Multifunctional smart pole traffic safety monitoring system and method based on AI algorithm

Abstract

The multifunctional intelligent pole traffic safety monitoring system and method based on AI algorithm of the present invention include lighting module, brightness sensor module, photoelectric conversion module, voltage storage module, voltage data comparison module and alarm module; the lighting module illuminates the surrounding road surface to form a regional light field screen; the brightness sensor module receives the light signal of the regional light field screen; the photoelectric conversion module converts the light signal into an electrical signal; the voltage storage module stores the electrical signal and forms a regional electric field screen accordingly; the voltage data comparison module is used to compare the regional electric field screen at a first time and the regional electric field screen at a second time, and judge whether to trigger the alarm module according to the comparison result of the regional electric field screen at the first time and the regional electric field screen at the second time; the present invention monitors the road traffic safety status in real time, and the system stores and processes data memory, which occupies a very low communication bandwidth capacity, does not need to process large-capacity memory of graphics and videos, reduces communication congestion, reduces costs, and has stable and reliable transmission.

Description

Technical Field

[0001] The present invention relates to the field of street lamp networking security technology, and in particular to a multifunctional smart pole traffic safety monitoring system and method based on AI algorithm. Background Art

[0002] In traditional road traffic safety monitoring, cameras across the road are generally used for shooting and monitoring. However, in the case of unstable road conditions such as blind spots in curves, blurry images in bad weather, and unclear images at long distances, especially on expressways and highways, if there is an emergency on the road, the monitoring camera cannot give a wide range of early warnings to the vehicles behind. However, urban road streetlights are a huge node network that coexists with vehicles at the same time and place, making them very suitable for tracking, real-time monitoring, and no blind spots. Through the streetlight network and empowering multi-functional smart poles, sudden road conditions can be monitored in real time, and real-time and extensive warnings can be issued through display terminals to ensure the safe passage of vehicles. However, real-time monitoring of road traffic safety conditions currently requires processing of a large amount of graphics and videos, and large-capacity memory needs to be set in communication equipment. In addition, graphics and videos occupy a high communication bandwidth capacity during communication, which can easily cause communication congestion, increase equipment and communication costs, and have poor communication stability. Summary of the invention

[0003] The main purpose of the present invention is to overcome the above shortcomings and deficiencies of the prior art and provide a multifunctional smart pole traffic safety monitoring system based on AI algorithm.

[0004] A multifunctional smart pole traffic safety monitoring system based on AI algorithm, including a lighting module, a brightness sensing module, a photoelectric conversion module, a voltage storage module, a voltage data comparison module and an alarm module;

[0005] The lighting module illuminates the surrounding road surface, and the light spot illuminated by the lighting module forms a regional light field screen;

[0006] The brightness sensing module receives the light signal of the regional light field screen;

[0007] The photoelectric conversion module is used to convert the optical signal received by the brightness sensor module into an electrical signal;

[0008] The voltage storage module is used to store the electrical signal and correspondingly form a regional electric field screen;

[0009] The voltage data comparison module is used to compare the regional electric field screen at the first time and the regional electric field screen at the second time, and determine whether to trigger the alarm module according to the comparison result of the regional electric field screen at the first time and the regional electric field screen at the second time.

[0010] Compared with the prior art, which requires powerful computing power and massive storage capacity to identify obstacles by transmitting the video stream of video surveillance to the background, the present invention collects brightness values ​​through the brightness sensor module and converts them into voltage values ​​to form a stable initial data model. When a risk point appears, the initial data model will be broken to generate a set of variable data models. Compared with the initial data model, the risk type after the change is analyzed through the voltage data comparison module, and the terminal warning can be directly issued after identification. The present invention solves the shortcomings of the prior art that require a large number of equipment, high computing power, high capacity and high energy consumption, and greatly reduces costs.

[0011] As a further improvement, it also includes an edge computing module or an AI computing module. The light signal of the regional light field screen is used by the edge computing module or the AI ​​computing module to establish the two-dimensional (x, y) coordinate position of the brightness point of the region using the pixel coordinate system method, and the vertical coordinate is added to the two-dimensional (x, y) coordinate position to obtain a three-dimensional coordinate data model of the brightness value of the light signal. The photoelectric conversion module converts the three-dimensional coordinate data model of the brightness value to obtain a three-dimensional coordinate data model of the voltage value.

[0012] As a further improvement, the lighting module includes a voltage storage unit for storing the LED light source, and a control chip unit is connected between the voltage storage unit and the voltage storage module. The control chip unit is used to obtain the voltage value of the ambient brightness by subtracting the voltage value of the voltage storage unit from the voltage value of the voltage storage module.

[0013] As a further improvement, the voltage data comparison module is also used to compare the voltage value of the ambient brightness at the first time and the voltage value of the ambient brightness at the second time, and to determine whether to compare the voltage value of the ambient brightness based on the comparison result of the regional electric field screen at the first time and the regional electric field screen at the second time, and to determine whether to trigger the alarm module based on the comparison structure of the voltage value of the ambient brightness.

[0014] As a further improvement, the voltage data comparison module is used to compare the initial value and the variable value, and if the variation range between the variable value and the initial value exceeds ±5%, the alarm module is triggered.

[0015] As a further improvement, the voltage data comparison module is an AI data comparator.

[0016] As a further improvement, it further includes a control module, which controls the alarm module to emit red light or yellow light, or issue an early warning through a display screen.

[0017] As a further improvement, it also includes a communication module and a cloud computing module. The communication module transmits the three-dimensional coordinate data model of the regional electric field screen of each smart lamp pole to the remote central system, and forms a complete big data model through the cloud computing module.

[0018] As a further improvement, a photoelectric sensor is further included, and the photoelectric sensor detects the distance between the target object entering the regional light field screen and the brightness sensing module by emitting an infrared light beam.

[0019] As a further improvement, a camera module is also included, and the camera module takes pictures of the regional light field screen.

[0020] The present application also provides a multifunctional smart pole traffic safety monitoring method based on an AI algorithm, which is applied to the above-mentioned multifunctional smart pole traffic safety monitoring system based on an AI algorithm, and the method comprises:

[0021] The lighting module illuminates the surrounding road surface, and the light spot illuminated by the lighting module forms a regional light field screen;

[0022] The brightness sensing module receives the light signal of the regional light field screen;

[0023] The photoelectric conversion module is used to convert the optical signal received by the brightness sensor module into an electrical signal;

[0024] The voltage storage module is used to store the electrical signal and correspondingly form a regional electric field screen;

[0025] The voltage data comparison module is used to compare the regional electric field screen at the first time and the regional electric field screen at the second time, and determine whether to trigger the alarm module according to the comparison result of the regional electric field screen at the first time and the regional electric field screen at the second time.

[0026] The beneficial effects of the multifunctional smart pole traffic safety monitoring system based on AI algorithm of the present invention are: compared with the existing technology, it can accurately improve the monitoring target road conditions, the system stores and processes data memory, occupies a very low communication bandwidth capacity, does not need to process large-capacity memory of graphics and videos, reduces communication congestion, greatly reduces equipment and communication costs, and the communication transmission is stable and reliable; by calculating the stable light field brightness distribution, it avoids the errors and time delays caused by monitoring visual recognition, simplifies the equipment interconnection channel, saves costs, and runs stably BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a schematic block diagram of a multifunctional smart pole traffic safety monitoring system based on an AI algorithm of the present invention;

[0028] Figure 2A schematic diagram of a photoelectric conversion circuit of a multifunctional smart pole traffic safety monitoring system based on an AI algorithm of the present invention;

[0029] Figure 3 It is a schematic diagram of the principle of the voltage comparison circuit of the multifunctional smart pole traffic safety monitoring system based on the AI ​​algorithm of the present invention;

[0030] Figure 4 It is a schematic block diagram of a multifunctional smart pole traffic safety monitoring system based on an AI algorithm of the present invention;

[0031] Figure 5 A schematic diagram of the coordinates of the regional light field screen in the multifunctional smart pole traffic safety monitoring system based on the AI ​​algorithm of the present invention;

[0032] Figure 6 It is a schematic diagram of the formation of the regional light field screen in the multifunctional smart pole traffic safety monitoring system based on the AI ​​algorithm of the present invention;

[0033] Figure 7 A schematic diagram of risk points of the regional light field screen in the multifunctional smart pole traffic safety monitoring system based on the AI ​​algorithm of the present invention;

[0034] Figure 8 A schematic diagram of the light path reflection of a water surface appearing in a pothole in the multifunctional smart pole traffic safety monitoring system based on the AI ​​algorithm of the present invention;

[0035] Fig. 9 A schematic diagram of the light path reflection on a pothole road surface in the multifunctional smart pole traffic safety monitoring system based on the AI ​​algorithm of the present invention;

[0036] Fig.10 A schematic diagram of the light path reflection of an obstacle somewhere on the road surface in the multifunctional smart pole traffic safety monitoring system based on the AI ​​algorithm of the present invention;

[0037] Fig.11 This is an operation block diagram of the multifunctional smart pole traffic safety monitoring system based on AI algorithm of the present invention;

[0038] Fig.12 This is a monitoring flow chart of the multifunctional smart pole traffic safety monitoring method based on AI algorithm of the present invention. DETAILED DESCRIPTION

[0039] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. The preferred embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thoroughly understood.

[0040] It should be noted that when an element is considered to be "connected" to another element, it may be directly connected to another element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are only for illustrative purposes and do not represent the only implementation method.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0042] Embodiment 1

[0043] See also Figure 1 , an embodiment of the present invention provides a multifunctional smart pole traffic safety monitoring system 100 based on an AI algorithm, comprising a lighting module 1, a brightness sensor module 2, a photoelectric conversion module 3, a voltage storage module 4, a voltage data comparison module 5 and an alarm module 6;

[0044] The lighting module 1 illuminates the surrounding road surface, and the light spot illuminated by the lighting module forms a regional light field screen;

[0045] The brightness sensor module 2 receives the light signal of the regional light field screen;

[0046] The photoelectric conversion module 3 is used to convert the optical signal received by the brightness sensor module 2 into an electrical signal;

[0047] The voltage storage module 4 is used to store the electric signal and to form a regional electric field screen accordingly;

[0048] The voltage data comparison module 5 is used to compare the regional electric field screen at the first time with the regional electric field screen at the second time, and judge whether to trigger the alarm module 6 according to the comparison result of the regional electric field screen at the first time and the regional electric field screen at the second time. Preferably, the voltage data comparison module 5 is an AI data comparator. Among them, the alarm module 6 includes but is not limited to the LED lamp head of the intelligent lighting fixture, the display screen, the rotatable spotlight or the voice alarm module, the LED lamp head can emit flashing red light or yellow light, the display screen issues an early warning through the subtitle display, and the rotatable spotlight emits a flashing light for early warning.

[0049] Among them, the brightness sensing module 2 is a brightness sensor or a brightness sensor or a light sensor, the regional electric field screen at the first time is the regional electric field screen at the initial time, and the regional electric field screen at the second time is the variable regional electric field screen. It can be understood that each street lamp smart pole covers a specific jurisdiction road area, and the street lamp irradiates light to the jurisdiction road area to form a regional light field screen of a certain size and area. The regional light field screen distributes enough brightness points to achieve a certain resolution, and the road surface flatness, high and low ups and downs and other features are clearly expressed with optical brightness energy. The regional light field screen with strong and weak energy corresponds to the regional electric field screen with high and low voltage, forming a vector relationship. Since the regional light field screen has a certain resolution, each point in the regional light field screen has a unique coordinate point. As long as the voltage value of the regional light field screen after being converted into the regional electric field screen changes, the voltage data comparison module 5 will compare it with the regional electric field screen value at the initial time to determine whether to trigger the alarm module 6.

[0050] More specifically, see Figure 2 The brightness sensing module 2 is a light sensor. The light sensors RG1 and RG2 absorb light signals. The photoelectric conversion module 3 includes signal operational amplifiers A1 and A2 and transistors Q1 and Q2. The light signals absorbed by the light sensors RG1 and RG2 are amplified by the signal operational amplifiers A1 and A2, and then a pulse voltage signal is formed by closing the switches of the transistors Q1 and Q2.

[0051] More specifically, see Figure 3 , Figure 3 The schematic diagram of the principle of the voltage comparison circuit is shown, and the voltage data comparison module 5 is preferably an AI comparator. The function of the AI ​​comparator is to compare two or more data items to determine whether they are equal, or to determine the size relationship and arrangement order between them. The voltage data comparison module 5 can also be other circuits or devices that implement this comparison function. The comparator is a circuit that compares an analog voltage signal with a reference voltage. The two inputs of the comparator are analog signals, and the output is a binary signal 0 or 1. When the difference of the input voltage increases or decreases and the positive and negative signs remain unchanged, the output remains constant.

[0052] More specifically, see Figure 4 , Figure 5 , Figure 6 and Figure 7The multifunctional smart pole traffic safety monitoring system based on AI algorithm also includes a computing module 7, which is an edge computing module or an AI computing module. The light signal of the regional light field screen uses the edge computing module or the AI ​​computing module to establish the two-dimensional (x, y) coordinate position of the brightness point of the region using the pixel coordinate system method, and adds the vertical coordinate to the two-dimensional (x, y) coordinate position to obtain the three-dimensional coordinate data model of the brightness value of the light signal. The photoelectric conversion module 3 converts the three-dimensional coordinate data model of the brightness value to obtain the three-dimensional coordinate data model of the voltage value, and stores it in the voltage storage module 4.

[0053] Among them, the initial regional light field screen defines its safety factor and the flatness of the position according to the road conditions, collects data of the initial regional light field screen through the brightness sensor module 2, and establishes an initial model of the regional light field screen through the edge computing module or the IA computing module as a reference condition for judging the changing factors.

[0054] More specifically, when potholes, water accumulation, snow accumulation, obstacles and other unsafe environmental factors appear on the road conditions under the jurisdiction of a street light smart pole, the initial brightness energy distribution of the light field screen in the area under the jurisdiction of the corresponding street light smart pole will be broken, and the edge computing module or AI computing module will instantly and accurately identify the unsafe position and shape characteristics of the area under its jurisdiction, and then use the voltage data comparison module 5 to compare the existing regional electric field screen with the initial regional electric field screen, and determine whether to trigger the alarm module 6 based on the comparison result of the existing regional electric field screen and the initial regional electric field screen. More specifically, when the existing variable value of the regional electric field screen voltage exceeds the initial value by more than ±5%, the alarm module 6 is triggered.

[0055] The numerical changes at specific coordinate locations include the following modes: water accumulation mode, pit mode, and raised obstacles.

[0056] Among them, see Figure 8 , Figure 8 The diagram shows the light path reflection of the water surface in the potholes. If the water surface appears in the original potholes or pits at a certain place of the regional light field screen, the water area will become larger with time, the rainfall becomes heavier or longer, and the brightness value of the coordinate area will decrease as a whole, and the uniformity will also decrease. It can be understood that the brightness value of a certain area of ​​the regional light field screen coordinates has changed, but not the data of the entire regional light field screen. The data of the coordinates of other areas without problems are still the same as the initial value.

[0057] Among them, see Fig. 9 , Fig. 9 The diagram shows the light path reflection of a pothole road. If there are potholes or pits on the road, the brightness energy at that location is reduced, and the voltage at the corresponding voltage point is reduced. As shown in the figure, the brightness value of the coordinate area on the left side of the pit becomes low and messy, and the brightness value of the area on the right side is almost zero.

[0058] Among them, see Fig.10 , Fig.10 The figure shows a schematic diagram of the light path reflection when there is an obstacle somewhere on the road surface. If there is an obstacle somewhere on the road surface, it will block or enhance the light exposure and transmission, increase the scattering surface or mirror surface, change the original light propagation, and form a shadow surface or a bright surface. The corresponding voltage point will be reduced or enhanced. As shown in the figure, the data received in the left and right areas of the obstacle are almost 0, and the data in the middle area is high, low, or messy.

[0059] For obstacles on the road, please refer to Figure 3 The multifunctional smart pole traffic safety monitoring system based on AI algorithm also includes a photoelectric sensor module 8, which detects the distance between the target object entering the regional light field screen and the brightness sensor module 2 by emitting an infrared beam. Preferably, the photoelectric sensor module 8 is a proximity photoelectric sensor for detecting the distance between the target object and the sensor. It usually adopts a transmitting-receiving integrated structure. The transmitting end emits an infrared beam. When an object approaches, the beam is scattered or reflected back by the object. The receiving end receives the light signal and converts it into an electrical signal, thereby judging the existence or distance of the object. The photoelectric sensor module 8 transmits the distance data of the obstructing target object to the alarm module 6.

[0060] More specifically, in order to prevent misjudgment and trigger the alarm module 6, a two-dimensional coordinate data model or a three-dimensional coordinate data model of the regional electric field screen voltage value is calculated by the edge computing module or the AI ​​computing module, and then an initial uniformity of the regional electric field screen is obtained. A comprehensive judgment is made through the changes in the uniformity and voltage value of the entire regional electric field screen to reduce the probability of misjudgment and improve the accuracy of the alarm module triggered by the entire traffic safety monitoring system.

[0061] More specifically, see Fig.11 , the voltage and uniformity of the regional electric field screen are determined as follows:

[0062] (1) When the current variable value of the regional electric field screen voltage does not vary by more than ±5% from the initial value, and the current uniformity of the regional electric field screen does not change from the initial uniformity of the regional electric field screen, it is considered normal and the alarm module 6 is not triggered.

[0063] (2) When the existing variable value of the regional electric field screen voltage varies by more than ±5% compared to the initial value, and the uniformity of the existing regional electric field screen does not vary by more than ±3% compared to the initial uniformity of the regional electric field screen, it can be determined that there are potholes, water accumulation in potholes, or obstacles. The pothole pattern, obstacle protrusion pattern, or water accumulation pattern is identified, and the alarm module 6 is triggered.

[0064] (3) When the existing variable value of the regional electric field screen voltage varies by more than 5% compared to the initial value, and the uniformity of the existing regional electric field screen increases by more than 3% compared to the initial uniformity of the regional electric field screen, it can be identified as snowy weather, the brightness value of the regional light field screen increases as a whole, the overall uniformity of the regional light field screen increases, the snow mode is identified, and the alarm module 6 is triggered.

[0065] (4) When the existing variable value of the regional electric field screen voltage varies by more than 5% from the initial value, and the uniformity of the existing regional electric field screen decreases by more than 3% compared with the initial uniformity of the regional electric field screen, it can be identified as rainy weather, the brightness value of the regional light field screen decreases as a whole, and the overall uniformity of the regional light field screen decreases. The rain pattern is identified and the alarm module 6 is triggered.

[0066] (5) When the existing variable value of the regional electric field screen voltage decreases by more than 5% compared with the initial value, and the uniformity of the existing regional electric field screen has not decreased as a whole compared with the initial uniformity of the regional electric field screen, it can be determined as smog weather, the brightness value of the regional light field screen decreases as a whole, and the overall uniformity of the regional light field screen has not decreased, the smog pattern is identified, and the alarm module 6 is triggered.

[0067] More specifically, see Figure 4 In order to further prevent misjudgment and trigger the alarm module 6, the multifunctional smart pole traffic safety monitoring system based on the AI ​​algorithm also includes a camera module 9, which takes pictures of the regional light field screen. For example, when the regional light field screen has regularly moving light and dark spots, so that the existing variable value of the regional electric field screen voltage varies by more than ±5% from the initial value, and the uniformity of the existing regional electric field screen exceeds ±3% compared with the initial uniformity of the regional electric field screen, the camera module 9 takes pictures of the regularly moving light and dark spots on the regional light field screen for comparison, and the regularly moving light and dark spots are regarded as vehicles and their lights affecting the regional light field screen, and the alarm module 6 will not be triggered.

[0068] More specifically, see Figure 4 In order to further prevent misjudgment and trigger the alarm module 6, the lighting module 1 includes a voltage storage unit 10 for storing the LED light source. A control chip unit 11 is connected between the voltage storage unit 10 and the voltage storage module 4. The control chip unit 11 is used to subtract the voltage value of the voltage storage unit 10 from the voltage value of the voltage storage module 4 to obtain the voltage value of the ambient brightness.

[0069] The voltage data comparison module 5 is also used to compare the voltage value of the initial ambient brightness with the voltage value of the variable time ambient brightness. The voltage data comparison module 5 first checks whether the existing variable value of the regional electric field screen voltage and the initial value change range exceed ±5%, and whether the uniformity change of the regional electric field screen exceeds ±3%;

[0070] 1. The voltage data comparison module 5 first compares the voltage value of the regional electric field screen with the initial value, and the change range of the current variable value does not exceed ±5%, and the uniformity of the regional electric field screen does not exceed ±3%; the voltage data comparison module 5 does not compare the voltage value of the ambient brightness.

[0071] 2. The voltage data comparison module 5 first compares the voltage value of the regional electric field screen with the initial value when the change range exceeds ±5% and the uniformity of the regional electric field screen exceeds ±3%. The voltage data comparison module 5 then compares the voltage value of the ambient brightness;

[0072] 2.1. If the voltage value of the variable time ambient brightness does not change from the voltage value of the initial ambient brightness, the alarm module 6 is triggered.

[0073] 2.2. If the voltage value of the variable time ambient brightness changes with the voltage value of the initial ambient brightness, the alarm module 6 will not be triggered.

[0074] More specifically, see Figure 4 The multifunctional smart pole traffic safety monitoring system based on AI algorithm also includes a control module 12, which controls the alarm module 6 to emit red light or yellow light, or issue an early warning through a display screen or through a voice prompt.

[0075] More specifically, see Figure 4 The multifunctional smart pole traffic safety monitoring system based on AI algorithm also includes a communication module 13 and a cloud computing module 14. The communication module 13 transmits the three-dimensional coordinate data model of the regional electric field screen of each smart lamp pole to the remote central system, and forms a complete big data model through the cloud computing module 14. The abnormal points at the location can be quickly identified and located through the communication module 13, the cloud computing module 14 and the remote central system, so as to send a safety warning signal to the display terminal in advance, and calculate the location distance of the unsafe factor point, as well as the risk level. For example, it can prompt that there is an obstacle ahead, how many kilometers ahead it is, and notify the road inspection to clear the obstacle. It can provide real-time early warning to traffic to ensure safe passage.

[0076] Embodiment 2

[0077] See also Fig.12 The present application also provides a multifunctional smart pole traffic safety monitoring method based on an AI algorithm, which is applied to the above-mentioned multifunctional smart pole traffic safety monitoring system based on an AI algorithm, and the method includes:

[0078] Step S201, the lighting module 1 illuminates the surrounding road surface, and the light spot illuminated by the lighting module 1 forms a regional light field screen; the brightness sensor module 2 receives the light signal of the regional light field screen and stores it as an initial value;

[0079] Step S202, the photoelectric conversion module 3 converts the optical signal received by the brightness sensor module 2 into an electrical signal, and establishes an initial model of the regional electric field screen corresponding to the regional light field screen through the edge computing module or the IA computing module 7;

[0080] Step S203, when the road surface is affected by environmental factors such as water accumulation, snow accumulation, and new obstacles, the original brightness energy distribution of the initial regional light field screen is broken, and the brightness sensor module 2 receives the brightness energy of the changed regional light field screen, and the photoelectric conversion module 3 converts the light signal received by the brightness sensor module 2 into an electrical signal and stores it as a variable value. The electrical signal stored in the voltage storage module 4 forms a variable regional electric field screen;

[0081] Step S204, the voltage data comparison module 5 compares the variable regional electric field screen with the initial regional electric field screen. When the difference value of the comparison result exceeds the critical value, the edge computing module or the IA computing module 7 quickly identifies and locates the abnormal point at the location, and reports it to the background control system through the communication module 13;

[0082] In step S205, the background control system sends a control instruction to the control module 12, and issues an early warning by emitting flashing red or yellow light through the LED lamp head of the intelligent lighting fixture, or by issuing an early warning through a display screen, or by adding a rotatable spotlight to emit a flashing light.

[0083] The beneficial effects of the multifunctional smart pole traffic safety monitoring system based on AI algorithm of the present invention are: compared with the existing technology, it can monitor the road traffic safety situation in real time, the system stores and processes data memory, occupies a very low communication bandwidth capacity, does not need to process large-capacity memory of graphics and videos, reduces communication congestion, greatly reduces equipment and communication costs, and the communication transmission is stable and reliable.

[0084] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A multifunctional smart pole traffic safety monitoring system based on AI algorithm, characterized in that: It includes a lighting module, a brightness sensing module, a photoelectric conversion module, a voltage storage module, a voltage data comparison module and an alarm module; The lighting module illuminates the surrounding road surface, and the light spot illuminated by the lighting module forms a regional light field screen; The brightness sensing module receives the light signal of the regional light field screen; The photoelectric conversion module is used to convert the optical signal received by the brightness sensor module into an electrical signal; The voltage storage module is used to store the electrical signal and correspondingly form a regional electric field screen; The voltage data comparison module is used to compare the regional electric field screen at the first time and the regional electric field screen at the second time, and determine whether to trigger the alarm module according to the comparison result of the regional electric field screen at the first time and the regional electric field screen at the second time; The lighting module includes a voltage storage unit for storing the LED light source, a control chip unit is connected between the voltage storage unit and the voltage storage module, and the control chip unit is used to obtain a voltage value of the ambient brightness by subtracting a voltage value of the voltage storage unit from a voltage value of the voltage storage module; The voltage data comparison module is also used to compare the voltage value of the ambient brightness at the first time and the voltage value of the ambient brightness at the second time, and to determine whether to compare the voltage value of the ambient brightness based on the comparison result of the regional electric field screen at the first time and the regional electric field screen at the second time, and to determine whether to trigger the alarm module based on the comparison structure of the voltage value of the ambient brightness.

2. The multifunctional smart pole traffic safety monitoring system based on AI algorithm according to claim 1 is characterized in that: It also includes an edge computing module or an AI computing module. The light signal of the regional light field screen is used by the edge computing module or the AI ​​computing module to establish the two-dimensional (x, y) coordinate position of the brightness point of the region using a pixel coordinate system method, and the vertical coordinate is added to the two-dimensional (x, y) coordinate position to obtain a three-dimensional coordinate data model of the brightness value of the light signal. The photoelectric conversion module converts the three-dimensional coordinate data model of the brightness value to obtain a three-dimensional coordinate data model of the voltage value.

3. The multifunctional smart pole traffic safety monitoring system based on AI algorithm according to any one of claims 1-2 is characterized in that: The voltage data comparison module is used to compare the initial value and the variable value. If the variation range between the variable value and the initial value exceeds ±5%, the alarm module is triggered.

4. The multifunctional smart pole traffic safety monitoring system based on AI algorithm according to any one of claims 1-2 is characterized in that: The voltage data comparison module is an AI data comparator.

5. The multifunctional smart pole traffic safety monitoring system based on AI algorithm according to any one of claims 1-2 is characterized in that: It also includes a control module, which controls the alarm module to emit red light or yellow light, or issue an early warning through a display screen.

6. The multifunctional smart pole traffic safety monitoring system based on AI algorithm according to any one of claims 1-2 is characterized in that: It also includes a communication module and a cloud computing module. The communication module transmits the three-dimensional coordinate data model of the regional electric field screen of each smart lamp pole to the remote central system, and forms a complete big data model through the cloud computing module.

7. The multifunctional smart pole traffic safety monitoring system based on AI algorithm according to any one of claims 1-2 is characterized in that: A photoelectric sensor is also included, and the photoelectric sensor detects the distance between the target object entering the regional light field screen and the brightness sensing module by emitting an infrared light beam.

8. The multifunctional smart pole traffic safety monitoring system based on AI algorithm according to any one of claims 1-2 is characterized in that: It also includes a camera module, which takes pictures of the regional light field screen.

9. A multifunctional smart pole traffic safety monitoring method based on AI algorithm, characterized in that: The multifunctional smart pole traffic safety monitoring system based on AI algorithm applied to any one of claims 1 to 8 comprises: The lighting module illuminates the surrounding road surface, and the light spot illuminated by the lighting module forms a regional light field screen; The brightness sensing module receives the light signal of the regional light field screen; The photoelectric conversion module is used to convert the optical signal received by the brightness sensor module into an electrical signal; The voltage storage module is used to store the electrical signal and correspondingly form a regional electric field screen; The voltage data comparison module is used to compare the regional electric field screen at the first time and the regional electric field screen at the second time, and determine whether to trigger the alarm module according to the comparison result of the regional electric field screen at the first time and the regional electric field screen at the second time; The lighting module includes a voltage storage unit for storing the LED light source, a control chip unit is connected between the voltage storage unit and the voltage storage module, and the control chip unit is used to obtain a voltage value of the ambient brightness by subtracting a voltage value of the voltage storage unit from a voltage value of the voltage storage module; The voltage data comparison module is also used to compare the voltage value of the ambient brightness at the first time and the voltage value of the ambient brightness at the second time, and to determine whether to compare the voltage value of the ambient brightness based on the comparison result of the regional electric field screen at the first time and the regional electric field screen at the second time, and to determine whether to trigger the alarm module based on the comparison structure of the voltage value of the ambient brightness.

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