehicle The end point; S405, the inspection vehicle may pass by charging stations: ; in, This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? That is, patrol vehicle After inspecting the nodes Afterwards, should you go to a charging station? Charge it; if so, take it. Otherwise, take ; S406, the number of vehicles used for inspection does not exceed a given number. m : ; S407, the travel time of any vehicle must not exceed the minimum cycle time of the visited parking points to meet the inspection interval requirements of each parking point: ; in, Indicates inspection nodes The inspection cycle; S408, the travel time of any vehicle shall not exceed the maximum duration: ; in, Represents arc The passage time, Represents a node Inspection time; S409, the relationship between the start times of two nodes in sequential service: ; in, Indicates patrol vehicle Reaching the node And the start time of the inspection, Indicates patrol vehicle Reaching the node And the start time of the inspection, It is a pre-defined positive number; S409 is used to constrain the order of inspection nodes. The time displayed for a later inspected node is greater than the time displayed for a previous node. Then, vehicle r immediately inspects node j after inspecting node i, and satisfies the relation... ;otherwise Since M is a very large positive number, it always satisfies the relation. .

[0026] S410, the access time meets the time window, that is, the access time is guaranteed to be within the inspection cycle of the parking point: ; S411, The range of values ​​for the decision variable: ; S412, if the inspection vehicle's mileage exceeds the threshold, it must proceed to a charging station during this inspection mission: ; ; ; ; in, Indicates patrol vehicle The cumulative mileage driven to complete a single inspection task. This indicates the mileage threshold for a single inspection by the patrol vehicle; Indicates patrol vehicle Has the cumulative mileage exceeded the preset threshold? express , express .

[0027] S5 defines the set of domain operators and the set of local search operators; In this embodiment, step S5 defines the neighborhood operator. Define local search operators ; S51, Define the first type of neighborhood operator The operations include: S511, from the current path planning scheme Randomly select a non-empty path Let its inspection point sequence be denoted as ; S512, Determine the length of the random sub-segment and in Randomly select continuous segments ; S513, for the selected sub-segment Perform a reverse operation to obtain the reversed subarray. ;Will sub-segments in Replace with A new path is obtained. The new inspection point sequence is as follows: ; S514, if new path If feasible, then use replace Update the path planning scheme for If not feasible, return to step S511 to reselect the path and sub-segment until a feasible neighborhood solution is obtained. A feasible path means that the new path satisfies all constraints.

[0028] S52, defining the second type of neighborhood operator The operations include: S521, from the current route planning scheme Two non-empty paths are randomly selected. and The inspection point sequences are as follows: , ; S522, Determine the length of the random sub-segment and in Randomly select continuous segments ,exist Randomly select continuous segments ; S523, from Delete And randomly select a position to insert Candidate paths are obtained. ;from Delete And randomly select a position to insert Candidate paths are obtained. ; S524, if candidate path and If feasible, then use and replace and Update the path planning scheme for If it is not feasible, return to step S521 to reselect the path and sub-segment until a feasible neighborhood solution is obtained.

[0029] S53, select the scheme with the smallest incremental value obtained in steps S51 and S52. ; Calculate the path planning scheme Incremental value and The incremental value is selected based on the smallest incremental value. ; S54, defining the first type of local search operator. The operations include: S541, from the current path planning scheme Two non-empty paths are randomly selected. and The inspection point sequences are as follows: , ; S542, Determine the length of the random sub-segment and in Randomly select continuous segments ,exist Randomly select continuous segments and sub-segment and Swap to the other's position in the path, and get and ; S543, if feasible, then use and replace and Update the path planning scheme for Calculate the increment value of the objective function at different locations. ;like , If the minimum increment is the current value, then record the combination of segments and update the current minimum increment to the current value. ; S544, after traversing all paths and all possible sub-segments, select the one that makes The minimum segment swap operation updates the path planning scheme to If all operations are not feasible, then keep constant.

[0030] S55 defines the second type of local search operator. The operations include: S551, from the current path planning scheme Traverse all non-empty paths Record the sequence of inspection points for each path. Determine the length of the random sub-segment ,exist Randomly select continuous segments ; S552, for the selected sub-segment Perform a reverse operation, inserting the original field positions in the sequence into the reversed sub-sequence, resulting in... ; S553, if feasible, then use replace Update the path planning scheme for Calculate the increment of the objective function of the new path planning scheme after performing the reverse operation. ;like If so, record the reverse operation of that sub-segment and update the current minimum increment to . ; S554, after traversing all paths and all possible sub-segments, select the one that makes The smallest sub-segment inversion operation updates the path planning scheme to the updated... for If all operations are not feasible, then keep constant.

[0031] S56, Select the scheme with the smallest incremental value obtained in steps S54 and S55 as... ; Calculate the path planning scheme Incremental value and The incremental value is selected based on the smallest incremental value. ; S6, based on the operator set, uses a variable domain algorithm to solve the inspection path planning model with specific constraints, and obtains the optimal inspection path planning scheme.

[0032] Please see Figure 3 , Figure 3The flowchart of the variable-neighborhood algorithm for the hybrid simulated annealing mechanism provided in this embodiment is shown below. In this embodiment, the specific process of step S6 is as follows: S61, Set and initialize the current iteration number. The maximum number of iterations is defined as A greedy algorithm is used to generate the initial feasible solution. Path planning scheme To make the optimal path planning scheme The algorithm accepts the simulated annealing rule and defines a temperature. To control the probability of accepting a new solution, calculate the first... Temperature ,in These are temperature control parameters; S62, Application Domain Operator Pairs The solution is obtained by perturbation. Applying local search operators to Perform a domain search to obtain the updated [number]. Path planning scheme of the generation ; Specifically, the process of step S62 is as follows: S621, Setting the set of domain operators Local search operator set Initialize the current solution to be optimized as the first... Initial path planning scheme Neighborhood perturbations are marked as feasible, and the optimal increment is searched locally. .

[0033] S622, Random selection of neighborhood operator For the current solution Execute the corresponding disturbance operation: If selected : Perform the operation according to step S51, for Randomly select a non-empty path and perform a segment reversal operation to generate a perturbed intermediate solution; if selected : Perform the operation according to step S52, for Perform a segment cross-insertion operation on two random non-empty paths to generate a perturbed intermediate solution.

[0034] If feasible, the intermediate solution of the perturbation is defined as the neighborhood perturbation solution. Proceed to step S63; If this is not feasible, select another neighborhood operator to perform the perturbation operation. If perturbation with both neighborhood operators is not feasible, then let... Proceed directly to step S623; S623, resolve neighborhood perturbation As the initial solution for the local search, the local search operators are called sequentially. Perform iterative optimization to gradually find local optima: Let the local search for the current solution traversal markers , This indicates that not all local search operators have been traversed. This indicates that the traversal is complete; Call the first local search operator Perform the operation according to step S53. Explore all possible combinations of two non-empty paths by performing segment swapping, and select the one that makes... The minimum feasible operation, if the minimum Then update For the corresponding optimal exchange solution, and The minimum ; Call the second local search operator : Perform the operation according to step S54, for Perform segment reverse probing on all non-empty paths to filter out those that make... The minimum feasible operation, if the minimum Then update For the corresponding optimal reverse order solution, and The minimum ; Finish , After traversal, let The final local search optimal solution Defined as the updated first Path planning scheme .

[0035] S624, obtained from local search based on simulated annealing. Perform an acceptance test to ensure the algorithm escapes local optima and calculate the original solution. With updated solution objective function value , And solve for the difference in the objective function. ; like :illustrate For a better solution, we directly accept this solution and determine it. For the first The final updated solution; like Generate uniformly distributed pseudo-random numbers based on the simulated annealing principle. Calculate the simulated annealing acceptance probability ; like : based on probability Accept inferior solutions It was determined to be the first The final updated solution; like Reject inferior solutions, The original solution remains unchanged.

[0036] S63, order ; like Then the optimal path planning scheme will be output. ; like Then calculate the first Next iteration temperature ,Will As the first Path planning scheme Return to step S62 and continue execution, where It is the cooling coefficient. .

[0037] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for planning inspection routes for urban roadside parking spaces, characterized in that, Includes the following steps: S1, Construct a secondary road network: Abstract the inspected road segments in the primary urban road network into nodes of the secondary road network, and the non-inspected road segments into arcs, and obtain the length of the inspected road segment corresponding to the node and the length of the non-inspected road segment corresponding to the arc. S2, calculate the travel time of the inspection vehicle on the inspected road segment and the non-inspected road segment, and generate the road network travel time matrix; S3 converts the length of the inspected road section and the length of the non-inspected road section into corresponding energy consumption costs; S4, based on the driving time matrix and energy consumption cost, establishes an inspection path planning model with specific constraints; S5 defines the set of domain operators and the set of local search operators; S6, based on the operator set, uses a variable domain algorithm to solve the inspection path planning model with specific constraints, and obtains the optimal inspection path planning scheme.

2. The method for planning inspection routes for urban roadside parking spaces according to claim 1, characterized in that, The specific process of step S1 is as follows: S11 abstracts the roadside parking spaces that need to be inspected in the city's primary road network into nodes of the secondary road network, and the roadside parking spaces that need to be inspected are the inspection sections. S12 defines the shortest travel path between the end point of the previous inspection segment and the start point of the next inspection segment as a non-inspection segment, and abstracts the non-inspection segment as an arc of the secondary road network. S13, Construct a secondary network ,in, N For a set of points, , The starting set, For the final set, For the set of inspection nodes, For charging stations Indicates an arc in a secondary road network. , Represents any node arrive The arc between; S14, obtain the length of the inspected road segment corresponding to the node and the length of the non-inspected road segment corresponding to the arc.

3. The method for planning inspection routes for urban roadside parking spaces according to claim 1, characterized in that, The specific process of step S2 is as follows: S21, Set inspection periods ; S22, Obtain Inspection Period Within the inspection area, the average speed of the inspection vehicles meets the speed limit and inspection requirements. S23, Calculate the travel time of the inspection vehicle in each inspection section based on the length of each inspection section and the average vehicle speed. S24, Obtain Inspection Period The maximum speed of the internal inspection vehicle between inspection nodes is used to calculate the travel time of the inspection vehicle on each non-inspection section based on the maximum speed. S25. Based on the travel time of the inspection vehicle on the inspection section and the non-inspection section, a road network travel time matrix is ​​generated.

4. The method for planning inspection routes for urban roadside parking spaces according to claim 1, characterized in that, The specific process of step S3 is as follows: S31, based on the same type of inspection vehicle, calibrates its energy consumption per unit mileage in inspection sections and non-inspection sections respectively. S32, separately count the mileage of inspected road sections and the mileage of non-inspected road sections; S33, the total energy consumption is calculated by weighting the mileage of the inspected road section and the mileage of the non-inspected road section and the corresponding energy consumption per kilometer.

5. The method for planning inspection routes for urban roadside parking spaces according to claim 1, characterized in that, The objective function of the multi-objective urban roadside parking space inspection path planning model constructed in step S4 is: ； in, For inspection vehicles during inspection periods E Fixed energy consumption within; Energy consumption per unit mileage of the inspection vehicle; This is the percentage coefficient; For inspection vehicle The starting point , , This represents the total number of inspection vehicles. For inspection vehicle Starting from the beginning Drive directly to the inspection point , If yes, then it is 1; otherwise, it is 0. From node The set of connected nodes; This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? That is, to arrive Is the next point inspected by the patrol vehicle a... If yes, the value is 1; otherwise, it is 0. Represents arc Length; Indicates the inspection section Length; This is the inspection period; Indicates patrol vehicle The longest single service time.

6. The method for planning inspection routes for urban roadside parking spaces according to claim 5, characterized in that, The constraints of the objective function of the multi-objective urban roadside parking space inspection path planning model are as follows: S401, at any parking spot, a vehicle is assigned to patrol it: ； S402, all vehicles must depart from its starting point: ； S403, the route for any vehicle from its starting point to its destination is continuous: ； in, This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? ; From node The set of connected nodes; S404, every vehicle must eventually reach the finish line: ； in, Represents a node i Belongs to node j Connect one point in the set of outgoing nodes; This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? ; S405, the inspection vehicle may pass by charging stations: ； in, This indicates the inspection vehicle in the path planning scheme. Does the inspection route include arcs? ; S406, the number of vehicles used for inspection does not exceed a given number. m : ； S407, the travel time of any vehicle must not exceed the minimum cycle time of the visited parking points to meet the inspection interval requirements of each parking point: ； in, Indicates inspection nodes The inspection cycle; S408, the travel time of any vehicle shall not exceed the maximum duration: ； in, Represents arc The passage time, Represents a node Inspection time; S409, the relationship between the start times of two nodes in sequential service: ； in, Indicates patrol vehicle Reaching the node And the start time of the inspection, Indicates patrol vehicle Reaching the node And the start time of the inspection, It is a pre-defined positive number; S410, the access time meets the time window, that is, the access time is guaranteed to be within the inspection cycle of the parking point: ； S411, The range of values ​​for the decision variable: ； S412, if the inspection vehicle's mileage exceeds the threshold, it must proceed to a charging station during this inspection mission: ； ； ； ； in, Indicates patrol vehicle The cumulative mileage driven to complete a single inspection task. This indicates the mileage threshold for a single inspection by the patrol vehicle; Indicates patrol vehicle Whether the cumulative mileage exceeds the preset threshold.

7. The method for planning inspection routes for urban roadside parking spaces according to claim 1, characterized in that, Step S5: Define the neighborhood operator Define local search operators ; S51, Define the first type of neighborhood operator The operations include: S511, from the current path planning scheme Randomly select a non-empty path Let its inspection point sequence be denoted as ; S512, Determine the length of the random sub-segment and in Randomly select continuous segments ; S513, for the selected sub-segment Perform a reverse operation to obtain the reversed subarray. ;Will sub-segments in Replace with A new path is obtained. The new inspection point sequence is as follows: ; S514, if new path If feasible, then use replace Update the path planning scheme for If not feasible, return to step S511 to reselect the path and sub-segment until a feasible neighborhood solution is obtained. S52, defining the second type of neighborhood operator The operations include: S521, based on the current route planning scheme Two non-empty paths are randomly selected. and The inspection point sequences are as follows: and ; S522, Determine the length of the random sub-segment and in Randomly select continuous segments ,exist Randomly select continuous segments ; S523, from Delete And randomly select a position to insert Candidate paths are obtained. ;from Delete And randomly select a position to insert Candidate paths are obtained. ; S524, if candidate path and If feasible, then use and replace and Update the path planning scheme for If not feasible, return to step S521 to reselect paths and sub-segments until a feasible neighborhood solution is obtained. S53, select the scheme with the smallest incremental value obtained in steps S51 and S52. ; S54, defining the first type of local search operator. The operations include: S541, from the current path planning scheme Two non-empty paths are randomly selected. and The inspection point sequences are as follows: and ; S542, Determine the length of the random sub-segment and in Randomly select continuous segments ,exist Randomly select continuous segments and sub-segment and Swap to the other's position in the path, and get and ; S543, if feasible, then use and replace and Update the path planning scheme for Calculate the increment value of the objective function at different locations. ;like , If the minimum increment is the current value, then record the combination of segments and update the current minimum increment to the current value. ; S544, after traversing all paths and all possible sub-segments, select the one that makes The minimum segment swap operation updates the path planning scheme to If all operations are not feasible, then keep constant; S55 defines the second type of local search operator. The operations include: S551, from the current path planning scheme Traverse all non-empty paths Record the sequence of inspection points for each path. Determine the length of the random sub-segment Select all possible continuous sub-segments; S552, reverse the selected sub-segment, inserting the original field positions from the sequence into the reversed sub-segment, resulting in... ; S553, if feasible, then use replace Update the path planning scheme for Calculate the increment of the objective function of the new path planning scheme after performing the reverse operation. ;like If so, record the reverse operation of that sub-segment and update the current minimum increment to . ; S554, after traversing all paths and all possible sub-segments, select the one that makes Reverse the smallest subarray and update. for If all operations are not feasible, then keep constant; S56, Select the scheme with the smallest incremental value obtained in steps S54 and S55 as... .

8. The method for planning inspection routes for urban roadside parking spaces according to claim 7, characterized in that, The specific process of step S6 is as follows: S61, Set and initialize the current iteration number. The maximum number of iterations is defined as A greedy algorithm is used to generate the initial feasible solution. Path planning scheme To make the optimal path planning scheme The algorithm accepts the simulated annealing rule and defines a temperature. To control the probability of accepting a new solution, calculate the first... Temperature ,in These are temperature control parameters; S62, Application Domain Operator Pairs The solution is obtained by perturbation. Applying the local search operator to perform a neighborhood search, we obtain the updated [number]th [unit]. Path planning scheme of the generation ; S63, order ,like Then the optimal path planning scheme will be output. ;like Then calculate the first... Next iteration temperature ,Will As the first Path planning scheme Return to step S62 and continue execution, where It is the cooling coefficient. .

9. A method for planning inspection routes for urban roadside parking spaces according to claim 8, characterized in that, The specific process of step S62 is as follows: S621, Setting the set of domain operators Local search operator set Initialize the current solution to be optimized as the first... Initial path planning scheme Neighborhood perturbations are marked as feasible, and the optimal increment is searched locally. ; S622, Randomly select the neighborhood operator For the current solution Execute the corresponding disturbance operation: If selected : Perform the operation according to step S51, for Randomly select a non-empty path and perform a segment reversal operation to generate a perturbed intermediate solution; if selected : Perform the operation according to step S52, for Perform a segment cross-insertion operation on two random non-empty paths to generate a perturbed intermediate solution; If feasible, the intermediate solution of the perturbation is defined as the neighborhood perturbation solution. Proceed to step S63; If this is not feasible, select another neighborhood operator to perform the perturbation operation. If perturbation with both neighborhood operators is not feasible, then let... Proceed directly to step S623; S623, resolve neighborhood perturbation As the initial solution for the local search, the local search operators are called sequentially. Perform iterative optimization to gradually find local optima: Let the local search for the current solution traversal markers , This indicates that not all local search operators have been traversed. This indicates that the traversal is complete; Call the first local search operator Perform the operation according to step S54. Explore all possible combinations of two non-empty paths by performing segment swapping, and select the one that makes... The minimum feasible operation, if the minimum Then update For the corresponding optimal exchange solution, and The minimum ; Call the second local search operator : Perform the operation according to step S55, for Perform segment reverse probing on all non-empty paths to filter out those that make... The minimum feasible operation, if the minimum Then update For the corresponding optimal reverse order solution, and The minimum ; Finish , After traversal, let The final local search optimal solution Defined as the updated first Path planning scheme ; S624, obtained from local search based on simulated annealing rules. Perform an acceptance test to ensure the algorithm escapes local optima and calculate the original solution. With updated solution objective function value , And solve for the difference in the objective function. ; like :illustrate For a better solution, we directly accept this solution and determine it. For the first The final updated solution; like Generate uniformly distributed pseudo-random numbers Calculate the simulated annealing acceptance probability ; like : based on probability Accept inferior solutions It was determined to be the first The final updated solution; like : Reject inferior solutions, The original solution remains unchanged.

10. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the urban roadside parking space inspection route planning method according to any one of claims 1-9.

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