Solar street lamp intelligent control system self-adaptive to environment

Abstract

The invention relates to the technical field of new energy, in particular to an environment-adaptive solar street lamp intelligent control system, which comprises a solar street lamp, a data acquisition module, a data processing module, a control module and a user interaction module, the data acquisition module is used for acquiring environmental data near the solar street lamp, the data processing module is used for generating adjustment parameters of the solar street lamp according to the environmental data acquired by the data acquisition module, and the control module is used for adjusting the solar street lamp according to the adjustment parameters. And the user interaction module is used for displaying the working parameters of each solar street lamp and receiving a user instruction. According to the scheme, the intelligent degree of the solar street lamp is improved.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to an environment-adaptive solar street lamp intelligent control system. Background Art

[0002] Street lights are an important part of urban infrastructure, but traditional street lights mainly rely on power grid for power supply. In order to cope with the shortcomings of traditional street lights, solar street lights are gradually becoming popular as a green and environmentally friendly lighting solution.

[0003] For example, the prior art of CN103857117A discloses a solar street light control system. Existing street lights are all powered directly to ensure that the street lights are lit and provide light sources for people, which consumes a lot of electricity. A solar street light control system includes a solar cell group, the solar cell group is sequentially connected to a charge and discharge controller, an AC converter and an AC load, the solar cell group is also connected to an alarm, and the charge and discharge controller is connected to a battery.

[0004] Another typical solar street light controller disclosed in the prior art CN101815385A includes a solar panel; a battery connected to the solar panel to form a charging circuit, in which a first branch, a second branch, a boost circuit module, a branch control switch module and a charging current monitoring circuit module are provided; a lamp connected to the battery to form a working circuit, in which a pulse width modulation discharge switch control circuit module and a discharge current monitoring circuit module are also connected in series; a battery voltage monitoring circuit module connected in parallel to both ends of the battery; a solar panel monitoring circuit module arranged in parallel at both ends of the solar panel; and a microcontroller unit for monitoring and control.

[0005] Let's take a look at a solar street light control system disclosed in the prior art such as CN104411050A, which includes: a lighting module, a switching module, a control module and a data acquisition module, wherein the lighting module is used to provide lighting light; the switching module is used to switch the illumination parameters of the lighting light emitted by the lighting module according to the control instruction; the data acquisition module is used to obtain control reference data; the control module is used to receive the control reference data and generate control instructions according to the control reference data; the illumination parameters specifically include light color, illumination range and illumination intensity.

[0006] At present, in the prior art, solar street lights are generally controlled according to a set control scheme without comprehensive consideration of environmental factors, and the degree of intelligence is low. In order to solve the common problems in this field, the present invention is made. Summary of the invention

[0007] The purpose of the present invention is to propose an environment-adaptive solar street light intelligent control system to address the current deficiencies.

[0008] In order to overcome the shortcomings of the prior art, the present invention adopts the following technical solutions: An environment-adaptive solar street light intelligent control system includes a solar street light, a data acquisition module, a data processing module, a control module and a user interaction module. The solar street light is used to collect solar energy and provide lighting. The data acquisition module is used to collect environmental data near the solar street light. The data processing module is used to generate adjustment parameters for the solar street light based on the environmental data collected by the data acquisition module. The control module is used to adjust the solar street light based on the adjustment parameters. The user interaction module is used to display the working parameters of each solar street light and receive user instructions.

[0009] Furthermore, the solar street light includes a solar panel, a lighting unit and a battery energy storage unit. The solar panel is used to collect solar energy and convert sunlight into electrical energy. The battery energy storage unit is used to store the electrical energy converted by the solar panel and transmit the electrical energy to the lighting unit. The lighting unit is used for lighting.

[0010] Furthermore, the data acquisition module includes a light intensity detection unit and an environment detection unit. The light intensity detection unit includes a sensor, and the light intensity detection unit is used to detect the light intensity of sunlight. The environment detection unit includes a camera, and the camera is used to capture images of the surrounding environment of the solar street light.

[0011] Furthermore, the data processing module includes a preprocessing unit, a traffic environment analysis unit, a weather environment analysis unit and a calculation unit. The preprocessing unit is used to preprocess the data collected by the data acquisition module, and the preprocessing includes classification and noise reduction. The traffic environment analysis unit is used to obtain the traffic congestion index of the road section where the solar street light is currently located based on the preprocessed captured images. The weather environment analysis unit is used to judge the weather of the road section where the solar street light is currently located based on the preprocessed captured images. The calculation unit is used to calculate the adjustment parameters of the solar street light based on the traffic congestion index, the analysis result of the weather environment analysis unit and the preprocessed data.

[0012] Furthermore, the control module includes a control command generating unit, which is used to convert the generated adjustment parameters into control commands according to the communication protocol and send the control commands to the lighting unit. The user interaction module includes a display unit and a user instruction receiving unit. The display unit is used to display the working parameters of each solar street lamp. The user instruction receiving unit is used to receive user instructions and convert the acquired user instructions into control commands according to the communication protocol and send the control commands to the lighting unit.

[0013] Furthermore, the workflow of the system includes the following steps: S1, the solar street light collects solar energy and works according to pre-set parameters; S2, data acquisition module collects environmental data near the solar street light; S3, the data processing module generates adjustment parameters according to the collected data of the data collection module; S4, the control module controls the solar street light according to the adjustment parameters; S5, the user interaction module displays the working parameters of each solar street light and receives user instructions.

[0014] Furthermore, the data processing module generates adjustment parameters according to the collected data of the data collection module, including the following steps: S31, the preprocessing unit preprocesses the collected data of the data collection module; S32, the traffic environment analysis unit obtains a traffic congestion index according to the preprocessed captured image; S33, the weather environment analysis unit determines the weather of the road section where the solar street lamp is currently located according to the preprocessed captured image; S34, the calculation unit calculates the adjustment parameters according to the traffic congestion index, the analysis result of the weather environment analysis unit and the pre-processed data.

[0015] The beneficial effects of this solution are: 1. By adjusting the brightness of solar street lights by considering the weather and traffic environment, it is helpful to make the brightness of solar street lights more adaptable to the environment, increase the brightness in traffic congestion and bad weather, help reduce the risk of traffic accidents, and help improve the intelligence of solar street lights.

[0016] 2. By taking pictures to obtain the congestion conditions and weather types of the current road section, image analysis technology and deep learning technology are fully utilized, the response speed is fast and the analysis results are more accurate, which is conducive to improving the overall intelligence level. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention can be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily drawn to scale, but the emphasis is placed on illustrating the principles of the embodiments. In different views, the same reference numerals designate the same parts.

[0018] Figure 1 It is a schematic diagram of the structure of the present invention.

[0019] Figure 2 It is the work flow chart of the present invention.

[0020] Figure 3 A flow chart for generating adjustment parameters for the data processing module of the present invention.

[0021] Figure 4 It is a relationship diagram between the optimized weather type parameters and the optimization coefficients of the present invention. DETAILED DESCRIPTION

[0022] The following is an explanation of the embodiments of the present invention through specific embodiments. Those skilled in the art can understand the advantages and effects of the present invention from the contents disclosed in this specification. The present invention can be implemented or applied through other different specific embodiments, and the details in this specification can also be modified and changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. In addition, the drawings of the present invention are only simple schematic illustrations and are not depicted according to actual sizes. It is stated in advance. The following embodiments will further explain the relevant technical contents of the present invention in detail, but the disclosed contents are not intended to limit the scope of protection of the present invention.

[0023] Embodiment 1: According to Figure 1 , Figure 2 , Figure 3 and Figure 4 The present embodiment provides an adaptive environment solar street light intelligent control system, including a solar street light, a data acquisition module, a data processing module, a control module and a user interaction module. The solar street light is used to collect solar energy and provide lighting. The data acquisition module is used to collect environmental data near the solar street light. The data processing module is used to generate adjustment parameters for the solar street light according to the environmental data collected by the data acquisition module. The control module is used to adjust the solar street light according to the adjustment parameters. The user interaction module is used to display the working parameters of each solar street light and receive user instructions.

[0024] Furthermore, the solar street light includes a solar panel, a lighting unit and a battery energy storage unit. The solar panel is used to collect solar energy and convert sunlight into electrical energy. The battery energy storage unit is used to store the electrical energy converted by the solar panel and transmit the electrical energy to the lighting unit. The lighting unit is used for lighting.

[0025] Furthermore, the data acquisition module includes a light intensity detection unit and an environment detection unit. The light intensity detection unit includes a sensor, and the light intensity detection unit is used to detect the light intensity of sunlight. The environment detection unit includes a camera, and the camera is used to capture images of the surrounding environment of the solar street light.

[0026] Furthermore, the data processing module includes a preprocessing unit, a traffic environment analysis unit, a weather environment analysis unit and a calculation unit. The preprocessing unit is used to preprocess the data collected by the data acquisition module, and the preprocessing includes classification and noise reduction. The traffic environment analysis unit is used to obtain the traffic congestion index of the road section where the solar street light is currently located based on the preprocessed captured images. The weather environment analysis unit is used to judge the weather of the road section where the solar street light is currently located based on the preprocessed captured images. The calculation unit is used to calculate the adjustment parameters of the solar street light based on the traffic congestion index, the analysis result of the weather environment analysis unit and the preprocessed data.

[0027] Specifically, the weather environment analysis unit includes a deep learning model, which is trained by technical personnel in this field using various photos under different weather environments as training sets. By inputting the captured image into the deep learning model, the deep learning model will input the weather type corresponding to the captured image, and the weather type includes but is not limited to haze, rain, sunny, snowy, etc.

[0028] Furthermore, the control module includes a control command generating unit, which is used to convert the generated adjustment parameters into control commands according to the communication protocol and send the control commands to the lighting unit. The user interaction module includes a display unit and a user instruction receiving unit. The display unit is used to display the working parameters of each solar street lamp. The user instruction receiving unit is used to receive user instructions and convert the acquired user instructions into control commands according to the communication protocol and send the control commands to the lighting unit.

[0029] Specifically, the communication protocol is set in advance by those skilled in the art; the control module sends a control command to the lighting unit to change the working parameters of the lighting unit, thereby achieving control over the solar street light.

[0030] Furthermore, the workflow of the system includes the following steps: S1, the solar street light collects solar energy and works in a specified period according to pre-set parameters; S2, data acquisition module collects environmental data near the solar street light; S3, the data processing module generates adjustment parameters according to the collected data of the data collection module; S4, the control module controls the solar street light according to the adjustment parameters; S5, the user interaction module displays the working parameters of each solar street light and receives user instructions.

[0031] Furthermore, the data processing module generates adjustment parameters according to the collected data of the data collection module, including the following steps: S31, the preprocessing unit preprocesses the collected data of the data collection module; S32, the traffic environment analysis unit obtains a traffic congestion index according to the preprocessed captured image; Specifically, the traffic congestion index is calculated according to the following formula: Among them, JTZB is the traffic congestion index, which is used to characterize the degree of traffic congestion. The larger the index is, the more congested the traffic is. e is a natural constant, S is the total number of pixels in the captured image, is the number of pixels occupied by motor vehicles in the captured image, set is the vehicle clearance constant, the value of which is set by those skilled in the art according to the proportion of motor vehicle lanes in the captured image to the entire image. The greater the proportion, the greater the value of the constant. The value range of the constant is 0.1*S to 0.2*S. A is the number of motor vehicles contained in the captured image. is the instantaneous speed of the a-th motor vehicle, which can be obtained by dividing the distance between the position of the motor vehicle's current image and the position of its previous frame's image by the time difference between the two frames. is the speed limit of the lane where the a-th motor vehicle is currently located.

[0032] S33, the weather environment analysis unit determines the weather of the road section where the solar street lamp is currently located according to the preprocessed captured image; S34, the calculation unit calculates the adjustment parameters according to the traffic congestion index, the analysis result of the weather environment analysis unit and the pre-processed data.

[0033] Specifically, the adjustment parameters are calculated according to the following formula: Among them, TZ is the adjustment parameter, LIGHT is the brightness of the solar street light after adjustment, light is the set brightness of the solar street light at the current moment, e is the natural constant, h is the intensity of the sunlight currently detected, H is the maximum intensity of the sunlight detected in the past when the solar street light was in working state, JTZB is the traffic congestion index, is the traffic congestion index threshold, which is set by those skilled in the art according to the traffic congestion index of the current road under ideal conditions (large traffic volume but no congestion), k is a weather type parameter, which is set by those skilled in the art between 1 and 1.5 according to the approximate visibility of different weather types. The greater the approximate visibility, the smaller the weather type parameter. For example, on a sunny day, the weather type parameter is 1, on a snowy day, the weather type parameter is 1.15, on a rainy day, the weather type parameter is 1.2, and on a foggy day, the weather type parameter is 1.5. is the current visibility, It is the maximum visibility on a sunny day in historical data.

[0034] Specifically, the current visibility can be obtained by the weather environment analysis unit by analyzing the captured image through existing image processing technology.

[0035] The following is the procedure for obtaining the adjustment parameters: import math def calculate_tz(light, h, e, k, dis, DIS, jtzb, jtzb_bet, tianqi_type): """ Function for calculating the tuning parameter TZ parameter: - light: The set brightness of the solar street light at the current moment (constant).

[0036] - h: The current detected sunlight intensity.

[0037] - e: natural constant (2.71828).

[0038] - k: Weather type parameter.

[0039] - dis: Current visibility.

[0040] - DIS: Maximum visibility on a clear day in historical data.

[0041] - jtzb: current traffic congestion indicator.

[0042] - jtzb_bet: Benchmark traffic congestion indicator.

[0043] - tianqi_type: weather type.

[0044] Return value: - TZ: Tuning parameters.

[0045] """ # Calculate LIGHT LIGHT = light * math.exp(h / e) * (jtzb / jtzb_bet) * k * (1 + (dis -DIS) / dis) # Calculate TZ TZ = LIGHT - light return TZ # Example input light = 100 # Assume the brightness is set to 100 h = 50 # Current sunlight intensity e = 2.71828 # natural constant k = 1.5 # Assuming weather type parameter dis = 10 # Current visibility DIS = 15 # Maximum visibility on a clear day jtzb = 1.2 # Current traffic congestion index jtzb_bet = 1.0 # Baseline traffic congestion indicator tianqi_type = 1.5 # haze # Calculate adjustment parameters tz = calculate_tz(light, h, e, k, dis, DIS, jtzb, jtzb_bet, tianqi_type) print(f"Adjust parameter TZ to: {tz:.2f}") The beneficial effects of this solution are: 1. By adjusting the brightness of solar street lights by considering the weather and traffic environment, it is helpful to make the brightness of solar street lights more adaptable to the environment, increase the brightness in traffic congestion and bad weather, help reduce the risk of traffic accidents, and help improve the intelligence of solar street lights.

[0046] 2. By taking pictures to obtain the congestion conditions and weather types of the current road section, image analysis technology and deep learning technology are fully utilized, the response speed is fast and the analysis results are more accurate, which is conducive to improving the overall intelligence level.

[0047] Embodiment 2: This embodiment should be understood to include all the features of any of the above embodiments, and further improves on the basis thereof, and also includes a method for optimizing weather type parameters. When the current weather is snowy, snow accumulation will cause reflection. If the lighting brightness is too high, the reflection will be more serious. Therefore, it is necessary to optimize the weather type parameters in the case of snowy days according to the actual situation of the road surface: The method optimizes the weather type parameter according to the following formula: Among them, K is the weather type parameter after optimization, k is the weather type parameter before optimization, hd is the optimization coefficient, S is the snow thickness, B is the number of pixels involving snow in the captured image, is the brightness value of the b-th pixel, which can be obtained by converting the image into a grayscale space and calculating the brightness component, is a pixel brightness threshold, which is set by a person skilled in the art according to corresponding captured image pixels when the reflective ability of snow is low.

[0048] like Figure 4 As shown, Figure 4 This is the relationship between the optimized weather type parameters and the optimization coefficient when k is equal to 1.15.

[0049] The beneficial effects of this embodiment are as follows: by adjusting the weather type parameters in consideration of the snow accumulation on snowy days, it is helpful to appropriately reduce the light level when there is too much snow accumulation, and to avoid excessive snow accumulation from reflecting the light of the solar street lamp.

[0050] The above disclosed contents are only preferred feasible embodiments of the present invention, and do not limit the protection scope of the present invention. Therefore, all equivalent technical changes made by using the contents of the present invention specification and drawings are included in the protection scope of the present invention. In addition, the elements therein can be updated with the development of technology. The above units are only examples. Those skilled in the art can use corresponding units according to different designs according to actual needs when implementing this solution.

Claims

1. An environment-adaptive solar street light intelligent control system, characterized in that: The system comprises a solar street light, a data acquisition module, a data processing module, a control module and a user interaction module. The solar street light is used to collect solar energy and provide lighting. The data acquisition module is used to collect environmental data near the solar street light. The data processing module is used to generate adjustment parameters for the solar street light according to the environmental data collected by the data acquisition module. The control module is used to adjust the solar street light according to the adjustment parameters. The user interaction module is used to display the working parameters of each solar street light and receive user instructions.

2. According to the environmentally adaptive solar street light intelligent control system of claim 1, it is characterized in that: The solar street light includes a solar panel, a lighting unit and a battery energy storage unit. The solar panel is used to collect solar energy and convert sunlight into electrical energy. The battery energy storage unit is used to store the electrical energy converted by the solar panel and transmit the electrical energy to the lighting unit. The lighting unit is used for lighting.

3. The environmentally adaptive solar street light intelligent control system according to claim 2 is characterized in that: The data acquisition module includes a light intensity detection unit and an environment detection unit. The light intensity detection unit includes a sensor for detecting the light intensity of sunlight. The environment detection unit includes a camera for capturing images of the surrounding environment of the solar street light.

4. The environment-adaptive solar street light intelligent control system according to claim 3 is characterized in that: The data processing module includes a preprocessing unit, a traffic environment analysis unit, a weather environment analysis unit and a calculation unit. The preprocessing unit is used to preprocess the data collected by the data collection module, and the preprocessing includes classification and noise reduction. The traffic environment analysis unit is used to obtain the traffic congestion index of the road section where the solar street lamp is currently located according to the preprocessed captured images. The weather environment analysis unit is used to judge the weather of the road section where the solar street lamp is currently located according to the preprocessed captured images. The calculation unit is used to calculate the adjustment parameters of the solar street lamp according to the traffic congestion index, the analysis result of the weather environment analysis unit and the preprocessed data.

5. The environment-adaptive solar street light intelligent control system according to claim 4 is characterized in that: The control module includes a control command generating unit, which is used to convert the generated adjustment parameters into control commands according to the communication protocol and send the control commands to the lighting unit. The user interaction module includes a display unit and a user instruction receiving unit. The display unit is used to display the working parameters of each solar street lamp. The user instruction receiving unit is used to receive user instructions and convert the acquired user instructions into control commands according to the communication protocol and send the control commands to the lighting unit.

6. The environmentally adaptive solar street light intelligent control system according to claim 5, characterized in that: The workflow of the system includes the following steps: S1, the solar street light collects solar energy and works according to pre-set parameters; S2, data acquisition module collects environmental data near the solar street light; S3, the data processing module generates adjustment parameters according to the collected data of the data collection module; S4, the control module controls the solar street light according to the adjustment parameters; S5, the user interaction module displays the working parameters of each solar street light and receives user instructions.

7. The environment-adaptive solar street light intelligent control system according to claim 6, characterized in that: The data processing module generates adjustment parameters according to the collected data of the data collection module, including the following steps: S31, the preprocessing unit preprocesses the collected data of the data collection module; S32, the traffic environment analysis unit obtains a traffic congestion index according to the preprocessed captured image; S33, the weather environment analysis unit determines the weather of the road section where the solar street lamp is currently located according to the preprocessed captured image; S34, the calculation unit calculates the adjustment parameters according to the traffic congestion index, the analysis result of the weather environment analysis unit and the pre-processed data.

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