An application method for parking project calculation

Abstract

The present invention discloses an application method for parking project measurement, which relates to the field of parking lot control technology. The application method for parking project measurement includes the following steps: S1, data collection; S2, network status evaluation; S3, obtaining an environmental compensation coefficient; S4, camera operation status evaluation. The present invention obtains a network anomaly evaluation value through the obtained network performance data, determines whether to perform network optimization based on the network anomaly evaluation value and a preset network anomaly threshold, then analyzes the main camera operation status data and environmental data obtained after optimization to obtain an environmental compensation coefficient, obtains an operation anomaly evaluation value based on the main camera operation status data, the environmental compensation coefficient and the network anomaly evaluation value, determines whether to adjust the main camera based on the operation anomaly evaluation value and the preset operation anomaly threshold, thereby improving the accuracy of parking guidance and solving the problem of low parking guidance accuracy in the parking guidance application of parking project measurement in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of parking lot control, and in particular to an application method for parking project measurement. Background Art

[0002] In parking project calculations, the primary reason for parking guidance stems from the mismatch between increasing parking demand and parking resources during modern urbanization, particularly in densely populated areas like large cities, shopping malls, and airports. Due to the large scale and complex distribution of parking spaces, especially in multi-story or multi-area parking lots, it can be difficult for drivers to quickly find available spaces. Traditional parking management methods largely rely on subjective on-site observation and analysis, as well as manual note-taking and field data recording. This requires significant manpower to manage and maintain parking spaces, resulting in low management efficiency and difficulties in achieving real-time monitoring and updating of parking status.

[0003] In the prior art, the parking situation of each parking space is collected by data collection equipment, and the obtained parking space information is then fed back to the user, and the user is guided to select a parking space, thereby realizing real-time monitoring of the parking space status, information transmission and guidance functions.

[0004] For example, the invention patent with announcement number: CN114863714B announces an intelligent guidance system for underground parking spaces and a method for reverse car search after parking, including: an information collection system, an information processing system, a control system, a service terminal and a light path belt; in view of the special environment of underground parking lots, information collection of license plates and parking spaces is realized based on NB-IOT, license plate recognition technology and parking space detection technology; considering the driver's visual and psychological factors, the shortest path algorithm is designed based on the improved ant colony algorithm, and the path is double-layer optimized to obtain the best parking guidance path; a color light path belt is used to guide the driver for parking guidance, helping the driver to quickly find the parking space and improve the utilization rate of the underground parking lot; and based on WiFi, laser scanners, 3D-MAP software and other equipment, reverse car search technology and 3D map navigation technology are introduced, and the light guidance path is combined with the real-time vehicle scene three-dimensional map model to design an integrated "parking-searching" service.

[0005] For example, the invention patent announcement with announcement number: CN117831338B discloses a data collaborative sharing method based on a parking lot intelligent guidance terminal, which includes the following steps: S101, collecting the status data of each parking space in real time through magnetic sensors installed on the parking space; S102, designing a database on the data center to store and manage the parking space status data of each parking lot, and using algorithms to conduct in-depth analysis; S103, establishing a data sharing mechanism to transmit parking lot data to urban traffic management departments and map navigation software; S104, displaying the sensor working status in real time through a visual interface and alarm mechanism.

[0006] However, in the process of implementing the technical solutions of the embodiments of the present application, the inventors of the present application discovered that the above technology has at least the following technical problems:

[0007] In the existing technology, due to various reasons such as the complexity of the parking environment, sensors may not be able to accurately detect the status of parking spaces, and unstable signals in underground parking lots affect the real-time update of parking space status. There is a problem of low parking space guidance accuracy in parking space guidance applications calculated by parking projects. Summary of the Invention

[0008] The embodiment of the present application solves the problem of low parking space guidance accuracy in parking space guidance applications based on parking project calculations in the prior art by providing an application method for parking project calculations, thereby improving the parking space guidance accuracy in parking space guidance applications based on parking project calculations.

[0009] An embodiment of the present application provides an application method for parking project measurement, including the following steps: S1, obtaining parking space status time data and monitoring data of a parking lot through a main camera, wherein the parking space status time data includes the parking space data update time and the parking space status information display time, and the monitoring data includes the main camera operation status data, environmental data and network performance data; S2, evaluating the network status through the network performance data to obtain a network anomaly evaluation value, and judging whether to perform network optimization based on the obtained network anomaly evaluation value and a preset network anomaly threshold, wherein the network anomaly evaluation value is used to quantify the degree of performance anomaly of the network device; S3, after the network performance is optimized, analyzing the optimized main camera operation status data and environmental data to obtain an environmental compensation coefficient; S4, evaluating the main camera according to the optimized main camera operation status data, the environmental compensation coefficient and the network anomaly evaluation value to obtain an operation anomaly evaluation value, and judging whether to adjust the main camera based on the obtained operation anomaly evaluation value and the preset operation anomaly threshold, wherein the operation anomaly evaluation value is used to quantify the degree of operation anomaly of the main camera.

[0010] Furthermore, the main camera operating status data includes information refresh time, average image frame rate and exposure time; the average image frame rate represents the average value of the image frame rate at a preset time point within a preset time period; the parking space status information display time is obtained by requesting the timestamp of displaying the parking space status information from the database through the network; the parking space data update time is obtained by detecting the timestamp of the parking space status update through the main camera of the parking lot; the information refresh time represents the time difference between the parking space status information display time and the parking space data update time; the environmental data represents the average value of the light intensity, humidity and temperature at a preset time point within a preset time period; the environmental data includes the average light intensity, average humidity and average temperature; the network performance data includes the average network throughput, the network connection interruption time and the average parking lot signal strength; the average network throughput represents the average value of the network throughput at a preset time point within the preset time period; the average parking lot signal strength represents the average value of the signal strength at a preset time point within the preset time period.

[0011] Furthermore, the specific method for evaluating the network status through network performance data to obtain a network anomaly evaluation value is as follows: obtaining preset network performance data and preset information refresh time from a preset database, the preset network performance data including preset network throughput, preset parking lot signal strength and preset network connection interruption time; obtaining network performance deviation based on the relative relationship analysis between the network performance data and the preset network performance data, the network performance deviation including network throughput deviation, parking lot signal strength deviation and network connection interruption time deviation, the network performance deviation indicating the deviation between the network performance data and the preset network performance data; obtaining information refresh time deviation based on the relative relationship analysis between the information refresh time and the preset information refresh time, the information refresh time deviation indicating the deviation between the information refresh time and the preset information refresh time; processing the network performance deviation and the information refresh time deviation to obtain a network anomaly evaluation value.

[0012] Furthermore, the specific process of determining whether to perform network optimization based on the obtained network anomaly evaluation value and the preset network anomaly threshold is as follows: A1, determining whether the obtained network anomaly evaluation value is less than the preset network anomaly threshold. If the network anomaly evaluation value is less than the preset network anomaly threshold, network optimization is not performed, otherwise A2 is executed; A2, optimizing data transmission, wherein the optimized data transmission includes bandwidth management, multi-path transmission, and adaptive path selection, wherein the adaptive path selection indicates dynamic selection of the optimal transmission path according to network performance data; A3, adjusting computing resources step by step, and when the information refresh time is less than the preset information refresh time, stopping adjusting computing resources and executing A4, otherwise continuing to adjust computing resources step by step until the computing resources are adjusted to the preset maximum computing resources and then executing A4; A4, optimizing parking lot signal strength, wherein optimizing parking lot signal strength includes optimizing the communication network and prompting preset personnel to deploy signal enhancers.

[0013] Furthermore, the specific process of the adaptive path selection is as follows: processing the network performance deviation of each communication path to obtain a transmission network coefficient; arranging the transmission network coefficients of each communication path in ascending order to obtain an optimal transmission network coefficient; recording the communication path corresponding to the optimal transmission network as the optimal transmission path, and determining whether the optimal transmission path is unique; if the optimal transmission path is not unique, screening the optimal transmission path according to the information refresh time to determine a unique optimal transmission path.

[0014] Furthermore, after the network performance is optimized, the specific process of analyzing the optimized main camera operation status data and environmental data to obtain the environmental compensation coefficient is as follows: C1, determine whether the number of the preset time period is less than 2. If the number of the preset time period is less than 2, the environmental compensation coefficient is recorded as 1, otherwise execute C2; ​​C2, according to the relative relationship analysis of the main camera operation status data of the current preset time period and the main camera operation status data of the previous preset time period, obtain the operation status change coefficient of the main camera, and the operation status change coefficient represents the change of the main camera operation status data of the current preset time period and the main camera operation status data of the previous preset time period; according to the relative relationship analysis of the environmental data of the current preset time period and the environmental data of the previous preset time period, obtain the environmental change coefficient, and the environmental change coefficient represents the change of the environmental data of the current preset time period and the environmental data of the previous preset time period; process the environmental change coefficient and the operation status change coefficient of the main camera to obtain the environmental compensation coefficient.

[0015] Furthermore, the specific method of evaluating the main camera to obtain the operation abnormality evaluation value based on the main camera operation status data, environmental compensation coefficient and network anomaly evaluation value obtained after optimization is as follows: obtaining the preset operation status data of the main camera from a preset database, the preset operation status data including a preset information refresh time, a preset image frame rate and a preset exposure time; obtaining the operation status deviation of the main camera based on the relative relationship analysis between the main camera operation status data and the preset operation status data, the operation status deviation of the main camera indicating the deviation between the operation status data of the main camera and the preset operation status data; and obtaining the operation abnormality evaluation value based on the operation status deviation, environmental compensation coefficient and network anomaly evaluation value of the main camera.

[0016] Furthermore, the specific limiting expression of the operation abnormality evaluation value is:

[0017]

[0018] Where i represents the number of the preset time period, i=1,2,...,T, T represents the total number of preset time periods, YIC i represents the operational abnormality evaluation value of the main camera in the i-th preset time period, HJB represents the environmental compensation coefficient, SYC i Indicates the operating state deviation of the main camera in the i-th preset time period, WA i Indicates the network anomaly assessment value of the i-th preset time period, CBC i Indicates the information refresh time of the i-th preset time period, TXZ i Indicates the average image frame rate of the i-th preset time period, BGS i represents the exposure time of the i-th preset time period, CBC0 represents the preset information refresh time, TXZ0 represents the preset image frame rate, and BGS0 represents the preset exposure time.

[0019] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0020] 1. Obtain a network anomaly assessment value through the acquired network performance data, and determine whether to perform network optimization. Then, obtain an environmental compensation coefficient based on the optimized main camera operating status data and environmental data. Finally, obtain an operating anomaly assessment value based on the main camera operating status data, environmental compensation coefficient, and network anomaly assessment value, and determine whether to adjust the main camera. This ensures the stable operation of the camera, thereby improving the accuracy of parking guidance and effectively solving the problem of low parking guidance accuracy in parking project measurement applications in the existing technology.

[0021] 2. The operating status change coefficient is obtained by obtaining the operating status data of the main camera in the current preset time period and the operating status data of the main camera in the previous preset time period, and then the environmental change coefficient is obtained according to the environmental data of the current preset time period and the environmental data of the previous preset time period. Finally, the environmental change coefficient and the operating status change coefficient are used to obtain the environmental compensation coefficient, thereby realizing the effective quantification of the environmental compensation coefficient, and then realizing the adjustment of the working status of the camera under different environmental conditions.

[0022] 3. The operating status deviation of the main camera is obtained by analyzing the relative relationship between the obtained main camera operating status data and the preset operating status data. Finally, the operating abnormality assessment value is obtained by processing the main camera operating status deviation, the environmental compensation coefficient and the network abnormality assessment value, thereby realizing a comprehensive quantitative assessment of the degree of abnormality of the main camera's operating status in the parking space guidance application calculated by the parking project, and further realizing the monitoring of the camera's operating problems in the parking space guidance application calculated by the parking project. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flowchart of an application method for parking project calculation provided in an embodiment of the present application;

[0024] Figure 2 A schematic diagram of the change of the operating state deviation provided in the embodiment of the present application;

[0025] Figure 3 A flowchart of the main camera adjustment provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiment of the present application solves the problem of low parking guidance accuracy in parking guidance applications of parking project calculation in the prior art by providing an application method for parking project calculation. A network anomaly evaluation value is obtained by acquiring network performance data, and whether network optimization is to be performed is determined based on the network anomaly evaluation value and a preset network anomaly threshold. Then, the optimized main camera operation status data and environmental data are analyzed to obtain an environmental compensation coefficient. An operation anomaly evaluation value is obtained according to the main camera operation status data, the environmental compensation coefficient and the network anomaly evaluation value. Based on the operation anomaly evaluation value and the preset operation anomaly threshold, it is determined whether the main camera should be adjusted, thereby improving the parking guidance accuracy in parking guidance applications of parking project calculation.

[0027] The technical solution in the embodiment of the present application is to solve the problem of low parking guidance accuracy in the parking guidance application calculated by the above-mentioned parking project. The overall idea is as follows:

[0028] The obtained network anomaly evaluation value is used to determine whether network optimization should be performed. Then, the environmental compensation coefficient is obtained based on the optimized main camera operation status data and environmental data. Finally, the operation anomaly evaluation value is obtained based on the main camera operation status data, the environmental compensation coefficient and the network anomaly evaluation value, and it is determined whether the main camera should be adjusted. This achieves the effect of improving the parking guidance accuracy in the parking guidance application calculated in the parking project.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0030] like Figure 1 As shown, it is a flow chart of an application method for parking project measurement provided by an embodiment of the present application, and the method includes the following steps: S1, data acquisition: obtaining parking space status time data and monitoring data of the parking lot through the main camera, the parking space status time data includes the parking space data update time and the parking space status information display time, and the monitoring data includes the main camera operation status data, environmental data and network performance data; S2, network status evaluation: evaluating the network status through the network performance data to obtain a network anomaly evaluation value, judging whether to perform network optimization based on the obtained network anomaly evaluation value and the preset network anomaly threshold, and the network anomaly evaluation value is used to quantify the performance anomaly degree of the network device ; S3, obtain the environmental compensation coefficient: after the network performance is optimized, the optimized main camera operation status data and environmental data are analyzed to obtain the environmental compensation coefficient. The environmental compensation coefficient represents the parameters used to adjust the main camera operation status to compensate for the fluctuations in the main camera operation status caused by environmental changes; S4, camera operation status evaluation: the main camera is evaluated according to the optimized main camera operation status data, the environmental compensation coefficient and the network anomaly evaluation value to obtain an operation anomaly evaluation value. Based on the obtained operation anomaly evaluation value and the preset operation anomaly threshold, it is determined whether to adjust the main camera. The operation anomaly evaluation value is used to quantify the degree of operation anomaly of the main camera.

[0031] In this embodiment, the parking space status time data represents the time data corresponding to the status information of each parking space in the parking lot (for example, whether the parking space is vacant or occupied); the parking space status information display time is obtained by requesting the timestamp of displaying the parking space status information from the database through the network, indicating the time when the parking space status information is presented to the user; the parking space data update time is obtained by detecting the timestamp of the parking space status update by the main camera of the parking lot, indicating the time when the parking space status data starts to be updated; the unit of the parking space status time data is uniformly in seconds; by obtaining the parking space status time data, the accuracy of parking space data update in the parking space guidance application calculated by the parking project is improved.

[0032] Specifically, the main camera operating status data represents the working status of the camera; the main camera operating status data includes information refresh time, average image frame rate and exposure time; the average image frame rate represents the average value of the image frame rate at a preset time point within a preset time period; the image frame rate is obtained through a frame rate tester; the exposure time is obtained through the open source computer vision library (OpenSource Computer Vision Library, OpenCV); the information refresh time represents the time difference between the parking space status information display time and the parking space data update time; by obtaining the main camera operating status data, the accuracy of the camera operating data in the parking guidance application for parking project measurement is improved.

[0033] Specifically, the environmental data represents the environmental conditions of the parking lot; the light intensity, humidity and temperature are obtained respectively through the light sensor, humidity sensor and temperature sensor; the environmental data represents the average value of the light intensity, humidity and temperature at a preset time point within a preset time period; the environmental data includes the average light intensity, average humidity and average temperature; by obtaining the environmental data, the accuracy of the environmental data in the parking space guidance application calculated by the parking project is improved.

[0034] Specifically, the network performance data represents the status of the network connected to the main camera; the network performance data includes the average network throughput, the network connection interruption time and the average parking lot signal strength; the network throughput is obtained by calculating the ratio between the number of frames received and the number of frames sent; the average network throughput represents the average value of the network throughput at a preset time point within a preset time period; the network connection interruption time is obtained by collecting and analyzing the control plane data of the Border Gateway Protocol (BGP); the parking lot signal strength is obtained through a WiFi signal tester; the average parking lot signal strength represents the average value of the signal strength at a preset time point within a preset time period; by obtaining network performance data, the accuracy of network performance data in the parking space guidance application calculated for parking projects is improved; network performance is improved through network optimization, and then the operating status of the main camera is adjusted through the environmental compensation coefficient to ensure that the main camera maintains its working status in complex environments.

[0035] Specifically, the preset time period is set to one hour, for example, the average temperature represents the average value of the temperatures collected within one hour; the preset time point is set to one minute, for example, the temperature is collected once every minute within one hour; the information refresh time reflects the time delay from the detection of the parking space status data captured by the main camera to the display of the data to the user; the average network throughput is used to measure the network transmission efficiency; the network connection interruption time represents the total interruption duration of the network connection with the main camera within the preset time period, which is used to measure the network stability; through the above parameters, strong support for subsequent data analysis and processing is achieved; by judging the operation abnormality evaluation value, the main camera is adjusted to ensure the accuracy and stability of the monitoring image, thereby achieving the improvement of the parking guidance accuracy in the parking guidance application calculated by the parking project.

[0036] Furthermore, the specific method for evaluating the network status through network performance data to obtain a network anomaly evaluation value is as follows: obtaining preset network performance data and preset information refresh time from a preset database, the preset network performance data including preset network throughput, preset parking lot signal strength and preset network connection interruption time; obtaining network performance deviation based on the relative relationship analysis between the network performance data and the preset network performance data, the network performance deviation including network throughput deviation Parking lot signal strength deviation and network connection interruption time deviation Network performance deviation indicates the deviation between network performance data and preset network performance data; the information refresh time deviation is obtained by analyzing the relative relationship between information refresh time and preset information refresh time. The information refresh time deviation indicates the deviation between the information refresh time and the preset information refresh time; the network performance deviation and the information refresh time deviation are processed to obtain the network anomaly assessment value;

[0037] The specific restricted expression of the network anomaly evaluation value is:

[0038]

[0039] Where i represents the number of the preset time period, i=1,2,...,T, T represents the total number of preset time periods, WA i Indicates the network anomaly evaluation value of the i-th preset time period, TUN i Indicates the average network throughput in the i-th preset time period, XHQ i represents the average parking lot signal strength during the i-th preset time period, XSX i Indicates the information refresh time of the i-th preset time period, LJS iIndicates the network connection interruption time of the i-th preset time period, TUN0 indicates the preset network throughput, XHQ0 indicates the preset parking lot signal strength, XSX0 indicates the preset information refresh time, and LJS0 indicates the preset network connection interruption time.

[0040] In this embodiment, the preset network performance data is obtained from a preset database, the preset network throughput is represented by the average value of the network throughput in the historical time period, the preset parking lot signal strength is represented by the average value of the parking lot signal strength in the historical time period, and the preset network connection interruption time is represented by the average value of the network connection interruption time in the historical time period; the preset information refresh time is obtained from the preset database and is represented by the average value of the information refresh time in the historical time period; the historical time period is set from the beginning of the previous month to the beginning of this month. For example, if it is currently August 11, the historical time period is set to July 1 to August 1.

[0041] The decrease in signal strength in the parking lot leads to a decrease in network throughput, because the packet loss rate and the number of retransmissions increase, which affects the data transmission speed, resulting in the inability to transmit data in a timely manner, and even triggering a network interruption. The network connection interruption time increases, and the lower the network throughput, the worse the network performance, and the higher the network anomaly assessment value. The weaker the signal strength, the more frequent network interruptions may occur, and the data transmission efficiency may also decrease, resulting in longer information refresh time. The weaker the signal strength, the more unstable the network status, and the higher the network anomaly assessment value. Frequent network connection interruptions directly affect network throughput, because it takes time to reconnect after each interruption, resulting in data transmission interruption or delay. The longer the network connection interruption time, the longer the information refresh time, because the data needs to be updated after reconnection. The longer the network connection interruption time, the less stable the network. The worse the signal strength is, the greater the network anomaly assessment value is; the signal strength changes dynamically, the longer the information refresh time is, the more difficult it is to capture the actual strength of the current signal in time, which affects the overall network status assessment. The longer the signal refresh time is, the slower the network response speed is and the larger the network anomaly assessment value is; therefore, the greater the average network throughput is, the smaller the network anomaly assessment value is; the greater the average parking lot signal strength is, the smaller the network anomaly assessment value is; the longer the information refresh time is, the larger the network anomaly assessment value is; the longer the network connection is interrupted, the larger the network anomaly assessment value is; the larger the network anomaly assessment value is, the worse the network status is; through the above steps, the effective quantification of the network status in the parking space guidance application calculated for the parking project is achieved, and then the monitoring of the network anomaly status in the parking space guidance application calculated for the parking project is achieved, avoiding subsequent mistakes caused by incorrect network anomaly assessment values.

[0042] Taking the preset time period as 1, the preset network throughput as 4Mbps, the preset parking lot signal strength as -60dBm, the preset network connection interruption time as 30 seconds, and the preset information refresh time as 2 seconds as an example, the statistical table of changes in network anomaly assessment values ​​is shown in Table 1:

[0043] Table 1 Statistics of changes in network anomaly assessment values

[0044]

[0045] It can be seen from the first and second groups of data in the table that the longer the network connection interruption time, the larger the network anomaly assessment value. It can be seen from the second and third groups of data that the longer the information refresh time, the larger the network anomaly assessment value. It can be seen from the third and fourth groups of data that the greater the average parking lot signal strength, the smaller the network anomaly assessment value. It can be seen from the fourth and fifth groups of data that the greater the average network throughput, the smaller the network anomaly assessment value. Therefore, the network anomaly assessment value is positively correlated with the network connection interruption time and the information refresh time, and is negatively correlated with the average parking lot signal strength and the average network throughput.

[0046] Furthermore, the specific process of determining whether to perform network optimization based on the obtained network anomaly evaluation value and the preset network anomaly threshold is as follows: A1, determining whether the obtained network anomaly evaluation value is less than the preset network anomaly threshold. If the network anomaly evaluation value is less than the preset network anomaly threshold, network optimization is not performed, otherwise A2 is executed; A2, optimizing data transmission. Optimizing data transmission includes bandwidth management, multi-path transmission, and adaptive path selection. Adaptive path selection means dynamically selecting the optimal transmission path based on network performance data; A3, adjusting computing resources step by step. When the information refresh time is less than the preset information refresh time, stop adjusting computing resources and execute A4. Otherwise, continue to adjust computing resources step by step until the computing resources are adjusted to the preset maximum computing resources and then execute A4; A4, optimizing parking lot signal strength. Optimizing parking lot signal strength includes optimizing the communication network and prompting preset personnel to deploy signal boosters.

[0047] In this embodiment, the preset network anomaly threshold is represented by the maximum value of the network anomaly evaluation value within the historical time period; computing resources generally include central processing unit, memory, hard disk and network bandwidth; the preset maximum computing resources represent the upper limit of the computing resources that the network can use, which is set according to the hardware performance. For example, for low-end network equipment, the central processing unit is 1-2 cores, the memory is 128-512 RAM, the hard disk is 1-4 GB, the network bandwidth is 100Mbps-1Gbps, the central processing unit usage upper limit is 80%, the memory usage upper limit is 90%, the hard disk usage upper limit is 80%, and the network bandwidth usage upper limit is 80%; bandwidth management means reducing the amount of data transmission through data compression technology; data compression technology includes lossy compression and lossless compression, and lossless compression technology is selected in this embodiment; multipath transmission means distributing data to each communication path through multipath transmission technology; Multipath Transmission Control Protocol is a protocol that supports multipath data The standard transmission protocol can use multiple network interfaces (such as Wi-Fi, 4G, and 5G) to transmit data simultaneously, and the common default number of paths is between 2 and 4; in this embodiment, the number of communication paths is set to 4; optimizing the communication network means prompting the preset personnel to deploy a wired communication network as a backup for the wireless network; increasing computing resources step by step until the information refresh time is less than the preset information refresh time or the computing resources are adjusted to the preset maximum computing resources; through data transmission optimization and computing resource adjustment, the network delay and packet loss rate in the parking space guidance application calculated by the parking project are effectively reduced, thereby improving the stability and reliability of the network in the parking space guidance application calculated by the parking project; technologies such as multi-path transmission and adaptive path selection enhance the network's recovery capabilities in the face of faults, ensuring continuous and reliable data transmission; the optimized network ensures faster response to user requests and improves the user experience in the parking space guidance application calculated by the parking project.

[0048] Furthermore, the specific process of adaptive path selection is as follows: the network performance deviation of each communication path is processed to obtain the transmission network coefficient, which represents the network status of each communication path; the transmission network coefficients of each communication path are arranged in ascending order to obtain the optimal transmission network coefficient, which represents the maximum value of the transmission network coefficient; the communication path corresponding to the optimal transmission network is recorded as the optimal transmission path, and it is determined whether the optimal transmission path is unique. If the optimal transmission path is not unique, the optimal transmission path is screened according to the information refresh time to determine the only optimal transmission path.

[0049] Among them, the specific restriction expression of the optimal transmission network coefficient is:

[0050] ZYC i.max =MAX{CSW i.m};

[0051]

[0052] Where i represents the number of the preset time period, i=1,2,...,T, T represents the total number of preset time periods, m represents the number of the communication path, m=1,2,3,4, ZYC i.max represents the optimal transmission network coefficient in the i-th preset time period, CSW i.m It represents the transmission network coefficient of the mth communication path in the i-th preset time period, TUN i Indicates the average network throughput in the i-th preset time period, XHQ i represents the average parking lot signal strength during the i-th preset time period, XSX i Indicates the information refresh time of the i-th preset time period, LJS i Indicates the network connection interruption time of the i-th preset time period, TUN0 indicates the preset network throughput, XHQ0 indicates the preset parking lot signal strength, XSX0 indicates the preset information refresh time, and LJS0 indicates the preset network connection interruption time.

[0053] In this embodiment, the stronger the average parking lot signal strength, the more stable the data transmission and the higher the average network throughput. The higher the average network throughput, the better the network transmission performance and the larger the transmission network coefficient. The stronger the average parking lot signal strength, the more stable the network connection and the shorter the network connection interruption time, which means higher network transmission stability and the larger the transmission network coefficient. The greater the average parking lot signal strength, the better the network performance and the larger the transmission network coefficient. The longer the network connection interruption time, the more information update delays and the longer the information refresh time. The higher the throughput, the faster the data transmission and processing and the shorter the information refresh time. The shorter the information refresh time, the higher the network transmission efficiency and the larger the transmission network coefficient.

[0054] The communication path represents a series of nodes or links that the data passes through during the transmission process. The nodes or links constitute the transmission channel for the data from the source to the destination. If the optimal transmission path is not unique, the information refresh time corresponding to each optimal transmission path is obtained, and the information refresh time corresponding to each optimal transmission path is arranged in ascending order to obtain the minimum information refresh time. The optimal transmission path corresponding to the minimum information refresh time is recorded as the only optimal transmission path. By selecting the optimal transmission path, the delay and packet loss rate of data transmission are effectively reduced, and the data transmission efficiency in the parking space guidance application calculated by the parking project is improved. The adaptive path selection dynamically adjusts the transmission path according to the changes in the network status, thereby enhancing the stability and reliability of the network in the parking space guidance application calculated by the parking project. By reasonably allocating network resources, the adaptive path selection effectively avoids network congestion and resource waste in the parking space guidance application calculated by the parking project, and improves the overall performance of the network.

[0055] Furthermore, after the network performance is optimized, the specific process of analyzing the optimized main camera operation status data and environmental data to obtain the environmental compensation coefficient is as follows: C1, determine whether the number of the preset time period is less than 2. If the number of the preset time period is less than 2, the environmental compensation coefficient is recorded as 1, otherwise execute C2; ​​C2, according to the relative relationship analysis of the main camera operation status data of the current preset time period and the main camera operation status data of the previous preset time period, the operation status change coefficient of the main camera is obtained, and the operation status change coefficient represents the change of the main camera operation status data of the current preset time period and the main camera operation status data of the previous preset time period; according to the relative relationship analysis of the environmental data of the current preset time period and the environmental data of the previous preset time period, the environmental change coefficient represents the change of the environmental data of the current preset time period and the environmental data of the previous preset time period; the environmental change coefficient and the operation status change coefficient of the main camera are processed to obtain the environmental compensation coefficient.

[0056] The specific limiting expression of the environmental compensation coefficient is:

[0057] HJB=e cosh(SXY-1)+cosh(HUAN-1) ;

[0058]

[0059] Where i represents the number of the preset time period, i=1,2,...,T, T represents the total number of preset time periods, HJB represents the environmental compensation coefficient, SXY represents the operating state change coefficient of the main camera, HUAN represents the environmental change coefficient, CBC i Indicates the information refresh time of the i-th preset time period, TXZ i Indicates the average image frame rate of the i-th preset time period, BGS i Indicates the exposure time of the i-th preset time period, GZQ i Indicates the average light intensity in the i-th preset time period, WEN i Indicates the average temperature of the i-th preset time period, SHI i Represents the average humidity in the i-th preset time period.

[0060] In this embodiment, the preset time period number being less than 2 indicates that when the preset time period number is 1, there is no data from the previous preset time period for comparison and analysis, so there is no need to calculate the environmental compensation coefficient. The environmental compensation coefficient is directly set to 1, thereby avoiding meaningless calculations when there is no reference data and reducing the usage of computing resources. By analyzing the relationship between the operating status of the main camera and environmental changes, the environmental compensation coefficient enables the camera to maintain a working state under different environmental conditions in the parking space guidance application for parking project measurement, thereby avoiding a decrease in shooting quality due to sudden changes in the external environment (such as sudden darkness).

[0061] A higher average image frame rate results in more real-time parking space status data updates, but this also increases data processing and transmission pressure. Insufficient data processing capabilities and network bandwidth may extend information refresh time. Longer exposure times reduce the number of images captured per unit time, thereby reducing the frame rate and potentially extending information refresh time. In brighter light conditions, the camera can be set to a shorter exposure time, resulting in a higher frame rate and a shorter refresh time. Higher temperatures may increase image noise, affecting image analysis efficiency in parking guidance applications used for parking project calculations, thereby indirectly affecting information refresh time. Higher humidity, especially in conditions with large ambient temperature differences, may cause mist to form on the camera lens surface, resulting in blurred images, reducing the frame rate and extending information refresh time. Higher humidity may affect camera hardware performance, indirectly affecting the frame rate and data processing speed. The greater the coefficient of variation between the main camera operating status data for the current preset time period and the previous preset time period, the greater the environmental compensation coefficient. The greater the coefficient of variation between the environmental data for the current preset time period and the previous preset time period, the greater the environmental compensation coefficient, indicating that the main camera's operating status has changed significantly due to environmental changes.

[0062] Furthermore, a specific method for evaluating the main camera to obtain an operation abnormality evaluation value based on the main camera operation status data, environmental compensation coefficient and network anomaly evaluation value obtained after optimization is as follows: obtaining the preset operation status data of the main camera from a preset database, the preset operation status data including a preset information refresh time, a preset image frame rate and a preset exposure time; obtaining the operation status deviation of the main camera based on the relative relationship analysis between the main camera operation status data and the preset operation status data, the operation status deviation of the main camera indicating the deviation between the operation status data of the main camera and the preset operation status data; and obtaining the operation abnormality evaluation value based on the operation status deviation, environmental compensation coefficient and network anomaly evaluation value of the main camera.

[0063] In this embodiment, the preset operating status data is obtained from a preset database, the preset information refresh time is represented by the average value of the information refresh time in the historical time period, the preset image frame rate is represented by the average value of the image frame rate in the historical time period, and the preset exposure time is represented by the average value of the exposure time in the historical time period; through the above steps, a comprehensive quantitative evaluation of the degree of abnormality in the operating status of the main camera in the parking space guidance application calculated by the parking project is realized, and then the monitoring of the operating problems of the main camera in the parking space guidance application calculated by the parking project is realized.

[0064] The specific limiting expression for the abnormal operation evaluation value is:

[0065]

[0066] Where i represents the number of the preset time period, i=1,2,...,T, T represents the total number of preset time periods, YIC i represents the operational abnormality evaluation value of the main camera in the i-th preset time period, HJB represents the environmental compensation coefficient, SYC i Indicates the operating state deviation of the main camera in the i-th preset time period, WA i Indicates the network anomaly assessment value of the i-th preset time period, CBC i Indicates the information refresh time of the i-th preset time period, TXZ i Indicates the average image frame rate of the i-th preset time period, BGS i represents the exposure time of the i-th preset time period, CBC0 represents the preset information refresh time, TXZ0 represents the preset image frame rate, and BGS0 represents the preset exposure time.

[0067] The algorithm of this embodiment combines the information refresh time, average image frame rate, exposure time, environmental compensation coefficient and network anomaly assessment value for comprehensive analysis to obtain the operation anomaly assessment value. The various parameters are related to each other and do not exist independently. They are a whole and cannot be obtained by simple combination and addition. For example, an increase in the network anomaly assessment value in the formula may cause the operation anomaly assessment value to increase, which does not necessarily mean that the operation anomaly assessment value has increased. At this time, the information refresh time, average image frame rate, exposure time and environmental compensation coefficient need to be considered. These parameters can also determine the operation anomaly assessment value at the next moment. Generally speaking, an increase in the environmental compensation coefficient indicates an increase in the change in the operation status compared to the previous preset time period. Therefore, the environmental compensation coefficient compensates for the current operation status of the main camera, so that the image quality is improved and the information refresh time is reduced; the higher the average image frame rate, the more frequently the parking guidance application can obtain parking space images, thereby reducing the information refresh time and reducing the operation anomaly assessment value; if the network anomaly assessment value is too high, the parking guidance application can obtain parking space images more frequently ... An increase in the valuation may extend the information refresh time, thereby increasing the operation abnormality assessment value; a longer exposure time will reduce the number of images captured per unit time, thereby reducing the frame rate, which may extend the information refresh time, thereby increasing the operation abnormality assessment value. The larger the operation abnormality assessment value, the more abnormal the operation status of the main camera is. The algorithm itself is obtained through comprehensive analysis, which can accurately and quantitatively analyze the abnormality of the operation status of the main camera, indicating the effect of accurately evaluating the operation abnormality assessment value. Therefore, a precise analysis is performed by establishing a mathematical form; the effective quantification of the operation abnormality status of the main camera in the parking guidance application of the parking project measurement is realized, avoiding subsequent mistakes caused by incorrect operation abnormality assessment values.

[0068] For example, if the information refresh time is between 1 second and 5 seconds, the average image frame rate is between 1 frame per second and 5 frames per second, the exposure time is between 1 second and 5 seconds, the preset information refresh time is 1 second, the preset image frame rate is 1 frame per second, and the preset exposure time is 1 second, Figure 2 As shown, it is a schematic diagram of the change of the operating state deviation provided in an embodiment of the present application. When the average image frame rate is 1 frame per second and the exposure time is 1 second, the longer the information refresh time is, the greater the operating state deviation is. When the information refresh time and the exposure time are 1 second, the greater the average image frame rate is, the smaller the operating state deviation is. When the information refresh time is 1 second and the average image frame rate is 1 frame per second, the greater the exposure time is, the greater the operating state deviation is. Therefore, the operating state deviation is negatively correlated with the average image frame rate, and the operating state deviation is positively correlated with the information refresh time and the exposure time.

[0069] Furthermore, the specific process of judging whether to adjust the main camera based on the obtained operation abnormality evaluation value and the preset operation abnormality threshold is as follows: B1, judging whether the obtained operation abnormality evaluation value is less than the preset operation abnormality threshold, if the operation abnormality evaluation value is less than the preset operation abnormality threshold, then the main camera adjustment is not performed, otherwise B2 is executed; B2, restarting the main camera, when the monitored main camera rear operation abnormality evaluation value is less than the preset operation abnormality threshold, the main camera adjustment is ended, otherwise B3 is executed (that is, the condition is still not met after restarting the main camera); B3, judging whether the frame rate of the main camera picture is less than the preset frame rate, if the frame rate of the main camera picture is less than the preset frame rate, then adjusting the image resolution, Otherwise, execute B4 (i.e., the frame rate of the main camera image is not less than the preset frame rate or the image resolution of the main camera is adjusted but the conditions are still not met); B4, determine whether the clarity of the main camera image is less than the preset clarity. If the clarity of the main camera image is less than the preset clarity, adjust the focal length of the main camera. Otherwise, execute B5 (i.e., the clarity of the main camera image is not less than the preset clarity or after adjusting the focal length of the main camera); B5, determine whether the adjusted operation abnormality assessment value is less than the preset operation abnormality threshold. If the adjusted operation abnormality assessment value is less than the preset operation abnormality threshold, no feedback is given; otherwise, feedback is given (i.e., the adjusted operation abnormality assessment value is not less than the preset operation abnormality threshold).

[0070] like Figure 3 As shown, this is a flowchart of the main camera adjustment provided by an embodiment of the present application. The preset operation abnormality threshold is obtained from a preset database and is represented by the maximum value of the operation abnormality evaluation value in the historical time period; the preset clarity is obtained from a preset database and is represented by the average value of the clarity of the picture in the historical time period; adjusting the image resolution means gradually reducing the image resolution until the frame rate of the main camera picture is not less than the preset frame rate or the image resolution is reduced to the preset minimum image resolution; judging whether the main camera supports the automatic focus adjustment function, if it supports the automatic focus adjustment function, the focus of the main camera is set to automatic mode, and the main camera automatically adjusts the focus according to the shooting scene and object, otherwise notifying the relevant personnel The staff manually adjusts the focus of the main camera; if the clarity obtained after the main camera automatically adjusts the focus is less than the preset clarity, the relevant personnel are notified to manually adjust the focus of the main camera; by judging the relationship between the operation abnormality assessment value and the preset threshold, the abnormal operation status of the main camera is discovered in time, and the problem is solved by adjusting measures such as restarting the main camera, adjusting the resolution and focal length of the main camera, etc. If the adjustment measures are effective, the operation status of the main camera returns to normal; if not, feedback is given so that further measures can be taken, thereby improving the reliability and stability of the main camera in the parking space guidance application calculated by the parking project, and ensuring the normal operation of the monitoring system in the parking space guidance application calculated by the parking project.

[0071] Furthermore, it includes deploying redundant cameras; the redundant cameras are used to replace the transmitted main camera image with the redundant camera image when the operation abnormality assessment value of the main camera is less than the preset operation abnormality threshold, so as to ensure that the monitoring image is stable and uninterrupted.

[0072] In this embodiment, the redundant camera refers to an additional camera installed in addition to the main camera, which can replace the main camera for monitoring when the main camera fails or cannot meet the monitoring needs; the monitoring area of ​​the redundant camera is the same as the monitoring area of ​​the main camera to ensure that there are no monitoring blind spots when switching, so the position and angle of the redundant camera need to be coordinated with the main camera to ensure effective coverage; the redundant camera is installed beside or diagonally to the main camera (depending on the specific scenario setting). For example, at the corner inside the parking lot, by installing a redundant camera beside the main camera, it is ensured that the vehicles at the corner are effectively monitored. In the spacious area inside the parking lot that needs to be fully monitored, redundant cameras are installed diagonally to form cross monitoring to ensure that every corner are effectively monitored to ensure that when the main camera's field of view is blocked, the redundant camera's field of view is not affected; the number of redundant cameras is set according to the risk level and monitoring needs of different areas. For example, in a parking lot, each entrance and exit is equipped with a main camera and a redundant camera; by deploying redundant cameras and setting a switching mechanism, the stability and reliability of the monitoring system in the parking guidance application calculated by the parking project are effectively improved; when the main camera fails or cannot meet the monitoring requirements, the redundant camera can immediately replace the main camera for monitoring, ensuring that the monitoring image in the parking guidance application calculated by the parking project will not be interrupted or lost, thereby improving the fault tolerance and emergency response capabilities of the monitoring system in the parking guidance application calculated by the parking project.

[0073] To sum up, the embodiment of the present application obtains a network anomaly evaluation value through the acquired network performance data, and determines whether to perform network optimization. Then, the environmental compensation coefficient is obtained based on the main camera operation status data and environmental data obtained after optimization. Finally, the operation anomaly evaluation value is obtained based on the main camera operation status data, the environmental compensation coefficient and the network anomaly evaluation value, and it is determined whether to adjust the main camera, thereby ensuring the stable operation of the camera, and further achieving an improvement in the accuracy of parking space guidance, effectively solving the problem of low parking space guidance accuracy in the parking space guidance application calculated by parking projects in the prior art.

[0074] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0078] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0079] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An application method for parking project calculation, characterized in that: The following steps are involved: S1, obtaining parking space status time data and monitoring data in the parking lot through the main camera, wherein the parking space status time data includes the parking space data update time and the parking space status information display time, and the monitoring data includes the main camera operation status data, environmental data and network performance data; S2, evaluating the network status using the network performance data to obtain a network anomaly evaluation value, and determining whether to perform network optimization based on the obtained network anomaly evaluation value and a preset network anomaly threshold, wherein the network anomaly evaluation value is used to quantify the degree of performance anomaly of the network device; The specific method for evaluating the network status using network performance data to obtain a network anomaly evaluation value is as follows: Obtaining preset network performance data and preset information refresh time from a preset database, wherein the preset network performance data includes preset network throughput, preset parking lot signal strength, and preset network connection interruption time; Analyzing the relative relationship between the network performance data and the preset network performance data to obtain a network performance deviation, wherein the network performance deviation includes a network throughput deviation, a parking lot signal strength deviation, and a network connection interruption time deviation. The network performance deviation indicates the deviation between the network performance data and the preset network performance data; The information refresh time deviation is obtained based on the relative relationship between the information refresh time and the preset information refresh time, wherein the information refresh time deviation indicates the deviation between the information refresh time and the preset information refresh time; The network performance deviation and information refresh time deviation are processed to obtain the network anomaly assessment value; S3, after the network performance is optimized, analyzing the optimized main camera operating status data and environmental data to obtain an environmental compensation coefficient; S4, evaluating the main camera according to the optimized main camera operation status data, environmental compensation coefficient and network anomaly evaluation value to obtain an operation anomaly evaluation value, and determining whether to adjust the main camera based on the obtained operation anomaly evaluation value and a preset operation anomaly threshold. The operation anomaly evaluation value is used to quantify the degree of operation anomaly of the main camera.

2. The parking project calculation application method according to claim 1, characterized in that: The main camera operating status data includes information refresh time, average image frame rate and exposure time; The average image frame rate represents the average value of the image frame rates at preset time points within a preset time period; The parking space status information display time is obtained from the timestamp of the parking space status information displayed in the database through a network request; The parking space data update time is obtained by detecting the timestamp of the parking space status update by the main camera of the parking lot; The information refresh time represents the time difference between the parking space status information display time and the parking space data update time; The environmental data represents the average values ​​of light intensity, humidity and temperature at preset time points within a preset time period; The environmental data include average light intensity, average humidity and average temperature; The network performance data includes average network throughput, network connection interruption time, and average parking lot signal strength; The average network throughput represents the average value of the network throughput at a preset time point within a preset time period; The average parking lot signal strength represents an average value of signal strengths at preset time points within a preset time period.

3. The parking project calculation application method according to claim 2, characterized in that: The specific process of determining whether to perform network optimization based on the obtained network anomaly assessment value and the preset network anomaly threshold is as follows: A1: Determine whether the obtained network anomaly assessment value is less than a preset network anomaly threshold. If the network anomaly assessment value is less than the preset network anomaly threshold, do not perform network optimization. Otherwise, execute A2. A2, optimized data transmission, including bandwidth management, multi-path transmission, and adaptive path selection. The adaptive path selection dynamically selects the optimal transmission path based on network performance data. A3, adjusting computing resources step by step. When the information refresh time is less than the preset information refresh time, stop adjusting computing resources and execute A4. Otherwise, continue adjusting computing resources step by step until the computing resources are adjusted to the preset maximum computing resources and then execute A4. A4, optimizing parking lot signal strength, said optimizing parking lot signal strength includes optimizing the communication network and prompting preset personnel to deploy signal boosters.

4. The parking project calculation application method according to claim 3, characterized in that: The specific process of the adaptive path selection is as follows: The network performance deviation of each communication path is processed to obtain the transmission network coefficient; Arrange the transmission network coefficients of each communication path in ascending order to obtain the optimal transmission network coefficient; The communication path corresponding to the optimal transmission network is recorded as the optimal transmission path, and whether the optimal transmission path is unique is determined. If the optimal transmission path is not unique, the optimal transmission path is screened according to the information refresh time to determine the only optimal transmission path.

5. The parking project calculation application method according to claim 1, characterized in that: The specific process of analyzing the optimized main camera operating status data and environmental data to obtain the environmental compensation coefficient after network performance optimization is as follows: C1, determine whether the number of the preset time period is less than 2. If the number of the preset time period is less than 2, the environmental compensation coefficient is recorded as 1, otherwise execute C2; C2, obtaining an operating state change coefficient of the main camera based on a relative relationship analysis between the operating state data of the main camera in the current preset time period and the operating state data of the main camera in the previous preset time period, wherein the operating state change coefficient represents a change between the operating state data of the main camera in the current preset time period and the operating state data of the main camera in the previous preset time period; An environmental change coefficient is obtained by analyzing the relative relationship between the environmental data of the current preset time period and the environmental data of the previous preset time period, wherein the environmental change coefficient represents the change between the environmental data of the current preset time period and the environmental data of the previous preset time period; The environmental variation coefficient and the operating state variation coefficient of the main camera are processed to obtain the environmental compensation coefficient.

6. The parking project calculation application method according to claim 1, characterized in that: The specific method for evaluating the main camera to obtain the operation abnormality evaluation value based on the optimized main camera operation status data, the environmental compensation coefficient and the network abnormality evaluation value is as follows: Obtaining preset operating status data of the main camera from a preset database, wherein the preset operating status data includes a preset information refresh time, a preset image frame rate, and a preset exposure time; Obtaining an operating state deviation of the main camera based on a relative relationship analysis between the operating state data of the main camera and the preset operating state data, wherein the operating state deviation of the main camera indicates a deviation between the operating state data of the main camera and the preset operating state data; The operation abnormality assessment value is obtained by processing the operation state deviation of the main camera, the environmental compensation coefficient and the network abnormality assessment value.

7. The parking project calculation application method according to claim 6, characterized in that: The specific limiting expression of the operation abnormality evaluation value is: ； ； Where i represents the number of the preset time period, i=1,2,…,T, T represents the total number of preset time periods, YIC i represents the operational abnormality evaluation value of the main camera in the i-th preset time period, HJB represents the environmental compensation coefficient, SYC i Indicates the operating state deviation of the main camera in the i-th preset time period, WA i Indicates the network anomaly assessment value of the i-th preset time period, CBC i Indicates the information refresh time of the i-th preset time period, TXZ i Indicates the average image frame rate of the i-th preset time period, BGS i represents the exposure time of the i-th preset time period, CBC0 represents the preset information refresh time, TXZ0 represents the preset image frame rate, and BGS0 represents the preset exposure time.

8. The parking project calculation application method according to claim 7, characterized in that: The specific process of determining whether to adjust the main camera based on the obtained operation abnormality assessment value and the preset operation abnormality threshold is as follows: B1, determining whether the obtained operation abnormality assessment value is less than a preset operation abnormality threshold. If the operation abnormality assessment value is less than the preset operation abnormality threshold, no main camera adjustment is performed. Otherwise, B2 is executed. B2: Restart the main camera. If the abnormality assessment value of the monitored main camera is less than the preset abnormality threshold, the main camera adjustment is terminated. Otherwise, execute B3. B3, determining whether the frame rate of the main camera image is less than the preset frame rate. If the frame rate of the main camera image is less than the preset frame rate, adjust the image resolution; otherwise, execute B4; B4, determining whether the clarity of the main camera image is less than a preset clarity. If the clarity of the main camera image is less than the preset clarity, adjust the focus of the main camera; otherwise, execute B5; B5, judging whether the adjusted operation abnormality assessment value is less than the preset operation abnormality threshold value. If the adjusted operation abnormality assessment value is less than the preset operation abnormality threshold value, no feedback is given; otherwise, feedback is given.

9. The parking project calculation application method according to claim 1, characterized in that: This includes deploying redundant cameras; The redundant camera is used to replace the transmitted main camera image with the redundant camera image when the operation abnormality assessment value of the main camera is less than a preset operation abnormality threshold, thereby ensuring that the monitoring image is stable and uninterrupted.

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