A system and method for identifying parking space locations based on intelligent charging piles

Abstract

The present invention discloses a system and method for identifying parking space positions based on an intelligent charging pile, which relates to the technical field of parking space identification. The system and method include starting an intelligent charging pile, using multi-sensor fusion technology to collect an original environmental data set in a parking lot, and preprocessing the collected original environmental data set to obtain an optimized environmental data set; applying a computer vision algorithm and a geometric analysis method to image data in the optimized environmental data set, extracting static element features and confirming the position and shape of fixed structures in the static element features to obtain a static element layer; extracting dynamic elements from video data in the optimized environmental data set by adopting a motion detection algorithm, mapping the position information of the dynamic elements to a spatial coordinate system to obtain a dynamic element layer; and extracting the position and shape features of static structures and moving objects from the static and dynamic element layers respectively by using a geometric feature extraction method.

Description

Technical Field

[0001] The present invention relates to the technical field of parking space recognition, and in particular to a system and method for identifying parking space positions based on a smart charging pile. Background Art

[0002] The parking space recognition system is an intelligent system that uses a variety of technical means to automatically detect and identify the location of vacant parking spaces in a parking lot. By integrating sensors, computer vision, machine learning and other advanced technologies, it can monitor the distribution of vehicles in the parking lot in real time and provide users with accurate parking space information.

[0003] In the field of parking space recognition, existing systems cannot adapt well to different types of parking lot environments, and their performance degrades under severe weather conditions. In addition, many parking lot management systems can only provide static parking space information, while ignoring the influence of vehicle movement and other dynamic factors. At the same time, existing systems rarely provide detailed three-dimensional models of parking lots, which limits users' understanding of the parking environment. Summary of the Invention

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

[0005] Therefore, the present invention provides a system and method for identifying parking space locations based on smart charging piles to solve the problems that existing systems cannot adapt well to different types of parking lot environments, their performance degrades under severe weather conditions, and many parking lot management systems can only provide static parking space information while ignoring the influence of vehicle movement and other dynamic factors.

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

[0007] In the first aspect, the present invention provides a method for identifying parking space locations based on smart charging piles, which comprises: starting the smart charging pile, using multi-sensor fusion technology to collect the original environmental data set in the parking lot, and preprocessing the collected original environmental data set to obtain an optimized environmental data set; applying computer vision algorithms and geometric analysis methods to the image data in the optimized environmental data set, extracting static element features and confirming the position and shape of fixed structures in the static element features to obtain a static element layer; extracting dynamic elements from the video data in the optimized environmental data set by adopting a motion detection algorithm, mapping the position information of the dynamic elements to a spatial coordinate system to obtain a dynamic element layer; extracting dynamic elements from the static and The position and shape features of static structures and moving objects are extracted from the dynamic element layer respectively. The relative positions of the position and shape features of the static structures and moving objects are determined using the feature matching method. The relative positions of the position and shape features of the static structures and moving objects are integrated using the 3D modeling platform Blender to obtain a 3D model of the parking lot. The multi-attribute decision analysis method is used to extract attribute indicators that affect the parking space score in the 3D model of the parking lot, and weights are assigned to each attribute indicator that affects the parking space score. The weighted summation method is used to calculate the comprehensive score of each parking space. Based on the comprehensive score of each parking space, the parking space with the highest comprehensive score is selected as the charging space. When the vehicle arrives at the optimal parking space, the parking space status is verified and the smart charging pile is activated for charging service.

[0008] As a preferred solution of the method for identifying parking space locations based on a smart charging pile described in the present invention, the method includes: starting the smart charging pile, using multi-sensor fusion technology to collect the original environmental data set in the parking lot, and pre-processing the collected original environmental data set to obtain an optimized environmental data set. The specific steps are: starting the smart charging pile and initializing the multiple sensors installed on the smart charging pile; after starting the smart charging pile, the smart charging pile checks the health status of the multiple sensors through the Self-Diagnosis Program; the smart charging pile controls the multiple sensors to collect the environment in the parking lot to obtain the original environmental data set; using an anomaly detection method to identify the original environmental data set and eliminate obviously erroneous data points, and applying a Kalman filter to remove high-frequency noise to obtain a preliminary optimized data set; performing Z-score normalization on the numerical data in the preliminary optimized data set to obtain a standardized optimized data set; using a network time protocol to synchronize the timestamps of the multiple sensors, and adjusting the spatial coordinate system of the multiple sensors through the external parameter information preset by the smart sensors to make it consistent with the global coordinate system to obtain an optimized environmental data set.

[0009] As a preferred solution of the method for identifying parking space locations based on smart charging piles described in the present invention, wherein: the computer vision algorithm and geometric analysis method are applied to the image data in the optimized environmental data set to extract static element features and confirm the position and shape of fixed structures in the static element features to obtain a static element layer. The specific steps are: using the Canny edge detection algorithm to identify the boundary contours of the image data in the optimized environmental data set, and using the Harris corner detection algorithm to find significant corner points in the image data in the optimized environmental data set. The expression is as follows:

[0010]

[0011] R(p)=det(M(p))-k·trac e 2 (M(p));

[0012] Among them, E(p) is the boundary contour strength of point p, is the gradient operator of the Gaussian filter, I(p) is the image grayscale value of point p, M(p) is the structure tensor matrix, det(M(p)) is the determinant of the matrix M(p), trac e (M(p)) is the trace of the matrix M(p), k is an empirical constant, and R(p) represents the corner response value. The boundary contour intensity E(p) and the corner response value R(p) are combined to obtain the feature set F1. Based on the feature set, the findContours function is used to outline the contours of the fixed structures in the feature set, and SIFT is used to compare the feature set with the contours in the optimized environment dataset. The expression is as follows:

[0013] C={p|p∈F1.E,isClosed(p)};

[0014]

[0015] Among them, F1.E is the boundary contour strength E(p) extracted from F1, isClosed(p) is used to determine whether point p forms a closed contour, and C i is the i-th contour, T i is the i-th template, γ is the adjustment coefficient, d(C i ,T i ) is the matching error between the i-th contour and the i-th template, S(C i ) represents the shape matching score of the i-th contour; the shape matching score S(C i ) are collected to obtain the intermediate feature set F2; the shape matching score S(C i) are integrated to obtain the static element feature F3; the spatial relationship score M(C i ), the expression is:

[0016]

[0017] in, is the adjustment coefficient, M(C i ) is the spatial relationship score of the i-th contour; the static element layer is constructed using the comprehensive feature scoring function F(E) combined with the static element feature F3, and the expression is:

[0018]

[0019] L={F1,M(C i ),Δ};

[0020] Among them, Δ is the static element feature score, n is the number of static elements, α i is the shape matching score weight of the i-th static element, F2.S(i) is the shape matching score of the i-th static element in the intermediate feature set, β i is the spatial relationship score weight of the i-th static element, F3.M(C i ) is the spatial relationship score of the i-th static element in the static element feature, m is the number of computing resources used in the feature extraction process, W j is the weight of the j-th computing resource, C j is the consumption of the jth computing resource, and L is the static element layer.

[0021] As a preferred solution of the method for identifying parking space positions based on smart charging piles described in the present invention, wherein: the method extracts dynamic elements from the video data in the optimized environmental data set by using a motion detection algorithm, maps the position information of the dynamic elements to the spatial coordinate system, and obtains a dynamic element layer. The specific steps are: selecting the optical flow method, calculating the pixel displacement between consecutive frames in the video data, and obtaining the velocity vector v(x, y) of the pixel point; using the obtained velocity vector v(x, y), using a threshold function to distinguish the static background and dynamic foreground in the velocity vector v(x, y), the expression is:

[0022]

[0023] Where D(x,y) is the binary dynamic element mask, x is the horizontal coordinate of the pixel, y is the vertical coordinate of the pixel, ∥v(x,y)∥ is the length of the velocity vector v(x,y) at the position (x,y), The threshold of the velocity vector length; when the velocity vector length of v(x,y) exceeds the set threshold The position is considered to belong to the dynamic foreground, otherwise it is considered to be the static background, and the position information of the dynamic foreground is obtained and marked as a dynamic element; the dynamic element is mapped to the global coordinate system to obtain the dynamic element layer Q.

[0024] As a preferred solution of the method for identifying parking space positions based on smart charging piles described in the present invention, wherein: the position and shape features of static structures and moving objects are extracted from the static and dynamic element layers respectively by the geometric feature extraction method, and the specific steps are: applying the edge detection algorithm and the corner point detection algorithm to extract the contours and key points of the static structure and moving objects in the static element layer and the dynamic element layer respectively; using the feature descriptor to encode the contours and key points of the static structure and moving objects, and obtaining the static structure feature set L static and the moving object feature set Q dstatic , the expression is:

[0025] L static ={(Z i ,τ i )|Z i ωA * ∪B * ,τ i =σ(Z i )};

[0026] Q dstatic ={(q j ,e j )|q j ωQ dynamic ∪Q dynamic ,h j =σ(q j )};

[0027] Among them, L static is the static structural feature set, Z i Represents the position coordinates of the i-th key point, A * Represents the edge point set in the static element layer, B * represents the set of corner points in the static element layer, τ i represents the feature descriptor of the i-th key point, σ(Z i ) represents the position Z i The feature vector obtained by applying the feature descriptor encoding function, q j Indicates the position coordinates of the jth key point, Q dynamic Represents the edge point set in the dynamic element layer, Q dynamic Represents the corner point set in the dynamic element layer, h j represents the feature descriptor of the jth key point, σ(q j ) represents the position q jThe feature vector obtained by applying the feature descriptor encoding function; the relative position of the position shape features of the static structure and the moving object is determined by using the feature matching method, and the relative position of the position shape features of the static structure and the moving object is integrated by using the 3D modeling platform Blender to obtain a 3D model of the parking lot. The specific steps are: using FLANN to match the descriptors in the static structure feature set and the moving object feature set to obtain a feature matching set ∩; based on the feature matching set ∩ and the static structure feature set L static and the moving object feature set Q dstatic , using the 3D modeling platform Blender, a 3D model of the parking lot is generated. The expression is:

[0028]

[0029] Among them, ∩ is the feature matching set, θ is the parking lot 3D model, L static is the feature set of the static structure in the static element layer, Q dstatic A feature set for moving objects in a dynamic element layer. It is a comprehensive function; based on the 3D model of the parking lot, it uses visualization rendering technology to output a complete 3D scene including static structures and dynamic objects.

[0030] As a preferred solution of the method for identifying parking space locations based on smart charging piles described in the present invention, wherein: the multi-attribute decision analysis method is used to extract attribute indicators that affect the parking space score from the three-dimensional model of the parking lot, and weights are assigned to each attribute indicator that affects the parking space score. A weighted summation method is used to calculate the comprehensive score of each parking space. The specific steps are: extracting attribute indicators that affect the parking space score from the three-dimensional model of the parking lot, specifically the distance from the parking space to the charging station, the area of ​​the barrier-free area around the parking space, the vehicle density near the parking space, the pedestrian flow near the parking space, and the safety score of the parking space; assigning weights to the attribute indicators to obtain a scoring function for the attribute indicators, and calculating the comprehensive score using the scoring function for the attribute indicators. The expression is:

[0031]

[0032] Among them, R is the comprehensive score of the parking space, W(k i ) is the nonlinear normalized weight of the i-th attribute index, Z i is the scoring function of the i-th attribute index, H j is the auxiliary adjustment function, and π is the number of auxiliary adjustment items.

[0033] As a preferred solution of the method for identifying parking space locations based on smart charging piles according to the present invention, the method selects the parking space with the highest comprehensive score as the charging parking space based on the comprehensive score of each parking space. When the vehicle arrives at the optimal parking space, the parking space status is verified and the smart charging pile is activated for charging service. The specific steps are: defining a selection function to select the parking space with the highest comprehensive score as the optimal parking space, which is expressed as:

[0034]

[0035] Among them, C is the optimal parking space, R i is the comprehensive score of the i-th parking space, Indicates the choice of making R i The largest index i; after determining the optimal parking space, the optimal parking space information is sent to the car owner through the wireless communication protocol, and navigation guidance is provided; during the navigation process to the optimal parking space, when the vehicle approaches the optimal parking space, the multi-sensor starts the monitoring function to detect whether the parking space is occupied; if the parking space is vacant, the vehicle is allowed to enter; otherwise, the car owner is notified to reselect a parking space.

[0036] In the second aspect, the present invention provides a system for identifying parking space locations based on smart charging piles, including a data acquisition and processing module, a static element extraction module, a dynamic element extraction module, a three-dimensional modeling module, and a parking space scoring and selection module; the data acquisition and processing module is used to start the smart charging pile and initialize multiple sensors, collect raw data on the environment in the parking lot, and then obtain an optimized environmental data set through steps such as cleaning, denoising, standardization, time synchronization, and spatial alignment; the static element extraction module is used to apply computer vision algorithms and geometric analysis methods to extract static element features from the optimized environmental data set; the dynamic element extraction module is used to adopt motion detection algorithms to extract static element features from the optimized environmental data set. Dynamic elements are extracted from the optimized environmental data set, the position information of the dynamic elements is mapped to the spatial coordinate system, and a dynamic element layer is constructed; the three-dimensional modeling module is used to integrate the static element layer and the dynamic element layer, use geometric feature extraction and feature matching methods to determine the relative position between static structures and moving objects, and use the three-dimensional modeling platform to generate a complete three-dimensional model of the parking lot; the parking space scoring selection module is used to extract attribute indicators that affect the parking space score from the three-dimensional model of the parking lot, calculate the comprehensive score of each parking space, select the optimal charging parking space and send it to the car owner through a wireless communication protocol, and when the vehicle arrives at the optimal parking space, verify the parking space status and activate the smart charging pile for charging service.

[0037] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for identifying parking space locations based on smart charging piles as described in the first aspect of the present invention is implemented.

[0038] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for identifying parking space locations based on smart charging piles as described in the first aspect of the present invention is implemented.

[0039] The beneficial effects of the present invention are as follows: by applying computer vision algorithms and geometric analysis methods to image data in the optimized environmental data set, static element features are extracted and the position and shape of fixed structures in the static element features are confirmed to obtain a static element layer, thereby achieving accurate extraction of static element features from the optimized environmental data set, thereby achieving the effect of enhancing the accuracy and reliability of parking space position recognition; by adopting a motion detection algorithm, dynamic elements are extracted from video data in the optimized environmental data set, and the position information of the dynamic elements is mapped to a spatial coordinate system to obtain a dynamic element layer, thereby achieving accurate tracking of dynamic objects, achieving the effect of real-time updating of the dynamic situation in the parking lot and assisting intelligent decision-making; by using the three-dimensional modeling platform Blender to integrate the relative positions of static structures and moving objects, a complete three-dimensional model of the parking lot is generated, which not only improves the user experience, but also provides a visualization tool for intelligent management and scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is a flow chart of the method for identifying parking space locations based on smart charging piles in Example 1.

[0042] Figure 2 This is a system diagram for identifying parking space locations based on smart charging piles in Example 1. DETAILED DESCRIPTION

[0043] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0044] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0045] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0046] Example 1, with reference to Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for identifying parking space locations based on a smart charging pile, comprising the following steps:

[0047] S1. Start the smart charging pile, use multi-sensor fusion technology to collect the original environmental data set in the parking lot, and pre-process the collected original environmental data set to obtain an optimized environmental data set;

[0048] Furthermore, the smart charging pile is started, and the multi-sensor fusion technology is used to collect the original environmental data set in the parking lot. The collected original environmental data set is preprocessed to obtain an optimized environmental data set. The specific steps are: start the smart charging pile and initialize the multiple sensors installed on the smart charging pile; after starting the smart charging pile, the smart charging pile checks the health status of the multiple sensors through the Self-Diagnosis Program; the smart charging pile controls the multiple sensors to collect the environment in the parking lot to obtain the original environmental data set; anomaly detection methods are used to identify and eliminate obviously erroneous data points in the original environmental data set, and a Kalman filter is applied to remove high-frequency noise to obtain a preliminary optimized data set; the numerical data in the preliminary optimized data set is Z-score standardized to obtain a standardized optimized data set; the network time protocol is used to synchronize the timestamps of the multiple sensors, and the spatial coordinate system of the multiple sensors is adjusted through the external parameter information preset by the smart sensor to make it consistent with the global coordinate system to obtain an optimized environmental data set.

[0049] It should be noted that the data collected through multi-sensor fusion technology undergoes a series of preprocessing steps, which not only ensures the quality and reliability of the original data, but also provides a solid foundation for subsequent feature extraction and analysis. In particular, the application of the Kalman filter effectively reduces high-frequency noise, and the Z-score normalization processing enables data from different sensors to be compared on the same scale, enhancing the consistency and accuracy of data analysis. In addition, timestamp synchronization and spatial coordinate system adjustment ensure the temporal consistency and spatial positioning accuracy of multi-source data, thereby improving the accuracy of three-dimensional model construction.

[0050] S2. Apply computer vision algorithms and geometric analysis methods to the image data in the optimized environmental data set to extract static element features and confirm the position and shape of fixed structures in the static element features to obtain a static element layer;

[0051] Furthermore, computer vision algorithms and geometric analysis methods are applied to the image data in the optimized environment dataset to extract static element features and confirm the position and shape of fixed structures in the static element features to obtain a static element layer. The specific steps are: using the Canny edge detection algorithm to identify the boundary contours of the image data in the optimized environment dataset, and using the Harris corner detection algorithm to find the significant corners in the image data in the optimized environment dataset. The expression is as follows:

[0052]

[0053] R(p)=det(M(p))-k·trac e 2 (M(p));

[0054] Among them, E(p) is the boundary contour strength of point p, is the gradient operator of the Gaussian filter, I(p) is the image grayscale value of point p, M(p) is the structure tensor matrix, det(M(p)) is the determinant of the matrix M(p), trac e (M(p)) is the trace of the matrix M(p), k is an empirical constant, and R(p) represents the corner response value. The boundary contour intensity E(p) and the corner response value R(p) are combined to obtain the feature set F1. Based on the feature set, the findContours function is used to outline the contours of the fixed structures in the feature set, and SIFT is used to compare the feature set with the contours in the optimized environment dataset. The expression is as follows:

[0055] C={p|p∈F1.E,isClosed(p)};

[0056]

[0057] Among them, F1.E is the boundary contour strength E(p) extracted from F1, isClosed(p) is used to determine whether point p forms a closed contour, and C i is the i-th contour, T i is the i-th template, γ is the adjustment coefficient, d(C i ,T i ) is the matching error between the i-th contour and the i-th template, S(C i ) represents the shape matching score of the i-th contour; the shape matching score S(C i) are collected to obtain the intermediate feature set F2; the shape matching score S(C i ) are integrated to obtain the static element feature F3; the spatial relationship score M(C i ), the expression is:

[0058]

[0059] in, is the adjustment coefficient, M(C i ) is the spatial relationship score of the i-th contour; the static element layer is constructed using the comprehensive feature scoring function F(E) combined with the static element feature F3, and the expression is:

[0060]

[0061] L={F1,M(C i ),Δ};

[0062] Among them, Δ is the static element feature score, n is the number of static elements, α i is the shape matching score weight of the i-th static element, F2.S(i) is the shape matching score of the i-th static element in the intermediate feature set, β i is the spatial relationship score weight of the i-th static element, F3.M(C i ) is the spatial relationship score of the i-th static element in the static element feature, m is the number of computing resources used in the feature extraction process, W j is the weight of the j-th computing resource, C j is the consumption of the jth computing resource, and L is the static element layer.

[0063] It should be noted that the joint application of Canny edge detection and Harris corner detection algorithms can effectively separate important geometric features from images. Features are crucial for identifying the position and shape of fixed structures. The accuracy of the contour is further verified by SIFT feature matching, which improves the robustness of static element feature extraction. The static element feature scoring function comprehensively considers the shape matching score and spatial relationship score, and introduces the weight of computing resource consumption, optimizing the construction process of the static element layer, which not only ensures high precision but also improves computing efficiency, laying the foundation for the subsequent extraction of dynamic elements and determination of relative positions.

[0064] S3. Extract dynamic elements from the video data in the optimized environment dataset by using a motion detection algorithm, map the position information of the dynamic elements to a spatial coordinate system, and obtain a dynamic element layer;

[0065] Furthermore, by adopting a motion detection algorithm, dynamic elements are extracted from the video data in the optimized environment dataset, and the position information of the dynamic elements is mapped to the spatial coordinate system to obtain a dynamic element layer. The specific steps are as follows: the optical flow method is selected to calculate the pixel displacement between consecutive frames in the video data to obtain the velocity vector v(x,y) of the pixel point; based on the obtained velocity vector v(x,y), a threshold function is used to distinguish the static background and dynamic foreground in the velocity vector v(x,y). The expression is:

[0066]

[0067] Where D(x,y) is the binary dynamic element mask, x is the horizontal coordinate of the pixel, y is the vertical coordinate of the pixel, ∥v(x,y)∥ is the length of the velocity vector v(x,y) at the position (x,y), The threshold of the velocity vector length; when the velocity vector length of v(x,y) exceeds the set threshold The position is considered to belong to the dynamic foreground, otherwise it is considered to be the static background, and the position information of the dynamic foreground is obtained and marked as a dynamic element; the dynamic element is mapped to the global coordinate system to obtain the dynamic element layer Q.

[0068] It should be noted that the application of the optical flow method can effectively distinguish between static background and dynamic foreground, improve the sensitivity and accuracy of dynamic object detection, and the use of the velocity vector length threshold function ensures the effective separation of dynamic foreground and static background, avoiding false detection and missed detection. The position information of dynamic elements is mapped to the global coordinate system, so that the system can grasp the precise position of moving objects such as vehicles in the parking lot in real time, providing important information for path planning and safety monitoring. The process realizes real-time update of the dynamic situation of the parking lot and assists the operation of the intelligent decision-making system.

[0069] S4. Extracting the position and shape features of the static structure and the moving object from the static and dynamic element layers respectively using a geometric feature extraction method, determining the relative positions of the position and shape features of the static structure and the moving object using a feature matching method, and integrating the relative positions of the position and shape features of the static structure and the moving object using a 3D modeling platform Blender to obtain a 3D model of the parking lot;

[0070] Furthermore, the position and shape features of static structures and moving objects are extracted from the static and dynamic element layers respectively through the geometric feature extraction method. The specific steps are as follows: applying the edge detection algorithm and the corner detection algorithm to extract the contours and key points of static structures and moving objects in the static element layer and the dynamic element layer respectively; using the feature descriptor to encode the contours and key points of static structures and moving objects, and obtaining the static structure feature set L static and the moving object feature set Qdstatic , the expression is:

[0071] L static ={(Z i ,τ i )|Z i ωA * ∪B * ,τ i =σ(Z i )};

[0072] Q dstatic ={(q j ,e j )|q j ωQ dynamic ∪Q dynamic ,h j =σ(q j )};

[0073] Among them, L static is the static structural feature set, Z i Represents the position coordinates of the i-th key point, A * Represents the edge point set in the static element layer, B * represents the set of corner points in the static element layer, τ i represents the feature descriptor of the i-th key point, σ(Z i ) represents the position Z i The feature vector obtained by applying the feature descriptor encoding function, q j Indicates the position coordinates of the jth key point, Q dynamic Represents the edge point set in the dynamic element layer, Q dynamic Represents the corner point set in the dynamic element layer, h j represents the feature descriptor of the jth key point, σ(q j ) represents the position q j The feature vector obtained by the feature descriptor encoding function is applied; the relative position of the position and shape features of the static structure and the moving object is determined by the feature matching method, and the relative position of the position and shape features of the static structure and the moving object is integrated by the 3D modeling platform Blender to obtain the 3D model of the parking lot. The specific steps are as follows: FLANN is used to match the descriptors in the static structure feature set and the moving object feature set to obtain the feature matching set ∩; based on the feature matching set ∩ and the static structure feature set L static and the moving object feature set Q dstatic , using the 3D modeling platform Blender, a 3D model of the parking lot is generated. The expression is:

[0074]

[0075] Among them, ∩ is the feature matching set, θ is the parking lot 3D model, L static is the feature set of the static structure in the static element layer, Q dstatic A feature set for moving objects in a dynamic element layer. It is a comprehensive function; based on the 3D model of the parking lot, it uses visualization rendering technology to output a complete 3D scene including static structures and dynamic objects.

[0076] It should be noted that the geometric feature extraction method and feature descriptor encoding provide unique "fingerprints" for static structures and moving objects, facilitating subsequent feature matching and identification. The application of the FLANN matching algorithm improves the speed and accuracy of feature matching and provides a reliable correspondence for 3D model generation. The 3D modeling platform Blender is used to integrate the relative positions of static structures and moving objects to generate a high-precision, interactive 3D parking lot scene, which not only improves the user experience, but also provides a visualization tool for intelligent management and scheduling. The 3D model supports a variety of application scenarios, such as parking space recommendation, path planning and security monitoring.

[0077] S5. Use a multi-attribute decision analysis method to extract attribute indicators that affect the parking space score from the three-dimensional model of the parking lot, assign a weight to each attribute indicator that affects the parking space score, and use a weighted summation method to calculate the comprehensive score of each parking space;

[0078] Furthermore, a multi-attribute decision analysis method is used to extract attribute indicators that affect the parking space score from the three-dimensional model of the parking lot, and weights are assigned to each attribute indicator that affects the parking space score. The weighted summation method is used to calculate the comprehensive score of each parking space. The specific steps are as follows: extract attribute indicators that affect the parking space score from the three-dimensional model of the parking lot, specifically the distance from the parking space to the charging station, the area of ​​the barrier-free area around the parking space, the vehicle density near the parking space, the pedestrian flow near the parking space, and the safety score of the parking space; assign weights to the attribute indicators to obtain the attribute indicator scoring function, and calculate the comprehensive score using the attribute indicator scoring function. The expression is:

[0079]

[0080] Among them, R is the comprehensive score of the parking space, W(k i ) is the nonlinear normalized weight of the i-th attribute index, Z i is the scoring function of the i-th attribute index, H j is the auxiliary adjustment function, and π is the number of auxiliary adjustment items.

[0081] It should be noted that the multi-attribute decision analysis method (MADM) ensures the scientificity and rationality of the scoring system by quantitatively evaluating the key attribute indicators that affect the parking space score and assigning appropriate weights. The weighted summation method comprehensively considers the influence of multiple factors, including the distance to the charging station, the area of ​​the barrier-free area, vehicle density, pedestrian flow and safety score, to obtain a comprehensive score for each parking space. This method not only improves the accuracy and rationality of parking space selection, but also provides users with more personalized parking suggestions, thereby improving the overall service quality and user experience.

[0082] S6. Based on the comprehensive scores of each parking space, the one with the highest comprehensive score is selected as the charging parking space. When the vehicle arrives at the optimal parking space, the parking space status is verified and the smart charging pile is activated for charging service.

[0083] Furthermore, based on the comprehensive score of each parking space, the one with the highest comprehensive score is selected as the charging parking space. When the vehicle arrives at the optimal parking space, the parking space status is verified and the smart charging pile is activated for charging service. The specific steps are: define a selection function to select the parking space with the highest comprehensive score as the optimal parking space from the comprehensive score. The expression is:

[0084]

[0085] Among them, C is the optimal parking space, R i is the comprehensive score of the i-th parking space, Indicates the choice of making R i The largest index i; after determining the optimal parking space, the optimal parking space information is sent to the car owner through the wireless communication protocol, and navigation guidance is provided; during the navigation process to the optimal parking space, when the vehicle approaches the optimal parking space, the multi-sensor starts the monitoring function to detect whether the parking space is occupied; if the parking space is vacant, the vehicle is allowed to enter; otherwise, the car owner is notified to reselect a parking space.

[0086] It should be noted that the selection function based on comprehensive scoring ensures that the selection of the optimal parking space is based on the results of a comprehensive evaluation, maximizing user convenience and satisfaction. The application of wireless communication protocols enables the optimal parking space information to be quickly transmitted to the car owner and provides navigation guidance, simplifying the parking process. The multi-sensor activation monitoring function detects the parking space status when the vehicle approaches the optimal parking space, ensuring the availability of the parking space and preventing the parking space from being occupied. When the parking space is empty, the vehicle is allowed to enter and the smart charging pile is activated for charging service, realizing the integrated management of smart parking and charging, and improving the operational efficiency and service level of the parking lot.

[0087] This embodiment also provides a system for identifying parking space locations based on smart charging piles, including: a data acquisition and processing module, a static element extraction module, a dynamic element extraction module, a three-dimensional modeling module, and a parking space scoring and selection module; the data acquisition and processing module is used to start the smart charging pile and initialize multiple sensors, collect raw data of the environment in the parking lot, and then obtain an optimized environmental data set through steps such as cleaning, denoising, standardization, time synchronization, and spatial alignment; the static element extraction module is used to apply computer vision algorithms and geometric analysis methods to extract static element features from the optimized environmental data set; the dynamic element extraction module is used to use motion detection algorithms to extract static element features from the optimized environmental data set The dynamic elements are extracted from the environmental data set, the position information of the dynamic elements is mapped to the spatial coordinate system, and the dynamic element layer is constructed; the 3D modeling module is used to integrate the static element layer and the dynamic element layer, and the geometric feature extraction and feature matching methods are used to determine the relative position between the static structure and the moving object, and the 3D modeling platform is used to generate a complete 3D model of the parking lot; the parking space scoring selection module is used to extract the attribute indicators that affect the parking space score from the 3D model of the parking lot, calculate the comprehensive score of each parking space, select the optimal charging parking space and send it to the car owner through the wireless communication protocol. When the vehicle arrives at the optimal parking space, the parking space status is verified and the smart charging pile is activated for charging service.

[0088] This embodiment also provides a computer device, which is suitable for the method of identifying the location of parking spaces based on smart charging piles, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method of identifying the location of parking spaces based on smart charging piles proposed in the above embodiment.

[0089] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0090] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for identifying parking space locations based on smart charging piles as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0091] In summary, the present invention applies computer vision algorithms and geometric analysis methods to image data in the optimized environmental data set, extracts static element features and confirms the position and shape of fixed structures in the static element features to obtain a static element layer, thereby achieving accurate extraction of static element features from the optimized environmental data set, thereby enhancing the accuracy and reliability of parking space position recognition. By adopting a motion detection algorithm, dynamic elements are extracted from video data in the optimized environmental data set, and the position information of the dynamic elements is mapped to a spatial coordinate system to obtain a dynamic element layer, thereby achieving accurate tracking of dynamic objects, achieving real-time updating of the dynamic situation in the parking lot and assisting intelligent decision-making. The three-dimensional modeling platform Blender is used to integrate the relative positions of static structures and moving objects to generate a complete three-dimensional model of the parking lot, which not only improves the user experience, but also provides a visualization tool for intelligent management and scheduling.

[0092] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for identifying parking space locations based on smart charging piles, characterized by: include: Start the smart charging pile and use multi-sensor fusion technology to collect the original environmental data set in the parking lot. Then pre-process the collected original environmental data set to obtain an optimized environmental data set. Apply computer vision algorithms and geometric analysis methods to the image data in the optimized environmental dataset to extract static element features and confirm the position and shape of fixed structures in the static element features to obtain a static element layer; By using motion detection algorithms, dynamic elements are extracted from the video data in the optimized environment dataset, and the position information of the dynamic elements is mapped to the spatial coordinate system to obtain a dynamic element layer; The position and shape features of static structures and moving objects are extracted from the static and dynamic element layers using geometric feature extraction methods. The relative positions of the position and shape features of static structures and moving objects are determined using feature matching methods. The relative positions of the position and shape features of static structures and moving objects are integrated using the 3D modeling platform Blender to obtain a 3D model of the parking lot. The specific steps are as follows: Apply edge detection algorithms and corner detection algorithms to extract the outlines and key points of static structures and moving objects in static element layers and dynamic element layers respectively; Use feature descriptors to encode the outlines and key points of static structures and moving objects, and obtain static structure feature sets and moving object feature sets; FLANN is used to match the descriptors in the static structure feature set and the moving object feature set to obtain a feature matching set; Based on the feature matching set, static structure feature set and moving object feature set, a 3D model of the parking lot is generated using the 3D modeling platform Blender. Based on the 3D model of the parking lot, a complete 3D scene including static structures and dynamic objects is output using visual rendering technology; A multi-attribute decision analysis method is used to extract attribute indicators that affect parking space scores from the three-dimensional model of the parking lot. A weight is assigned to each attribute indicator that affects the parking space score, and a weighted summation method is used to calculate the comprehensive score of each parking space. Based on the comprehensive score of each parking space, the one with the highest comprehensive score is selected as the charging parking space. When the vehicle arrives at the optimal parking space, the parking space status is verified and the smart charging pile is activated for charging service.

2. The method for identifying parking space locations based on a smart charging pile according to claim 1, characterized in that: The smart charging pile is started, and the original environmental data set in the parking lot is collected using multi-sensor fusion technology. The collected original environmental data set is pre-processed to obtain an optimized environmental data set. The specific steps are as follows: Start the smart charging pile and initialize the multiple sensors installed on the smart charging pile; After starting the smart charging pile, it checks the health status of multiple sensors through the Self-Diagnosis Program; The smart charging pile controls multiple sensors to collect the environment in the parking lot and obtain the original environmental data set; The original environmental data set is identified using anomaly detection methods to remove obviously erroneous data points, and a Kalman filter is applied to remove high-frequency noise to obtain a preliminary optimized data set. Perform Z-score normalization on the numerical data in the preliminary optimized data set to obtain a standardized optimized data set; The network time protocol is used to synchronize the timestamps of multiple sensors, and the spatial coordinate system of multiple sensors is adjusted through the external parameter information preset by the intelligent sensor to make it consistent with the global coordinate system, thereby obtaining an optimized environmental data set.

3. The method for identifying parking space locations based on a smart charging pile according to claim 2, characterized in that: The computer vision algorithm and geometric analysis method are applied to the image data in the optimized environment data set to extract static element features and confirm the position and shape of the fixed structure in the static element features to obtain the static element layer. The specific steps are as follows: The Canny edge detection algorithm is used to identify the boundary contours of the image data in the optimized environment dataset, and the Harris corner detection algorithm is used to find the significant corner points in the image data in the optimized environment dataset. The expression is as follows: ； ; in, for point The boundary contour strength, is the gradient operator of the Gaussian filter, for point The grayscale value of the image, is the structure tensor matrix, is a matrix The determinant of is a matrix traces, is an empirical constant, Indicates the corner point response value; Boundary contour strength and corner response values Get feature set ; Based on the feature set, the findContours function is used to outline the contours of the fixed structure in the feature set, and SIFT is used to compare the feature set with the contours in the optimized environment dataset. The expression is as follows: ; ； in, It is from The extracted boundary contour strength , It is a judgment point Whether a closed contour is formed, It is A silhouette, It is templates, is the adjustment coefficient, It is The first contour and the The matching error between templates is Indicates the Shape matching score of each contour; The outline With the Shape matching score of contours Perform collection to obtain the intermediate feature set ; The feature set and intermediate feature sets Middle Shape matching score of contours Integrate to obtain static element features ; Calculating static element features Middle The spatial relationship score of the contours , the expression is: ; in, is the adjustment coefficient, For the The spatial relationship score of the contours; Using comprehensive feature scoring function Combined with static element features , construct a static element layer, the expression is: ; ; in, Score static element features, is the number of static elements, For the The shape matching score weight of static elements, is the first feature in the intermediate feature set The shape matching score of static elements, For the The spatial relationship score weight of static elements, For static element features The spatial relationship score of static elements, is the number of computing resources used in the feature extraction process, For the The weight of computing resources, For the The consumption of computing resources, This is a static element layer.

4. The method for identifying parking space locations based on a smart charging pile according to claim 3, characterized in that: The method uses a motion detection algorithm to extract dynamic elements from the video data in the optimized environment data set, maps the position information of the dynamic elements to a spatial coordinate system, and obtains a dynamic element layer. The specific steps are as follows: Select the optical flow method to calculate the pixel displacement between consecutive frames in the video data and obtain the velocity vector of the pixel point ; The velocity vector obtained by , using a threshold function to distinguish velocity vectors The static background and dynamic foreground in are expressed as: ； in, is the binary dynamic element mask, is the horizontal coordinate of the pixel point, is the vertical coordinate of the pixel point, For location The velocity vector at length, is the threshold of velocity vector length; when The velocity vector length exceeds the set threshold , then the position is considered to belong to the dynamic foreground, otherwise it is considered to be the static background, and the position information of the dynamic foreground is obtained and marked as a dynamic element; Map the dynamic elements to the global coordinate system to obtain the dynamic element layer .

5. The method for identifying parking space locations based on a smart charging pile according to claim 4, characterized in that: The geometric feature extraction method is used to extract the position and shape features of static structures and moving objects from the static and dynamic element layers respectively. The specific steps are as follows: Apply edge detection algorithms and corner detection algorithms to extract the outlines and key points of static structures and moving objects in static element layers and dynamic element layers respectively; Use feature descriptors to encode the contours and key points of static structures and moving objects, and obtain a static structure feature set and moving object feature set , the expression is: ； ； in, is a set of static structural features, Indicates the The position coordinates of the key points, Represents the edge point set in the static element layer, Represents the corner point set in the static element layer. Indicates the The feature descriptor of key points, Indicates position The feature vector obtained by applying the feature descriptor encoding function, Indicates the The position coordinates of the key points, Represents the edge point set in the dynamic element layer, Represents the corner point set in the dynamic element layer. Indicates the The feature descriptor of key points, Indicates position Applying the feature descriptor encoding function to obtain the feature vector; The feature matching method is used to determine the relative positions of the position and shape features of the static structure and the moving object, and the 3D modeling platform Blender is used to integrate the relative positions of the position and shape features of the static structure and the moving object to obtain a 3D model of the parking lot. The specific steps are as follows: FLANN is used to match the descriptors in the static structure feature set and the moving object feature set to obtain the feature matching set. ; Feature matching set , static structural feature set and moving object feature set , using the 3D modeling platform Blender, a 3D model of the parking lot is generated. The expression is: ； in, is the feature matching set, For the 3D model of the parking lot, It is the feature set of the static structure in the static element layer. A feature set for moving objects in a dynamic element layer. is a comprehensive function; Based on the 3D model of the parking lot, visualization rendering technology is used to output a complete 3D scene including static structures and dynamic objects.

6. The method for identifying parking space locations based on a smart charging pile according to claim 5, characterized in that: The multi-attribute decision analysis method is used to extract attribute indicators that affect the parking space score from the three-dimensional model of the parking lot, and a weight is assigned to each attribute indicator that affects the parking space score. The weighted summation method is used to calculate the comprehensive score of each parking space. The specific steps are as follows: Extract attribute indicators that affect parking space scores from the 3D parking lot model, specifically the distance from the parking space to the charging station, the area of ​​the barrier-free area around the parking space, the vehicle density near the parking space, the pedestrian flow near the parking space, and the parking space safety score; Assign weights to the attribute indicators and obtain the scoring function of the attribute indicators. The comprehensive score is calculated by the scoring function of the attribute indicators. The expression is: ; in, The comprehensive rating of the parking space. For the The nonlinear normalized weight of the attribute index, For the The scoring function of the attribute index is To assist in adjusting the function, is the number of auxiliary adjustments.

7. The method for identifying parking space locations based on a smart charging pile according to claim 6, characterized in that: Based on the comprehensive score of each parking space, the highest comprehensive score is selected as the charging parking space. When the vehicle arrives at the optimal parking space, the parking space status is verified and the smart charging pile is activated for charging service. The specific steps are as follows: Define a selection function to select the parking space with the highest score from the comprehensive score as the optimal parking space. The expression is: ； in, For the best parking space, For the The comprehensive rating of parking spaces, Indicates that the choice makes Largest index ; After determining the optimal parking space, the optimal parking space information is sent to the car owner through the wireless communication protocol and navigation guidance is provided; When navigating to the optimal parking space, multiple sensors activate the monitoring function when the vehicle approaches the optimal parking space to detect whether the parking space is occupied; If the parking space is empty, the vehicle is allowed to enter; otherwise, the owner is notified to reselect a parking space.

8. A system for identifying parking spaces based on a smart charging pile, based on the method for identifying parking spaces based on a smart charging pile according to any one of claims 1 to 7, characterized in that: It includes data acquisition and processing module, static element extraction module, dynamic element extraction module, 3D modeling module and parking space scoring and selection module; The data acquisition and processing module is used to start the smart charging pile and initialize multiple sensors to collect raw data of the parking lot environment, and then obtain an optimized environmental data set through steps such as cleaning, denoising, standardization, time synchronization and spatial alignment; The static element extraction module is used to extract static element features from the optimized environmental data set by applying computer vision algorithms and geometric analysis methods; The dynamic element extraction module is used to extract dynamic elements from the optimized environmental data set using a motion detection algorithm, map the position information of the dynamic elements to a spatial coordinate system, and construct a dynamic element layer; The 3D modeling module is used to extract the position and shape features of static structures and moving objects from the static and dynamic element layers using a geometric feature extraction method, determine the relative positions of the position and shape features of the static structures and moving objects using a feature matching method, and integrate the relative positions of the position and shape features of the static structures and moving objects using the 3D modeling platform Blender to obtain a 3D model of the parking lot. The specific steps are as follows: Apply edge detection algorithms and corner detection algorithms to extract the outlines and key points of static structures and moving objects in static element layers and dynamic element layers respectively; Use feature descriptors to encode the outlines and key points of static structures and moving objects, and obtain static structure feature sets and moving object feature sets; FLANN is used to match the descriptors in the static structure feature set and the moving object feature set to obtain a feature matching set; Based on the feature matching set, static structure feature set and moving object feature set, a 3D model of the parking lot is generated using the 3D modeling platform Blender. Based on the 3D model of the parking lot, a complete 3D scene including static structures and dynamic objects is output using visual rendering technology; The parking space scoring selection module is used to extract attribute indicators that affect the parking space score from the three-dimensional parking lot model, calculate the comprehensive score of each parking space, select the optimal charging parking space and send it to the car owner through a wireless communication protocol. When the vehicle arrives at the optimal parking space, it verifies the parking space status and activates the smart charging pile for charging service.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for identifying parking space locations based on a smart charging pile are implemented as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for identifying parking space locations based on a smart charging pile according to any one of claims 1 to 7 are implemented.

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