Intelligent parking lot management method, system and device based on UWB and storage medium

Abstract

The invention relates to an intelligent parking lot management method, system and device based on UWB, and a storage medium, the method comprises the steps: obtaining the real-time position information of a vehicle in a parking lot, and the real-time position information is obtained through the cooperative positioning of a UWB tag installed on the vehicle and a plurality of UWB base stations in the parking lot; obtaining parking space state data in the parking lot, integrating the real-time position information of the vehicle with the parking space state data, and generating a parking space dynamic map in the parking lot; real-time traffic flow information is acquired, and an optimized driving path of the vehicle is generated based on the parking space dynamic map and the real-time traffic flow information; based on a preset parking space prediction model, generating a corresponding parking space demand condition, and dynamically adjusting a parking space distribution reservation strategy according to the parking space demand condition; the abnormal event condition of the parking lot is monitored in real time, and when the occurrence of the abnormal event is detected, an abnormal processing mechanism is automatically triggered based on an abnormal detection algorithm to process the abnormal event. The method has the effect of improving the parking lot management efficiency.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent parking lot management, and in particular to an intelligent parking lot management method, system, device and storage medium based on UWB. Background Art

[0002] At present, with the acceleration of urbanization and the increase in the number of cars, parking difficulties have become an important issue in urban management. Intelligent parking management systems have gradually become an effective means to solve this problem. By introducing technologies such as the Internet of Things, wireless communications, and big data analysis, intelligent parking systems can achieve real-time monitoring of parking space information, vehicle guidance and navigation, and other functions, thereby improving the efficiency of parking lots and the parking experience of car owners.

[0003] Most existing smart parking systems rely on video surveillance, RFID technology, geomagnetic induction technology, etc. to detect parking spaces and identify vehicles. However, these technologies have problems such as insufficient accuracy, poor real-time performance, and significant environmental interference, resulting in low parking management efficiency. Especially in high-density parking lots, existing technologies are difficult to accurately achieve real-time monitoring and allocation of vehicles and parking spaces, resulting in car owners spending a lot of time looking for parking spaces, which easily causes congestion.

[0004] The above-mentioned existing technical solutions have the following defects: the existing intelligent parking system has problems such as insufficient accuracy, poor real-time performance, and significant environmental interference, which makes it difficult to accurately realize real-time monitoring and allocation of vehicles and parking spaces, and easily causes congestion. Therefore, there is room for improvement. Summary of the invention

[0005] In order to improve the management efficiency of parking lots, the present application provides a UWB-based intelligent parking lot management method, system, device and storage medium.

[0006] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A UWB-based intelligent parking lot management method, the UWB-based intelligent parking lot management method comprising: Acquire real-time location information of vehicles in the parking lot, the real-time location information being obtained by collaborative positioning of a UWB tag installed on the vehicle and a plurality of UWB base stations in the parking lot; Acquire parking space status data in the parking lot, integrate the real-time location information of the vehicle with the parking space status data, and generate a dynamic map of parking spaces in the parking lot; Acquire real-time traffic flow information, and generate an optimized driving path for the vehicle based on the dynamic parking map and the real-time traffic flow information; Based on the preset parking space prediction model, generate corresponding parking space demand conditions, and dynamically adjust the parking space allocation and reservation strategy according to the parking space demand conditions; The abnormal events in the parking lot are monitored in real time, and when an abnormal event is detected, an abnormality handling mechanism is automatically triggered based on an abnormality detection algorithm to handle the abnormal event.

[0007] By adopting the above technical solution, by obtaining the real-time position information of the vehicle in the parking lot, the specific position of the vehicle in the parking lot can be accurately determined, and the accuracy of vehicle positioning can be improved, thereby providing reliable data support for the vehicle's driving path optimization and parking space matching; by obtaining the parking space status data in the parking lot and integrating the real-time position information of the vehicle with the parking space status data, the parking space usage of the parking lot can be dynamically updated, thereby realizing efficient utilization of parking space resources and avoiding vacant or repeated occupation of parking spaces; by obtaining real-time traffic flow information and generating an optimized driving path for the vehicle based on the parking space dynamic map and real-time traffic flow information, the time for vehicles to search for parking spaces in the parking lot can be reduced, thereby reducing vehicle congestion in the parking lot and improving the traffic efficiency of the parking lot; by generating the corresponding parking space demand based on the preset parking space prediction model and dynamically adjusting the parking space allocation and reservation strategy according to the parking space demand, the parking space supply and demand allocation of the parking lot can be reasonably planned, thereby improving the utilization efficiency of parking resources and reducing the parking difficulty caused by the mismatch between parking space supply and demand.

[0008] In one example, the present application may be further configured as follows: the obtaining of real-time location information of vehicles in the parking lot specifically includes: The UWB tag sends a unique identifier and communicates with a plurality of the UWB base stations, and the plurality of UWB base stations determine the precise location of the vehicle through a multi-anchor collaborative positioning algorithm; The collaborative positioning algorithm performs weighted processing according to the signal strength and signal quality of each base station to generate the precise positioning of the vehicle.

[0009] By adopting the above technical solution, a unique identifier is sent through the UWB tag and communicates with multiple UWB base stations, and the precise position of the vehicle is determined through a multi-anchor collaborative positioning algorithm. Multi-point fusion can be performed based on the positioning data of multiple base stations to improve the accuracy of vehicle positioning, thereby reducing the positioning error caused by signal interference or obstacle obstruction in the parking environment, and ensuring that the parking management system has accurate control of the vehicle position; through the collaborative positioning algorithm, weighted processing is performed according to the signal strength and signal quality of each base station to generate the precise positioning of the vehicle, which can effectively reduce positioning deviation and improve the stability of positioning results, thereby ensuring that the path calculation of the vehicle during driving and parking is more accurate, and improving the parking experience of car owners.

[0010] In one example, the present application may be further configured as follows: the acquiring of real-time traffic flow information, based on the parking space dynamic map and the real-time traffic flow information, and then generating an optimized driving path for the vehicle, specifically includes: Obtaining the parking type of the vehicle, and determining the target parking space according to the parking type and the parking space dynamic map; According to the target parking space, a multi-vehicle collaborative scheduling algorithm is used to calculate the optimized driving path of the vehicle. The multi-vehicle collaborative scheduling algorithm adopts a spatiotemporal avoidance strategy to avoid contact between different vehicles at the same time point.

[0011] By adopting the above technical solution, by obtaining the parking type of the vehicle and determining the target parking space according to the parking type and the dynamic map of the parking space, intelligent parking space allocation can be performed based on the needs and parking preferences of the car owner, thereby improving the efficiency of the car owner in finding a suitable parking space, reducing parking time, and improving the parking experience; by using a multi-vehicle collaborative scheduling algorithm to calculate the optimized driving path of the vehicle according to the target parking space, and adopting a time-space avoidance strategy, it can effectively avoid congestion or collision of vehicles in the parking lot due to crossing driving paths, thereby improving the traffic efficiency of the parking lot, reducing the waiting time during vehicle driving, and improving the overall operation efficiency of the parking lot.

[0012] In one example, the present application may be further configured as follows: the acquiring of the parking type of the vehicle and determining the target parking space according to the parking type and the parking space dynamic map specifically includes: Obtaining the vehicle owner's preference settings and / or parking demand information, and comprehensively analyzing the preference settings and / or the parking demand information to determine the parking type of the vehicle; According to the parking type, correlation matching is performed on each parking space in the parking space dynamic map, and then the target parking space is screened out according to the corresponding matching degree.

[0013] By adopting the above technical solution, by obtaining the owner's preference settings and / or parking demand information and conducting a comprehensive analysis of the preference settings and / or parking demand information, the parking type of the vehicle is determined, and parking spaces can be allocated based on the owner's personalized needs, thereby improving the intelligence of parking, avoiding the owner's repeated search for parking spaces due to parking spaces not meeting expected needs, and improving parking efficiency and user experience; by performing correlation matching of each parking space in the parking space dynamic map according to the parking type, and screening out the target parking space according to the degree of matching, the geographical location, type, current usage and other factors of the parking space can be comprehensively considered to ensure that the owner can quickly find the most suitable parking space, thereby reducing parking time and improving the resource utilization of the parking lot.

[0014] In one example, the present application may be further configured as follows: the multi-vehicle collaborative scheduling algorithm is used to calculate the optimized driving path of the vehicle according to the target parking space, specifically including: Obtaining driving information and target parking space information of all vehicles in the parking lot; A multi-vehicle collaborative scheduling algorithm is used to globally optimize all the vehicles, and the driving paths of all the vehicles are calculated based on the driving information of the vehicles and the target parking space information; According to the driving path, the intersection time-space points where all the vehicles come into contact with other vehicles during driving are determined, and the vehicle paths where the intersection time-space points appear are adjusted through the time-space avoidance strategy to obtain the optimized driving path.

[0015] By adopting the above technical solution, by obtaining the driving information and target parking space information of all vehicles in the parking lot, the distribution and dynamic changes of vehicles in the parking lot can be fully grasped, thereby providing an accurate data basis for multi-vehicle collaborative scheduling; by adopting a multi-vehicle collaborative scheduling algorithm to globally optimize all vehicles, and calculating the driving paths of all vehicles based on the driving information and target parking space information of the vehicles, the vehicles can be scheduled on a global scale to ensure that each vehicle has the optimal path during driving, avoid congestion in local areas, and improve the overall traffic efficiency of the parking lot; by determining the intersection time and space points where all vehicles come into contact with other vehicles during driving according to the driving paths, and adjusting the vehicle paths at the intersection time and space points through the time and space avoidance strategy, it is possible to effectively avoid traffic conflicts in the parking lot, thereby reducing congestion caused by the intersection of vehicle paths, improving the smoothness of vehicle traffic, and improving the overall operation efficiency of the parking lot.

[0016] In one example, the present application may be further configured as follows: the construction of the preset parking space prediction model specifically includes: Collecting historical parking space usage data and corresponding external environmental factors in the parking lot; Extracting the time, space and environment factors of the historical parking space usage data and the external environmental factors respectively, generating corresponding time feature vectors, space feature vectors and environment feature vectors, and then generating a model training data set; The preset long short-term memory network model is iteratively trained according to the model training data set, and the corresponding parking space demand is predicted by adjusting the network parameters using historical parking space data and environmental characteristics, thereby obtaining the parking space prediction model.

[0017] By adopting the above technical solution, by collecting historical parking space usage data and corresponding external environmental factors in the parking lot, it is possible to analyze the parking space demand based on the historical data of the parking lot, so as to understand the usage rules of the parking spaces and improve the accuracy of parking space prediction; by extracting the features of time, space and environmental factors from the historical parking space usage data and external environmental factors respectively, and generating corresponding time feature vectors, space feature vectors and environmental feature vectors, and then generating a model training data set, it is possible to integrate the influencing factors of different dimensions and improve the model's prediction ability for parking demand, thereby making parking management more intelligent and efficient; by iteratively training the preset long short-term memory network model according to the model training data set, and by adjusting the network parameters, using the historical parking space data and environmental characteristics to predict the corresponding parking space demand, and then obtaining a parking space prediction model, it is possible to ensure the accuracy and adaptability of the parking space prediction model, thereby improving the parking lot's ability to reasonably plan future parking space demand and improving the overall utilization of parking resources.

[0018] In one example, the present application may be further configured as follows: generating corresponding parking space demand conditions based on a preset parking space prediction model, and dynamically adjusting the parking space allocation and reservation strategy according to the parking space demand conditions, specifically including: Acquire real-time external environmental factors and real-time parking space status, and input the external environmental factors and the real-time parking space status into the parking space prediction model to generate the parking space demand situation; Based on the parking space demand situation, the demand for parking spaces in different areas and different types in the parking lot is determined, and excessive parking space demand and vacant parking spaces are identified, and then the corresponding allocation and reservation strategies are adjusted according to the identification results.

[0019] By adopting the above technical solution, by obtaining real-time external environmental factors and real-time parking space status, and inputting the external environmental factors and real-time parking space status into the parking space prediction model, the parking space demand situation is generated, which can enable the parking space demand prediction model to adapt to the real-time changing parking lot environment, thereby ensuring the accuracy of parking space demand prediction and improving the rationality of parking space allocation; by determining the demand for different areas and different types of parking spaces in the parking lot based on the parking space demand situation, and identifying excessive parking space demand and vacant parking spaces, and then adjusting the corresponding allocation and reservation strategies according to the identification results, it can effectively solve the problem of unbalanced supply and demand of parking spaces in the parking lot, thereby improving the utilization rate of parking resources, avoiding long-term vacancy or shortage of parking spaces, and improving the overall operation efficiency of the parking lot and the parking experience of users.

[0020] The second object of the invention is achieved by the following technical solutions: A UWB-based intelligent parking lot management system, the UWB-based intelligent parking lot management system comprising: A vehicle positioning module is used to obtain real-time location information of vehicles in the parking lot, wherein the real-time location information is obtained by co-positioning a UWB tag installed on the vehicle and a plurality of UWB base stations in the parking lot; A parking space management module, used to obtain parking space status data in the parking lot, integrate the real-time location information of the vehicle with the parking space status data, and generate a dynamic map of parking spaces in the parking lot; A path optimization module, used for obtaining real-time traffic flow information, and generating an optimized driving path for the vehicle based on the parking space dynamic map and the real-time traffic flow information; The parking space prediction and scheduling module is used to generate corresponding parking space demand conditions based on the preset parking space prediction model, and dynamically adjust the parking space allocation and reservation strategy according to the parking space demand conditions; The abnormality monitoring module is used to monitor the abnormal events in the parking lot in real time, and when an abnormal event is detected, automatically trigger an abnormality handling mechanism based on an abnormality detection algorithm to handle the abnormal event.

[0021] By adopting the above technical solution, by obtaining the real-time position information of the vehicle in the parking lot, the specific position of the vehicle in the parking lot can be accurately determined, and the accuracy of vehicle positioning can be improved, thereby providing reliable data support for the vehicle's driving path optimization and parking space matching; by obtaining the parking space status data in the parking lot and integrating the real-time position information of the vehicle with the parking space status data, the parking space usage of the parking lot can be dynamically updated, thereby realizing efficient utilization of parking space resources and avoiding vacant or repeated occupation of parking spaces; by obtaining real-time traffic flow information and generating an optimized driving path for the vehicle based on the parking space dynamic map and real-time traffic flow information, the time for vehicles to search for parking spaces in the parking lot can be reduced, thereby reducing vehicle congestion in the parking lot and improving the traffic efficiency of the parking lot; by generating the corresponding parking space demand based on the preset parking space prediction model and dynamically adjusting the parking space allocation and reservation strategy according to the parking space demand, the parking space supply and demand allocation of the parking lot can be reasonably planned, thereby improving the utilization efficiency of parking resources and reducing the parking difficulty caused by the mismatch between parking space supply and demand.

[0022] The third objective of the present application is achieved through the following technical solutions: A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned UWB-based intelligent parking lot management method are implemented.

[0023] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned UWB-based intelligent parking lot management method are implemented.

[0024] In summary, this application includes the following beneficial technical effects: 1. By acquiring the real-time location information of vehicles in the parking lot, the specific location of the vehicle in the parking lot can be accurately determined, and the accuracy of vehicle positioning can be improved, thereby providing reliable data support for vehicle driving path optimization and parking space matching; by acquiring the parking space status data in the parking lot and integrating the real-time location information of the vehicle with the parking space status data, the parking space usage of the parking lot can be dynamically updated, thereby realizing efficient utilization of parking space resources and avoiding vacant or repeated occupancy of parking spaces; by acquiring real-time traffic flow information and generating optimized driving paths for vehicles based on parking space dynamic maps and real-time traffic flow information, the time for vehicles to search for parking spaces in the parking lot can be reduced, thereby reducing vehicle congestion in the parking lot and improving the traffic efficiency of the parking lot; by generating corresponding parking space demand based on a preset parking space prediction model and dynamically adjusting the parking space allocation and reservation strategy according to the parking space demand, the parking space supply and demand allocation of the parking lot can be reasonably planned, thereby improving the utilization efficiency of parking resources and reducing the parking difficulty caused by the mismatch between parking space supply and demand; 2. Send a unique identifier through the UWB tag and communicate with multiple UWB base stations, and determine the precise location of the vehicle through a multi-anchor collaborative positioning algorithm. It can perform multi-point fusion based on the positioning data of multiple base stations to improve the accuracy of vehicle positioning, thereby reducing the positioning error caused by signal interference or obstacle obstruction in the parking environment, and ensuring that the parking management system can accurately control the vehicle position; through the collaborative positioning algorithm, weighted processing is performed according to the signal strength and signal quality of each base station to generate the precise positioning of the vehicle, which can effectively reduce positioning deviation and improve the stability of positioning results, thereby ensuring that the path calculation of the vehicle during driving and parking is more accurate, and improving the parking experience of the car owner; 3. By obtaining the parking type of the vehicle and determining the target parking space based on the parking type and dynamic parking space map, intelligent parking space allocation can be performed based on the needs and parking preferences of the car owner, thereby improving the efficiency of the car owner in finding a suitable parking space, reducing parking time, and improving the parking experience; by using a multi-vehicle collaborative scheduling algorithm to calculate the optimized driving path of the vehicle according to the target parking space, and adopting a time-space avoidance strategy, it can effectively avoid congestion or collision of vehicles in the parking lot due to crossing driving paths, thereby improving the traffic efficiency of the parking lot, reducing the waiting time during vehicle driving, and improving the overall operation efficiency of the parking lot. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1is a flow chart of a UWB-based intelligent parking lot management method in an embodiment of the present application; Figure 2 It is a flowchart for implementing step S10 in the UWB-based intelligent parking lot management method in one embodiment of the present application; Figure 3 It is a flowchart for implementing step S30 in the UWB-based intelligent parking lot management method in one embodiment of the present application; Figure 4 It is a flowchart for implementing step S31 in the UWB-based intelligent parking lot management method in one embodiment of the present application; Figure 5 It is a flowchart for implementing step S32 in the UWB-based intelligent parking lot management method in one embodiment of the present application; Figure 6 It is a flowchart for implementing the construction of a parking space prediction model in a UWB-based intelligent parking lot management method in one embodiment of the present application; Figure 7 is a flowchart for implementing step S40 in the UWB-based intelligent parking lot management method in one embodiment of the present application; Figure 8 It is a principle block diagram of an intelligent parking lot management system based on UWB in one embodiment of the present application; Fig. 9 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The present application is further described in detail below in conjunction with the accompanying drawings.

[0027] In one embodiment, if Figure 1 As shown, the present application discloses a UWB-based intelligent parking lot management method, which specifically includes the following steps: S10: Acquire real-time location information of vehicles in the parking lot. The real-time location information is obtained by co-locating a UWB tag installed on the vehicle and multiple UWB base stations in the parking lot.

[0028] Specifically, the UWB tags installed on the vehicles will periodically send unique identifier signals to multiple UWB base stations in the parking lot. The UWB base stations in the parking lot receive the signals through a multi-anchor collaborative positioning algorithm and use a weighted processing method to calculate the precise location of the vehicle based on the signal strength and signal quality received by each base station. This enables high-precision positioning to be achieved through collaboration between multiple base stations, improves positioning accuracy, and ensures the accuracy and real-time nature of vehicle location data.

[0029] S20: Acquire parking space status data in the parking lot, integrate the real-time location information of the vehicle with the parking space status data, and generate a dynamic map of the parking spaces in the parking lot.

[0030] Specifically, the parking space status data in the parking lot is collected in real time by UWB base stations and other sensors such as geomagnetic sensors, video surveillance, etc., and the vacancy and occupancy status of the parking spaces are then displayed in a graphical manner based on the collected data. The real-time location information of the vehicle is matched and integrated with the parking space status data, and combined with the spatial information and usage frequency of each parking space to generate a real-time updated dynamic map of the parking lot, which includes the vacancy or occupancy status of the parking spaces, and can also dynamically adjust the parking space status according to the needs of the car owner and the parking time period.

[0031] S30: Acquire real-time traffic flow information, and generate an optimized driving path for the vehicle based on the parking space dynamic map and the real-time traffic flow information.

[0032] Specifically, sensors, cameras and on-board equipment installed at key locations in the parking lot monitor the flow of vehicles in the parking lot in real time. Traffic flow information includes vehicle speed, direction and traffic density. Combined with the dynamic map of parking spaces, the parking lot management system will generate the optimal driving route based on the owner's location, target parking space and real-time traffic flow data. The route planning will take into account avoiding congested areas, avoiding collisions with other vehicles, and combining the shortest time with the optimal space utilization to ensure that the vehicle reaches the target parking space quickly and safely.

[0033] S40: Generate corresponding parking space demand conditions based on the preset parking space prediction model, and dynamically adjust parking space allocation and reservation strategies according to the parking space demand conditions.

[0034] Specifically, by inputting real-time parking space usage data and external environmental data such as weather, holidays, and surrounding shopping mall activities into the preset parking space prediction model, the model will generate predictions for future parking space demand based on these data. According to the prediction results, the demand for parking spaces in different areas and different types will be evaluated, and areas with excessive parking space demand and vacant parking spaces will be determined. The system will then adjust the parking space allocation and reservation strategy. For example, more parking spaces may be reserved in areas with higher demand, and fewer reserved parking spaces may be reserved in areas with lower demand, thereby optimizing the resource utilization of the parking lot.

[0035] S50: Monitor abnormal events in the parking lot in real time, and when abnormal events are detected, automatically trigger an abnormality handling mechanism based on an abnormality detection algorithm to handle the abnormal events.

[0036] Specifically, the parking system will use real-time monitoring systems and sensors to continuously monitor abnormal situations in the parking lot, including but not limited to illegal parking, loss of UWB signals, equipment failures, abnormal parking space occupancy, etc. The anomaly detection algorithm will analyze the data from sensors and cameras in real time, identify any abnormal events and calculate potential safety risks. If the system detects an abnormal event, it will automatically trigger a predetermined processing mechanism, such as automatically adjusting the allocation of parking spaces in the parking lot, issuing a warning signal, or notifying the parking lot manager of relevant information. Through these automated processes, the safety and management efficiency of the parking lot are ensured.

[0037] In one embodiment, if Figure 2 As shown, in step S10, the real-time position information of the vehicle in the parking lot is obtained, which specifically includes: S11: The UWB tag sends a unique identifier and communicates with multiple UWB base stations, which determine the precise location of the vehicle through a multi-anchor collaborative positioning algorithm.

[0038] Specifically, the UWB tag regularly sends a unique identifier signal in each car, which is received by multiple UWB base stations in the parking lot. The base stations collaborate to use a multi-anchor collaborative positioning algorithm, using the signal arrival time difference between each base station and the vehicle to calculate the vehicle's position. The combination of multiple base station signals can improve the accuracy of positioning and ensure that the accuracy of vehicle positioning can reach the centimeter level. Each base station participates in the calculation based on the quality and strength of the received signal. The more base stations there are, the more accurate the positioning results are, further ensuring the accuracy and real-time nature of parking space management.

[0039] S12: The collaborative positioning algorithm performs weighted processing based on the signal strength and signal quality of each base station to generate the precise positioning of the vehicle.

[0040] Specifically, the multi-anchor collaborative positioning algorithm weights the signal strength and signal quality received by each base station, and uses weighted processing to improve positioning accuracy. Base stations with stronger signal strengths have higher weights in positioning, and base stations with poorer signal quality have less impact on the final positioning. The algorithm combines the weights of each base station to generate a more accurate positioning result, ensuring that the vehicle's location can be displayed in real time and accurately on the dynamic map of parking spaces in the parking lot.

[0041] In one embodiment, if Figure 3 As shown, in step S30, real-time traffic flow information is obtained, and based on the parking space dynamic map and the real-time traffic flow information, an optimized driving path for the vehicle is generated, which specifically includes: S31: Obtain the parking type of the vehicle, and determine the target parking space according to the parking type and the parking space dynamic map.

[0042] Specifically, the parking type of the vehicle is determined based on the owner's preference settings or parking demand information. For example, the owner may choose short-term parking, long-term parking or priority parking. The parking type will be clarified through the preference information entered in advance by the owner or recorded in historical data. For example, short-term parking vehicles may give priority to parking spaces near entrances and exits, while long-term parking vehicles may choose parking spaces away from crowded areas. Based on the parking type, the dynamic map of parking spaces in the parking lot will select the target parking spaces that best meet the needs by matching the characteristics of the parking spaces, such as parking space type, availability, location, etc., to ensure that the vehicle can quickly find the most suitable parking space.

[0043] S32: Based on the target parking space, a multi-vehicle collaborative scheduling algorithm is used to calculate the optimal driving path of the vehicle. The multi-vehicle collaborative scheduling algorithm adopts a spatiotemporal avoidance strategy to avoid contact between different vehicles at the same time point.

[0044] Specifically, a multi-vehicle collaborative scheduling algorithm is adopted to calculate the driving path of each vehicle by analyzing the real-time location information and target parking spaces of all vehicles in the parking lot. The algorithm will take into account the traffic volume, parking space occupancy, relative position and movement direction between vehicles to avoid collisions between vehicles at the same time and space points. The time and space avoidance strategy is dynamically adjusted according to the driving speed, path and expected arrival time of each vehicle to ensure that vehicles avoid cross paths and potential collision risks during driving. Finally, the safe path of each vehicle is calculated to ensure that all vehicles can reach the target parking space efficiently and safely.

[0045] In one embodiment, if Figure 4 As shown, in step S31, the parking type of the vehicle is obtained, and the target parking space is determined according to the parking type and the parking space dynamic map, which specifically includes: S311: Obtain the vehicle owner's preference settings and / or parking demand information, and conduct a comprehensive analysis of the preference settings and / or parking demand information to determine the parking type of the vehicle.

[0046] Specifically, the car owner inputs parking demand information through the vehicle-mounted device or mobile terminal, such as parking duration, whether a parking space for electric vehicle charging is needed, or whether there are special needs such as a disabled parking space. This information will be combined with the car owner's historical parking behavior and preference settings, such as commonly used parking areas or parking space types. The parking type of the vehicle is determined by analyzing past parking data or the rules set by the car owner. For example, if the car owner sets a preference for a parking space near a shopping mall, the system will identify it as short-term parking and match the vacant parking space closest to the mall entrance in the dynamic map of the parking lot.

[0047] S312: According to the parking type, correlation matching is performed on each parking space in the parking space dynamic map, and then the target parking space is selected according to the corresponding matching degree.

[0048] Specifically, the dynamic map of parking spaces in the parking lot will display the vacancy status and type of each parking space. The system will screen the parking spaces according to the parking type of the vehicle and the matching degree of the parking space. If the vehicle is a short-term parking type, the system will give priority to parking spaces near the parking lot entrance, close to elevators or shopping centers and other key locations. If the vehicle is a long-term parking space, the system will choose parking spaces away from high-traffic areas. By comprehensively considering the parking space type, distance and vacancy status, the system will dynamically screen out the optimal target parking space.

[0049] In one embodiment, if Figure 5 As shown, in step S32, the optimized driving path of the vehicle is calculated using a multi-vehicle collaborative scheduling algorithm according to the target parking space, which specifically includes: S321: Obtain driving information and target parking space information of all vehicles in the parking lot.

[0050] Specifically, the driving information of all vehicles in the parking lot includes the current location information, target parking space information, driving direction, etc., as well as the status of the target parking space, such as whether it is idle or occupied, etc., and then integrates the data of all vehicles in the parking lot and the relevant information of the target parking space as the basic data for calculating the optimized path.

[0051] S322: A multi-vehicle collaborative scheduling algorithm is used to perform global optimization on all vehicles, and the driving paths of all vehicles are calculated based on the vehicle driving information and target parking space information.

[0052] Specifically, with the help of the multi-vehicle collaborative scheduling algorithm, the system globally optimizes all vehicles in the parking lot, and calculates the optimal driving path for each vehicle by comprehensively considering factors such as traffic flow, parking space distribution, relative positions between vehicles, vehicle types and driving directions. Through this algorithm, it can ensure that all vehicles avoid path overlap and mutual interference in the process of reaching the target parking space, thereby improving parking efficiency and reducing the risk of congestion.

[0053] S323: Determine the intersection time and space points where all vehicles come into contact with other vehicles during the driving process according to the driving path, and adjust the vehicle paths where the intersection time and space points appear through the time and space avoidance strategy to obtain the optimized driving path.

[0054] Specifically, in the process of calculating the path, the system will simulate the driving trajectory of each vehicle, predict the time and space points where each vehicle may intersect during its driving process, identify potential path conflicts by analyzing parameters such as the speed, driving direction and arrival time of all vehicles, and make adjustments through time and space avoidance strategies. By adjusting the vehicle's speed, driving route or scheduled arrival time, it can avoid vehicles from colliding at the same time and space point, ensuring that all vehicles can reach the target parking space safely and efficiently.

[0055] In one embodiment, if Figure 6 As shown, in step S40, the construction of the preset parking space prediction model specifically includes: S401: Collect historical parking space usage data and corresponding external environmental factors in the parking lot.

[0056] Specifically, the parking lot management system will continuously collect and store historical parking space usage data, which includes the vacancy and occupancy status of parking spaces, usage time, parking space types such as ordinary parking spaces, electric vehicle charging spaces, etc., and combined with external environmental factors such as weather, holidays, and surrounding shopping mall activities. These historical data provide the necessary input information for training the parking space demand prediction model.

[0057] S402: Extract the time, space and environmental factors of historical parking space usage data and external environmental factors respectively, generate corresponding time feature vectors, space feature vectors and environmental feature vectors, and then generate a model training data set.

[0058] Specifically, historical parking space usage data and external environmental factors will undergo a feature extraction process to extract time factors such as date, hour, weekdays and non-working days, spatial factors such as parking space location, parking space type, and environmental factors such as weather, holidays, etc., and generate time feature vectors, spatial feature vectors, and environmental feature vectors respectively. Finally, these features are synthesized into a training data set for training the parking space demand prediction model.

[0059] S403: Iteratively train the preset long short-term memory network model according to the model training data set, adjust the network parameters, use the historical parking space data and environmental characteristics to predict the corresponding parking space demand, and then obtain a parking space prediction model.

[0060] Specifically, the training data set is iteratively trained using a long short-term memory network model, and the network optimizes the prediction accuracy of parking space demand by adjusting parameters. Through back propagation and optimization of historical parking space data and environmental feature data, the model can accurately predict the parking space demand of the parking lot in the future, thereby obtaining a reliable parking space prediction model.

[0061] In one embodiment, if Figure 7 As shown, in step S40, based on the preset parking space prediction model, the corresponding parking space demand situation is generated, and the parking space allocation and reservation strategy is dynamically adjusted according to the parking space demand situation, specifically including: S41: Acquire real-time external environmental factors and real-time parking space status, and input the external environmental factors and real-time parking space status into a parking space prediction model to generate parking space demand conditions.

[0062] Specifically, real-time parking space status data and external environmental information such as weather changes, holiday information, and traffic flow changes will be collected in real time and input into the parking space prediction model. The model will calculate and generate predicted parking space demand to understand future demand fluctuations in the parking lot.

[0063] S42: Based on the parking space demand situation, determine the demand for different areas and different types of parking spaces in the parking lot, identify excessive parking space demand and vacant parking spaces, and then adjust the corresponding allocation and reservation strategies according to the identification results.

[0064] Specifically, based on the demand for parking spaces, the system will analyze the demand in different areas and parking space types, identify areas with excessive parking space demand and vacant parking spaces, and automatically increase the number of reserved parking spaces in areas with higher parking space demand, and reduce the number of reserved parking spaces in areas with lower demand, thereby ensuring the rational use of parking lot space, optimizing parking space allocation and reservation strategies, and improving the overall efficiency of the parking lot.

[0065] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0066] In one embodiment, a UWB-based intelligent parking lot management system is provided, and the UWB-based intelligent parking lot management system corresponds one-to-one with the UWB-based intelligent parking lot management method in the above embodiment. Figure 8 As shown, the UWB-based intelligent parking management system includes a vehicle positioning module, a parking space management module, a path optimization module, a parking space prediction and scheduling module, and an abnormality monitoring module. The detailed description of each functional module is as follows: The vehicle positioning module is used to obtain the real-time location information of the vehicle in the parking lot. The real-time location information is obtained by co-positioning the UWB tag installed on the vehicle and multiple UWB base stations in the parking lot; The parking space management module is used to obtain the parking space status data in the parking lot, integrate the real-time location information of the vehicle with the parking space status data, and generate a dynamic map of the parking spaces in the parking lot; The path optimization module is used to obtain real-time traffic flow information and generate an optimized driving path for the vehicle based on the parking space dynamic map and real-time traffic flow information; The parking space prediction and scheduling module is used to generate corresponding parking space demand conditions based on the preset parking space prediction model, and dynamically adjust the parking space allocation and reservation strategy according to the parking space demand conditions; The abnormality monitoring module is used to monitor abnormal events in the parking lot in real time, and when an abnormal event is detected, it automatically triggers the abnormality handling mechanism based on the abnormality detection algorithm to handle the abnormal event.

[0067] Optionally, the vehicle positioning module specifically includes: UWB communication submodule, used for UWB tag to send unique identifier and communicate with multiple UWB base stations, and multiple UWB base stations determine the precise location of the vehicle through a multi-anchor collaborative positioning algorithm; The signal processing submodule is used to coordinate the positioning algorithm to perform weighted processing based on the signal strength and signal quality of each base station to generate the precise positioning of the vehicle.

[0068] Optionally, the path optimization module specifically includes: The parking type identification submodule is used to obtain the parking type of the vehicle and determine the target parking space according to the parking type and the parking space dynamic map; The path planning submodule is used to calculate the optimal driving path of the vehicle according to the target parking space using a multi-vehicle collaborative scheduling algorithm. The multi-vehicle collaborative scheduling algorithm adopts a spatiotemporal avoidance strategy to prevent different vehicles from contacting each other at the same time.

[0069] Optionally, the parking type identification submodule specifically includes: A parking demand analysis unit, used to obtain the vehicle owner's preference settings and / or parking demand information, and conduct a comprehensive analysis of the preference settings and / or parking demand information to determine the parking type of the vehicle; The parking space matching unit is used to perform correlation matching on each parking space in the parking space dynamic map according to the parking type, and then select the target parking space according to the corresponding matching degree.

[0070] Optionally, the path planning submodule specifically includes: The vehicle data collection unit is used to obtain the driving information and target parking space information of all vehicles in the parking lot; The multi-vehicle scheduling optimization unit is used to perform global optimization on all vehicles using a multi-vehicle collaborative scheduling algorithm and calculate the driving paths of all vehicles based on the vehicle driving information and target parking space information; The space-time avoidance unit is used to determine the intersection space-time points where all vehicles come into contact with other vehicles during driving according to the driving path, and adjust the vehicle paths where the intersection space-time points appear through the space-time avoidance strategy to obtain the optimized driving path.

[0071] Optionally, the construction of the preset parking space prediction model specifically includes: A data collection module is used to collect historical parking space usage data and corresponding external environmental factors in the parking lot; The feature extraction module is used to extract the time, space and environmental factors of the historical parking space usage data and external environmental factors, generate the corresponding time feature vector, space feature vector and environmental feature vector, and then generate the model training data set; The model training module is used to iteratively train the preset long short-term memory network model according to the model training data set, adjust the network parameters, use historical parking space data and environmental characteristics to predict the corresponding parking space demand, and then obtain the parking space prediction model.

[0072] Optionally, the parking space prediction and scheduling module specifically includes: A real-time data input submodule is used to obtain real-time external environmental factors and real-time parking space status, and input the external environmental factors and real-time parking space status into the parking space prediction model to generate parking space demand conditions; The parking space allocation optimization submodule is used to determine the demand for different areas and types of parking spaces in the parking lot based on the parking space demand situation, and to identify excessive parking space demand and vacant parking spaces, and then adjust the corresponding allocation and reservation strategies based on the identification results.

[0073] For the specific definition of the UWB-based intelligent parking management system, please refer to the definition of the UWB-based intelligent parking management method above, which will not be repeated here. Each module in the above-mentioned UWB-based intelligent parking management system can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0074] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Fig. 9 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a UWB-based intelligent parking lot management method is implemented.

[0075] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Obtain the real-time location information of vehicles in the parking lot. The real-time location information is obtained by co-locating the UWB tags installed on the vehicles and multiple UWB base stations in the parking lot. Obtain parking space status data in the parking lot, integrate the real-time location information of the vehicle with the parking space status data, and generate a dynamic map of the parking spaces in the parking lot; Obtain real-time traffic flow information, and generate optimized driving routes for vehicles based on dynamic parking maps and real-time traffic flow information; Generate corresponding parking space demand based on the preset parking space prediction model, and dynamically adjust parking space allocation and reservation strategies according to parking space demand; Abnormal events in the parking lot are monitored in real time, and when abnormal events are detected, the abnormal handling mechanism is automatically triggered based on the anomaly detection algorithm to handle the abnormal events.

[0076] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Obtain the real-time location information of vehicles in the parking lot. The real-time location information is obtained by co-locating the UWB tags installed on the vehicles and multiple UWB base stations in the parking lot. Obtain parking space status data in the parking lot, integrate the real-time location information of the vehicle with the parking space status data, and generate a dynamic map of the parking spaces in the parking lot; Obtain real-time traffic flow information, and generate optimized driving routes for vehicles based on dynamic parking maps and real-time traffic flow information; Generate corresponding parking space demand based on the preset parking space prediction model, and dynamically adjust parking space allocation and reservation strategies according to parking space demand; Abnormal events in the parking lot are monitored in real time, and when abnormal events are detected, the abnormal handling mechanism is automatically triggered based on the anomaly detection algorithm to handle the abnormal events.

[0077] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0078] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0079] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A UWB-based intelligent parking lot management method, characterized in that: The UWB-based intelligent parking lot management method includes: Acquire real-time location information of vehicles in the parking lot, the real-time location information being obtained by collaborative positioning of a UWB tag installed on the vehicle and a plurality of UWB base stations in the parking lot; Acquire parking space status data in the parking lot, integrate the real-time location information of the vehicle with the parking space status data, and generate a dynamic map of parking spaces in the parking lot; Acquire real-time traffic flow information, and generate an optimized driving path for the vehicle based on the dynamic parking map and the real-time traffic flow information; Based on the preset parking space prediction model, generate corresponding parking space demand conditions, and dynamically adjust the parking space allocation and reservation strategy according to the parking space demand conditions; The abnormal events in the parking lot are monitored in real time, and when an abnormal event is detected, an abnormality handling mechanism is automatically triggered based on an abnormality detection algorithm to handle the abnormal event.

2. The UWB-based intelligent parking lot management method according to claim 1 is characterized in that: The real-time location information of the vehicle in the parking lot is obtained, specifically including: The UWB tag sends a unique identifier and communicates with a plurality of the UWB base stations, and the plurality of UWB base stations determine the precise location of the vehicle through a multi-anchor collaborative positioning algorithm; The collaborative positioning algorithm performs weighted processing according to the signal strength and signal quality of each base station to generate the precise positioning of the vehicle.

3. The UWB-based intelligent parking lot management method according to claim 1 is characterized in that: The acquiring of real-time traffic flow information and generating an optimized driving path for the vehicle based on the parking space dynamic map and the real-time traffic flow information specifically includes: Obtaining the parking type of the vehicle, and determining the target parking space according to the parking type and the parking space dynamic map; According to the target parking space, a multi-vehicle collaborative scheduling algorithm is used to calculate the optimized driving path of the vehicle. The multi-vehicle collaborative scheduling algorithm adopts a spatiotemporal avoidance strategy to avoid contact between different vehicles at the same time point.

4. The UWB-based intelligent parking lot management method according to claim 3 is characterized in that: The acquiring the parking type of the vehicle and determining the target parking space according to the parking type and the parking space dynamic map specifically includes: Obtaining the vehicle owner's preference settings and / or parking demand information, and comprehensively analyzing the preference settings and / or the parking demand information to determine the parking type of the vehicle; According to the parking type, correlation matching is performed on each parking space in the parking space dynamic map, and then the target parking space is screened out according to the corresponding matching degree.

5. The UWB-based intelligent parking lot management method according to claim 3 is characterized in that: The step of calculating the optimized driving path of the vehicle using a multi-vehicle collaborative scheduling algorithm according to the target parking space specifically includes: Obtaining driving information and target parking space information of all vehicles in the parking lot; A multi-vehicle collaborative scheduling algorithm is used to globally optimize all the vehicles, and the driving paths of all the vehicles are calculated based on the driving information of the vehicles and the target parking space information; According to the driving path, the intersection time-space points where all the vehicles come into contact with other vehicles during driving are determined, and the vehicle paths where the intersection time-space points appear are adjusted through the time-space avoidance strategy to obtain the optimized driving path.

6. The UWB-based intelligent parking lot management method according to claim 1, characterized in that: The construction of the preset parking space prediction model specifically includes: Collecting historical parking space usage data and corresponding external environmental factors in the parking lot; Extracting the time, space and environment factors of the historical parking space usage data and the external environmental factors respectively, generating corresponding time feature vectors, space feature vectors and environment feature vectors, and then generating a model training data set; The preset long short-term memory network model is iteratively trained according to the model training data set, and the corresponding parking space demand is predicted by adjusting the network parameters using historical parking space data and environmental characteristics, thereby obtaining the parking space prediction model.

7. The UWB-based intelligent parking lot management method according to claim 1, characterized in that: The method of generating corresponding parking space demand conditions based on a preset parking space prediction model and dynamically adjusting the parking space allocation and reservation strategy according to the parking space demand conditions specifically includes: Acquire real-time external environmental factors and real-time parking space status, and input the external environmental factors and the real-time parking space status into the parking space prediction model to generate the parking space demand situation; Based on the parking space demand situation, the demand for parking spaces in different areas and different types in the parking lot is determined, and excessive parking space demand and vacant parking spaces are identified, and then the corresponding allocation and reservation strategies are adjusted according to the identification results.

8. An intelligent parking lot management system based on UWB, characterized in that: The UWB-based intelligent parking lot management system includes: A vehicle positioning module is used to obtain real-time location information of vehicles in the parking lot, wherein the real-time location information is obtained by co-positioning a UWB tag installed on the vehicle and a plurality of UWB base stations in the parking lot; A parking space management module, used to obtain parking space status data in the parking lot, integrate the real-time location information of the vehicle with the parking space status data, and generate a dynamic map of parking spaces in the parking lot; A path optimization module, used for obtaining real-time traffic flow information, and generating an optimized driving path for the vehicle based on the parking space dynamic map and the real-time traffic flow information; The parking space prediction and scheduling module is used to generate corresponding parking space demand conditions based on the preset parking space prediction model, and dynamically adjust the parking space allocation and reservation strategy according to the parking space demand conditions; The abnormality monitoring module is used to monitor the abnormal events in the parking lot in real time, and when an abnormal event is detected, automatically trigger an abnormality handling mechanism based on an abnormality detection algorithm to handle the abnormal event.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the UWB-based intelligent parking lot management method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the UWB-based intelligent parking lot management method as claimed in any one of claims 1 to 7 are implemented.

Images