Intelligent road lighting control system based on multi-sensor fusion

Abstract

The invention relates to the technical field of illumination control, in particular to an intelligent road illumination control system based on multi-sensor fusion. The system comprises a fusion calculation unit, a response speed unit, a priority unit and a delay control unit. According to the invention, the priority unit calculates the dynamic adjustment priority of the intelligent street lamps according to the static processing priority of the intelligent street lamps, and then rearranges the processing queue of the intelligent street lamps according to the dynamic adjustment priority of the intelligent street lamps; it is ensured that tasks with high processing speed of the intelligent street lamp are executed preferentially in the initial state, the request response speed for controlling the lighting switch of the intelligent street lamp is increased, and meanwhile, the priority of the intelligent street lamp is dynamically adjusted on the basis of the static processing priority of the intelligent street lamp, so that the intelligent street lamp can continuously control the lighting switch in the process of continuously controlling the lighting switch. The delay of the control response speed of the intelligent street lamp caused by continuous control accumulation is avoided, so that the response speed of the continuous control of the intelligent street lamp is improved.

Description

Technical Field

[0001] This invention relates to the field of lighting control technology, and more specifically, to an intelligent road lighting control system based on multi-sensor fusion. Background Technology

[0002] With the rapid development of IoT technology, smart sensors are increasingly being applied to the field of road lighting. By connecting various smart sensors (such as lighting sensors, pedestrian sensors, and vehicle flow sensors) and lighting equipment to an IoT platform, new possibilities have been brought for intelligent lighting management and control of roads. Utilizing multi-sensor fusion technology, data from different types of sensors can be integrated to obtain more comprehensive and accurate road environment information. For example, by combining ambient light intensity sensed by a light sensor and pedestrian activity detected by a human infrared sensor, precise control of lighting equipment can be achieved.

[0003] However, existing intelligent road lighting control still has significant shortcomings in terms of control algorithms, data processing, and system integration. In intelligent road lighting scenarios, due to changes in traffic flow, intelligent streetlights will face the situation of continuous control of switching lights on and off. During peak traffic hours, vehicles and pedestrians pass by frequently, and intelligent streetlights need to frequently perform switching operations, thus generating a large number of control tasks.

[0004] If task scheduling is not reasonable, it will lead to task backlog and an increase in the number of tasks waiting to be processed. Under high load, the time for the controller of the smart street light to process each task will increase significantly, which will slow down the data processing and instruction execution speed in the smart street light. This will result in a delay in the control response speed of the smart street light when continuously controlling the switching on and off, reduce the control response speed of the smart street light, affect the timeliness and effectiveness of road lighting, and fail to fully meet the actual needs of smart road lighting.

[0005] Therefore, to solve the above problems, we provide an intelligent road lighting control system based on multi-sensor fusion. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent road lighting control system based on multi-sensor fusion to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides an intelligent road lighting control system based on multi-sensor fusion, including a fusion computing unit, a response speed unit, a priority unit, and a delay control unit;

[0008] The fusion computing unit monitors data from multiple smart sensors on the road and merges it into smart street light status data. It then obtains the CPU utilization rate and lighting switch processing time of smart street lights in a specific area of ​​the smart road, and calculates the average processing time for controlling the continuous lighting switch of smart street lights and the average utilization rate of the smart street lights.

[0009] The response speed unit performs unit dimension processing on the average processing time of the continuous lighting switch of the smart street light in the fusion computing unit. It evaluates the processing performance score of the control of the smart street light lighting switch by the average utilization rate of the control of the smart street light in the fusion computing unit and the dimensionless average processing time, and then determines whether there is a control response speed delay in the continuous lighting switch of the smart street light.

[0010] The priority unit receives a control response speed delay command from the response speed unit regarding the continuous lighting switch of the smart street light. It calculates the priority of static processing of the smart street light based on the average processing time of the continuous lighting switch of the smart street light controlled by the fusion calculation unit. Then, it calculates the priority of dynamic adjustment of the smart street light and reorders the smart street light processing queue based on the dynamic adjustment priority of the smart street light, prioritizing the processing of data with higher priority.

[0011] The delay control unit receives data commands with higher priority from the priority unit and processes them first. It determines whether the smart street light has insufficient control precision under continuous lighting switching by calculating the delay ratio of the smart street light lighting switch. When it is determined that there is insufficient control precision, it predicts the processing time required for the smart street light lighting switch and then calculates the control precision of the smart street light under continuous lighting switching to control and adjust the smart street light.

[0012] As a further improvement to this technical solution, the response speed unit acquires historical data and extracts the reference time and the square of the reference time, respectively, and performs unit dimension processing on the processing time variance of the continuous lighting switch of the smart street light in the utilization module and the average processing time of the continuous lighting switch of the smart street light in the fusion averaging module. The processing performance score of the control of the smart street light lighting switch is evaluated by controlling the average utilization of the smart street light, the dimensionless processing time variance, and the dimensionless average processing time. A stable processing performance score means that the smart street light can respond to the switching lighting command in a timely manner and provide stable lighting for the road. Whether vehicles are driving or pedestrians are walking, they can carry out activities more safely in a stable lighting environment, avoiding the situation of flickering or sudden blackout of lighting caused by the delay in street light control response, thus improving the user experience and sense of security.

[0013] As a further improvement to this technical solution, the priority unit utilizes a dynamic priority adjustment algorithm to calculate the dynamic adjustment priority of the smart streetlights based on the dimensionless processing time, the priority of static processing of the smart streetlights, and the dimensionless average processing time in the response speed unit. Then, the processing queue of the smart streetlights is reordered through dynamic priority adjustment, prioritizing the processing of higher-priority data. Dynamic priority adjustment allows for more precise control of streetlight illumination switching and brightness according to actual needs. For streetlights with low usage or short processing times, their priority can be reduced, decreasing illumination time or brightness when necessary, thereby achieving energy-saving goals. For example, in areas with few pedestrians late at night, reducing the priority of streetlights and appropriately dimming the lights can meet basic lighting needs while effectively saving energy.

[0014] As a further improvement to this technical solution, the control accuracy module receives a command from the delay judgment module indicating insufficient control accuracy of the smart street light during continuous lighting switching. By using the dimensionless processing time variance and dimensionless average processing time in the response speed unit, it predicts the processing time required for the smart street light to switch on and off. This allows the module to know in advance the processing time required for the street light to turn off, enabling it to issue a shutdown command at the appropriate time and avoid energy waste caused by delayed shutdown. Furthermore, for street light control tasks with long processing times, the module can prepare in advance to ensure that the street light is shut off as soon as possible when lighting is not needed, thus improving energy efficiency.

[0015] As a further improvement to this technical solution, the control accuracy module utilizes an optimized control algorithm to calculate the control accuracy of the smart streetlights under continuous lighting switching conditions, based on the dynamic adjustment priority of the smart streetlights in the priority unit, the dimensionless processing time variance in the response speed unit, and the processing time required to predict the switching of the smart streetlights. By controlling the control accuracy under continuous lighting switching conditions, the smart streetlights are adjusted. The calculated control accuracy accurately reflects the degree to which the smart streetlights match the expected control target during continuous lighting switching. Real-time monitoring of the control accuracy allows for timely detection and correction of control deviations. For example, if the streetlights are expected to turn on at a specific time and achieve a certain brightness, but the actual brightness is insufficient after control, high-precision control accuracy calculations can quickly adjust the control parameters, ensuring accurate streetlight response and improving the response accuracy of the smart streetlights.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0017] 1. In this intelligent road lighting control system based on multi-sensor fusion, the priority unit calculates the dynamic adjustment priority of the intelligent streetlights based on the dimensionless processing time, the priority of static processing of the intelligent streetlights, and the dimensionless average processing time. Then, the intelligent streetlight processing queue is reordered through the dynamic adjustment priority of the intelligent streetlights, prioritizing the processing of data with higher priority. By using the priority of static processing of the intelligent streetlights, it ensures that tasks with faster processing speeds in the initial state are executed first, improving the response speed of requests to control the intelligent streetlight lighting switches. At the same time, the dynamic adjustment priority of the intelligent streetlights is performed based on the static processing priority, so that the execution order of intelligent streetlight lighting tasks can be optimized in real time during continuous control of the lighting switches, avoiding delays in the intelligent streetlight control response speed caused by continuous control backlog, thereby improving the response speed of continuous control of the intelligent streetlights.

[0018] 2. In this intelligent road lighting control system based on multi-sensor fusion, the control accuracy module predicts the processing time required for the intelligent street light to switch on and off by using the dimensionless processing time variance and the dimensionless average processing time. Then, based on the dynamic adjustment priority of the intelligent street light, the dimensionless processing time variance, and the predicted processing time required for the intelligent street light to switch on and off, it calculates the control accuracy of the intelligent street light under continuous lighting switching conditions. By controlling the control accuracy of the intelligent street light under continuous lighting switching conditions, the system adjusts the intelligent street light accordingly. By comprehensively considering the influence of priority and processing time, the system makes the calculation of the control accuracy of the intelligent street light under continuous lighting switching conditions more accurate, which helps in the allocation and scheduling of lighting resources for the intelligent street light and improves the control accuracy of the intelligent street light under continuous lighting switching conditions. Attached Figure Description

[0019] Figure 1 This is a block diagram of the overall system structure of the present invention;

[0020] Figure 2 This is a block diagram of the module units of the present invention.

[0021] The meanings of the labels in the diagram are as follows:

[0022] 1. Fusion computing unit; 11. Fusion averaging module; 12. Utilization module;

[0023] 2. Response speed unit; 3. Priority unit;

[0024] 4. Delay control unit; 41. Delay judgment module; 42. Control accuracy module. Detailed Implementation

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1

[0027] This invention provides an intelligent road lighting control system based on multi-sensor fusion. Please refer to [link / reference]. Figures 1-2 It includes a fusion computing unit 1, a response speed unit 2, a priority unit 3, and a delay control unit 4;

[0028] The fusion computing unit 1 includes a fusion averaging module 11 and a utilization module 12;

[0029] The fusion averaging module 11 is connected in real time to multiple smart sensors (such as light sensors, vehicle flow sensors, pedestrian sensors, and sound sensors) distributed on the road, and monitors the lighting, vehicle flow, and sound data of specific areas of the smart road (such as main road sections and intersections) in real time. It merges the monitored lighting, vehicle flow, and sound data into smart street light status data, and then obtains the CPU utilization rate U of smart street lights in specific areas of the smart road triggered by the sound sensors of the smart sensors during peak periods and the smart street light lighting switch processing time t from the smart street light status data. It also records the number of smart street light lighting switch processing times n and the number of smart street light CPU sampling times m.

[0030] Using a processing time averaging algorithm, the average processing time for controlling the continuous lighting switch of a smart street light is calculated based on the processing time and the number of times the smart street light is switched on and off. Specific algorithm formula: Among them, t i This refers to the processing time of the i-th intelligent street light switching;

[0031] The utilization module 12 uses a processing time variance algorithm to calculate the processing time variance σ of the continuous lighting switch of the smart street light by fusing the average processing time, the processing time of the smart street light switch, and the number of times the smart street light switch is processed in the averaging module 11. 2 Specific algorithm formula:

[0032] This formula can more easily identify outliers by calculating variance. High variance indicates that the smart streetlights in a specific area of ​​the road have insufficient processing capacity during peak hours. Load balancing can be achieved by dynamically adjusting resource allocation and optimizing task scheduling, thereby improving the control response speed of smart streetlights in that specific area of ​​the road.

[0033] Simultaneously, by utilizing the utilization rate averaging algorithm, the average utilization rate of the controlled smart streetlights is calculated by integrating the smart streetlight CPU utilization rate and the number of smart streetlight CPU samplings in the averaging module 11. Among them, U j This refers to the CPU utilization rate of the smart street light in the j-th iteration;

[0034] Response speed unit 2 acquires historical data and extracts the reference time t0 and the square of the reference time from the historical data. By using the processing time variance of the continuous lighting switch of the smart street light controlled in the rate module 12 and the average processing time of the continuous lighting switch of the smart street light controlled in the fusion averaging module 11, and performing unit dimension processing on the reference time and the square of the reference time, a dimensionless processing time variance is obtained. and dimensionless average processing time

[0035] The average utilization rate, dimensionless processing time variance, and dimensionless average processing time of the smart streetlights controlled in the utilization rate module 12 are used as input data to the linear regression model of machine learning. The linear regression model of machine learning learns from the input data and outputs the average utilization rate weight coefficient α, the processing time variance weight coefficient β, and the average processing time weight coefficient γ.

[0036] Historical data includes the base time t0 and the square of the base time.

[0037] The processing performance score S of controlling the intelligent street light switch is evaluated by controlling the average utilization rate, dimensionless processing time variance, the weighting coefficients of dimensionless average processing time and average utilization rate, the weighting coefficient of processing time variance, and the weighting coefficient of average processing time. The specific algorithm formula is as follows: The formula can promptly detect the declining trend of the processing performance of smart street light switches by evaluating the processing performance score of smart street light switches. At the same time, high usage rate and high variance may indicate potential faults or bottlenecks of smart street lights, which can provide early warning and avoid the impact on the lighting effect of specific areas of the road due to the delayed response speed of the control smart street light switches.

[0038] When the processing performance score of the intelligent street light switch is known, a processing performance score threshold is set. The processing performance score of the intelligent street light switch and the processing performance score threshold are used to determine whether there is a control response speed delay in the continuous lighting switch of the intelligent street light. When the processing performance score of the intelligent street light switch is greater than the processing performance score threshold, it is determined that there is a control response speed delay in the continuous lighting switch of the intelligent street light, which slows down the data processing and command execution speed of the intelligent street light.

[0039] Priority unit 3 receives a control response speed delay command from response speed unit 2 regarding the continuous lighting switch of the smart street light, and calculates the total processing time of the smart street light lighting switch by controlling the average processing time of the continuous lighting switch through fusion averaging module 11. Then, using the static priority allocation algorithm, the priority P of the smart street light static processing is calculated based on the smart street light switching processing time and the total smart street light switching processing time. s Specific algorithm formula: Among them, by prioritizing the processing of short-duration smart street light switching tasks, the control response speed of smart street lights can be significantly improved, the delay in the turn-on time of smart street lights can be reduced, thereby improving the lighting experience in specific areas of the road;

[0040] When the priority of static processing for intelligent streetlights is known, the processing time of the intelligent streetlight lighting switch is processed in units of dimension by the reference time in response speed unit 2 and the fusion averaging module 11 to obtain the dimensionless processing time.

[0041] Using a dynamic priority adjustment algorithm, the dynamic adjustment priority P of the smart street light is calculated based on the dimensionless processing time, the priority of static processing of the smart street light, and the average dimensionless processing time in response speed unit 2. d Specific algorithm formula: Here, δ refers to the adjustment coefficient. This formula dynamically increases the priority of tasks with short processing times or urgent tasks, enabling smart streetlights to respond to these tasks more quickly, thereby reducing the delay in the turn-on time of smart streetlights.

[0042] By dynamically adjusting the priorities of smart streetlights and reordering their processing queues, higher-priority data is processed first. The static processing priorities ensure that tasks with faster processing speeds are executed first in the initial state, improving the response speed of requests to control the streetlights' lighting switches. Furthermore, by dynamically adjusting priorities based on the static processing priorities, the execution order of lighting tasks can be optimized in real time during continuous control of the streetlights, avoiding delays in control response speed caused by continuous control backlog, thereby improving the response speed of continuous control of the smart streetlights.

[0043] The delay control unit 4 includes a delay judgment module 41 and a control accuracy module 42;

[0044] The delay judgment module 41 receives the data command from the priority unit 3 that prioritizes the processing of high-priority data, and calculates the delay ratio of the smart street light lighting switch by combining the dimensionless processing time in the priority unit 3 with the number of processing times of the smart street light lighting switch by the fusion averaging module 11.

[0045] When the delay ratio of the smart street light switch is calculated, a delay ratio threshold is set. The delay ratio and the delay ratio threshold are used to determine whether the smart street light has insufficient control precision under continuous lighting switching. When the delay ratio of the smart street light switch is greater than the delay ratio threshold, it is determined that the smart street light has insufficient control precision under continuous lighting switching. When the delay ratio of the smart street light switch is less than the delay ratio threshold, it is determined that the smart street light does not have insufficient control precision under continuous lighting switching, and no operation is performed.

[0046] The control accuracy module 42 receives a delay judgment module 41 command indicating insufficient control accuracy of the smart street light during continuous lighting switching. It then predicts the required processing time for the smart street light's lighting switch by using the dimensionless processing time variance and dimensionless average processing time in the response speed unit 2. Wherein, ε refers to the weighting factor, which is used to control the degree of influence of the processing time variance of the continuous lighting switch of the smart street light on the processing time required to predict the smart street light lighting switch.

[0047] Knowing the processing time required to predict the switching of the smart street light, an optimized control algorithm is used. Based on the dynamic priority adjustment of the smart street light in priority unit 3, the dimensionless processing time variance in response speed unit 2, and the processing time required to predict the switching of the smart street light, the control accuracy A of controlling the smart street light under continuous lighting switching conditions is calculated. The specific algorithm formula is as follows: Wherein, ∈ refers to the adjustment factor, which is used to balance the impact of the dynamic adjustment priority of smart streetlights and the processing time required to predict the lighting switch of smart streetlights. By comprehensively considering the impact of priority and processing time, this formula makes the calculation of the control accuracy of smart streetlights under continuous lighting switch conditions more accurate, which helps the allocation and scheduling of lighting resources of smart streetlights and improves the control accuracy of smart streetlights under continuous lighting switch conditions.

[0048] By controlling the precision of intelligent streetlights during continuous lighting switching, the control strategy of intelligent streetlights can be optimized to ensure the stability and efficiency of road lighting.

[0049] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent road lighting control system based on multi-sensor fusion, characterized in that: It includes a fusion computing unit (1), a response speed unit (2), a priority unit (3), and a delay control unit (4); The fusion computing unit (1) monitors data from multiple smart sensors on the road and merges it into smart street light status data. Then, it obtains the CPU utilization rate of smart street lights and the processing time of smart street light lighting switches in a specific area of ​​the smart road, calculates the average processing time for controlling the continuous lighting switch of smart street lights and the average utilization rate of smart street lights. The response speed unit (2) performs unit dimension processing on the average processing time of the control of the continuous lighting switch of the smart street light in the fusion calculation unit (1). It evaluates the processing performance score of the control of the smart street light lighting switch by the average utilization rate of the control of the smart street light and the dimensionless average processing time in the fusion calculation unit (1), and then determines whether there is a control response speed delay in the continuous lighting switch of the smart street light. The priority unit (3) receives the control response speed delay command for the continuous lighting switch of the smart street light from the response speed unit (2), calculates the priority of the static processing of the smart street light by the average processing time of the continuous lighting switch of the smart street light controlled by the fusion calculation unit (1), calculates the priority of the dynamic adjustment of the smart street light, and reorders the processing queue of the smart street light by the dynamic adjustment priority of the smart street light, and processes the data with higher priority first. The delay control unit (4) receives data commands with higher priority from the priority unit (3) and determines whether the intelligent street light has insufficient control precision under continuous lighting switch by calculating the delay ratio of the intelligent street light. When it is determined that there is insufficient control precision, it predicts the processing time required for the intelligent street light to switch and then calculates the control precision of the intelligent street light under continuous lighting switch to control and adjust the intelligent street light.

2. The intelligent road lighting control system based on multi-sensor fusion according to claim 1, characterized in that: The fusion computing unit (1) includes a fusion averaging module (11) and a utilization module (12); The fusion averaging module (11) monitors lighting, traffic flow and sound data of a specific area of ​​the smart road through multiple smart sensors distributed on the road, and merges them into smart street light status data, and then obtains the CPU utilization rate of smart street lights and the processing time of smart street light lighting switch in a specific area of ​​the smart road. The average processing time for controlling the continuous lighting switch of the smart street light is calculated based on the processing time of the smart street light lighting switch using the processing time averaging algorithm.

3. The intelligent road lighting control system based on multi-sensor fusion according to claim 2, characterized in that: The utilization module (12) uses the processing time variance algorithm to calculate the processing time variance of the intelligent street light continuous lighting switch based on the average processing time of the intelligent street light continuous lighting switch and the processing time of the intelligent street light lighting switch in the fusion averaging module (11). Using the utilization rate averaging algorithm, the average utilization rate of the controlled smart street light is calculated based on the CPU utilization rate of the smart street light in the fusion averaging module (11).

4. The intelligent road lighting control system based on multi-sensor fusion according to claim 3, characterized in that: The response speed unit (2) acquires historical data and extracts the reference time and the square of the reference time, respectively, and performs unit dimension processing on the processing time variance of the continuous lighting switch of the smart street light in the utilization module (12) and the average processing time of the continuous lighting switch of the smart street light in the fusion averaging module (11).

5. The intelligent road lighting control system based on multi-sensor fusion according to claim 4, characterized in that: The response speed unit (2) evaluates the processing performance score of the control of the intelligent street light lighting switch by controlling the average utilization rate of the intelligent street light, the dimensionless processing time variance, and the dimensionless average processing time. Set a processing performance score threshold, and use the processing performance score of the intelligent street light switch and the processing performance score threshold to determine whether there is a control response speed delay in the continuous lighting switch of the intelligent street light.

6. The intelligent road lighting control system based on multi-sensor fusion according to claim 4, characterized in that: The priority unit (3) receives the control response speed delay command for the continuous lighting switch of the smart street light from the response speed unit (2), obtains the total processing time of the smart street light lighting switch by controlling the average processing time of the continuous lighting switch of the smart street light through the fusion averaging module (11), calculates the priority of the static processing of the smart street light, and performs unit dimension processing by using the reference time in the response speed unit (2) and the processing time of the smart street light lighting switch in the fusion averaging module (11).

7. The intelligent road lighting control system based on multi-sensor fusion according to claim 6, characterized in that: The priority unit (3) uses a dynamic priority adjustment algorithm to calculate the dynamic priority of the smart street light based on the dimensionless processing time, the priority of the smart street light static processing, and the dimensionless average processing time in the response speed unit (2). Then, the smart street light processing queue is reordered through the dynamic priority adjustment of the smart street light, and the data with higher priority is processed first.

8. The intelligent road lighting control system based on multi-sensor fusion according to claim 7, characterized in that: The delay control unit (4) includes a delay judgment module (41) and a control accuracy module (42); The delay judgment module (41) receives the data command with high priority from the priority unit (3), calculates the delay ratio of the smart street light lighting switch through the dimensionless processing time in the priority unit (3), sets the delay ratio threshold, and uses the smart street light lighting switch delay ratio and the delay ratio threshold to determine whether the smart street light has insufficient control precision under continuous lighting switch conditions.

9. The intelligent road lighting control system based on multi-sensor fusion according to claim 8, characterized in that: The control precision module (42) receives the command from the delay judgment module (41) that the intelligent street light has insufficient control precision when the lighting is continuously switched on and off. It predicts the processing time required for the intelligent street light to switch on and off by using the dimensionless processing time variance and dimensionless average processing time in the response speed unit (2).

10. The intelligent road lighting control system based on multi-sensor fusion according to claim 9, characterized in that: The control accuracy module (42) uses an optimized control algorithm to calculate the control accuracy of the intelligent street light under continuous lighting switching based on the dynamic adjustment priority of the intelligent street light in the priority unit (3), the dimensionless processing time variance in the response speed unit (2), and the processing time required to predict the intelligent street light lighting switch. The intelligent streetlights are controlled and adjusted by controlling the control precision during continuous lighting switching.

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