Real-time parking data analysis method for parking lot

Abstract

The invention discloses a real-time parking data analysis method for a parking lot, and relates to the technical field of parking lot management, and the analysis steps are as follows: real-time updating is carried out on parking spaces in an applet based on real-time parking data in the parking lot, and a vehicle owner checks the parking spaces online through the applet to know the change of the parking spaces in the parking lot; the laser radar scans and collects point cloud data in the parking garage, constructs a plane map in the parking lot and uploads the plane map to the applet, and a vehicle owner knows the parking point of the vehicle through the plane map and quickly finds the vehicle; and based on the real-time parking data in the parking lot, acquiring conditions of vehicles entering and exiting from the parking lot at present. According to the invention, based on the real-time parking data in the parking lot, the access condition of the current vehicle in the parking lot is analyzed, the access route with fewer vehicles is intelligently generated, the vehicle owner is guided to quickly access the vehicle through an applet, the waiting time and the congestion condition are reduced, convenience is brought to the vehicle owner, and the management of the parking lot is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of parking lot management, and in particular to a real-time parking data analysis method for a parking lot. Background Art

[0002] With the acceleration of urbanization and the sharp increase in the number of cars, the problem of difficult parking in cities has become increasingly prominent. Parking lot management is a comprehensive task involving multiple aspects, aiming to ensure the normal operation of parking lots and provide car owners with a safe, convenient and comfortable parking environment. Although the parking lot management systems on the market have achieved vehicle access management and parking guidance functions to a certain extent, they still have many shortcomings in real-time parking data analysis. Traditional systems often rely on manual records or simple sensor equipment, with low data collection frequency and poor accuracy, and it is difficult to reflect the actual situation of parking spaces in parking lots in real time. Since it is impossible to obtain parking space information in real time, car owners often need to spend a lot of time and energy when looking for parking spaces, resulting in a poor parking experience. In addition, when users enter and exit the garage, they cannot understand the congestion of the entrances and exits, resulting in concentrated congestion of vehicles at the same entrance and exit during peak hours, increasing congestion. In this regard, we propose a real-time parking data analysis method for parking lots. Summary of the invention

[0003] In order to solve the above technical problems, a real-time parking data analysis method for a parking lot is provided. This technical solution solves the above problems.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is: a real-time parking data analysis method for a parking lot, the analysis steps comprising:

[0005] The parking spaces in the mini program are updated in real time based on the real-time parking data in the parking lot. Car owners can check the parking spaces online through the mini program to understand the changes in the parking spaces in the parking lot.

[0006] The laser radar scans the parking garage, collects point cloud data, constructs a plane map of the parking lot, and uploads it to the mini program. Car owners can use the plane map to understand their parking spots and quickly find their vehicles.

[0007] Based on the real-time parking data in the parking lot, the system obtains the current situation of vehicles entering and exiting the parking lot. According to the situation of vehicles entering and exiting the parking lot, it intelligently generates entrance and exit routes with fewer vehicles to guide car owners to enter and exit the vehicles quickly.

[0008] Preferably, the real-time update step of parking data is performed by installing a geomagnetic sensor in the parking space, obtaining in real time whether there is a vehicle in the parking space based on the geomagnetic sensor, uploading the obtained data to the cloud server, and updating the vehicle status in the mini program based on the cloud server. After the cloud server obtains the change of the vehicle in the parking space, it pushes the information to the mini program in time and displays it on the interactive page for the car owner to view; the car owner can intuitively view the parking space status in the current parking lot through the interactive page of the mini program.

[0009] Preferably, the steps for constructing a planar map in the mini program are:

[0010] A1. Prepare the LiDAR, ensure the equipment is working properly, and plan the scanning path according to the size of the parking lot;

[0011] A2. Start the laser radar and move along the planned path. The laser radar emits laser pulses to the surroundings and receives the reflected light beams to collect point cloud data in the parking garage.

[0012] A3. Check the data collection of the LiDAR to ensure data integrity and adjust the scanning path parameters in time for supplementary scanning;

[0013] A4. Import the collected point cloud data into the point cloud software and pre-process the data;

[0014] A5. Use the spatial features and attribute information of point cloud data to segment and classify different objects in the parking lot;

[0015] A6. Extract the plane information in the parking lot through the classified point cloud data;

[0016] A7. Based on map editing software, convert point cloud data into plane map;

[0017] A8. Draw the outline of the parking lot, parking spaces, roads and facilities on the plan map;

[0018] A9. Optimize and output the map, and upload it to the mini program.

[0019] Preferably, in step A1, the scanning path is planned to determine the size of the parking lot in advance according to the area and number of parking lots, and the entrance and exit locations of the parking lot are identified, and the parking lot is divided into large parking lots, medium-sized parking lots and small parking lots. For large parking lots, a block scanning strategy is adopted to divide the parking lot into several small areas, which are scanned one by one, and the scanning path of each small area is planned separately; for medium-sized parking lots, a surrounding scanning strategy is adopted, starting from a corner of the parking lot, and performing a surrounding scan along the outer lanes and parking space boundaries, gradually advancing inward until the entire parking lot is covered; for small parking lots, a spiral scanning strategy is adopted, starting from the center of the parking lot, and expanding the scanning range outward in a spiral manner.

[0020] Preferably, the point cloud data segmentation formula in step A5 is:

[0021]

[0022] Where A is the objective function after point cloud segmentation, k is the number of clusters in the cluster, ci is the i-th cluster, x is the point in the cluster, μ i is the mean of cluster ci. The point cloud data is divided into multiple clusters through the formula, and the spatial position and shape points are gathered together to form different objects;

[0023] Point cloud classification is based on neural network classification. Each layer converts input into output through weight and activation function. The formula is:

[0024] B=σ(W·d+e)

[0025] Where B is the output value of the input conversion, σ is the activation function, W is the weight matrix, d is the input value, and e is the bias vector;

[0026] The parking lot plane information extraction form in step A6 is expressed as:

[0027] G v +H y +J z +f=0

[0028] Among them G v , H y , J z is the normal vector of the plane, f is the intercept, and the formula is used to find the minimum sum of the squares of the distances from the point to the plane; users can quickly find their vehicles based on the parking lot map on the mini program.

[0029] Preferably, the real-time parking data acquisition method in the parking lot is based on sensor acquisition. Sensors are installed on the gates of the entrances and exits of the parking lot to monitor the entry and exit of vehicles. The monitored data is uploaded to the analysis system, and the data is analyzed to obtain the vehicle data in the current parking garage. The vehicle data acquisition is based on the total number of vehicles entering and exiting each entrance and exit per unit time; the interval time of vehicle entry and exit is analyzed based on the time feedback from the sensor, and the obtained interval time is averaged to obtain the average interval time of vehicle entry and exit at each entrance and exit. The vehicle intervals at each entrance and exit are compared to obtain the entrance and exit position with the longest interval time. The mini program performs navigation planning for users according to the currently obtained entrance and exit positions to guide car owners to enter.

[0030] Preferably, the average interval time between vehicle entry and exit is calculated as follows:

[0031]

[0032] Among them, K is the average interval time, L1 is the interval time for the first group of vehicles to enter and exit the warehouse, L2 is the interval time for the second group of vehicles to enter and exit the warehouse, Ln is the interval time for the nth group of vehicles to enter and exit the warehouse, and M is the total interval time. The average time interval between vehicles entering and exiting is obtained through the formula.

[0033] Preferably, the average interval time comparison formula for inbound and outbound storage is:

[0034] O=K1-K2

[0035] Among them, O is the interval time difference, K1 is the average interval time of the first group, and K2 is the average interval time of the second group. If the value of O is greater than 1, it means that the K1 value is greater than the K2 value. If the value of O is less than 1, it means that the K1 value is less than the K2 value. If the value of O is equal to 0, it means that the K1 value is equal to the K2 value. By repeated calculation, the entrance and exit position with the largest interval time is obtained, and navigation is provided for users through the mini program.

[0036] Preferably, the mini program provides a text box for users to enter specific text feedback, and allows users to upload screenshots, photos and short videos to submit user feedback.

[0037] Preferably, after the feedback is submitted, the collected feedback is manually reviewed to ensure the authenticity and validity of the information.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention develops a special parking lot management applet through a management unit. The applet can receive and process parking data in the parking lot in real time. The parking space information in the applet is updated in real time according to the real-time parking data in the parking lot, ensuring that the car owner obtains the latest and most accurate parking space information. The car owner can clearly see the parking spot of his vehicle through the floor map in the applet, so as to quickly find the vehicle. Based on the real-time parking data in the parking lot, the entry and exit situation of the vehicles in the current parking lot is analyzed, and the entrance and exit routes with fewer vehicles are intelligently generated. The car owner is guided to quickly enter and exit the vehicle through the applet, which reduces waiting time and congestion, brings convenience to the car owner, and facilitates the management of the parking lot. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is a flow chart of the management steps of the present invention;

[0041] Figure 2 The present invention is a flow chart of the plan map construction steps. DETAILED DESCRIPTION

[0042] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0043] Reference Figure 1 As shown, a real-time parking data analysis method for a parking lot, the analysis steps include:

[0044] The parking spaces in the mini program are updated in real time based on the real-time parking data in the parking lot. Car owners can check the parking spaces online through the mini program to understand the changes in the parking spaces in the parking lot.

[0045] The laser radar scans the parking garage, collects point cloud data, constructs a plane map of the parking lot, and uploads it to the mini program. Car owners can use the plane map to understand their parking spots and quickly find their vehicles.

[0046] Based on the real-time parking data in the parking lot, the system obtains the current situation of vehicles entering and exiting the parking lot. According to the situation of vehicles entering and exiting the parking lot, it intelligently generates entrance and exit routes with fewer vehicles to guide car owners to enter and exit the vehicles quickly.

[0047] The parking space information that can be updated in real time in this application allows car owners to easily find vacant parking spaces, avoiding the trouble of blindly searching for parking spaces. The plane map constructed by the lidar allows car owners to quickly locate their vehicles and save time in finding cars. The intelligently generated vehicle entry and exit routes reduce waiting time and congestion, and improve the travel efficiency of car owners. Real-time parking data analysis provides management units with a scientific and accurate basis for decision-making, which helps to optimize the resource allocation and management strategy of parking lots. By collecting car owners' opinions and suggestions through the feedback mechanism, management units can promptly understand car owners' needs and pain points, thereby continuously improving service quality and management efficiency. Real-time updated parking space information helps car owners quickly find vacant parking spaces, thereby improving the utilization and turnover rate of parking lots. The intelligently generated vehicle entry and exit routes reduce the ineffective movement and waiting time of vehicles in parking lots, and further improve the operating efficiency of parking lots.

[0048] The real-time update step of parking data is to install a geomagnetic sensor in the parking space, obtain the presence of a vehicle in the parking space in real time based on the geomagnetic sensor, upload the obtained data to the cloud server, and update the vehicle status in the mini program based on the cloud server. After the cloud server obtains the change of the vehicle in the parking space, it will push the information to the mini program in time and display it on the interactive page for the car owner to view; the car owner can intuitively view the parking space status in the current parking lot through the mini program interactive page.

[0049] The car owners of this application can obtain the latest parking space status in the parking lot in real time through the mini program without having to check in person, which greatly saves time and energy. Car owners only need to use the interactive page of the mini program to intuitively understand the current parking space distribution and occupancy status in the parking lot, so as to quickly find vacant parking spaces and improve the convenience of parking. Real-time updated parking data helps parking lot managers to better understand the use of parking spaces, and then optimize the allocation of parking resources, improve the utilization rate and turnover rate of parking spaces. Through the cooperation of geomagnetic sensors and cloud servers, parking lot managers can realize accurate monitoring and management of parking space status, reducing resource waste and management loopholes caused by human misjudgment or negligence.

[0050] Reference Figure 2 As shown, the steps for constructing a flat map in the applet are:

[0051] A1. Prepare the LiDAR, ensure the equipment is working properly, and plan the scanning path according to the size of the parking lot;

[0052] A2. Start the laser radar and move along the planned path. The laser radar emits laser pulses to the surroundings and receives the reflected light beams to collect point cloud data in the parking garage.

[0053] A3. Check the data collection of the LiDAR to ensure data integrity and adjust the scanning path parameters in time for supplementary scanning;

[0054] A4. Import the collected point cloud data into the point cloud software and pre-process the data;

[0055] A5. Use the spatial features and attribute information of point cloud data to segment and classify different objects in the parking lot;

[0056] A6. Extract the plane information in the parking lot through the classified point cloud data;

[0057] A7. Based on map editing software, convert point cloud data into plane map;

[0058] A8. Draw the outline of the parking lot, parking spaces, roads and facilities on the plan map;

[0059] A9. Optimize and output the map, and upload it to the mini program.

[0060] In step A1, the scanning path is planned to determine the size of the parking lot in advance according to the area and number of parking lots, identify the entrance and exit locations of the parking lot, and divide the parking lot into large parking lots, medium-sized parking lots and small parking lots. For large parking lots, a block scanning strategy is adopted to divide the parking lot into several small areas, which are scanned one by one, and the scanning path of each small area is planned separately; for medium-sized parking lots, a surrounding scanning strategy is adopted, starting from a corner of the parking lot, and performing a surrounding scan along the outer lanes and parking space boundaries, gradually advancing inward until the entire parking lot is covered; for small parking lots, a spiral scanning strategy is adopted, starting from the center of the parking lot, and expanding the scanning range outward in a spiral manner.

[0061] The point cloud data segmentation formula in step A5 is:

[0062]

[0063] Where A is the objective function after point cloud segmentation, k is the number of clusters in the cluster, ci is the i-th cluster, x is the point in the cluster, μ i is the mean of cluster ci. The point cloud data is divided into multiple clusters through the formula, and the spatial position and shape points are gathered together to form different objects;

[0064] Point cloud classification is based on neural network classification. Each layer converts input into output through weight and activation function. The formula is:

[0065] B=σ(W·d+e)

[0066] Where B is the output value of the input conversion, σ is the activation function, W is the weight matrix, d is the input value, and e is the bias vector;

[0067] The parking lot plane information extraction form in step A6 is expressed as:

[0068] G v +H y +J z +f=0

[0069] Among them G v , H y , J z is the normal vector of the plane, f is the intercept, and the formula is used to find the minimum sum of the squares of the distances from the point to the plane; users can quickly find their vehicles based on the parking lot map on the mini program.

[0070] In this application, the scanning path is planned according to the size of the parking lot, which can ensure that the lidar covers all areas where data needs to be collected during the scanning process, while reducing unnecessary repeated scanning and improving scanning efficiency. Moving along the planned path can ensure that all areas in the parking lot are scanned, including corners and areas behind obstacles that are difficult to directly observe. The scanning path parameters are adjusted according to the data collection situation, which reflects the flexibility and adaptability of the process and ensures the accuracy and completeness of data collection. Through segmentation and classification, information on different objects in the parking lot, such as walls, pillars, and parking spaces, can be extracted from the point cloud data. The plane map generated by lidar scanning and point cloud data processing provides car owners with accurate parking lot layout information, which helps car owners quickly find parking spaces and exit key locations and improve parking efficiency. The automated data collection and processing process reduces manual intervention and error rates, reduces the management cost of parking lots, and detailed map annotations and convenient access methods improve car owners' parking experience and enhance user satisfaction and loyalty to parking lot services.

[0071] The real-time parking data acquisition method in the parking lot is based on sensor acquisition. Sensors are installed on the gates of the entrances and exits of the parking lot to monitor the entry and exit of vehicles. The monitored data is uploaded to the analysis system, and the data is analyzed to obtain the vehicle data in the current parking garage. The vehicle data acquisition is based on the total number of vehicles entering and exiting each entrance and exit per unit time; the interval time of vehicle entry and exit is analyzed based on the time feedback from the sensor, and the obtained interval time is averaged to obtain the average interval time of vehicle entry and exit at each entrance and exit. The vehicle intervals at each entrance and exit are compared to obtain the entrance and exit position with the longest interval time. The mini program performs navigation planning for users according to the currently obtained entrance and exit positions to guide car owners in.

[0072] The average interval time between vehicle entry and exit is calculated as follows:

[0073]

[0074] Among them, K is the average interval time, L1 is the interval time for the first group of vehicles to enter and exit the warehouse, L2 is the interval time for the second group of vehicles to enter and exit the warehouse, Ln is the interval time for the nth group of vehicles to enter and exit the warehouse, and M is the total interval time. The average time interval between vehicles entering and exiting is obtained through the formula.

[0075] The comparison formula for the average interval time between inbound and outbound storage is:

[0076] O=K1-K2

[0077] Among them, O is the interval time difference, K1 is the average interval time of the first group, and K2 is the average interval time of the second group. If the value of O is greater than 1, it means that the K1 value is greater than the K2 value. If the value of O is less than 1, it means that the K1 value is less than the K2 value. If the value of O is equal to 0, it means that the K1 value is equal to the K2 value. By repeated calculation, the entrance and exit position with the largest interval time is obtained, and navigation is provided for users through the mini program.

[0078] This application can identify the currently relatively idle entrances and exits by analyzing the average interval time of vehicle entry and exit. Based on this, the mini program provides navigation planning for car owners and guides them to choose the optimal route to enter the parking lot, thereby reducing waiting time and the time spent looking for empty parking spaces. By monitoring and analyzing the vehicle flow at each entrance and exit, parking lot managers can allocate resources more reasonably, such as adjusting staffing and optimizing parking area divisions, to improve overall operational efficiency.

[0079] The mini program provides a text box for users to enter specific text feedback, and allows users to upload screenshots, photos and short videos to submit user feedback. After submitting the feedback, the collected feedback will be manually reviewed to ensure the authenticity and validity of the information.

[0080] Users of this application can express their feedback in multiple forms such as text, pictures, and videos, making the feedback content richer and more intuitive, helping users to convey problems and suggestions more accurately. Users can submit feedback through the mini program anytime and anywhere without waiting or looking for other channels, which improves the immediacy of feedback. Multimedia forms such as pictures and videos can provide more intuitive and specific feedback content, helping developers or managers to understand the problem more accurately. Combined with the manual review mechanism, it can ensure the authenticity and effectiveness of the collected feedback information and reduce the interference of false or invalid feedback.

[0081] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A real-time parking data analysis method for a parking lot, characterized in that: The analysis steps include: The parking spaces in the mini program are updated in real time based on the real-time parking data in the parking lot. Car owners can check the parking spaces online through the mini program to understand the changes in the parking spaces in the parking lot. The laser radar scans the parking garage, collects point cloud data, constructs a plane map of the parking lot, and uploads it to the mini program. Car owners can use the plane map to understand their parking spots and quickly find their vehicles. Based on the real-time parking data in the parking lot, the system obtains the current situation of vehicles entering and exiting the parking lot. According to the situation of vehicles entering and exiting the parking lot, it intelligently generates entrance and exit routes with fewer vehicles to guide car owners to enter and exit the vehicles quickly.

2. A method for analyzing real-time parking data of a parking lot according to claim 1, characterized in that: The real-time update step of parking data is to install a geomagnetic sensor in the parking space, obtain the presence of a vehicle in the parking space in real time based on the geomagnetic sensor, upload the obtained data to the cloud server, and update the vehicle status in the mini program based on the cloud server. After the cloud server obtains the change of the vehicle in the parking space, it will push the information to the mini program in time and display it on the interactive page for the car owner to view; Car owners can intuitively view the current parking space situation in the parking lot through the interactive page of the mini program.

3. The real-time parking data analysis method of a parking lot according to claim 1, characterized in that: The steps for building a flat map with the applet are: A1. Prepare the LiDAR, ensure the equipment is working properly, and plan the scanning path according to the size of the parking lot; A2. Start the laser radar and move along the planned path. The laser radar emits laser pulses to the surroundings and receives the reflected light beams to collect point cloud data in the parking garage. A3. Check the data collection of the LiDAR to ensure data integrity and adjust the scanning path parameters in time for supplementary scanning; A4. Import the collected point cloud data into the point cloud software and pre-process the data; A5. Use the spatial features and attribute information of point cloud data to segment and classify different objects in the parking lot; A6. Extract the plane information in the parking lot through the classified point cloud data; A7. Based on map editing software, convert point cloud data into plane map; A8. Draw the outline of the parking lot, parking spaces, roads and facilities on the plan map; A9. Optimize and output the map, and upload it to the mini program.

4. The real-time parking data analysis method of a parking lot according to claim 3, characterized in that: In step A1, the scanning path is planned to determine the size of the parking lot in advance according to the area and number of parking lots, identify the entrance and exit locations of the parking lot, and divide the parking lot into large parking lots, medium-sized parking lots and small parking lots. For large parking lots, a block scanning strategy is adopted to divide the parking lot into several small areas, which are scanned one by one, and the scanning path of each small area is planned separately; for medium-sized parking lots, a surrounding scanning strategy is adopted, starting from a corner of the parking lot, and performing a surrounding scan along the outer lanes and parking space boundaries, gradually advancing inward until the entire parking lot is covered; for small parking lots, a spiral scanning strategy is adopted, starting from the center of the parking lot, and expanding the scanning range outward in a spiral manner.

5. The real-time parking data analysis method of a parking lot according to claim 3, characterized in that: The point cloud data segmentation formula in step A5 is: Where A is the objective function after point cloud segmentation, k is the number of clusters in the cluster, ci is the i-th cluster, x is the point in the cluster, μ i is the mean of cluster ci. The point cloud data is divided into multiple clusters through the formula, and the spatial position and shape points are gathered together to form different objects; Point cloud classification is based on neural network classification. Each layer converts input into output through weight and activation function. The formula is: B=σ(W·d+e) Where B is the output value of the input conversion, σ is the activation function, W is the weight matrix, d is the input value, and e is the bias vector; The parking lot plane information extraction form in step A6 is expressed as: G v +H y +J z +f=0 Among them G v , H y , J z is the normal vector of the plane, f is the intercept, and the formula is used to find the minimum sum of the squares of the distances from the point to the plane; users can quickly find their vehicles based on the parking lot map on the mini program.

6. The real-time parking data analysis method of a parking lot according to claim 1, characterized in that: The real-time parking data acquisition method in the parking lot is based on sensor acquisition. Sensors are installed on the gates at the entrances and exits of the parking lot to monitor the entry and exit of vehicles. The monitored data is uploaded to the analysis system, and the data is analyzed to obtain the vehicle data in the current parking garage. The vehicle data acquisition is based on the total number of vehicles entering and exiting each entrance and exit within a unit time. The interval time of vehicle entry and exit is analyzed based on the time feedback from the sensor, and the obtained interval time is averaged to obtain the average interval time of vehicle entry and exit at each entrance and exit. The vehicle intervals at each entrance and exit are compared to obtain the entrance and exit position with the longest interval time. The mini program will perform navigation planning for users according to the currently obtained entrance and exit positions to guide car owners in.

7. A method for analyzing real-time parking data of a parking lot according to claim 6, characterized in that: The average interval time between vehicle entry and exit is calculated as follows: Among them, K is the average interval time, L1 is the interval time for the first group of vehicles to enter and exit the warehouse, L2 is the interval time for the second group of vehicles to enter and exit the warehouse, Ln is the interval time for the nth group of vehicles to enter and exit the warehouse, and M is the total interval time. The average time interval between vehicles entering and exiting is obtained through the formula.

8. The method for real-time parking data analysis of a parking lot according to claim 6, characterized in that: The comparison formula for the average interval time between inbound and outbound storage is: O=K1-K2 Among them, O is the interval time difference, K1 is the average interval time of the first group, and K2 is the average interval time of the second group. If the value of O is greater than 1, it means that the K1 value is greater than the K2 value. If the value of O is less than 1, it means that the K1 value is less than the K2 value. If the value of O is equal to 0, it means that the K1 value is equal to the K2 value. By repeated calculation, the entrance and exit position with the largest interval time is obtained, and navigation is provided for users through the mini program.

9. The real-time parking data analysis method of a parking lot according to claim 1, characterized in that: The mini program provides a text box for users to enter specific text feedback, and allows users to upload screenshots, photos and short videos to submit user feedback.

10. A method for analyzing real-time parking data of a parking lot according to claim 9, characterized in that: After submitting the feedback, the collected feedback will be manually reviewed to ensure the authenticity and validity of the information.

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