Intelligent energy-saving street lamp control method and system based on AI visual identification

Abstract

The invention discloses an intelligent energy-saving street lamp control method and system based on AI visual identification, and relates to the technical field of street lamp illumination, and the method comprises the steps: collecting and processing an output voltage value of a light sensor; collecting real-time image data and carrying out image processing and analysis by adopting an AI visual recognition algorithm; judging whether the lamp needs to be turned on or not according to the light intensity, if so, judging whether a pedestrian and a vehicle pass in a first sensing range or not according to an image analysis result, if not, turning off the street lamp, and returning to the step of collecting the output voltage value of the light sensor; if people and vehicles pass, a street lamp turn-on control signal is sent to the digital-to-analog conversion circuit; converting the street lamp turn-on control signal into an analog signal; the constant-current power supply control circuit is used for turning on the street lamp for illumination. On the basis that whether the lamp needs to be turned on or not is judged according to the illumination intensity collected by the light sensor, whether people and vehicles traveling together within the illumination range of the street lamp or not is judged through AI visual recognition, and energy waste can be effectively avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of street lamp lighting, and in particular to an intelligent energy-saving street lamp control method and system based on AI visual recognition. Background Art

[0002] The street light system is a kind of urban infrastructure. It not only provides lighting functions, but also realizes intelligent control, data collection and information transmission by integrating advanced information technology and Internet of Things technology. The most important and basic function of the street light system is to provide nighttime lighting to ensure traffic safety and pedestrian safety. Traditional street light systems mainly rely on manual control of switches and brightness adjustment. With the development of technology, smart street light systems have become popular. Smart street lights integrate sensors, communication equipment, surveillance cameras, etc., and can realize multiple functions such as remote control, environmental monitoring, security monitoring, traffic management and information release. With the progress of society and the development of science and technology, smart street lights are also widely used on rural roads. As road construction becomes more and more developed, more and more street lights are needed, and the functional requirements of street light systems are getting higher and higher, and energy-saving requirements are also urgent.

[0003] Traditional street light systems control the on and off of street lights through timing or by relying on light sensors and infrared sensors. Light sensors sense the intensity of light and automatically control the on-off of street lights according to the set threshold; infrared sensors can automatically turn on street lights by sensing whether there are pedestrians or vehicles near the street lights. The timed street light system only turns on and off street lights according to fixed time periods. The algorithm is too rigid to meet flexible dynamic needs and cannot save electricity. The control method relying on light sensors is similar. As long as the light brightness drops to the control threshold, the street lights will remain on. The control method of infrared sensors mainly has detection accuracy problems. The detection of static objects is greatly affected by light and electromagnetic interference, and is greatly affected by the infrared radiation wavelength and surface reflectivity of the object.

[0004] Currently, the visual recognition used in street lighting systems mainly adopts neural network algorithms, which are less robust to complex scenes and transformations. Although neural networks perform well in processing certain types of image recognition tasks, they may not be robust enough for complex scenes, objects of different scales, and image transformations such as rotation, scaling, and translation. This limits the application of neural networks in complex environments and dynamically changing scenes. The performance of neural network algorithms is affected by a variety of parameters, such as network structure, learning rate, weight initialization, etc. The selection and adjustment of these parameters requires rich experience and a large number of experiments. The complexity of parameter selection increases the difficulty and cost of algorithm application. Summary of the invention

[0005] The present invention provides an intelligent energy-saving street lamp control method and system based on AI visual recognition, so as to overcome at least one technical problem existing in the prior art.

[0006] On the one hand, an embodiment of the present invention provides an intelligent energy-saving street lamp control method based on AI visual recognition, comprising:

[0007] Collect the output voltage value of the light sensor;

[0008] Processing the output voltage value of the light sensor to obtain light intensity;

[0009] Collect real-time image data;

[0010] Using an AI visual recognition algorithm to perform image processing and analysis on the real-time image data to obtain an image analysis result;

[0011] Judging whether it is necessary to turn on the light according to the light intensity, if it is not necessary to turn on the light, returning to the step of collecting the output voltage value of the light sensor; if it is necessary to turn on the light, judging whether there are people or vehicles passing within the first sensing range according to the image analysis result, if no vehicles are passing, turning off the street light, and returning to the step of collecting the output voltage value of the light sensor; if there are people or vehicles passing, sending a street light turn-on control signal to the digital-to-analog conversion circuit;

[0012] The digital-to-analog conversion circuit is used to convert the street lamp on control signal into an analog signal, and sends the analog signal to the constant current power supply control circuit;

[0013] The constant current power supply control circuit is used to turn on the street lamp lighting.

[0014] Optionally, after performing image processing and analysis on the real-time image data using an AI visual recognition algorithm and obtaining an image analysis result, the method further includes:

[0015] According to the image analysis result, it is determined whether an alarm event occurs within the second sensing range. If so, the alarm system is triggered to alarm; if not, the process returns to the step of collecting real-time image data.

[0016] Optionally, the output voltage value of the light sensor is processed, specifically:

[0017] According to the output voltage value of the light sensor, an interpolation table lookup process is performed to obtain the light intensity.

[0018] Optionally, an AI visual recognition algorithm is used to perform image processing and analysis on the real-time image data, specifically:

[0019] The real-time image data is identified, classified and marked to obtain events contained in the image range.

[0020] Optionally, it also includes:

[0021] The light intensity and the image analysis result are sent to the background monitoring system through the background monitoring data interface for storage and call.

[0022] On the other hand, the present invention also provides an intelligent energy-saving street lamp control system based on AI visual recognition, comprising:

[0023] A collection module, used for collecting the output voltage value of the light sensor;

[0024] A camera, used to collect real-time image data;

[0025] A calculation control unit, used to process the output voltage value of the light sensor to obtain the light intensity; and also used to perform image processing and analysis on the real-time image data using an AI visual recognition algorithm to obtain an image analysis result;

[0026] The calculation control unit is further used to determine whether it is necessary to turn on the light according to the light intensity, and if it is not necessary to turn on the light, return to the step of collecting the output voltage value of the light sensor; if it is necessary to turn on the light, determine whether there are people or vehicles passing within the first perception range according to the image analysis result, and if there are no people or vehicles passing, turn off the street light and return to the step of collecting the output voltage value of the light sensor; if there are people or vehicles passing, send a street light turn-on control signal to the digital-to-analog conversion circuit;

[0027] A digital-to-analog conversion circuit, used for converting the street lamp on control signal into an analog signal and sending it to a constant current power supply control circuit;

[0028] Constant current power supply control circuit, used to turn on street lighting.

[0029] Optionally, an alarm system is also included;

[0030] The calculation control unit is also used to determine whether an alarm event occurs within the second perception range based on the image analysis result. If so, trigger the alarm system to alarm; if not, return to the step of collecting real-time image data.

[0031] Optionally, when the calculation control unit processes the output voltage value of the light sensor, it is specifically used to:

[0032] According to the output voltage value of the light sensor, an interpolation table lookup process is performed to obtain the light intensity.

[0033] Optionally, when the computing control unit performs image processing and analysis on the real-time image data, it is specifically used to: identify, classify and mark the real-time image data, and obtain events contained in the image range.

[0034] Optionally, it also includes:

[0035] The background monitoring data interface is used to send the light intensity and the image analysis result to the background monitoring system for storage and call.

[0036] The innovative features of the embodiments of the present invention include:

[0037] In this embodiment, on the basis of judging whether it is necessary to turn on the lights by using the light intensity collected by the light sensor, AI visual recognition is used to judge whether there are people or vehicles traveling together within the lighting range of the street lights, so as to further control the turning on or off of the street lights according to actual needs. This can effectively avoid energy waste, especially on some roads with fewer pedestrians and vehicles, and can save a lot of electricity. This is one of the innovative points of the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 A flow chart of a street lamp control method provided by an embodiment of the present invention;

[0040] Figure 2 Another flow chart of the street lamp control method provided by the embodiment of the present invention;

[0041] Figure 3 Another flow chart of the street lamp control method provided by the embodiment of the present invention;

[0042] Figure 4 A schematic diagram of the structure of a street light control system provided by an embodiment of the present invention;

[0043] Figure 5 Another structural schematic diagram of a street lamp control system provided by an embodiment of the present invention;

[0044] Figure 6 A schematic diagram of another structure of a street lamp control system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only 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.

[0046] It should be noted that the terms "including" and "having" and any variations thereof in the embodiments of the present invention and the accompanying drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products or devices.

[0047] The embodiment of the present invention discloses an intelligent energy-saving street lamp control method and system based on AI visual recognition, which are described in detail below.

[0048] Figure 1 For a flow chart of a street lamp control method provided by an embodiment of the present invention, please refer to Figure 1 The intelligent energy-saving street lamp control method based on AI visual recognition provided by the embodiment of the present invention includes:

[0049] Step 1, collecting the output voltage value of the light sensor;

[0050] Step 2, processing the output voltage value of the light sensor to obtain the light intensity;

[0051] Step 3, collecting real-time image data;

[0052] Step 4: Use AI visual recognition algorithm to perform image processing and analysis on real-time image data to obtain image analysis results;

[0053] Step 5, judging whether it is necessary to turn on the light according to the light intensity, if not, returning to the step of collecting the output voltage value of the light sensor; if it is necessary to turn on the light, judging whether there are people or vehicles passing within the first sensing range according to the image analysis result, if no vehicles are passing, turning off the street light, and returning to the step of collecting the output voltage value of the light sensor; if there are people or vehicles passing, sending a street light turn-on control signal to the digital-to-analog conversion circuit;

[0054] Step 6, using a digital-to-analog conversion circuit to convert the street lamp on control signal into an analog signal, and send it to a constant current power supply control circuit;

[0055] Step 7, using the constant current power supply control circuit to turn on the street light.

[0056] Specifically, since the time of day and night varies in regions with different longitudes and latitudes and in different seasons, and in the face of extreme weather or solar eclipses, a fixed time control strategy for the street lighting system cannot adapt. Therefore, the present invention does not adopt a timing mechanism, but instead makes a policy based on light intensity and real-time identification of people and vehicles in traffic through AI vision.

[0057] Please refer to Figure 1 , the intelligent energy-saving street lamp control method based on AI visual recognition provided by the embodiment of the present invention first collects the output voltage value of the light sensor through step 1. Since the voltage value output by the light sensor cannot directly reflect the intensity of the light, the output voltage value of the light sensor is processed through step 2 to obtain the light intensity. Here, the output voltage value of the light sensor is processed, for example, an interpolation table lookup process can be performed according to the output voltage value of the light sensor to obtain the light intensity value corresponding to the voltage value. Using the light intensity value, the current light intensity can be judged, so as to judge whether the street lamp needs to be turned on.

[0058] After obtaining the light intensity, the present invention also uses a camera to collect real-time image data of the current environment in step 3, and the camera here is a camera with night vision function. After obtaining the real-time image data, in step 4, an AI visual recognition algorithm is used to perform image processing and analysis on the real-time image data to obtain image analysis results, for example, to analyze whether there are people, cars, animals, etc. in the image, so that it can be judged whether there are people and cars passing on the road according to the image analysis results, and the opening and closing of the street lights can be controlled more accurately based on the light intensity.

[0059] The Vision Transformer (ViT) algorithm is an innovative method that applies the Transformer architecture to visual recognition tasks. It captures feature relationships in images through a self-attention mechanism, and therefore is able to capture dependencies between all positions in the input image. This means that no matter how far apart two elements are in the image, ViT can directly calculate the relationship between them, thereby better understanding the global context. And because ViT makes fewer structural assumptions about the input data and relies on a self-attention mechanism rather than a fixed convolution operation, ViT can be more widely adapted to various types of data, including images of different sizes and resolutions.

[0060] Based on this, in this embodiment, the ViT algorithm is used to perform image processing and analysis on real-time image data, and the feature relationship in the image is captured through the self-attention mechanism, and the real-time image data is identified, classified and marked. For example, people, cars, animals, etc. contained in the image are identified, classified and marked. In this way, the events contained in the image range can be obtained.

[0061] After obtaining the light intensity and image analysis results, in step 5, first determine whether it is necessary to turn on the light according to the relationship between the light intensity and the threshold. For example, when the light intensity is greater than the threshold, it means that the light is sufficient at this time and the light does not need to be turned on. Return to the step of collecting the output voltage value of the light sensor, re-collect the output voltage value of the light sensor and process it; and when the light intensity is less than or equal to the threshold, it means that the light is insufficient at this time and the light needs to be turned on.

[0062] If it is determined based on the light intensity that the lights need to be turned on, but there are no pedestrians on the road, turning on the street lights will cause a waste of energy. Therefore, when the light intensity is low, the present invention determines whether there are people or vehicles passing within the first perception range based on the image analysis results. When there are no people or vehicles passing, in order to avoid wasting energy, lighting is not needed, so the street lights are turned off, and then the step of collecting the output voltage value of the light sensor is returned to perform the next collection. When there are people or vehicles passing within the first perception range, it means that the lights need to be turned on, so a street light turn-on control signal is sent to the digital-to-analog conversion circuit. The first perception range here includes the area contained in the road within the camera shooting range.

[0063] Since the street lamp on control signal received by the digital-to-analog conversion circuit is a digital signal, it cannot be used to directly turn on the street lamp. Therefore, after the digital-to-analog conversion circuit receives the street lamp on control signal, in step 6, the street lamp on control signal is converted into an analog signal and sent to the constant current power supply control circuit. After the constant current power supply control circuit receives the analog signal sent by the digital-to-analog conversion circuit, in step 7, the street lamp lighting is turned on according to the received analog signal.

[0064] The intelligent energy-saving street lamp control method based on AI visual recognition provided by the present invention determines whether it is necessary to turn on the light based on the light intensity collected by the light sensor, and then determines whether there are people or vehicles traveling together within the lighting range of the street lamp through AI visual recognition, so as to further control the turning on or off of the street lamp according to actual needs. This method can not only effectively avoid energy waste, especially for some sections with fewer pedestrians and vehicles, but also save a lot of electric energy; it can also solve the accuracy problem of infrared detection, and the detection of static objects will not be interfered by light, electromagnetic and other interferences.

[0065] Compared with neural network-based algorithms, the self-attention mechanism allows ViT to dynamically adjust the attention weights according to the characteristics of the input image, thereby adaptively extracting features. This flexibility enables ViT to capture more useful information when processing complex images, and because ViT has strong global context capture capabilities and good flexibility, it performs well in tasks such as image classification and object detection.

[0066] Optionally, Figure 2 For another flow chart of the street light control method provided by the embodiment of the present invention, please refer to Figure 2 , using AI visual recognition algorithm to perform image processing and analysis on real-time image data, after obtaining the image analysis result, it also includes: step 41, judging whether an alarm event occurs within the second perception range according to the image analysis result, if so, triggering the alarm system to alarm; if not, returning to the step of collecting real-time image data.

[0067] Specifically, please refer to Figure 2 In order to make full use of resources, after obtaining the image analysis results, this embodiment also determines in step 41 whether an alarm event occurs within the second perception range according to the image analysis results. When it is determined according to the image analysis results that there is an alarm event within the camera shooting range, such as a warning of a dangerous event such as a traffic accident or a natural disaster, the alarm system is triggered to alarm; when there is no alarm event, the real-time image data collection step is returned to re-collect. By adding a warning function for dangerous events such as traffic accidents and natural disasters to the street light system, this embodiment can not only avoid the loss of life and property due to the failure to notify rescue personnel in time, but also make full use of camera resources.

[0068] Optionally, Figure 3 For another flow chart of the street light control method provided by the embodiment of the present invention, please refer to Figure 3 The intelligent energy-saving street lamp control method based on AI visual recognition provided by an embodiment of the present invention also includes: step 42, sending the light intensity and image analysis results to the background monitoring system through the background monitoring data interface for storage and call.

[0069] Specifically, in this embodiment, after obtaining the light intensity and image analysis results, in step 42, the light intensity and image analysis results are sent to the background monitoring system through the background monitoring data interface and stored so that they can be used for subsequent calls.

[0070] Based on the same inventive concept, the present invention also provides an intelligent energy-saving street lamp control system based on AI visual recognition. Figure 4 A schematic diagram of a street light control system provided by an embodiment of the present invention is shown in FIG. Figure 4 The intelligent energy-saving street lamp control system 100 based on AI visual recognition provided by the embodiment of the present invention includes:

[0071] The acquisition module 101 is used to acquire the output voltage value of the light sensor;

[0072] Camera 102, used to collect real-time image data;

[0073] The calculation control unit 103 is used to process the output voltage value of the light sensor to obtain the light intensity; and is also used to use the AI ​​visual recognition algorithm to perform image processing and analysis on the real-time image data to obtain the image analysis result;

[0074] The calculation control unit 103 is further used to determine whether it is necessary to turn on the light according to the light intensity. If it is not necessary to turn on the light, the step of returning to the step of collecting the output voltage value of the light sensor is returned; if it is necessary to turn on the light, the step of determining whether there are people or vehicles passing within the first sensing range according to the image analysis result. If there are no people or vehicles passing, the street light is turned off and the step of returning to the step of collecting the output voltage value of the light sensor is returned; if there are people or vehicles passing, the street light is sent to the digital-to-analog conversion circuit 104.

[0075] The digital-to-analog conversion circuit 104 is used to convert the street lamp on control signal into an analog signal and send it to the constant current power supply control circuit 105;

[0076] The constant current power supply control circuit 105 is used to turn on the street lamp lighting.

[0077] Specifically, since the time of day and night varies in regions with different longitudes and latitudes and in different seasons, and in the face of extreme weather or solar eclipses, a fixed time control strategy for the street lighting system cannot adapt. Therefore, the present invention does not adopt a timing mechanism, but instead makes a policy based on light intensity and real-time identification of people and vehicles in traffic through AI vision.

[0078] Please refer to Figure 4 , the intelligent energy-saving street lamp control system 100 based on AI visual recognition provided by the embodiment of the present invention first collects the output voltage value of the light sensor through the acquisition module 101. Since the voltage value output by the light sensor cannot directly reflect the intensity of the light, the output voltage value of the light sensor is processed by the calculation control unit 103 to obtain the light intensity. Here, the output voltage value of the light sensor is processed, for example, an interpolation table lookup process can be performed according to the output voltage value of the light sensor to obtain the light intensity value corresponding to the voltage value. Using the light intensity value, the current light intensity can be judged, so as to determine whether the street lamp needs to be turned on.

[0079] After obtaining the light intensity, the present invention also uses the camera 102 to collect real-time image data of the current environment. The camera 102 here is a camera 102 with night vision function. After obtaining the real-time image data, the computing control unit 103 uses the AI ​​visual recognition algorithm to perform image processing and analysis on the real-time image data to obtain image analysis results, for example, to analyze whether there are people, cars, animals, etc. in the image, so as to judge whether there are people and cars passing on the road according to the image analysis results, and control the opening and closing of the street lamp more accurately based on the light intensity.

[0080] The Vision Transformer (ViT) algorithm is an innovative method that applies the Transformer architecture to visual recognition tasks. It captures feature relationships in images through a self-attention mechanism, and therefore is able to capture dependencies between all positions in the input image. This means that no matter how far apart two elements are in the image, ViT can directly calculate the relationship between them, thereby better understanding the global context. And because ViT makes fewer structural assumptions about the input data and relies on a self-attention mechanism rather than a fixed convolution operation, ViT can be more widely adapted to various types of data, including images of different sizes and resolutions.

[0081] Based on this, in this embodiment, the ViT algorithm is used to perform image processing and analysis on real-time image data, and the feature relationship in the image is captured through the self-attention mechanism, and the real-time image data is identified, classified and marked. For example, people, cars, animals, etc. contained in the image are identified, classified and marked. In this way, the events contained in the image range can be obtained.

[0082] After obtaining the light intensity and image analysis results, the calculation control unit 103 first determines whether it is necessary to turn on the light according to the relationship between the light intensity and the threshold. For example, when the light intensity is greater than the threshold, it means that the light is sufficient at this time and the light does not need to be turned on. The step of collecting the output voltage value of the light sensor is returned to re-collect the output voltage value of the light sensor and process it; and when the light intensity is less than or equal to the threshold, it means that the light is insufficient at this time and the light needs to be turned on.

[0083] If it is determined based on the light intensity that the lights need to be turned on, but there are no pedestrians on the road, turning on the street lights will cause a waste of energy. Therefore, when the light intensity is low, the present invention determines whether there are people or vehicles passing by in the first perception range based on the image analysis results. When there are no people or vehicles passing by, in order to avoid wasting energy, lighting is not needed, so the street lights are turned off, and then the step of collecting the output voltage value of the light sensor is returned to perform the next collection. When there are people or vehicles passing by in the first perception range, it means that the lights need to be turned on, so a street light turn-on control signal is sent to the digital-to-analog conversion circuit 104. The first perception range here includes the area contained in the road within the shooting range of the camera 102.

[0084] Since the street lamp on control signal received by the digital-to-analog conversion circuit 104 is a digital signal, it cannot be directly used to turn on the street lamp. Therefore, after receiving the street lamp on control signal, the digital-to-analog conversion circuit 104 converts the street lamp on control signal into an analog signal and sends it to the constant current power supply control circuit 105. After receiving the analog signal sent by the digital-to-analog conversion circuit 104, the constant current power supply control circuit 105 turns on the street lamp lighting according to the received analog signal.

[0085] The intelligent energy-saving street lamp control system based on AI visual recognition provided by the present invention determines whether it is necessary to turn on the light on the basis of using the light intensity collected by the light sensor, and then uses AI visual recognition to determine whether there are people or vehicles traveling together within the lighting range of the street lamp, thereby further controlling the turning on or off of the street lamp according to actual needs, which can effectively avoid energy waste, especially on some sections of road with fewer pedestrians and vehicles, and can save a lot of electricity.

[0086] Optionally, Figure 5 For another structural diagram of a street light control system provided by an embodiment of the present invention, please refer to Figure 5 The intelligent energy-saving street lamp control system 100 based on AI visual recognition provided by an embodiment of the present invention also includes an alarm system 106; the computing control unit 103 is also used to determine whether an alarm event occurs within the second perception range according to the image analysis result, and if so, trigger the alarm system 106 to alarm; if not, return to the step of collecting real-time image data.

[0087] Specifically, please refer to Figure 5 In order to make full use of resources, after obtaining the image analysis results, this embodiment also enables the calculation control unit 103 to determine whether an alarm event occurs within the second perception range based on the image analysis results. When it is determined based on the image analysis results that there is an alarm event within the shooting range of the camera 102, such as a warning of a dangerous event such as a traffic accident or a natural disaster, the alarm system 106 is triggered to alarm; if there is no alarm event, the real-time image data collection step is returned to re-collect. By adding a warning function for dangerous events such as traffic accidents and natural disasters to the street light system, this embodiment can not only avoid the loss of life and property caused by the failure to notify rescue personnel in time, but also make full use of the resources of the camera 102.

[0088] Optionally, Figure 6 For another structural diagram of a street light control system provided by an embodiment of the present invention, please refer to Figure 6 The intelligent energy-saving street lamp control system 100 based on AI visual recognition provided by the embodiment of the present invention also includes: a background monitoring data interface 107, which is used to send the light intensity and image analysis results to the background monitoring system for storage and call.

[0089] Specifically, please refer to Figure 6 In this embodiment, after obtaining the light intensity and image analysis results, the light intensity and image analysis results are sent to the background monitoring system through the background monitoring data interface 107 and stored so that they can be used for subsequent calls.

[0090] Those skilled in the art can understand that the accompanying drawings are only schematic diagrams of an embodiment, and the modules or processes in the accompanying drawings are not necessarily required to implement the present invention.

[0091] Those skilled in the art can understand that the modules in the device in the embodiment can be distributed in the device in the embodiment according to the description of the embodiment, or can be changed accordingly and located in one or more devices different from the embodiment. The modules in the above embodiment can be combined into one module, or can be further divided into multiple sub-modules.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent energy-saving street lamp control method based on AI visual recognition, characterized in that: include: Collect the output voltage value of the light sensor; Processing the output voltage value of the light sensor to obtain light intensity; Collect real-time image data; Using an AI visual recognition algorithm to perform image processing and analysis on the real-time image data to obtain an image analysis result; Determine whether to turn on the light according to the light intensity, and if the light does not need to be turned on, return to the step of collecting the output voltage value of the light sensor; If it is necessary to turn on the light, it is determined whether there are people or vehicles passing within the first sensing range according to the image analysis result. If no vehicle is passing, the street light is turned off and the step of collecting the output voltage value of the light sensor is returned; if there are people or vehicles passing, a street light turn-on control signal is sent to the digital-to-analog conversion circuit; The digital-to-analog conversion circuit is used to convert the street lamp on control signal into an analog signal, and sends the analog signal to the constant current power supply control circuit; The constant current power supply control circuit is used to turn on the street lamp lighting.

2. The intelligent energy-saving street lamp control method based on AI visual recognition according to claim 1 is characterized in that: After the real-time image data is processed and analyzed using an AI visual recognition algorithm and the image analysis result is obtained, the following steps are also included: According to the image analysis result, it is determined whether an alarm event occurs within the second sensing range. If so, the alarm system is triggered to alarm; if not, the process returns to the step of collecting real-time image data.

3. The intelligent energy-saving street lamp control method based on AI visual recognition according to claim 1 is characterized in that: The output voltage value of the light sensor is processed, specifically: According to the output voltage value of the light sensor, an interpolation table lookup process is performed to obtain the light intensity.

4. The intelligent energy-saving street lamp control method based on AI visual recognition according to claim 1 is characterized in that: The AI ​​visual recognition algorithm is used to perform image processing and analysis on the real-time image data, specifically: The real-time image data is identified, classified and marked to obtain events contained in the image range.

5. The intelligent energy-saving street lamp control method based on AI visual recognition according to claim 1 is characterized in that: Also includes: The light intensity and the image analysis result are sent to the background monitoring system through the background monitoring data interface for storage and call.

6. An intelligent energy-saving street lamp control system based on AI visual recognition, characterized in that: include: A collection module, used for collecting the output voltage value of the light sensor; A camera, used to collect real-time image data; A calculation control unit, used to process the output voltage value of the light sensor to obtain the light intensity; and also used to perform image processing and analysis on the real-time image data using an AI visual recognition algorithm to obtain an image analysis result; The calculation control unit is further used to determine whether the light needs to be turned on according to the light intensity, and if the light does not need to be turned on, return to the step of collecting the output voltage value of the light sensor; If it is necessary to turn on the light, it is determined whether there are people or vehicles passing within the first sensing range according to the image analysis result. If no vehicle is passing, the street light is turned off and the step of collecting the output voltage value of the light sensor is returned. If there are people or vehicles passing, a street light turn-on control signal is sent to the digital-to-analog conversion circuit. A digital-to-analog conversion circuit, used for converting the street lamp on control signal into an analog signal and sending it to a constant current power supply control circuit; Constant current power supply control circuit, used to turn on street lighting.

7. The intelligent energy-saving street lamp control system based on AI visual recognition according to claim 6 is characterized in that: Also includes alarm system; The calculation control unit is also used to determine whether an alarm event occurs within the second perception range based on the image analysis result. If so, trigger the alarm system to alarm; if not, return to the step of collecting real-time image data.

8. The intelligent energy-saving street lamp control system based on AI visual recognition according to claim 6 is characterized in that: When the calculation control unit processes the output voltage value of the light sensor, it is specifically used to: According to the output voltage value of the light sensor, an interpolation table lookup process is performed to obtain the light intensity.

9. The intelligent energy-saving street lamp control system based on AI visual recognition according to claim 6 is characterized in that: When the computing control unit performs image processing and analysis on the real-time image data, it is specifically used to: identify, classify and mark the real-time image data, and obtain events contained in the image range.

10. The intelligent energy-saving street lamp control system based on AI visual recognition according to claim 6 is characterized in that: Also includes: The background monitoring data interface is used to send the light intensity and the image analysis result to the background monitoring system for storage and call.

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