Light environment planning management method based on traffic and pedestrian perception information

Abstract

The invention discloses a light environment planning management method based on traffic and pedestrian perception information, and belongs to the technical field of data processing methods specially suitable for the management purpose. Historical information of an intersection is collected, a traffic flow prediction model under the influence of weather and a pedestrian flow prediction model under the influence of weather are constructed, and the traffic flow prediction model and the pedestrian flow prediction model are constructed; substituting target time to obtain a traffic flow predicted value and a pedestrian flow predicted value, calculating a traffic comprehensive congestion value according to a traffic comprehensive congestion calculation strategy, collecting a light brightness value in an intersection environment, calculating a light environment brightness output value according to a light environment brightness output value calculation strategy, and calculating a light environment brightness output value according to the light environment brightness output value calculation strategy. And the brightness of the light environment module at the intersection is adjusted according to the calculated light environment brightness output value, and the output brightness of the light environment module is adjusted according to the real-time condition of the intersection, so that the safety of passing at the intersection and the intelligence of adjusting the light environment are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing methods specially suitable for management purposes, and more specifically, is a light environment planning and management method based on traffic and pedestrian perception information. Background Art

[0002] Light environment refers to the general term for the light conditions and light intensity in the surrounding environment. Light environment has an important impact on people's lives and work, including factors such as illumination, light color, light direction, and light reflection. A well-designed light environment is of great significance to people's health and comfort. An inappropriate light environment at an intersection can easily limit the sight line of traffic, reduce visibility, and easily cause traffic accidents.

[0003] The prior art lacks a traffic prediction model constructed according to historical information of intersections, and then adjusting the brightness of the intersection light environment in combination with the actual intersection light brightness value. For example, a Chinese patent with application publication number CN116782464A discloses an adjustable lighting system based on ambient light and traffic information. The invention relates to a lighting system, specifically to an adjustable lighting system based on ambient light and traffic information, including a control unit installed inside a roadside distributed lighting device, the control unit acquires a surrounding environment image through a first image acquisition module, and uses an ambient light intensity detection module to detect the ambient light intensity using the surrounding environment image; the control unit detects the road surface through a road detection module, and uses an echo signal processing module to process the echo signal obtained by the road detection module, the control unit acquires distance information and speed information of multiple detection points based on the echo signal processing result through a detection point information acquisition module, and uses a point trace determination module to determine a valid point trace based on the echo signal processing result.

[0004] In order to adjust the output brightness of the light environment module according to the real-time situation of the intersection, improve the safety of traffic at the intersection and adjust the light environment intelligently, the present invention designs a light environment planning and management method based on traffic and pedestrian perception information. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention proposes a light environment planning and management method based on traffic and pedestrian perception information. The present invention collects historical information of intersections, constructs a vehicle flow prediction model under the influence of weather and a pedestrian flow prediction model under the influence of weather, substitutes the target time to obtain the vehicle flow prediction value and the pedestrian flow prediction value, calculates the comprehensive traffic congestion value according to the comprehensive traffic congestion calculation strategy, collects the light brightness value in the intersection environment, calculates the light environment brightness output value according to the light environment brightness output value calculation strategy, adjusts the brightness of the intersection light environment module according to the calculated light environment brightness output value, and adjusts the output brightness of the light environment module according to the real-time situation of the intersection, which helps to improve the safety of traffic at the intersection and the intelligence of adjusting the light environment.

[0006] To achieve the above objectives, the light environment planning and management method based on traffic and pedestrian perception information provides the following technical solutions: The light environment planning and management method based on traffic and pedestrian perception information includes the following specific steps: Step 1: Collect historical information of intersections, build a traffic flow prediction model under the influence of weather, and substitute the target time to obtain the traffic flow prediction value; Step 2: Collect historical information of intersections, build a pedestrian flow prediction model under the influence of weather, and substitute the target time to obtain the pedestrian flow prediction value; Step 3: Substituting the predicted vehicle flow and pedestrian flow at the target time into the traffic comprehensive congestion calculation strategy to calculate the traffic comprehensive congestion value; Step 4: Collect the light brightness value in the intersection environment, substitute the light brightness value and the comprehensive traffic congestion value in the intersection environment into the light environment brightness output value calculation strategy to calculate the light environment brightness output value, and adjust the brightness of the intersection light environment module according to the calculated light environment brightness output value.

[0007] Specifically, the step 1 includes the following specific steps: S11. Collect historical information of the intersection and store it in the vehicle information storage module, wherein the historical information of the intersection includes weather information, time and vehicle flow, wherein the weather information includes temperature, precipitation and visibility, and substitute the weather information into the bad weather value calculation strategy to calculate the bad weather value, wherein the bad weather value calculation strategy includes a bad weather value calculation formula, and the bad weather value calculation formula is: ,in, is the collected intersection temperature, is the intersection temperature threshold, is the collected precipitation at the intersection, is the precipitation threshold at the intersection, is the collected intersection visibility, is the intersection visibility threshold, is the intersection temperature ratio coefficient, is the ratio of precipitation at the intersection, is the intersection visibility ratio, and ; It should be noted that the intersection temperature is monitored by a temperature sensor, the intersection precipitation is monitored by a rain and snow sensor, and the intersection visibility is monitored by an infrared sensor; the intersection temperature threshold, intersection precipitation threshold, and intersection visibility threshold are determined according to the needs of the scene; S12, dividing the intersection historical information data set into two subsets, wherein the first 85% is a training data set and the second 15% is a test data set, inputting the first 85% of the training data set into the traffic flow neural network prediction model under the influence of weather for training, obtaining an initial traffic flow neural network prediction model under the influence of weather, and then using the second 15% of the test data set to test the initial traffic flow neural network prediction model under the influence of weather, and outputting the traffic flow neural network prediction model under the influence of weather with the highest accuracy in judging traffic flow; S13. The output strategy formula of a specific neuron in the traffic flow neural network prediction model under the influence of weather is: ,in, The neural network prediction model of traffic flow under weather conditions layer The output of the neuron, The neural network prediction model of traffic flow under weather conditions Layer of neurons and layer The connection weights of the neurons, The neural network prediction model of traffic flow under weather conditions Layer of neurons The output, The neural network prediction model of traffic flow under weather conditions Layer of neurons and layer The linear relationship between the term neurons is biased, is the Sigmoid activation function; S14, the traffic flow neural network prediction model under the weather influence with the highest traffic flow judgment accuracy is judged by the traffic flow prediction error value, and the traffic flow prediction error value calculation formula is: ,in, Indicates that under the influence of weather The weight coefficient of the traffic flow prediction value of the cycle, , is the prediction period of traffic flow, which is half an hour. Indicates that under the influence of weather The traffic flow prediction value of the cycle, The traffic volume under the influence of weather is Cycle A neural network prediction model for traffic flow under weather conditions The forecast error of the period; S15, inputting the target time and the severe weather value at the target time into the vehicle flow neural network prediction model under the influence of weather to obtain the vehicle flow prediction value at the target time.

[0008] Specifically, the step 2 includes the following specific steps: S21, collecting historical information of the intersection and storing it in a pedestrian information storage module, wherein the historical information of the intersection includes weather information, time and pedestrian flow, dividing the historical information data set of the intersection into two subsets, wherein the first 85% is a training data set and the second 15% is a test data set, inputting the first 85% of the training data set into a neural network prediction model for pedestrian flow under the influence of weather for training, obtaining an initial neural network prediction model for pedestrian flow under the influence of weather, and then using the second 15% of the test data set to test the initial neural network prediction model for pedestrian flow under the influence of weather, and outputting a neural network prediction model for pedestrian flow under the influence of weather with the highest accuracy in judging pedestrian flow; S22. The output strategy formula of a specific neuron in the pedestrian flow neural network prediction model under the influence of weather is: ,in, The neural network prediction model of pedestrian flow under weather conditions layer The output of the neuron, The neural network prediction model of pedestrian flow under weather conditions Layer of neurons and layer The connection weights of the neurons, The neural network prediction model of pedestrian flow under weather conditions Layer of neurons The output, The neural network prediction model of pedestrian flow under weather conditions Layer of neurons and layer The linear relationship between the term neurons is biased, is the Sigmoid activation function; S23, the pedestrian flow neural network prediction model under the weather influence with the highest accuracy in judging pedestrian flow is judged by the pedestrian flow prediction error value, and the pedestrian flow prediction error value calculation formula is: ,in, Indicates that under the influence of weather The weight coefficient of the predicted value of passenger flow in each period, , The forecast period for human traffic is Indicates that under the influence of weather The predicted value of passenger flow in a cycle, The flow of people under the influence of weather is Cycle A neural network prediction model for pedestrian flow under weather conditions The forecast error of the period; S24, inputting the target time and the severe weather value at the target time into the pedestrian flow neural network prediction model under the influence of weather to obtain the pedestrian flow prediction value at the target time.

[0009] Specifically, the step three includes the following specific steps: Substitute the obtained vehicle flow prediction value and the passenger flow prediction value at the target time into the traffic comprehensive congestion calculation strategy to calculate the traffic comprehensive congestion value. The traffic comprehensive congestion calculation strategy includes a traffic comprehensive congestion calculation formula, which is: ,in, is the traffic flow prediction value under the influence of weather, is the predicted value of passenger flow under the influence of weather. is the coefficient of traffic volume under the influence of weather, is the coefficient of passenger flow under the influence of weather, and .

[0010] Specifically, the step 4 includes the following specific steps: S41, collecting the light brightness value in the intersection environment, substituting the light brightness value and the comprehensive traffic congestion value in the intersection environment into the light environment brightness output value calculation strategy to calculate the light environment brightness output value, the light environment brightness output value calculation strategy includes a light environment brightness output value calculation formula, the light environment brightness output value calculation formula is: ,in, is the brightness value of the intersection, is the set rated brightness; It should be noted here that the light brightness value in the intersection environment is monitored by a light intensity sensor; S42: adjusting the brightness of the intersection light environment module according to the calculated light environment brightness output value.

[0011] It should be noted here that , , , The value of the set rated brightness is as follows: select 5000 groups of historical information of intersections, obtain weather, time, vehicle flow and pedestrian flow, calculate the impact of vehicle flow under weather influence and pedestrian flow under weather influence on the comprehensive traffic congestion value, substitute the traffic congestion value and intersection brightness value into the calculation formula of the comprehensive value of light environment brightness to calculate the comprehensive value of light environment brightness, employ 500 experts in this field to score the brightness of the light environment module required by the intersection, import the comprehensive value of light environment brightness and the brightness value obtained by scoring into the relevant fitting software, and output a group of values ​​that meet the highest accuracy of the brightness value obtained by scoring. , , , and the set rated brightness value.

[0012] The light environment planning and management system based on traffic and pedestrian perception information is implemented based on the above-mentioned light environment planning and management method based on traffic and pedestrian perception information, and includes: An information collection module is used to collect historical information of the intersection and the brightness value of the intersection, wherein the historical information of the intersection includes weather, time, vehicle flow and pedestrian flow; The information storage module is used to store historical information of the intersection, and is divided into a vehicle information storage module and a pedestrian information storage module; The traffic flow neural network prediction module is used to obtain historical information of the intersection, build a traffic flow prediction model under the influence of weather, and substitute the target time to obtain the traffic flow prediction value; The pedestrian flow neural network prediction module is used to obtain historical information of the intersection, build a pedestrian flow prediction model under the influence of weather, and substitute the target time to obtain the pedestrian flow prediction value; A traffic comprehensive congestion value module is used to substitute the obtained vehicle flow prediction value at the target time and the passenger flow prediction value at the target time into the traffic comprehensive congestion calculation strategy to calculate the traffic comprehensive congestion value; A light environment brightness comprehensive value calculation module is used to obtain the light brightness value in the intersection environment, substitute the light brightness value in the intersection environment and the comprehensive traffic congestion value into the light environment brightness output value calculation strategy to calculate the light environment brightness output value; The control module is used to control the operation of the information collection module, the information storage module, the vehicle flow neural network prediction module, the pedestrian flow neural network prediction module, the traffic comprehensive congestion value module and the light environment brightness comprehensive value calculation module.

[0013] An electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes the above-mentioned light environment planning and management method based on traffic and pedestrian perception information by calling the computer program stored in the memory.

[0014] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned light environment planning and management method based on traffic and pedestrian perception information.

[0015] The beneficial effects of the present invention are as follows: collecting historical information of intersections, constructing a vehicle flow prediction model under the influence of weather and a pedestrian flow prediction model under the influence of weather, substituting the target time to obtain a vehicle flow prediction value and a pedestrian flow prediction value, calculating a comprehensive traffic congestion value according to a comprehensive traffic congestion calculation strategy, collecting light brightness values ​​in the intersection environment, calculating a light environment brightness output value according to a light environment brightness output value calculation strategy, adjusting the brightness of a light environment module at the intersection according to the calculated light environment brightness output value, and adjusting the output brightness of the light environment module according to the real-time situation at the intersection, which helps to improve the safety of traffic at the intersection and the intelligence of adjusting the light environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of the light environment planning and management method based on traffic and pedestrian perception information of the present invention; Figure 2 It is a schematic diagram of the overall framework of the light environment planning and management system based on traffic and pedestrian perception information of the present invention. DETAILED DESCRIPTION

[0017] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present invention will be more comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0018] Example 1 See also Figure 1 The present invention provides an embodiment: a light environment planning and management method based on traffic and pedestrian perception information, which includes the following specific steps: Step 1: Collect historical information of intersections, build a traffic flow prediction model under the influence of weather, and substitute the target time to obtain the traffic flow prediction value; In this embodiment, step one includes the following specific steps: S11. Collect historical information of the intersection and store it in the vehicle information storage module. The historical information of the intersection includes weather information, time and traffic volume. The weather information includes temperature, precipitation and visibility. Substitute the weather information into the bad weather value calculation strategy to calculate the bad weather value. The bad weather value calculation strategy includes a bad weather value calculation formula. The bad weather value calculation formula is: ,in, is the collected intersection temperature, is the intersection temperature threshold, is the collected precipitation at the intersection, is the precipitation threshold at the intersection, is the collected intersection visibility, is the intersection visibility threshold, is the intersection temperature ratio coefficient, is the ratio of precipitation at the intersection, is the intersection visibility ratio, and ; It should be noted here that the temperature at the intersection is monitored by a temperature sensor, the precipitation at the intersection is monitored by a rain and snow sensor, and the visibility at the intersection is monitored by an infrared sensor; the intersection temperature threshold, intersection precipitation threshold and intersection visibility threshold here are determined according to the needs of the scene. Changes in temperature will affect the condition of the road surface. For example, low temperatures may cause icing, increasing the risk of vehicle driving; high temperatures may cause the road surface to become soft, affecting the stability of the vehicle; an increase in precipitation will reduce the friction of the road surface and increase the possibility of vehicle skidding, especially in rainy and snowy weather, which will increase the chance of traffic accidents; reduced visibility will affect the driver's field of vision and increase the possibility of accidents such as rear-end collisions.

[0019] S12, dividing the intersection historical information data set into two subsets, wherein the first 85% is a training data set and the second 15% is a test data set, inputting the first 85% of the training data set into the traffic flow neural network prediction model under the influence of weather for training, obtaining an initial traffic flow neural network prediction model under the influence of weather, and then using the second 15% of the test data set to test the initial traffic flow neural network prediction model under the influence of weather, and outputting the traffic flow neural network prediction model under the influence of weather with the highest accuracy in judging traffic flow; S13. The output strategy formula of a specific neuron in the traffic flow neural network prediction model under weather influence is: ,in, The neural network prediction model of traffic flow under weather conditions layer The output of the neuron, The neural network prediction model of traffic flow under weather conditions Layer of neurons and layer The connection weights of the neurons, The neural network prediction model of traffic flow under weather conditions Layer of neurons The output, The neural network prediction model of traffic flow under weather conditions Layer of neurons and layer The linear relationship between the term neurons is biased, is the Sigmoid activation function; S14. The traffic flow neural network prediction model under the weather influence with the highest traffic flow judgment accuracy is judged by the traffic flow prediction error value. The traffic flow prediction error value calculation formula is: ,in, Indicates that under the influence of weather The weight coefficient of the traffic flow prediction value of the cycle, , is the prediction period of traffic flow, which is half an hour. Indicates that under the influence of weather The traffic flow prediction value of the cycle, The traffic volume under the influence of weather is Cycle A neural network prediction model for traffic flow under weather conditions The forecast error of the period; S15, inputting the target time and the severe weather value at the target time into the vehicle flow neural network prediction model under the influence of weather to obtain the vehicle flow prediction value at the target time.

[0020] Step 2: Collect historical information of intersections, build a pedestrian flow prediction model under the influence of weather, and substitute the target time to obtain the pedestrian flow prediction value; In this embodiment, step 2 includes the following specific steps: S21. Collect historical information of the intersection and store it in a pedestrian information storage module. The historical information of the intersection includes weather information, time, and pedestrian flow. The historical information data set of the intersection is divided into two subsets, of which the first 85% is a training data set and the second 15% is a test data set. The first 85% of the training data set is input into a neural network prediction model for pedestrian flow under weather influence for training to obtain an initial neural network prediction model for pedestrian flow under weather influence. The second 15% of the test data set is used to test the initial neural network prediction model for pedestrian flow under weather influence, and output a neural network prediction model for pedestrian flow under weather influence with the highest accuracy in judging pedestrian flow. S22. The output strategy formula of a specific neuron in the neural network prediction model of pedestrian flow under weather influence is: ,in, The neural network prediction model of pedestrian flow under weather conditions layer The output of the neuron, The neural network prediction model of pedestrian flow under weather conditions Layer of neurons and layer The connection weights of the neurons, The neural network prediction model of pedestrian flow under weather conditions Layer of neurons The output, The neural network prediction model of pedestrian flow under weather conditions Layer of neurons and layer The linear relationship between the term neurons is biased, is the Sigmoid activation function; S23. The pedestrian flow neural network prediction model under the weather with the highest pedestrian flow judgment accuracy is judged by the pedestrian flow prediction error value. The pedestrian flow prediction error value calculation formula is: ,in, Indicates that under the influence of weather The weight coefficient of the predicted value of passenger flow in each period, , The forecast period for human traffic is Indicates that under the influence of weather The predicted value of passenger flow in a cycle, The flow of people under the influence of weather is Cycle A neural network prediction model for pedestrian flow under weather conditions The forecast error of the period; S24, inputting the target time and the severe weather value at the target time into the pedestrian flow neural network prediction model under the influence of weather to obtain the pedestrian flow prediction value at the target time.

[0021] Step 3: Substituting the predicted vehicle flow and pedestrian flow at the target time into the traffic comprehensive congestion calculation strategy to calculate the traffic comprehensive congestion value; In this embodiment, step three includes the following specific steps: Substitute the predicted value of vehicle flow at the target time and the predicted value of passenger flow at the target time into the traffic comprehensive congestion calculation strategy to calculate the traffic comprehensive congestion value. The traffic comprehensive congestion calculation strategy includes a traffic comprehensive congestion calculation formula, which is: ,in, is the traffic flow prediction value under the influence of weather, is the predicted value of passenger flow under the influence of weather. is the coefficient of traffic volume under the influence of weather, is the coefficient of passenger flow under the influence of weather, and .

[0022] Step 4: Collect the light brightness value in the intersection environment, substitute the light brightness value and the comprehensive traffic congestion value in the intersection environment into the light environment brightness output value calculation strategy to calculate the light environment brightness output value, and adjust the brightness of the intersection light environment module according to the calculated light environment brightness output value.

[0023] It should be noted here that the light brightness value in the intersection environment is monitored by an illuminance sensor. In the intersection environment, the level of light brightness will affect the driver's visual ability and judgment ability, thereby affecting the safety and smoothness of traffic; when the light brightness is too low, the driver's visual ability will be limited, and it will be difficult to clearly see the signs, traffic lights, other vehicles and pedestrians on the road, which is prone to traffic accidents; when the light brightness is too high, it will cause glare, affect the driver's line of sight, cause eye fatigue and distraction, and also increase the risk of traffic accidents; therefore, when designing and managing the intersection light environment, it is necessary to reasonably control the brightness value.

[0024] In this embodiment, step 4 includes the following specific steps: S41, collecting the light brightness value in the intersection environment, substituting the light brightness value and the comprehensive traffic congestion value in the intersection environment into the light environment brightness output value calculation strategy to calculate the light environment brightness output value, the light environment brightness output value calculation strategy includes a light environment brightness output value calculation formula, and the light environment brightness output value calculation formula is: ,in, is the brightness value of the intersection, is the set rated brightness; S42: adjusting the brightness of the intersection light environment module according to the calculated light environment brightness output value.

[0025] It should be noted here that , , , The value of the set rated brightness is as follows: select 5000 groups of historical information of intersections, obtain weather, time, vehicle flow and pedestrian flow, calculate the impact of vehicle flow under weather influence and pedestrian flow under weather influence on the comprehensive traffic congestion value, substitute the traffic congestion value and intersection brightness value into the calculation formula of the comprehensive value of light environment brightness to calculate the comprehensive value of light environment brightness, employ 500 experts in this field to score the brightness of the light environment module required by the intersection, import the comprehensive value of light environment brightness and the brightness value obtained by scoring into the relevant fitting software, and output a group of values ​​that meet the highest accuracy of the brightness value obtained by scoring. , , , and the set rated brightness value.

[0026] Example 2 See also Figure 2 , a light environment planning and management system based on traffic and pedestrian perception information, which is implemented based on the above-mentioned light environment planning and management method based on traffic and pedestrian perception information, and includes: An information collection module is used to collect historical information of intersections and light brightness values ​​of intersections. The historical information of intersections includes weather, time, vehicle flow and pedestrian flow; The information storage module is used to store historical information of the intersection, and is divided into a vehicle information storage module and a pedestrian information storage module; The traffic flow neural network prediction module is used to obtain historical information of the intersection, build a traffic flow prediction model under the influence of weather, and substitute the target time to obtain the traffic flow prediction value; The pedestrian flow neural network prediction module is used to obtain historical information of the intersection, build a pedestrian flow prediction model under the influence of weather, and substitute the target time to obtain the pedestrian flow prediction value; A traffic comprehensive congestion value module is used to substitute the obtained vehicle flow prediction value at the target time and the passenger flow prediction value at the target time into the traffic comprehensive congestion calculation strategy to calculate the traffic comprehensive congestion value; A light environment brightness comprehensive value calculation module is used to obtain the light brightness value in the intersection environment, substitute the light brightness value in the intersection environment and the comprehensive traffic congestion value into the light environment brightness output value calculation strategy to calculate the light environment brightness output value; The control module is used to control the operation of the information collection module, the information storage module, the vehicle flow neural network prediction module, the pedestrian flow neural network prediction module, the traffic comprehensive congestion value module and the light environment brightness comprehensive value calculation module.

[0027] Example 3 This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes the above-mentioned light environment planning and management method based on traffic and pedestrian perception information by calling the computer program stored in the memory.

[0028] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, and the computer program is loaded and executed by the processor to implement the light environment planning and management method based on traffic and pedestrian perception information provided by the above method embodiment, which includes the following specific steps: collecting historical information of intersections, constructing a vehicle flow prediction model under the influence of weather and a pedestrian flow prediction model under the influence of weather, substituting the target time to obtain a vehicle flow prediction value and a pedestrian flow prediction value, calculating a comprehensive traffic congestion value according to a comprehensive traffic congestion calculation strategy, collecting light brightness values ​​in the intersection environment, calculating a light environment brightness output value according to a light environment brightness output value calculation strategy, and adjusting the brightness of the intersection light environment module according to the calculated light environment brightness output value.

[0029] Example 4 This embodiment provides a computer-readable storage medium storing instructions, which, when a computer program is executed on a computer device, enables the computer device to execute the above-mentioned light environment planning and management method based on traffic and pedestrian perception information.

[0030] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0031] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0032] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0033] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

Claims

1. A light environment planning and management method based on traffic and pedestrian perception information, characterized in that: It includes the following specific steps: Step 1: Collect historical information of intersections, build a traffic flow prediction model under the influence of weather, and substitute the target time to obtain the traffic flow prediction value; Step 2: Collect historical information of intersections, build a pedestrian flow prediction model under the influence of weather, and substitute the target time to obtain the pedestrian flow prediction value; Step 3: Substituting the predicted vehicle flow and pedestrian flow at the target time into the traffic comprehensive congestion calculation strategy to calculate the traffic comprehensive congestion value; Step 4: Collect the light brightness value in the intersection environment, substitute the light brightness value and the comprehensive traffic congestion value in the intersection environment into the light environment brightness output value calculation strategy to calculate the light environment brightness output value, and adjust the brightness of the intersection light environment module according to the calculated light environment brightness output value.

2. The light environment planning and management method based on traffic and pedestrian perception information as claimed in claim 1, characterized in that: The step 1 includes the following specific steps: S11. Collect historical information of the intersection and store it in the vehicle information storage module, wherein the historical information of the intersection includes weather information, time and vehicle flow, wherein the weather information includes temperature, precipitation and visibility, and substitute the weather information into the bad weather value calculation strategy to calculate the bad weather value, wherein the bad weather value calculation strategy includes a bad weather value calculation formula, and the bad weather value calculation formula is: ,in, is the collected intersection temperature, is the intersection temperature threshold, is the collected precipitation at the intersection, is the precipitation threshold at the intersection, is the collected intersection visibility, is the intersection visibility threshold, is the intersection temperature ratio coefficient, is the ratio of precipitation at the intersection, is the intersection visibility ratio, and ; S12, dividing the intersection historical information data set into two subsets, wherein the first 85% is a training data set and the second 15% is a test data set, inputting the first 85% of the training data set into the traffic flow neural network prediction model under the influence of weather for training, obtaining an initial traffic flow neural network prediction model under the influence of weather, and then using the second 15% of the test data set to test the initial traffic flow neural network prediction model under the influence of weather, and outputting the traffic flow neural network prediction model under the influence of weather with the highest accuracy in judging traffic flow; S13. The output strategy formula of a specific neuron in the traffic flow neural network prediction model under the influence of weather is: ,in, The neural network prediction model of traffic flow under weather conditions layer The output of the neuron, The neural network prediction model of traffic flow under weather conditions Layer of neurons and layer The connection weights of the neurons, The neural network prediction model of traffic flow under weather conditions Layer of neurons The output, The neural network prediction model of traffic flow under weather conditions Layer of neurons and layer The linear relationship between the term neurons is biased, is the Sigmoid activation function.

3. The light environment planning and management method based on traffic and pedestrian perception information as claimed in claim 2, characterized in that: The step 1 also includes the following specific steps: S14, the traffic flow neural network prediction model under the weather influence with the highest traffic flow judgment accuracy is judged by the traffic flow prediction error value, and the traffic flow prediction error value calculation formula is: ,in, Indicates that under the influence of weather The weight coefficient of the traffic flow prediction value of the cycle, , is the prediction period of traffic flow, Indicates that under the influence of weather The traffic flow prediction value of the cycle, The traffic volume under the influence of weather is The actual value of the cycle, The neural network prediction model for traffic flow under weather influence is The forecast error of the period; S15, inputting the target time and the severe weather value at the target time into the vehicle flow neural network prediction model under the influence of weather to obtain the vehicle flow prediction value at the target time.

4. The light environment planning and management method based on traffic and pedestrian perception information as claimed in claim 3, characterized in that: The step 2 includes the following specific steps: S21, collecting historical information of the intersection and storing it in a pedestrian information storage module, wherein the historical information of the intersection includes weather information, time and pedestrian flow, dividing the historical information data set of the intersection into two subsets, wherein the first 85% is a training data set and the second 15% is a test data set, inputting the first 85% of the training data set into a neural network prediction model for pedestrian flow under the influence of weather for training, obtaining an initial neural network prediction model for pedestrian flow under the influence of weather, and then using the second 15% of the test data set to test the initial neural network prediction model for pedestrian flow under the influence of weather, and outputting a neural network prediction model for pedestrian flow under the influence of weather with the highest accuracy in judging pedestrian flow; S22. The output strategy formula of a specific neuron in the pedestrian flow neural network prediction model under the influence of weather is: ,in, The neural network prediction model of pedestrian flow under weather conditions layer The output of the neuron, The neural network prediction model of pedestrian flow under weather conditions Layer of neurons and layer The connection weights of the neurons, The neural network prediction model of pedestrian flow under weather conditions Layer of neurons The output, The neural network prediction model of pedestrian flow under weather conditions Layer of neurons and layer The linear relationship between the term neurons is biased, is the Sigmoid activation function; S23, the pedestrian flow neural network prediction model under the weather influence with the highest accuracy in judging pedestrian flow is judged by the pedestrian flow prediction error value, and the pedestrian flow prediction error value calculation formula is: ,in, Indicates that under the influence of weather The weight coefficient of the predicted value of passenger flow in each period, , The forecast period for human traffic is Indicates that under the influence of weather The predicted value of passenger flow in a cycle, The flow of people under the influence of weather is Cycle A neural network prediction model for pedestrian flow under weather conditions The forecast error of the period; S24, inputting the target time and the severe weather value at the target time into the pedestrian flow neural network prediction model under the influence of weather to obtain the pedestrian flow prediction value at the target time.

5. The light environment planning and management method based on traffic and pedestrian perception information as claimed in claim 4, characterized in that: The step three includes the following specific steps: Substitute the obtained vehicle flow prediction value and the passenger flow prediction value at the target time into the traffic comprehensive congestion calculation strategy to calculate the traffic comprehensive congestion value. The traffic comprehensive congestion calculation strategy includes a traffic comprehensive congestion calculation formula, which is: ,in, is the traffic flow prediction value under the influence of weather, is the predicted value of passenger flow under the influence of weather. is the coefficient of traffic volume under the influence of weather, is the coefficient of passenger flow under the influence of weather, and .

6. The light environment planning and management method based on traffic and pedestrian perception information as claimed in claim 5, characterized in that: The step 4 includes the following specific steps: S41, collecting the light brightness value in the intersection environment, substituting the light brightness value and the comprehensive traffic congestion value in the intersection environment into the light environment brightness output value calculation strategy to calculate the light environment brightness output value, the light environment brightness output value calculation strategy includes a light environment brightness output value calculation formula, the light environment brightness output value calculation formula is: ,in, is the brightness value of the intersection, is the set rated brightness; S42: adjusting the brightness of the intersection light environment module according to the calculated light environment brightness output value.

7. A light environment planning and management system based on traffic and pedestrian perception information, which is implemented based on the light environment planning and management method based on traffic and pedestrian perception information as claimed in any one of claims 1 to 6, characterized in that: It includes: An information collection module is used to collect historical information of the intersection and the brightness value of the intersection, wherein the historical information of the intersection includes weather, time, vehicle flow and pedestrian flow; The information storage module is used to store historical information of the intersection, and is divided into a vehicle information storage module and a pedestrian information storage module; The traffic flow neural network prediction module is used to obtain historical information of the intersection, build a traffic flow prediction model under the influence of weather, and substitute the target time to obtain the traffic flow prediction value; The pedestrian flow neural network prediction module is used to obtain historical information of the intersection, build a pedestrian flow prediction model under the influence of weather, and substitute the target time to obtain the pedestrian flow prediction value; A traffic comprehensive congestion value module is used to substitute the obtained vehicle flow prediction value at the target time and the passenger flow prediction value at the target time into the traffic comprehensive congestion calculation strategy to calculate the traffic comprehensive congestion value; A light environment brightness comprehensive value calculation module is used to obtain the light brightness value in the intersection environment, substitute the light brightness value in the intersection environment and the comprehensive traffic congestion value into the light environment brightness output value calculation strategy to calculate the light environment brightness output value; The control module is used to control the operation of the information collection module, the information storage module, the vehicle flow neural network prediction module, the pedestrian flow neural network prediction module, the traffic comprehensive congestion value module and the light environment brightness comprehensive value calculation module.

8. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program that can be called by the processor, and the processor executes the light environment planning and management method based on traffic and pedestrian perception information as described in any one of claims 1 to 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the light environment planning and management method based on traffic and pedestrian perception information as described in any one of claims 1 to 6.

Images