An outdoor lighting device risk analysis system and method based on data prediction, a lighting device, and a storage medium

Abstract

The application discloses an outdoor lighting equipment risk analysis system and method based on data prediction, lighting equipment and a storage medium, relates to the technical field of equipment risk analysis, and collects traffic information data of a target area from a plurality of preset data sources to form a precondition for intelligent on-off of outdoor lighting equipment; when the outdoor lighting equipment is intelligently turned on, the light pollution data range and the equipment lighting range are determined; target body movement data and position data in the target area are collected in real time to determine the priority relationship between the light pollution data range and the equipment lighting range; the outdoor lighting equipment risk analysis instruction is determined; and the intelligent control of the outdoor lighting equipment is realized. The application ensures the lighting processing at night and when the traffic is low, reduces the safety hidden danger caused by light pollution while guaranteeing the basic lighting demand, forms the intelligent risk analysis of the outdoor lighting equipment, and improves the intelligent level of the outdoor lighting equipment.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of device risk analysis, and particularly relates to an outdoor lighting device risk analysis system and method based on data prediction, a lighting device and a storage medium. BACKGROUND

[0002] At present, with the rapid development of the Internet of Things and digital technology, setting corresponding digital chips on outdoor lighting devices to realize remote automatic control gradually enters people's field of vision. The outdoor lighting device is one of the necessities for night travel of personnel or vehicles, and relieves the visual pressure while causing certain light pollution problems.

[0003] Especially for outdoor lighting street lamps at night, in order to ensure a large lighting range, most of the outdoor lighting street lamps on the market exist in different inclination angles, that is, they are not parallel to the horizontal line of the ground. In this case, due to the scattering of light, certain range of light pollution problems will be caused, which will cause certain influence on vehicles passing at night and exist safety hazards. Therefore, how to reduce the safety hazards caused by light pollution on the premise of ensuring normal lighting is one of the main problems of the intelligent risk analysis of outdoor lighting devices. SUMMARY

[0004] The application aims to provide an outdoor lighting device risk analysis system and method based on data prediction, a lighting device and a storage medium, which are used to solve the problem of reducing safety hazards caused by light pollution on the premise of ensuring normal lighting.

[0005] In a first aspect, the application provides an outdoor lighting device risk analysis method based on data prediction, which comprises the following steps:

[0006] Collecting traffic information data of a target area from a plurality of preset data sources to form a precondition for intelligent on-off of the outdoor lighting device;

[0007] Based on the precondition for intelligent on-off of the outdoor lighting device, determining a light pollution data range and a device lighting range when the outdoor lighting device is intelligently turned on;

[0008] Collecting target body movement data and position data in the target area in real time to determine a priority relationship between the light pollution data range and the device lighting range;

[0009] Based on the priority relationship between the light pollution data range and the device lighting range, determining an outdoor lighting device risk analysis instruction;

[0010] Based on the outdoor lighting device risk analysis instruction, intelligently controlling the outdoor lighting device.

[0011] In conjunction with the first aspect, in an implementation of the first aspect of the present application, the traffic information data of the target area includes:

[0012] The sum of vehicle and pedestrian flow data in the target area;

[0013] Utilize multiple preset data sources to collect vehicle and pedestrian flow data in the target area, set a fixed time period, and continuously calculate the flow information data of the target area based on the fixed time period;

[0014] Set a traffic information data threshold for the target area, and determine the duration of the traffic information data in the target area being lower than the traffic information data threshold as a precondition for the intelligent on / off of the outdoor lighting equipment;

[0015] System settings can adjust the duration , the duration of the traffic information data in the target area being lower than the traffic information data threshold in the target area reaches When the traffic information data in the target area is higher than the traffic information data threshold of the target area, it is determined that the outdoor lighting equipment is intelligently turned on.

[0016] In conjunction with the first aspect, in an implementation of the first aspect of the present application, determining the light pollution data range and the device lighting range includes:

[0017] Obtaining location information of outdoor lighting equipment, and when the outdoor lighting equipment is turned on, using a brightness measurement device to determine the lighting ranges of several groups of equipment, and taking an average value based on the lighting ranges of the several groups of equipment as the final determined lighting range of the equipment;

[0018] The light pollution data range includes same-direction light pollution and reverse light pollution;

[0019] The same-direction light pollution refers to the pollution of the light of the outdoor lighting equipment to the target object on the same side as the outdoor lighting equipment; the reverse light pollution refers to the pollution of the light of the outdoor lighting equipment to the target object on the opposite side of the outdoor lighting equipment;

[0020] The target objects include motor vehicles, non-motor vehicles and pedestrians; wherein, the non-motor vehicles and pedestrians do not have a light pollution data range;

[0021] When the outdoor lighting equipment is turned on, a brightness measurement device is used to measure several groups of pollution angles of the same-direction light pollution and the reverse light pollution, and the pollution angles of the same-direction light pollution and the reverse light pollution are determined based on the average value of the several groups of pollution angles;

[0022] The pollution angle refers to the angle between the first straight line and the vertical plane where the outdoor lighting device is located, and the first straight line refers to the straight line formed by the edge light point of the outdoor lighting device and the target object's eyes.

[0023] In conjunction with the first aspect, in an implementation of the first aspect of the present application, the priority relationship between the light pollution data range and the device lighting range includes:

[0024] The first priority relationship means that the light pollution data range is higher than the equipment lighting range, which means that the target object has priority to enter the light pollution data range;

[0025] The second priority relationship means that the light pollution data range is equal to the device lighting range, which means that multiple targets enter the light pollution data range and the device lighting range at the same time.

[0026] In conjunction with the first aspect, in an implementation of the first aspect of the present application, determining the priority relationship between the light pollution data range and the device lighting range includes:

[0027] Distance thresholds are set for each target, including motor vehicle thresholds, non-motor vehicle thresholds, and pedestrian thresholds. When the distance between each target and the light pollution data range or the device lighting range is equal to the distance threshold, the movement data and position data of each target are collected.

[0028] The travel data is based on the moving speed of the previous time period as a standard, and the specific time value of the previous time period is set by the system;

[0029] Based on the travel data and position data of each target, the data prediction method is used to calculate the time point when each target reaches the light pollution data range and the lighting range of the equipment for the same outdoor lighting equipment;

[0030] If a target object reaches the light pollution data range before other target objects, the first risk analysis instruction output is determined;

[0031] If one or more targets reach the light pollution data range and another target reaches the device lighting range at the same time, a second risk analysis instruction output is determined;

[0032] If a target object reaches the light pollution data range and there are other targets whose distance from the target object is less than the motor vehicle length of the target object, the third risk analysis instruction is output.

[0033] In conjunction with the first aspect, in an implementation of the first aspect of the present application, determining the first risk analysis instruction output includes:

[0034] Reduce the brightness of outdoor lighting equipment and set the system to low brightness to reduce the impact of light pollution data range;

[0035] Determining the second risk analysis instruction output includes:

[0036] Turn off several light bulbs of outdoor lighting equipment close to a certain target object to form a new light pollution data range, and determine that no other motor vehicles exist in the formed new light pollution data range.

[0037] In conjunction with the first aspect, in an implementation of the first aspect of the present application, determining the output of the third risk analysis instruction includes:

[0038] Output a second risk analysis instruction, mark other targets whose position distance from a certain target is less than the motor vehicle length of the certain target, and delete the data information of the marked target; wherein, the position distance refers to the overlapping distance of the motor vehicle body; wherein, if the horizontal distance of other targets to the outdoor lighting equipment is higher than that of a certain target, then the other targets are not marked.

[0039] In a second aspect, the present application provides an outdoor lighting equipment risk analysis system based on data prediction, the outdoor lighting equipment risk analysis system comprising:

[0040] The pre-on / off management module is used to collect traffic information data of the target area from multiple preset data sources to form the preconditions for intelligent on / off of outdoor lighting equipment;

[0041] The data processing module determines the light pollution data range and the equipment lighting range when the outdoor lighting equipment is intelligently turned on based on the preconditions of the outdoor lighting equipment's intelligent on / off.

[0042] The real-scene analysis module is used to collect the target movement data and position data in the target area in real time, and determine the priority relationship between the light pollution data range and the equipment lighting range;

[0043] The risk analysis module determines risk analysis instructions for outdoor lighting equipment based on the priority relationship between the light pollution data range and the equipment lighting range;

[0044] The command control module realizes intelligent control of outdoor lighting equipment based on risk analysis instructions of outdoor lighting equipment.

[0045] On the third aspect, please provide an outdoor lighting device based on data prediction, the outdoor lighting device includes: an outdoor lighting device base, a lamp pole, an outdoor lighting lamp head and a chip; the lamp pole is installed on the outdoor lighting device base, the outdoor lighting lamp head is connected to the lamp pole, and the chip is embedded in the outdoor lighting lamp head. The chip includes a memory and at least one processor, and instructions are stored in the memory; the at least one processor calls the instructions in the memory to enable the outdoor lighting device to execute the outdoor lighting device risk analysis method based on data prediction.

[0046] In a fourth aspect, the present application also provides a computer readable storage medium, which stores instructions, and the instructions are executed by a processor to implement the data prediction-based outdoor lighting equipment risk analysis method.

[0047] Compared with the prior art, the present application has the beneficial effect that the present application proposes the central idea of intelligently controlling the outdoor lighting equipment in the field of outdoor lighting equipment, analyzes and processes the data of multiple data sources to ensure the lighting processing at night and when the traffic is low, reduces the safety hazards caused by light pollution while ensuring the basic lighting demand, forms the intelligent risk analysis of the outdoor lighting equipment, and improves the intelligent level of the outdoor lighting equipment. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 FIG. 1 is a flowchart of the data prediction-based outdoor lighting equipment risk analysis method of the present application;

[0049] Figure 2 FIG. 2 is a schematic diagram of the module connection of the data prediction-based outdoor lighting equipment risk analysis system of the present application;

[0050] Figure 3 FIG. 3 is a schematic diagram of the data prediction-based outdoor lighting equipment of the present application;

[0051] In the figure, 1 is an outdoor lighting equipment base, 2 is a lamp pole, 3 is an outdoor lighting lamp head, and 4 is a chip. DETAILED DESCRIPTION

[0052] The embodiments of the present application provide a smart identification digital twin display method and device, equipment and storage medium. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the term "includes" or "has" and any variation thereof is intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily limit to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0053] For ease of understanding, the specific process of the embodiments of the present application is described below. Please refer to Figure 1 In the embodiments of the present application, one embodiment of the data prediction-based outdoor lighting equipment risk analysis method includes:

[0054] Collect traffic information data in the target area from multiple preset data sources to form the prerequisite for intelligent on / off of outdoor lighting equipment;

[0055] Specifically, first, determine which data sources can provide traffic information about the target area, such as sensor networks, satellite data, and IoT devices. These data sources should be able to collect the location parameters of the target objects in real time or periodically. Next, establish a stable and efficient data collection system, using communication protocols and data transmission technologies to store and transmit the collected traffic information data in the target area, ensuring data integrity, real-time availability, and accessibility.

[0056] The traffic information data of the target area includes:

[0057] The sum of vehicle and pedestrian flow data in the target area;

[0058] Utilize multiple preset data sources to collect vehicle and pedestrian flow data in the target area, set a fixed time period, and continuously calculate the flow information data of the target area based on the fixed time period;

[0059] Specifically, the fixed time period can be set to 1 minute, 5 minutes, etc., and the data information generated by the above-mentioned data sources, by borrowing the communication protocol and data transmission technology, the flow data within the fixed time period can be measured. For example, taking 1 minute as an example, the data will form a set of vehicle flow and pedestrian flow data summed up in the target area every 1 minute. The system sets the flow information data threshold of the target area, and the duration of the flow information data of the target area being lower than the flow information data threshold of the target area is determined as the precondition for the intelligent on and off of the outdoor lighting equipment;

[0060] Specifically, the total of the vehicle flow and pedestrian flow data of the target area within each minute is determined and compared with the flow information data threshold of the target area. If the flow information data of the target area is lower than the flow information data threshold of the target area, it is recorded as 1 minute. If the flow information data of the target area in the next minute is also lower than the flow information data threshold of the target area, the duration is 1+1=2 minutes. Conversely, if the flow information data of the target area in the next minute is not lower than the flow information data threshold of the target area, the duration is reset to zero. The above steps can specifically provide accurate preconditions for this application. Based on the premise that traffic is relatively sparse at night, if it is in a traffic peak period, the impact of vehicle lights under the traffic peak and the impact of the traffic peak itself are much higher than the impact of outdoor lighting equipment. Therefore, this application is more accurate in setting the preconditions, which is used to reduce the safety risks of car owners driving at high speeds when traffic is relatively sparse at night.

[0061] System settings can adjust the duration , the duration of the traffic information data in the target area being lower than the traffic information data threshold in the target area reaches When the traffic information data in the target area is higher than the traffic information data threshold of the target area, it is determined that the outdoor lighting equipment is intelligently turned on.

[0062] Based on the preconditions for intelligent on / off of outdoor lighting equipment, when the outdoor lighting equipment is intelligently turned on, the light pollution data range and the equipment lighting range are determined;

[0063] Determining the light pollution data range and the device lighting range includes:

[0064] Obtaining location information of outdoor lighting equipment, and when the outdoor lighting equipment is turned on, using a brightness measurement device to determine the lighting ranges of several groups of equipment, and taking an average value based on the lighting ranges of the several groups of equipment as the final determined lighting range of the equipment;

[0065] The light pollution data range includes same-direction light pollution and reverse light pollution;

[0066] The same-direction light pollution refers to the pollution of the light of the outdoor lighting equipment to the target object on the same side as the outdoor lighting equipment; the reverse light pollution refers to the pollution of the light of the outdoor lighting equipment to the target object on the opposite side of the outdoor lighting equipment;

[0067] Specifically, in this application, outdoor lighting equipment takes street lamps as an example. As the most common outdoor lighting equipment, the light pollution caused by street lamps is the most non-negligible. Street lamps are often installed at one end of the road. For most roads, there are two-way lanes. When vehicles in the same lane drive towards the street lamp, the light pollution points formed on one side of the street lamp are called same-way light pollution. For vehicles in the opposite lane, when vehicles in the opposite lane drive towards the street lamp, the light pollution points formed on the other side of the street lamp are called reverse light pollution.

[0068] The target objects include motor vehicles, non-motor vehicles and pedestrians; wherein, the non-motor vehicles and pedestrians do not have a light pollution data range; wherein, the non-motor vehicles and pedestrians only consider the lighting range problem.

[0069] When the outdoor lighting equipment is turned on, a brightness measurement device is used to measure several groups of pollution angles of the same-direction light pollution and the reverse light pollution, and the pollution angles of the same-direction light pollution and the reverse light pollution are determined based on the average value of the several groups of pollution angles;

[0070] The pollution angle refers to the angle between the first straight line and the vertical plane where the outdoor lighting device is located, and the first straight line refers to the straight line formed by the edge light point of the outdoor lighting device and the target object's eyes;

[0071] Specifically, the pollution angle is essentially a standard measurement method and is not limited to being formed with any plane. In this application, it is specifically defined as the angle with the vertical plane where the outdoor lighting equipment is located. During the measurement process, it is only necessary to ensure that each measurement is made with the same plane. In this application, the vertical plane where the outdoor lighting equipment is located is taken as an example, that is, the base of the lighting equipment is taken as the base point, and the plane where the entire lamp pole is located is the vertical plane where the outdoor lighting equipment is located. After confirming the pollution angle, the light pollution range is measured each time according to the vertical plane where the outdoor lighting equipment is located and the pollution angle. In the subsequent adjustment process, new light pollution areas can be quickly identified;

[0072] Collect the target movement data and position data in the target area in real time, and determine the priority relationship between the light pollution data range and the equipment lighting range;

[0073] The priority relationship between the light pollution data range and the device lighting range includes:

[0074] The first priority relationship means that the light pollution data range is higher than the equipment lighting range, which means that the target object has priority to enter the light pollution data range;

[0075] The second priority relationship means that the light pollution data range is equal to the device lighting range, which means that multiple targets enter the light pollution data range and the device lighting range at the same time.

[0076] Specifically, this application aims to reduce light pollution while ensuring lighting needs. Therefore, the priority processing is mainly to reduce light pollution when lighting is not needed, and automatically adjust when lighting is needed, so as to be compatible with both.

[0077] Determining the priority relationship between the light pollution data range and the device lighting range includes:

[0078] Distance thresholds are set for each target, including motor vehicle thresholds, non-motor vehicle thresholds, and pedestrian thresholds. When the distance between each target and the light pollution data range or the device lighting range is equal to the distance threshold, the movement data and position data of each target are collected.

[0079] The travel data is based on the moving speed of the previous time period as a standard, and the specific time value of the previous time period is set by the system;

[0080] Specifically, the system can set a time, for example, 1 minute, and calculate the moving speed within 1 minute as the basis for data prediction, so as to determine the time it takes for each target object to move into the corresponding light pollution data range or equipment lighting range, and thus perform priority processing;

[0081] Based on the travel data and position data of each target object, the time point at which each target object reaches its light pollution data range and the device lighting range for the same outdoor lighting device is calculated;

[0082] If a target object reaches the light pollution data range before other target objects, the first risk analysis instruction output is determined;

[0083] If one or more targets reach the light pollution data range and another target reaches the device lighting range at the same time, a second risk analysis instruction output is determined;

[0084] If a target object reaches the light pollution data range and there are other targets whose distance from the target object is less than the motor vehicle length of the target object, the third risk analysis instruction is output.

[0085] Determining the first risk analysis instruction output includes:

[0086] Reduce the brightness of outdoor lighting equipment and set the system to low brightness to reduce the impact of light pollution data range;

[0087] Determining the second risk analysis instruction output includes:

[0088] Turn off several light bulbs of outdoor lighting equipment close to a certain target object to form a new light pollution data range, and determine that no other motor vehicles exist in the formed new light pollution data range.

[0089] Determining the third risk analysis instruction output includes:

[0090] Output a second risk analysis instruction, mark other targets whose position distance from a certain target is less than the motor vehicle length of the certain target, and delete the data information of the marked target; wherein, the position distance refers to the overlapping distance of the motor vehicle body; wherein, if the horizontal distance of other targets to the outdoor lighting equipment is higher than that of a certain target, then the other targets are not marked.

[0091] Specifically, the third risk analysis instruction is mainly intended to reduce the processing power consumption of outdoor lighting equipment. In actual scenarios, due to the obstruction of light, when vehicles overlap, the overlapping vehicles will not be affected by light pollution. In this case, the system does not need to perform calculations again. In this embodiment, taking three lanes as an example, taking the opposite lane as an example, if the vehicle in the middle lane reaches the light pollution data range first, and the vehicles in the other two lanes overlap with the vehicle in the middle lane, and the overlapping distance is less than the distance of the motor vehicle in the middle lane itself, it will result in the vehicle in the outer lane (i.e., the lane farthest from the outdoor lighting equipment) being obscured by the vehicle in the middle lane and not affected by light pollution, while the vehicle in the inner lane (i.e., the lane closest to the outdoor lighting equipment) will not be obscured by the vehicle in the middle lane, but will still be affected by light pollution. Therefore, this application introduces a new technical feature, namely, the horizontal distance of other targets to the outdoor lighting equipment for measurement. The horizontal distance is based on the road as the entire plane, the straight line where the base of the outdoor lighting equipment is located as the horizontal line, and the straight-line distance of the vehicle to the horizontal line is recorded as the horizontal distance. In essence, it is to measure the overlapping impact between each motor vehicle.

[0092] Based on the risk analysis instructions of outdoor lighting equipment, intelligent control of outdoor lighting equipment is achieved.

[0093] The above describes an outdoor lighting equipment risk analysis based on data prediction in the embodiment of the present application. The following describes an outdoor lighting equipment risk analysis system based on data prediction in the embodiment of the present application. Figure 2 In an embodiment of the present application, an embodiment of a risk analysis system for outdoor lighting equipment based on data prediction includes:

[0094] The pre-on / off management module 101 is used to collect traffic information data of the target area from multiple preset data sources to form the preconditions for intelligent on / off of outdoor lighting equipment;

[0095] The data processing module 102 determines the light pollution data range and the device lighting range when the outdoor lighting device is intelligently turned on based on the preconditions for the intelligent on / off of the outdoor lighting device;

[0096] The real scene analysis module 103 is used to collect the target object movement data and position data in the target area in real time, and determine the priority relationship between the light pollution data range and the equipment lighting range;

[0097] The risk analysis module 104 determines a risk analysis instruction for outdoor lighting equipment based on a priority relationship between the light pollution data range and the equipment lighting range;

[0098] The instruction control module 105 implements intelligent control of outdoor lighting equipment based on the risk analysis instructions of outdoor lighting equipment.

[0099] At the same time, the present application also provides an outdoor lighting device based on data prediction, which includes: an outdoor lighting device base 1, a lamp pole 2, an outdoor lighting lamp head 3 and a chip 4; the lamp pole 2 is installed on the outdoor lighting device base 1, the outdoor lighting lamp head 3 is connected to the lamp pole 2, and the chip 4 is embedded in the outdoor lighting lamp head 3. The chip 4 includes a memory and at least one processor, and instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the outdoor lighting device based on data prediction executes the outdoor lighting device risk analysis method based on data prediction.

[0100] like Figure 3 As shown, the outdoor lighting lamp head 3 uses an array of lamp beads. In the determination of the second risk analysis instruction output, it is mentioned that several bulbs of the outdoor lighting equipment close to a certain target object are turned off to form a new light pollution data range, which may specifically include:

[0101] By turning off a row of array lamp beads close to a certain target object, the light pollution data range formed will be closer to the front than the original light pollution data range. After the vehicle passes the original light pollution data range, the turned-off lamp beads will be turned on again, and the light pollution data range will return to its original position. During the driving process of the vehicle, the impact of light pollution can be reduced.

[0102] The present application also provides a computer-readable storage medium having instructions stored thereon, and when the instructions are executed by a processor, the method for risk analysis of outdoor lighting equipment based on data prediction is implemented.

[0103] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A risk analysis method for outdoor lighting equipment based on data prediction, characterized by: The method includes: Collect traffic information data in the target area from multiple preset data sources to form the prerequisite for intelligent on / off of outdoor lighting equipment; Based on the preconditions for intelligent on / off of outdoor lighting equipment, when the outdoor lighting equipment is intelligently turned on, the light pollution data range and the equipment lighting range are determined; Collect the target movement data and position data in the target area in real time, and determine the priority relationship between the light pollution data range and the equipment lighting range; Determine risk analysis instructions for outdoor lighting equipment based on the priority relationship between light pollution data range and equipment lighting range; Based on the risk analysis instructions of outdoor lighting equipment, realize intelligent control of outdoor lighting equipment; The priority relationship between the light pollution data range and the device lighting range includes: The first priority relationship means that the light pollution data range is higher than the equipment lighting range, which means that the target object has priority to enter the light pollution data range; The second priority relationship means that the light pollution data range is equal to the device lighting range, which means that multiple targets enter the light pollution data range and the device lighting range at the same time; Determining the priority relationship between the light pollution data range and the device lighting range includes: Distance thresholds are set for each target, including motor vehicle thresholds, non-motor vehicle thresholds, and pedestrian thresholds. When the distance between each target and the light pollution data range or the device lighting range is equal to the distance threshold, the movement data and position data of each target are collected. The travel data is based on the moving speed of the previous time period as a standard, and the specific time value of the previous time period is set by the system; Based on the travel data and position data of each target, the data prediction method is used to calculate the time point when each target reaches the light pollution data range and the lighting range of the equipment for the same outdoor lighting equipment; If a target object reaches the light pollution data range before other target objects, the first risk analysis instruction output is determined; If one or more targets reach the light pollution data range and another target reaches the device lighting range at the same time, a second risk analysis instruction output is determined; If a target object reaches the light pollution data range and the distance between the target object and another target object is less than the motor vehicle length of the target object, the third risk analysis instruction is output; Determining the first risk analysis instruction output includes: Reduce the brightness of outdoor lighting equipment and set the system to low brightness to reduce the impact of light pollution data range; Determining the second risk analysis instruction output includes: Turn off several bulbs of outdoor lighting equipment close to a target object to form a new light pollution data range, and determine that no other motor vehicles exist in the formed new light pollution data range; Determining the third risk analysis instruction output includes: Output a second risk analysis instruction, mark other targets whose position distance from a certain target is less than the motor vehicle length of the certain target, and delete the data information of the marked target; wherein, the position distance refers to the overlapping distance of the motor vehicle body; wherein, if the horizontal distance of other targets to the outdoor lighting equipment is higher than that of a certain target, then the other targets are not marked.

2. The outdoor lighting equipment risk analysis method based on data prediction according to claim 1, characterized in that: The traffic information data of the target area includes: The sum of vehicle and pedestrian flow data in the target area; Utilize multiple preset data sources to collect vehicle and pedestrian flow data in the target area, set a fixed time period, and continuously calculate the flow information data of the target area based on the fixed time period; Set a traffic information data threshold for the target area, and determine the duration of the traffic information data in the target area being lower than the traffic information data threshold as a precondition for the intelligent on / off of the outdoor lighting equipment; The system sets an adjustable duration T0. When the flow information data in the target area is lower than the flow information data threshold of the target area for a duration of T0, it is determined that the outdoor lighting equipment is intelligently turned on; when the flow information data in the target area is higher than the flow information data threshold of the target area, it is determined that the outdoor lighting equipment is intelligently turned off.

3. The outdoor lighting equipment risk analysis method based on data prediction according to claim 1, characterized in that: Determining the light pollution data range and the device lighting range includes: Obtaining location information of outdoor lighting equipment, and when the outdoor lighting equipment is turned on, using a brightness measurement device to determine the lighting ranges of several groups of equipment, and taking an average value based on the lighting ranges of the several groups of equipment as the final determined lighting range of the equipment; The light pollution data range includes same-direction light pollution and reverse light pollution; The same-direction light pollution refers to the pollution of the light of the outdoor lighting equipment to the target object on the same side as the outdoor lighting equipment; the reverse light pollution refers to the pollution of the light of the outdoor lighting equipment to the target object on the opposite side of the outdoor lighting equipment; The target objects include motor vehicles, non-motor vehicles and pedestrians; wherein, the non-motor vehicles and pedestrians do not have a light pollution data range; When the outdoor lighting equipment is turned on, a brightness measurement device is used to measure several groups of pollution angles of the same-direction light pollution and the reverse light pollution, and the pollution angles of the same-direction light pollution and the reverse light pollution are determined based on the average value of the several groups of pollution angles; The pollution angle refers to the angle between the first straight line and the vertical plane where the outdoor lighting device is located, and the first straight line refers to the straight line formed by the edge light point of the outdoor lighting device and the target object's eyes.

4. A data-forecasting-based risk analysis system for outdoor lighting equipment, configured to implement the data-forecasting-based risk analysis method for outdoor lighting equipment as claimed in claim 1, characterized in that: The outdoor lighting equipment risk analysis system includes: The pre-on / off management module is used to collect traffic information data of the target area from multiple preset data sources to form the preconditions for intelligent on / off of outdoor lighting equipment; The data processing module determines the light pollution data range and the equipment lighting range when the outdoor lighting equipment is intelligently turned on based on the preconditions of the outdoor lighting equipment's intelligent on / off. The real-scene analysis module is used to collect the target movement data and position data in the target area in real time, and determine the priority relationship between the light pollution data range and the equipment lighting range; The risk analysis module determines risk analysis instructions for outdoor lighting equipment based on the priority relationship between the light pollution data range and the equipment lighting range; The command control module realizes intelligent control of outdoor lighting equipment based on risk analysis instructions of outdoor lighting equipment.

5. An outdoor lighting device based on data prediction, characterized by: The outdoor lighting equipment includes: an outdoor lighting equipment base, a lamp pole, an outdoor lighting lamp head and a chip; the lamp pole is installed on the outdoor lighting equipment base, the outdoor lighting lamp head is connected to the lamp pole, and the chip is embedded in the outdoor lighting lamp head. The chip includes a memory and at least one processor, and instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the outdoor lighting equipment executes a data prediction-based outdoor lighting equipment risk analysis method as described in any one of claims 1-3.

6. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, a risk analysis method for outdoor lighting equipment based on data prediction according to any one of claims 1 to 3 is implemented.

Images