CN-121982918-A - Multifunctional traffic baton function test method and system based on intelligent sensor

CN121982918ACN 121982918 ACN121982918 ACN 121982918ACN-121982918-A

Abstract

The invention discloses a multifunctional traffic baton function test method and system based on an intelligent sensor, and relates to the field of data processing. The method comprises the steps of extracting a first historical record in historical ambient light intensity records, obtaining an ambient light intensity prediction model, integrating the ambient light intensity prediction model into a traffic guidance rod, obtaining real-time ambient light influence characteristic parameters, inputting the real-time ambient light influence characteristic parameters into the ambient light intensity prediction model to obtain real-time predicted ambient light intensity, obtaining actual ambient light intensity of the traffic guidance rod, and comparing the actual ambient light intensity with the actual ambient light intensity to obtain an ambient light intensity difference. The method solves the technical problems that the traditional traffic baton is greatly influenced by weather conditions, cannot meet traffic command requirements under complex environments, cannot automatically adjust brightness or color according to changes of ambient light intensity, and is low in intelligent degree, and achieves more efficient traffic command technical effects by evaluating the intelligent level of the traffic baton.

Inventors

XIONG ZHENGFANG

Assignees

南通汉诺安警用电子科技开发有限公司

Dates

Publication Date: 20260505
Application Date: 20251223

Claims (9)

1. The multifunctional traffic baton function test method based on the intelligent sensor is characterized by comprising the following steps of: Extracting a first history record in a history ambient light intensity record, the first history record comprising a first history ambient light influencing parameter and a first history ambient light intensity; Performing machine learning on the first historical ambient light influence parameter and a first data set constructed by the first historical ambient light intensity to obtain an ambient light intensity prediction model, and integrating the ambient light intensity prediction model into an traffic baton; The traffic baton is subjected to multidimensional environmental light influence characteristic monitoring to obtain real-time environmental light influence characteristic parameters, and the real-time environmental light influence characteristic parameters are input into the environmental light intensity prediction model to obtain real-time predicted environmental light intensity; acquiring the actual ambient light intensity of the traffic baton, and comparing to obtain the ambient light intensity difference between the actual ambient light intensity and the real-time predicted ambient light intensity; the real-time prediction environment light intensity is used for carrying out light adaptability prediction compensation control on the traffic guidance rod, and the environment light intensity difference is used for representing the control precision of the light adaptability prediction compensation control of the traffic guidance rod.
2. The intelligent sensor-based multifunctional traffic guidance bar function test method according to claim 1, comprising: Reading a preset influence dimension, and traversing the first historical ambient light influence parameters based on the preset influence dimension to obtain a first historical traversing result, wherein the first historical traversing result comprises a plurality of dimension parameter sets of a plurality of influence dimensions; Matching a first set of dimension parameters of a first influencing dimension among the plurality of sets of dimension parameters, the first influencing dimension being any one of the plurality of influencing dimensions; weighting the normalized first dimension parameter set to obtain a first history dimension influence index of the first influence dimension; the first data set is constructed based on the first historical dimension impact index and the first historical ambient light intensity.
3. The intelligent sensor-based multi-functional traffic guidance rod function testing method of claim 2, wherein the predetermined impact dimensions include a natural dimension including solar elevation, solar azimuth, and light intensity, an environmental dimension including topography and vegetation coverage, and an artificial dimension including building elevation, artificial illumination intensity, and air pollution index.
4. The intelligent sensor-based multifunctional traffic guidance bar function test method according to claim 1, comprising: Acquiring a target control performance test time period, and extracting a first target time in the target control performance test time period; the real-time predicted ambient light intensity comprises real-time and predicted intensity; Generating an ambient light intensity prediction timing based on the real-time and the predicted intensity; the actual ambient light intensity includes the real-time and an actual intensity, and an ambient light intensity timing is generated based on the real-time and the actual intensity; traversing and matching in the environment light intensity prediction time sequence and the environment light intensity time sequence based on the first target time in sequence to respectively obtain first target predicted intensity and first target actual intensity; And (3) recording the intensity deviation between the first target predicted intensity and the first target actual intensity as the ambient light intensity difference.
5. The intelligent sensor-based multifunctional traffic guidance bar function test method according to claim 4, further comprising: Judging whether the first target time is included in the ambient light intensity prediction time sequence; If the environment light intensity prediction time sequence is not included, a first trend analysis instruction is sent out; performing trend analysis on the environment light intensity prediction time sequence based on the first trend analysis instruction to obtain a first target prediction result; The first target prediction result is recorded as the first target prediction intensity at the first target time.
6. The intelligent sensor-based multifunctional traffic guidance bar function test method according to claim 4, further comprising: Judging whether the first target time is included in the ambient light intensity time sequence; If the environment light intensity time sequence is not included, a second trend analysis instruction is sent out; performing trend analysis on the environment light intensity time sequence based on the second trend analysis instruction to obtain a second target prediction result; The second target prediction result is recorded as the first target actual intensity at the first target time.
7. The intelligent sensor-based multifunctional traffic guidance bar function test method according to claim 5, comprising: Performing regression fit on the environment light intensity prediction scatter diagram of the environment light intensity prediction time sequence based on the first trend analysis instruction to obtain a first fit polynomial; And inputting the first target time into the first fitting polynomial to obtain the first target prediction result.
8. The intelligent sensor-based multifunctional traffic guidance bar function test method according to claim 6, comprising: Performing multi-domain feature collection on the ambient light intensity time sequence based on the second trend analysis instruction to obtain a first multi-domain feature set; taking the first multi-domain feature set as input information of an intensity prediction model to obtain an output result, and marking the output result as the second target prediction result; The intensity prediction model is an intelligent prediction model trained based on the neural network principle.
9. The intelligent sensor-based multifunctional traffic guidance rod function test system is characterized by comprising the steps for implementing the intelligent sensor-based multifunctional traffic guidance rod function test method according to any one of claims 1 to 8, wherein the intelligent sensor-based multifunctional traffic guidance rod function test system comprises: The data extraction module is used for extracting a first historical record in the historical ambient light intensity records, wherein the first historical record comprises a first historical ambient light influence parameter and a first historical ambient light intensity; the model integration module is used for performing machine learning on the first historical ambient light influence parameter and the first data set formed by the first historical ambient light intensity to obtain an ambient light intensity prediction model, and integrating the ambient light intensity prediction model into the traffic baton; The intensity prediction module is used for carrying out multidimensional environmental light influence characteristic monitoring on the traffic baton to obtain real-time environmental light influence characteristic parameters, and inputting the real-time environmental light influence characteristic parameters into the environmental light intensity prediction model to obtain real-time predicted environmental light intensity; the intensity difference calculation module is used for obtaining the actual environment light intensity of the traffic baton and comparing the actual environment light intensity with the environment light intensity difference of the real-time predicted environment light intensity; the real-time prediction environment light intensity is used for carrying out light adaptability prediction compensation control on the traffic guidance rod, and the environment light intensity difference is used for representing the control precision of the light adaptability prediction compensation control of the traffic guidance rod.

Description

Multifunctional traffic baton function test method and system based on intelligent sensor Technical Field The application relates to the technical field of data processing, in particular to a multifunctional traffic baton function test method and system based on an intelligent sensor. Background With the continuous development of urban traffic, the traffic baton is used as an important tool for traffic police to conduct traffic guidance on the road surface, and the performance of the traffic baton directly influences the efficiency and safety of traffic guidance. At present, the multifunctional traffic baton has become a main stream product in the market, and has not only basic command functions, but also various functions such as illumination, warning and the like, so as to adapt to different traffic environments. However, the traditional traffic baton has single function, can be used only under the condition of sufficient light, is greatly influenced by weather conditions, and cannot meet traffic guidance requirements under complex environments. The existing multifunctional traffic baton integrates multiple functions, but has low intelligent degree, and cannot automatically adjust brightness or color according to the change of ambient light intensity, so that the commanding effect is not ideal under certain conditions. In summary, the conventional traffic baton is greatly affected by weather conditions, cannot meet traffic guidance requirements in complex environments, cannot automatically adjust brightness or color according to changes of ambient light intensity, and has low intelligent degree. Disclosure of Invention Based on the above, it is necessary to provide a multifunctional traffic guidance rod function test method and system based on an intelligent sensor, which can solve the technical problems that the traditional traffic guidance rod is greatly influenced by weather conditions, cannot meet traffic guidance requirements in complex environments, cannot automatically adjust brightness or color according to changes of ambient light intensity, and has low intelligent degree, and evaluate the intelligent level of the traffic guidance rod, including whether the brightness or color can be automatically adjusted according to changes of ambient light intensity, and whether the traffic guidance rod can be linked with other traffic management systems, so as to realize more efficient traffic guidance. The intelligent sensor-based multifunctional traffic guidance rod function test method comprises the steps of extracting a first historical record in a historical ambient light intensity record, wherein the first historical record comprises a first historical ambient light influence parameter and a first historical ambient light intensity, performing machine learning on a first data set constructed by the first historical ambient light influence parameter and the first historical ambient light intensity to obtain an ambient light intensity prediction model, integrating the ambient light intensity prediction model into an traffic guidance rod, performing multidimensional ambient light influence characteristic monitoring on the traffic guidance rod to obtain a real-time ambient light influence characteristic parameter, inputting the real-time ambient light influence characteristic parameter into the ambient light intensity prediction model to obtain a real-time predicted ambient light intensity, obtaining the actual ambient light intensity of the traffic guidance rod, and comparing to obtain the ambient light intensity difference of the actual ambient light intensity and the real-time predicted ambient light intensity, wherein the real-time predicted ambient light intensity is used for performing light-adaptive prediction compensation control on the traffic guidance rod, and the ambient light intensity difference is used for representing the accuracy of light-adaptive prediction compensation control on the traffic guidance rod. The intelligent sensor-based multifunctional traffic guidance rod function test system comprises a data extraction module, a model integration module, an intensity prediction module and an intensity difference calculation module, wherein the data extraction module is used for extracting a first historical record in a historical environment light intensity record, the first historical record comprises a first historical environment light influence parameter and a first historical environment light intensity, the model integration module is used for carrying out machine learning on the first historical environment light influence parameter and a first data set established by the first historical environment light intensity to obtain an environment light intensity prediction model, the environment light intensity prediction model is integrated to an traffic guidance rod, the intensity prediction module is used for carrying out multidimensional environment light influence characteristic moni