Energy-saving access control system and its control system

Abstract

The invention discloses an energy-saving access control and a control system thereof, and relates to the technical field of energy-saving access control, comprising: a data acquisition module, used for acquiring actual data and basic data of vehicle flow from time Q-W to current time Q, where Q and W are both integers greater than 0; a first regression module, used for inputting the actual data and basic data of vehicle flow into a preset vehicle flow prediction model, and obtaining future data of vehicle flow from future time E to future time E+R, where E and R are both integers greater than 0; a comparison module, used for comparing the future data of vehicle flow with a preset vehicle flow data threshold, and if the future data of vehicle flow is greater than or equal to the preset vehicle flow data threshold, Q=Q+X is set, and the data acquisition module is returned, and if the future data of vehicle flow is less than the preset vehicle flow data threshold, an access light energy-saving control interval and an access light non-energy-saving control interval are generated; the invention reduces energy consumption when no vehicle passes.

Description

Technical Field

[0001] The present invention relates to the technical field of door access control energy saving, and in particular to an energy-saving door access control and a control system thereof. Background Art

[0002] With the sharp increase in the number of cars, the demand for parking lots is also increasing. The management system of the parking lot plays a vital role in ensuring the safety and efficient passage of vehicles. Among them, the access control system, as a key component of the parking lot, is mainly responsible for controlling the entry and exit of vehicles. However, the traditional access control system will still generate unnecessary energy consumption when no vehicles pass through, which not only increases the operating cost of the parking lot, but also has an adverse impact on the environment. Therefore, it is necessary to provide an energy-saving access control system.

[0003] At present, the prior art lacks an intelligent energy-saving access control system. However, it can be observed from life that the existing access control energy-saving control mainly performs energy-saving control by sensing environmental changes and sensing vehicle passing conditions, that is, by using sensors to monitor environmental data in real time to adjust the light brightness. Of course, there are some similar energy-saving control technologies that use infrared sensors to monitor whether there are vehicles passing. For example, a Chinese patent with publication number CN110248451B proposes an automatic management control system for residential street lights and a control method thereof. Although the above method and the prior art can realize energy-saving control of access control, it is found in the actual application process that the above method and the prior art have at least the following defects:

[0004] (1) There is a lag in the energy-saving control of the access control system. The current access control system can only be opened and closed based on real-time environmental data or real-time vehicle flow. To avoid delays in parking, the access control system is mostly kept in operation, making it impossible to control the access control system for energy saving based on relevant data in the parking lot. As a result, the access control system remains in operation even when no vehicles are passing through, increasing energy consumption.

[0005] (2) There is a single consideration of the conditions affecting access control energy saving. Most of the time, environmental data is used to analyze access control energy saving. Using environmental data as an energy-saving condition to control access control will lead to control disorder of access control energy saving control. Summary of the invention

[0006] The purpose of the present invention is to provide an energy-saving access control system and a control system thereof to solve the deficiencies in the background technology.

[0007] An energy-saving access control system and a control system thereof, wherein the system is applied to the energy-saving access control system, comprising:

[0008] A data collection module is used to obtain the actual data and basic data of vehicle flow from time QW to the current time Q, where Q and W are both integers greater than 0;

[0009] The first regression module is used to input the actual traffic flow data and basic data into the preset traffic flow prediction model to obtain the future traffic flow data from the future time E to the future time E+R, where E and R are both integers greater than 0;

[0010] A comparison module is used to compare the future traffic flow data with a preset traffic flow data threshold. If the future traffic flow data is greater than or equal to the preset traffic flow data threshold, Q is set to be within Q+X and returned to the data acquisition module. If the future traffic flow data is less than the preset traffic flow data threshold, an access light energy-saving control interval and an access light non-energy-saving control interval are generated.

[0011] The first control module is used to start or shut down the access control light according to the energy-saving control interval of the access control light;

[0012] The second regression module is used to obtain the environmental data of the access control setting area and the access control light operation data according to the energy-saving non-control interval of the access control light, input the environmental data and the access control light operation data into the preset optimal energy-saving brightness analysis model, and generate the optimal energy-saving brightness;

[0013] The second control module is used to adjust the current access control light brightness according to the optimal energy-saving brightness;

[0014] The basic data include seasonal climate data, holiday data and traffic coefficient at each moment;

[0015] The generation logic of the traffic coefficient is:

[0016] The parking lot related data at each moment is collected, and the parking lot related data includes the total number of parking spaces in the parking lot, the number of vehicles passing through the parking lot entrance and exit at each moment, and the vehicle speed. The traffic coefficient is calculated based on the parking lot related data. The calculation formula of the traffic coefficient is:

[0017] Among them, T is the traffic coefficient, N is the total number of parking spaces in the parking lot, K is the total time for collecting parking lot related data, and v i is the speed of the vehicle passing through the parking lot entrance and exit at the i-th moment, V1 is the preset standard value of the vehicle speed passing through the parking lot entrance and exit, S i is the number of vehicles passing through the parking lot entrance and exit at the i-th moment, S i It is the preset standard value for the number of vehicles passing through the parking lot entrance and exit.

[0018] Furthermore, the generation logic of the vehicle flow prediction model is:

[0019] Acquire historical big data of vehicle flow, and divide the historical big data of vehicle flow into a first training set and a first test set; wherein the historical big data of vehicle flow includes actual data and basic data of vehicle flow, and corresponding future data of vehicle flow;

[0020] The actual traffic flow data and basic data in the first training set are used as input data of the first regression network, and the future traffic flow data in the first training set are used as output data of the first regression network, and the first regression network is trained to obtain an initial traffic flow prediction regression network;

[0021] The vehicle flow prediction regression network is validated through the first test set, and the vehicle flow prediction regression network with a test error less than or equal to the preset test error is output as the preconfigured vehicle flow prediction model.

[0022] Furthermore, the environmental data includes environmental brightness data, environmental temperature data and environmental visibility data, and the access control light operation data includes access control light input power and access control light surface temperature.

[0023] Furthermore, the generation logic of the optimal energy-saving brightness analysis model is:

[0024] Obtaining access control light control big data, and dividing the access control light control big data into a second training set and a second test set, wherein the access control light control big data includes environmental data, access control light operation data, and corresponding optimal energy-saving brightness;

[0025] Construct a second regression network, use the environmental data and access control light operation data in the second training set as input data of the second regression network, use the optimal energy-saving brightness in the second training set as output data of the second regression network, train the second regression network, and obtain an initial optimal energy-saving brightness analysis network;

[0026] The initial optimal brightness analysis network is verified by the second test set, and the initial optimal energy-saving brightness analysis network with a value less than or equal to the preset test error is output as the preconfigured optimal energy-saving brightness analysis model.

[0027] Furthermore, the generation logic of the optimal energy-saving brightness is:

[0028] S1: Set the initial test brightness of the access control light to Z, and the initial value of Z is 0;

[0029] S2: Adjust the initial test brightness, set Y=Z+B, and obtain the adjusted brightness Y, where B and Y are integers greater than zero;

[0030] S3: Under the adjusted brightness Y, the actual clarity of the vehicle image collected by the access control is obtained. If the actual clarity is greater than or equal to the preset standard clarity threshold, the adjusted brightness Y is used as the optimal energy-saving brightness; if the actual clarity is less than the preset standard clarity threshold, B=B+A is set, and the process returns to step S2, where A is an integer greater than zero;

[0031] S4: Repeat the above steps S2 to S3 until the optimal energy-saving brightness is obtained, and then end the cycle.

[0032] An energy-saving access control system includes at least a power module, a central processing module, an identification module, a storage module, a hardware control module and an energy-saving access control system.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present application discloses an energy-saving access control and its control system, the energy-saving access control system comprising: a data acquisition module, used to obtain actual data and basic data of vehicle flow from time QW to current time Q, Q and W are both integers greater than 0; a first regression module, used to input the actual data and basic data of vehicle flow into a preset vehicle flow prediction model, and obtain future data of vehicle flow from future time E to future time E+R, E and R are both integers greater than 0; a comparison module, used to compare the future data of vehicle flow with a preset vehicle flow data threshold, if the future data of vehicle flow is greater than or equal to the preset vehicle flow data threshold, then set Q=Q+X, and return to the data acquisition module, if the future data of vehicle flow is less than the preset vehicle flow data threshold, then generate an access light energy-saving control interval and an access light non-energy-saving control interval; a first control module, used to start or shut down the access light according to the access light energy-saving control interval; a second regression module, used to start or shut down the access light according to the access light energy-saving control interval; In the energy-saving non-control interval of the access control light, the environmental data of the access control setting area and the access control light operation data are obtained, and the environmental data and the access control light operation data are input into the preset optimal energy-saving brightness analysis model to generate the optimal energy-saving brightness; the second control module is used to adjust the current access control light brightness according to the optimal energy-saving brightness; based on the above modules, the present invention obtains the access control light energy-saving control interval of the access control light through comprehensive analysis, and intelligently controls the access control light through the access control light energy-saving control interval of the access control light, so as to ensure that the access control can reduce the energy consumption when there are no vehicles passing on the basis of rapid and accurate identification of vehicles, thereby achieving the beneficial effect of energy-saving control of the access control; in the non-energy-saving control interval of the access control light, by giving priority to adjusting the brightness of the access control light, the energy consumption of the access control light is optimized on the basis of ensuring effective identification of the vehicle, and the energy-saving control effect of the access control is further achieved under different environments and under different access control light operation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0036] Figure 1 A module diagram of an energy-saving access control system and its control system provided in Example 1 of the present invention;

[0037] Figure 2 A schematic diagram of an energy-saving access control structure provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] Example 1

[0040] See also Figure 1 As shown, in order to improve the intelligent control level of the access control lights set in the parking lot, thereby reducing the energy consumption of the access control lights, this implementation will focus on how to control the direction of opening and closing the access control lights according to the access control light energy-saving control range of the access control lights to achieve energy-saving settings for the access control. This embodiment discloses an energy-saving access control and a control system thereof, which are applied to energy-saving access control, and the system includes:

[0041] The data collection module 101 is used to obtain the actual data and basic data of the vehicle flow from time QW to the current time Q, where Q and W are both integers greater than 0;

[0042] Specifically, the actual vehicle flow data is the actual vehicle flow at each time point from time QW to the current time point Q.

[0043] It should be noted that the access control entry and exit records vehicle flow rate through unit infrared sensors and counters.

[0044] Specifically, the basic data includes seasonal climate data, holiday data and traffic coefficient at each moment;

[0045] It should be noted that: the seasonal climate data is obtained through the API interface provided by the online weather service, and the API interface includes but is not limited to Weather API, OpenWeatherMap, AccuWeathe, and the seasonal climate data reflects the access control situation in different seasons and weather. For example, due to the long days and short nights in summer, people get off work at [17:30, 18:30] in summer, and in winter, due to the short days and long nights, people get off work at [17:00, 18:00], resulting in different time periods for vehicles to pass through the access control in different seasons; the holiday data is determined according to the records of the online calendar, and the holiday data includes but is not limited to statutory holidays, festivals and important event anniversaries. The vehicle flow through the access control is also different during the holiday time period and non-holiday time period. The traffic coefficient is the influence coefficient of the relevant data of the parking lot on the future vehicle flow at each moment;

[0046] The generation logic of the traffic coefficient is:

[0047] The parking lot related data at each moment is collected, and the parking lot related data includes the total number of parking spaces in the parking lot, the number of vehicles passing through the parking lot entrance and exit at each moment, and the vehicle speed. The traffic coefficient is calculated based on the parking lot related data. The calculation formula of the traffic coefficient is:

[0048] Among them, T is the traffic coefficient, N is the total number of parking spaces in the parking lot, K is the total time for collecting parking lot related data, and v i is the speed of the vehicle passing through the parking lot entrance and exit at the i-th moment, V1 is the preset standard value of the vehicle speed passing through the parking lot entrance and exit, S i is the number of vehicles passing through the parking lot entrance and exit at the i-th moment, S i It is the preset standard value of the number of vehicles passing through the parking lot entrance and exit;

[0049] It should be noted that the traffic coefficient T is used to measure the traffic congestion of the parking lot at different times from time QW to the current time Q. The larger the traffic coefficient T, the more vehicles currently pass through the access control, the larger the number of parking lots, and the higher the frequency of use of the access control. The smaller the traffic coefficient T value, the less vehicles currently pass through the access control, the smaller the number of parking lots, and the lower the frequency of use of the access control.

[0050] The first regression module 102 is used to input the actual traffic flow data and the basic data into a preset traffic flow prediction model to obtain the future traffic flow data from the future time E to the future time E+R, where E and R are both integers greater than 0;

[0051] Specifically, the generation logic of the vehicle flow prediction model is:

[0052] Acquire historical big data of vehicle flow, and divide the historical big data of vehicle flow into a first training set and a first test set; wherein the historical big data of vehicle flow includes actual data and basic data of vehicle flow, and corresponding future data of vehicle flow;

[0053] It should be noted that the future traffic flow data is the actual traffic flow in the future time period [E, E+R], and the acquisition method of the access control entry and exit predicted traffic flow is the same as the acquisition method of the access control entry and exit recorded traffic flow;

[0054] The actual traffic flow data and basic data in the first training set are used as input data of the first regression network, and the future traffic flow data in the first training set are used as output data of the first regression network, and the first regression network is trained to obtain an initial traffic flow prediction regression network;

[0055] The vehicle flow prediction regression network is model-verified through the first test set, and a vehicle flow prediction regression network with a test error less than or equal to a preset test error is output as a preconfigured vehicle flow prediction model;

[0056] It should be noted that: the first regression network is specifically one of the models such as support vector machine, decision tree, random forest, deep neural network, etc.

[0057] The comparison module 103 is used to compare the future traffic flow data with the preset traffic flow data threshold. If the future traffic flow data is greater than or equal to the preset traffic flow data threshold, Q=Q+X is set and the data collection module 101 is returned. If the future traffic flow data is less than the preset traffic flow data threshold, an access light energy-saving control interval and an access light non-energy-saving control interval are generated.

[0058] It should be noted that the time range of the access light energy-saving control interval is [E, E+R], the future time E is the start time of the access light energy-saving control, the future time E+R is the end time of the access light energy-saving control, and the time range of the access light non-energy-saving control interval is the time other than [E, E+R];

[0059] It is important to understand that: if the predicted traffic volume for access control is greater than or equal to the preset predicted traffic volume threshold, it means that within the time range from future time E to future time E+R, there are many vehicles passing through the access control. The access control should take fast and accurate identification of vehicles passing through as its main purpose, and energy saving as its secondary purpose during the identification process. The access control lights need to be continuously illuminated. On the contrary, if the predicted traffic volume for access control is less than the preset predicted traffic volume threshold, it means that within the time range from future time E to future time E+R, there will be no vehicles passing through the access control. Within this time range, the access control should take energy saving as its main purpose, and the access control lights need to be turned off.

[0060] The first control module 104 is used to start or shut down the access light according to the access light energy-saving control interval;

[0061] In a specific implementation, the step of starting or shutting down the access light according to the access light energy-saving control interval includes:

[0062] Get the energy-saving control interval of the access control light and extract the start time of the energy-saving control interval of the access control light;

[0063] Control the access control light to start according to the start time of the access control light energy saving control;

[0064] In another specific implementation, the step of starting or shutting down the access light according to the access light energy-saving control interval includes:

[0065] Get the energy-saving control interval of the access control light and extract the end time of the energy-saving control interval of the access control light;

[0066] Control the access control light to turn off according to the end time of the access control light energy saving control;

[0067] This embodiment obtains the access control light energy-saving control range of the access control light through comprehensive analysis, and intelligently regulates the access control light through the access control light energy-saving control range of the access control light, thereby ensuring that the access control can reduce energy consumption when there are no vehicles passing on the basis of rapid and accurate identification of vehicles, thereby achieving the beneficial effect of energy-saving control of the access control.

[0068] Example 2

[0069] according to Figure 1 As shown, based on the energy-saving non-control interval of the access control light in the above embodiment 1, in order to further improve the energy-saving effect of the access control light, thereby further reducing the energy consumption of the access control, this embodiment will focus on the access control light control in the energy-saving non-control interval of the access control light, and make further improvements and designs. This embodiment discloses an energy-saving access control system, and the system also includes:

[0070] The second regression module 105 is used to obtain the environmental data of the access control setting area and the access control light operation data according to the access control light energy-saving non-control interval, input the environmental data and the access control light operation data into a preset optimal energy-saving brightness analysis model, and generate the optimal energy-saving brightness;

[0071] Specifically, the environmental data includes environmental brightness data, environmental temperature data and environmental visibility data, and the access control light operation data includes access control light input power and access control light surface temperature;

[0072] It should be noted that: the environmental data can be collected by multiple types of sensors installed in the access control setting area, including photosensors, temperature sensors, and visibility sensors. The input power of the access control light is collected by a power detector installed at the power supply of the access control light circuit, and the surface temperature of the access control light is collected by a temperature sensor installed on the access control light housing;

[0073] The generation logic of the optimal energy-saving brightness analysis model is:

[0074] Obtaining access control light control big data, and dividing the access control light control big data into a second training set and a second test set, wherein the access control light control big data includes environmental data, access control light operation data, and corresponding optimal energy-saving brightness;

[0075] Construct a second regression network, use the environmental data and access control light operation data in the second training set as input data of the second regression network, use the optimal energy-saving brightness in the second training set as output data of the second regression network, train the second regression network, and obtain an initial optimal energy-saving brightness analysis network;

[0076] The initial optimal brightness analysis network is verified by the second test set, and an initial optimal energy-saving brightness analysis network having a value less than or equal to a preset test error is output as a preconfigured optimal energy-saving brightness analysis model;

[0077] It should be noted that: like the above-mentioned first regression network, the second regression network is specifically one of the models such as support vector machine, decision tree, random forest, deep neural network, etc.;

[0078] The generation logic of the optimal energy-saving brightness is:

[0079] S1: Set the initial test brightness of the access control light to Z, and the initial value of Z is 0;

[0080] S2: Adjust the initial test brightness, set Y=Z+B, and obtain the adjusted brightness Y, where B and Y are integers greater than zero;

[0081] S3: Under the adjusted brightness Y, the actual clarity of the vehicle image collected by the access control is obtained. If the actual clarity is greater than or equal to the preset standard clarity threshold, the adjusted brightness Y is used as the optimal energy-saving brightness; if the actual clarity is less than the preset standard clarity threshold, B=B+A is set, and the process returns to step S2, where A is an integer greater than zero;

[0082] It should be noted that the definition of the vehicle image is determined based on whether the vehicle can be effectively identified, that is, whether the most basic vehicle image recognition function in the prior art, such as license plate recognition, can be achieved under the condition of low energy consumption of access control lights.

[0083] S4: Repeat the above steps S2 to S3 until the optimal energy-saving brightness is obtained, and then end the cycle.

[0084] The second control module 106 is used to adjust the current access control light brightness according to the optimal energy-saving brightness;

[0085] In a specific embodiment, the optimal energy-saving brightness in the energy-saving non-control interval of the access control light is extracted;

[0086] According to the optimal energy-saving brightness, adjust the brightness of the current access control light to the optimal energy-saving brightness.

[0087] Based on Example 1, this embodiment further optimizes the energy saving of the access control. In the non-energy-saving control range of the access control light, the brightness of the access control light is preferentially adjusted, thereby optimizing the energy consumption of the access control light while ensuring effective identification of the vehicle. In different environments and with different access control light operating data, the energy-saving control effect of the access control is further achieved.

[0088] Real-time Example 3

[0089] according to Figure 2 As shown, this embodiment discloses an energy-saving access control system, which includes at least a power module, a central processing module, an identification module, a storage module, a hardware control module and an energy-saving access control system.

[0090] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD) or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0091] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0092] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0093] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only for an energy-saving access control and its control. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0094] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0095] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0096] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0097] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An energy-saving access control system, characterized in that: The system is applied to energy-saving access control, including: Data acquisition module, used to obtain the time To the current time The actual and basic data of traffic flow in and All are integers greater than 0; The first regression module is used to input the actual traffic flow data and basic data into the preset traffic flow prediction model to obtain the traffic flow prediction model at the future time. To the future Future data on traffic flow within and All are integers greater than 0; The comparison module is used to compare the future traffic flow data with the preset traffic flow data threshold. If the future traffic flow data is greater than or equal to the preset traffic flow data threshold, and returns to the data collection module. If the future traffic flow data is less than the preset traffic flow data threshold, the access light energy-saving control interval and the access light non-energy-saving control interval are generated; The first control module is used to start or shut down the access control light according to the energy-saving control interval of the access control light; The second regression module is used to obtain the environmental data of the access control setting area and the access control light operation data according to the energy-saving non-control interval of the access control light, input the environmental data and the access control light operation data into the preset optimal energy-saving brightness analysis model, and generate the optimal energy-saving brightness; The second control module is used to adjust the current access control light brightness according to the optimal energy-saving brightness; The basic data include seasonal climate data, holiday data and traffic coefficient at each moment; The generation logic of the traffic coefficient is: The parking lot related data at each moment is collected, and the parking lot related data includes the total number of parking spaces in the parking lot, the number of vehicles passing through the parking lot entrance and exit at each moment, and the vehicle speed. The traffic coefficient is calculated based on the parking lot related data. The calculation formula of the traffic coefficient is: ; in, is the traffic coefficient, is the total number of parking spaces in the parking lot, The total amount of time to collect parking lot related data, For the The speed of vehicles passing through the parking lot entrance and exit at a certain moment, is the preset standard value of vehicle speed passing through the parking lot entrance and exit. For the The number of vehicles passing through the parking lot entrance and exit at a given moment, It is the preset standard value for the number of vehicles passing through the parking lot entrance and exit.

2. The system according to claim 1, characterized in that The generation logic of the traffic flow prediction model is: Acquire historical big data of vehicle flow, and divide the historical big data of vehicle flow into a first training set and a first test set; wherein the historical big data of vehicle flow includes actual data and basic data of vehicle flow, and corresponding future data of vehicle flow; The actual traffic flow data and basic data in the first training set are used as input data of the first regression network, and the future traffic flow data in the first training set are used as output data of the first regression network, and the first regression network is trained to obtain an initial traffic flow prediction regression network; The vehicle flow prediction regression network is model verified through the first test set, and a vehicle flow prediction regression network with a test error less than or equal to a preset test error is output as a preconfigured vehicle flow prediction model.

3. The system according to claim 2, characterized in that The environmental data includes environmental brightness data, environmental temperature data and environmental visibility data, and the access control light operation data includes access control light input power and access control light surface temperature.

4. The system according to claim 3, characterized in that The generation logic of the optimal energy-saving brightness analysis model is: Obtaining access control light control big data, and dividing the access control light control big data into a second training set and a second test set, wherein the access control light control big data includes environmental data, access control light operation data, and corresponding optimal energy-saving brightness; Construct a second regression network, use the environmental data and access control light operation data in the second training set as input data of the second regression network, use the optimal energy-saving brightness in the second training set as output data of the second regression network, train the second regression network, and obtain an initial optimal energy-saving brightness analysis network; The initial optimal brightness analysis network is verified through the second test set, and an initial optimal energy-saving brightness analysis network with a test error less than or equal to a preset test error is output as a preconfigured optimal energy-saving brightness analysis model.

5. The system according to claim 4, characterized in that The generation logic of the optimal energy-saving brightness is: S1: Set the initial test brightness of the access control light to , The initial value of is 0; S2: Adjust the initial test brightness, set Y=Z+B, and obtain the adjusted brightness Y, where B and Y are integers greater than zero; S3: Under the adjusted brightness Y, the actual clarity of the vehicle image collected by the access control is obtained. If the actual clarity is greater than or equal to the preset standard clarity threshold, the adjusted brightness Y is used as the optimal energy-saving brightness; If the actual definition is less than the preset standard definition threshold, then set B=B+A, and return to step S2, where A is an integer greater than zero; S4: Repeat the above steps S2 to S3 until the optimal energy-saving brightness is obtained, and then end the cycle.

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