CN119445849A - Intelligent traffic guiding device based on machine learning - Google Patents
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Abstract
The invention relates to the technical field of traffic guidance, in particular to an intelligent traffic guiding device based on machine learning, which comprises an intelligent system and a guiding device body, wherein the intelligent system comprises an acquisition module, an analysis module, a processing module, a learning module and a prediction module, wherein the acquisition module is used for acquiring environmental information, the analysis module is used for analyzing the cause of the environmental information to obtain an analysis result, the processing module is used for processing the environmental information to obtain a processing result, the learning module is used for creating a machine learning model and training the machine learning model according to the processing result, the prediction module is used for predicting future traffic conditions through the machine learning model to obtain a prediction result and sending the prediction result to a control module, and the control module is used for adjusting a guiding strategy of the guiding device body according to the prediction result. According to the invention, the machine learning model is built and trained, and the future traffic condition is predicted through the machine learning model, so that the traffic pressure is effectively relieved, and the road traffic efficiency is improved.
Description
Technical Field
The invention relates to the technical field of traffic guidance, in particular to an intelligent traffic guidance device based on machine learning.
Background
With the acceleration of the urban process, urban population is continuously increased, and the number of vehicles is rapidly increased, so that the problem of traffic jam is increasingly serious. Traditional traffic management means and techniques are difficult to cope with increasingly complex traffic conditions, real-time and efficient traffic scheduling cannot be realized, and machine learning techniques have been developed accordingly. Machine learning is a branch of artificial intelligence that enables computers to learn from data and improve their performance without explicit programming. The heart of machine learning is to enable a computer system to learn from data and make predictions or decisions based on learned knowledge through algorithms and statistical models.
At present, most of the existing traffic guiding devices rely on preset routes and traffic signals, cannot be dynamically adjusted according to real-time traffic conditions, and have the problem of insufficient flexibility.
In view of the foregoing, the problem of insufficient flexibility of most existing traffic guiding devices has become a major issue in the art, and therefore, an intelligent traffic guiding device based on machine learning is needed.
Disclosure of Invention
In order to solve the problems, the invention provides the intelligent traffic guiding device based on machine learning, which effectively relieves traffic pressure and improves road passing efficiency by establishing and training a machine learning model and predicting future traffic conditions through the machine learning model.
In order to achieve the purpose, the intelligent traffic guiding device based on machine learning comprises an intelligent system and a guiding device body, wherein the intelligent system comprises an acquisition module, an analysis module, a processing module, a learning module, a prediction module and a control module.
The acquisition module is used for acquiring the current environmental information and transmitting the acquired environmental information to the analysis module.
And the analysis module is used for analyzing the cause of the environmental information, obtaining an analysis result and transmitting the analysis result to the processing module.
The processing module is used for processing the acquired environmental information according to the analysis result of the analysis module to obtain a processing result and transmitting the processing result to the learning module.
And the learning module is used for creating a machine learning model and training the machine learning model according to the processing result of the processing module.
And the prediction module is used for predicting the future traffic condition through the machine learning model to obtain a prediction result and transmitting the prediction result to the control module.
And the control module is used for adjusting the guiding strategy of the guiding device body according to the prediction result.
Further, the environmental information includes the current traffic flow and the traffic flow density per unit time.
Further, when the processing module processes the environment information, the external index of the data set is collected through the Internet large model, and then clustering operation is carried out on the external index, and the specific formula of the process is as follows, D= { x 1,x2,…,xm } (1).
Where D is the external index of the dataset and x 1、x2 and x m are both the samples taken.
In the clustering process, the clustering result is compared with a reference model, the reference model is obtained by dividing results provided by experts, the evaluation results are marked as external indexes, meanwhile, the clustering result is evaluated in a direct evaluation mode, and the evaluation results are marked as internal indexes.
The external index evaluation process is as follows:
Dividing clusters given by clustering into Cluster partitioning given by reference modelThe samples were paired pairwise and the following definitions were made:。
。
。
。
。
Wherein, Representing a sampleThe categories in the cluster model are selected,Representing a sampleClass in reference model, a represents element number of SS set, SS set is contained inAre all affiliated to the same cluster and are inIn the sample pairs also belonging to the same cluster, b represents the number of elements of the set SD, the SD set being contained inAre all affiliated to the same cluster and are inSample pairs belonging to different clusters, c represents the number of elements of a DS set, and the DS set is contained inIs subject to different clusters and isSample pairs belonging to the same cluster, d represents the element number of a DD set, and the DD set is contained inIs subject to different clusters and isIn the pairs of samples belonging to different clusters, m represents the total number of samples, and i and j each represent the serial number of the sample.
。
JC is a Jacquard coefficient, the interval of JC is in the range of [0,1], and the consistency of clustering results and actual conditions is positively related to the Jacquard index.
。
Wherein RI is Rankine index, and the interval is in the range of [0,1 ].
。
The ARI is an adjusted Rankine index, the interval of the ARI is within the range of [ -1,1], and the consistency of the clustering result and the actual situation is positively correlated with the adjusted Rankine index.
The internal index evaluation formula is as follows:。
Where SSE is the sum of squares of the errors, which represents the sum of squares of the data-to-class center distance loss in the class.
。
。
Wherein SC is the contour coefficient,Representing the contour coefficient of the i-th sample,Representing a sampleAverage distance from other samples in the cluster,Representation ofThe range of SC is [ -1,1], the magnitude of which has a positive correlation with the clustering result.
。
。
。
Wherein CH is Calinski-Harabasz index,AndIs the covariance matrix of the inter-cluster and intra-cluster data,AndRepresenting the center points of cluster q and dataset D respectively,Represents the number of data sets belonging to cluster q,Representing the trace of the matrix in the inter-cluster covariance matrix,The CH index represents the ratio between the distance between clusters and the distance in the clusters, the ratio is positively correlated with the clustering effect, and k represents the number of clusters.
The technical principle of the scheme is as follows:
The acquisition module acquires the environmental information of the current environment at first, transmits the environmental information to the analysis module, analyzes the environmental information by the analysis module to obtain an analysis result, and transmits the analysis result to the processing module, and the processing module processes the environmental information according to the analysis result to obtain a processing result. The processing module sends the processing result to the learning module, and establishes a machine learning model through the learning module, and the machine learning model is trained according to the processing result. The prediction module predicts the future traffic situation through the machine learning model to obtain a prediction result, and sends the prediction result to the control module, and the control module adjusts the strategy of the guiding device body.
The adoption of the scheme has the following beneficial effects:
1. According to the invention, through real-time acquisition and analysis of the current environmental information, the change of traffic conditions can be rapidly identified, and meanwhile, the possible traffic jam or smooth situation can be predicted in advance by combining with the prediction capability of the machine learning model, so that the guiding strategy can be timely adjusted, the traffic pressure can be effectively relieved, and the road traffic efficiency can be improved.
2. According to the invention, potential traffic safety hazards, such as suddenly increased traffic flow or abnormal driving behaviors, can be timely found and pre-warned through real-time monitoring of the environmental information. By taking measures in advance, the risk of traffic accidents can be effectively reduced.
3. The invention optimizes traffic guidance, reduces waiting time and unnecessary running of vehicles, thereby reducing fuel consumption and exhaust emission, improving air quality and protecting environment.
Further, the guiding device body comprises a bottom plate, the bottom plate top fixedly connected with fixed plate, fixed plate top fixedly connected with control box, bottom wall fixedly connected with controller and alarm in the control box, the controller is used for controlling the operation of alarm, the bottom plate is along its lateral wall circumference fixedly connected with a plurality of cameras, the controller is used for controlling the operation of camera, the bottom plate top is equipped with the guide component that is used for showing guide information and is used for driving the drive assembly of guide component operation.
The camera has the beneficial effects that the camera can monitor the surrounding traffic environment in real time, so that the comprehensive understanding of traffic conditions is ensured. The controller is combined to control the camera, so that the traffic conditions of different areas can be accurately captured. Meanwhile, when the guiding device body is abnormal, the controller can rapidly start the alarm to send out a warning signal to inform a worker to go to maintenance.
Further, the transmission assembly comprises a driving frame fixedly connected to one side of the bottom plate, a first driving part is fixedly connected to the inner bottom wall of the driving frame, the controller is used for controlling the operation of the first driving part, an output shaft of the first driving part is fixedly connected with a transmission gear, the transmission gear is meshed with a first gear ring, the first gear ring is in running fit with the top of the bottom plate, a plurality of moving wheels are meshed on one side, away from the transmission gear, of the first gear ring, a second gear ring is meshed on one side, away from the first gear ring, of the moving wheels, and the second gear ring is fixedly connected with the side wall of the fixed plate.
The transmission gear, the first gear ring, the movable wheel and the second gear ring form a stable transmission chain, so that the movable wheel can move or adjust the position according to a preset track and speed.
Further, the guide assembly comprises a movable rod which is coaxially and fixedly connected with the movable wheel, a plurality of guide frames are arranged above the bottom plate, the tops of the adjacent movable rods are in running fit with the bottoms of the adjacent guide frames, and replacement assemblies for replacing different guide modes are arranged in the guide frames.
The intelligent traffic guidance system has the beneficial effects that the replacement component can conveniently replace different guidance modes according to actual needs or changes of traffic conditions, the flexibility ensures that the guidance component can adapt to different traffic environments and needs, and the pertinence and the effectiveness of traffic guidance are improved.
Further, change the subassembly and include fixed connection in the second driving piece of guide frame inside wall, the controller is used for controlling the operation of second driving piece, the coaxial fixedly connected with connecting rod of second driving piece output shaft, connecting rod keep away from second driving piece output shaft one end fixedly connected with stopper.
The guide frame inside wall normal running fit has the axis of rotation, coaxial fixedly connected with spacing wheel in the axis of rotation, and it has a plurality of spacing grooves to open to spacing wheel the equidistance, stopper and the equal sliding fit in spacing groove.
Coaxial equidistance fixedly connected with a plurality of first bevel gears on the axis of rotation, first bevel gear all meshes there is the second bevel gear, and the equal coaxial fixedly connected with switching-over pole of second bevel gear, equal fixedly connected with guide post on the switching-over pole, the one end that the second bevel gear was kept away from to the switching-over pole all with guide frame inner roof normal running fit.
The connecting rod and the limiting block can accurately move through the driving of the second driving piece and are meshed with the limiting groove on the limiting wheel. The design ensures that the replacement process of the guide mode is quick and accurate, and reduces the display error of the guide information caused by improper replacement. Meanwhile, due to the meshing relationship of the limiting block and the limiting groove and the linkage action of the rotating shaft, the first bevel gear, the second bevel gear and the reversing rod, the whole replacement process is more stable and reliable, and replacement failure caused by mechanical failure is reduced.
Further, the guide posts are all triangular columns.
The triangular column structure is relatively simple, easy to manufacture and process. Meanwhile, the traffic guiding device is convenient to install and debug due to regular shape, so that the production cost and time of the traffic guiding device are reduced, and the economical efficiency and the practicability of the traffic guiding device are improved.
Further, guide plates representing different guide strategies are fixedly connected to the side walls of the guide columns.
The guiding plate has the beneficial effects that the guiding plate can intuitively display different guiding strategies such as direction indication, lane allocation, speed limitation and the like. The driver can understand the current traffic guiding information only by quick glance without needing to worry about reading complex signs or symbols, and the intuitiveness is helpful for improving the attention and the response speed of the driver, and especially under the condition of heavy traffic or limited sight, the driver can quickly make a correct driving decision.
Further, a plurality of universal wheels are fixedly connected to the bottom of the bottom plate.
The universal wheel has the beneficial effects that the design of the universal wheel enables the traffic guiding device to be more convenient in the transportation process, and meanwhile, when the universal wheel is deployed, workers can quickly adjust the position and the angle of the device so as to adapt to different traffic environments and requirements.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
Fig. 1 is a schematic structural diagram of an intelligent system according to the present invention.
Fig. 2 is an isometric schematic diagram of the intelligent traffic guiding device based on machine learning.
Fig. 3 is an isometric view of a replacement component in the intelligent traffic guiding apparatus based on machine learning according to the present invention.
Fig. 4 is a schematic front sectional view of a driving frame in the intelligent traffic guiding apparatus based on machine learning.
The reference numerals in the attached drawings of the specification comprise 1, a bottom plate, 2, a fixing plate, 3, a control box, 4, a camera, 5, a driving frame, 6, a transmission gear, 7, a first gear ring, 8, a moving wheel, 9, a second gear ring, 10, a moving rod, 11, a guide frame, 12, a connecting rod, 13, a limiting block, 14, a rotating shaft, 15, a limiting wheel, 16, a first bevel gear, 17, a second bevel gear, 18, a reversing rod, 19, a guide post, 20, a guide plate, 21 and a universal wheel.
Detailed Description
The following is a further detailed description of the embodiments:
Embodiment 1 As shown in figures 1 and 2, the intelligent traffic guiding device based on machine learning comprises an intelligent system and a guiding device body, wherein the intelligent system comprises an acquisition module for acquiring environment information, an analysis module for analyzing the environment information, a processing module for processing the environment information, a learning module for establishing and training a machine learning model, a prediction module for predicting future traffic conditions and a control module for adjusting guiding strategies of the guiding device body.
The functions of each module are explained in detail in the following sequence:
The system comprises an acquisition module, an analysis module and a control module, wherein the acquisition module is used for acquiring current environmental information and transmitting the acquired environmental information to the analysis module, wherein the environmental information comprises current traffic flow and traffic flow density in unit time.
And the analysis module is used for analyzing the cause of the environmental information, obtaining an analysis result and transmitting the analysis result to the processing module.
The processing module is used for processing the acquired environmental information according to the analysis result of the analysis module to obtain a processing result and transmitting the processing result to the learning module.
And the learning module is used for creating a machine learning model and training the machine learning model according to the processing result of the processing module.
And the prediction module is used for predicting the future traffic condition through the machine learning model to obtain a prediction result and transmitting the prediction result to the control module.
And the control module is used for adjusting the guiding strategy of the guiding device body according to the prediction result.
When the processing module processes environment information, firstly, external indexes of a dataset are collected through an Internet large model, and then clustering operation is carried out on the external indexes, wherein the specific formula of the process is as follows, D= { x 1,x2,…,xm } (1).
Where D is the external index of the dataset and x 1、x2 and x m are both the samples taken.
In the clustering process, the clustering result is compared with a reference model, the reference model is obtained by dividing results provided by experts, the evaluation results are marked as external indexes, meanwhile, the clustering result is evaluated in a direct evaluation mode, and the evaluation results are marked as internal indexes.
The external index evaluation process is as follows:
Dividing clusters given by clustering into Cluster partitioning given by reference modelThe samples were paired pairwise and the following definitions were made:。
。
。
。
。
Wherein, Representing a sampleThe categories in the cluster model are selected,Representing a sampleClass in reference model, a represents element number of SS set, SS set is contained inAre all affiliated to the same cluster and are inIn the sample pairs also belonging to the same cluster, b represents the number of elements of the set SD, the SD set being contained inAre all affiliated to the same cluster and are inSample pairs belonging to different clusters, c represents the number of elements of a DS set, and the DS set is contained inIs subject to different clusters and isSample pairs belonging to the same cluster, d represents the element number of a DD set, and the DD set is contained inIs subject to different clusters and isIn the pairs of samples belonging to different clusters, m represents the total number of samples, and i and j each represent the serial number of the sample.
。
JC is a Jacquard coefficient, the interval of JC is in the range of [0,1], and the consistency of clustering results and actual conditions is positively related to the Jacquard index.
。
Wherein RI is Rankine index, and the interval is in the range of [0,1 ].
。
The ARI is an adjusted Rankine index, the interval of the ARI is within the range of [ -1,1], and the consistency of the clustering result and the actual situation is positively correlated with the adjusted Rankine index.
The internal index evaluation formula is as follows:。
Where SSE is the sum of squares of the errors, which represents the sum of squares of the data-to-class center distance loss in the class.
。
。
Wherein SC is the contour coefficient,Representing the contour coefficient of the i-th sample,Representing a sampleAverage distance from other samples in the cluster,Representation ofThe range of SC is [ -1,1], the magnitude of which has a positive correlation with the clustering result.
。
。
。
Wherein CH is Calinski-Harabasz index,AndIs the covariance matrix of the inter-cluster and intra-cluster data,AndRepresenting the center points of cluster q and dataset D respectively,Represents the number of data sets belonging to cluster q,Representing the trace of the matrix in the inter-cluster covariance matrix,The CH index represents the ratio between the distance between clusters and the distance in the clusters, the ratio is positively correlated with the clustering effect, and k represents the number of clusters.
The specific implementation process is that the acquisition module firstly acquires the traffic flow in the current environment and the traffic flow density in unit time as environment information, and in the embodiment, 5min is taken as one unit time. The acquisition module sends the acquired environmental information to the analysis module, the analysis module analyzes the cause of the environmental information to obtain an analysis result, the analysis result is sent to the processing module, and the processing module processes the environmental information.
In the processing process, the processing module firstly collects the external index of the data set through the Internet large model, and then performs clustering operation on the environment information, wherein the specific formula of the process is as follows, D= { x 1,x2,…,xm } (1).
Where D is the external index of the dataset and x 1、x2 and x m are both the samples taken.
In the clustering process, a reference model is established according to the dividing result provided by an expert, the clustering result is compared with the reference model and evaluated, the evaluation result is marked as an external index, meanwhile, a direct evaluation mode is adopted, the clustering result is not directly evaluated by the reference model, and the evaluation result is marked as an internal index.
The external index evaluation process is as follows:
Dividing clusters given by clustering into Cluster partitioning given by reference modelThe samples were paired pairwise and the following definitions were made:。
。
。
。
。
Wherein, Representing a sampleThe categories in the cluster model are selected,Representing a sampleClass in reference model, a represents element number of SS set, SS set is contained inAre all affiliated to the same cluster and are inIn the sample pairs also belonging to the same cluster, b represents the number of elements of the set SD, the SD set being contained inAre all affiliated to the same cluster and are inSample pairs belonging to different clusters, c represents the number of elements of a DS set, and the DS set is contained inIs subject to different clusters and isSample pairs belonging to the same cluster, d represents the element number of a DD set, and the DD set is contained inIs subject to different clusters and isIn the pairs of samples belonging to different clusters, m represents the total number of samples, and i and j each represent the serial number of the sample.
。
JC is a Jacquard coefficient, the interval of JC is in the range of [0,1], and the consistency of clustering results and actual conditions is positively related to the Jacquard index.
。
Wherein RI is Rankine index, and the interval is in the range of [0,1 ].
。
The ARI is an adjusted Rankine index, the interval of the ARI is within the range of [ -1,1], the consistency of the clustering result and the actual situation is positively correlated with the adjusted Rankine index, and the larger the value is, the more the clustering result is consistent with the actual situation.
The internal index evaluation formula is as follows:。
Where SSE is the sum of squares of the errors, which represents the sum of squares of the data-to-class center distance loss in the class.
。
。
Wherein SC is the contour coefficient,Representing the contour coefficient of the i-th sample,Representing a sampleAverage distance from other samples in the cluster,Representation ofThe range of SC is [ -1,1], the magnitude of which is positively correlated with the clustering result, the larger the value of which indicates the better the clustering effect, the smaller the distance between samples in the same cluster and the larger the distance between samples in different clusters.
。
。
。
Wherein CH is Calinski-Harabasz index,AndIs the covariance matrix of the inter-cluster and intra-cluster data,AndRepresenting the center points of cluster q and dataset D respectively,Represents the number of data sets belonging to cluster q,Representing the trace of the matrix in the inter-cluster covariance matrix,The CH index represents the ratio between the distance between clusters and the distance in the clusters, the ratio is positively correlated with the clustering effect, and k represents the number of clusters.
The processing module obtains a processing result after the processing of the environmental information is completed, the processing result is sent to the learning module, the learning module firstly creates a machine learning model, trains the machine learning model through the processing result of the processing module, and after the training is completed, the prediction module predicts the future traffic condition through the machine learning model to obtain a prediction result, and sends the prediction result to the control module, and the control module adjusts the guiding strategy of the guiding device body.
Example 2:
As shown in fig. 2, 3 and 4, the guiding device body is different from embodiment 1 in that the guiding device body comprises a bottom plate 1, a fixing plate 2 is integrally formed at the top of the bottom plate 1, a control box 3 is fixedly connected to a bolt at the top of the fixing plate 2, a controller and an alarm are fixedly connected to a bottom wall bolt in the control box 3, the controller is used for controlling the operation of the alarm, a plurality of cameras 4 are fixedly connected to the bottom plate 1 along the circumferential screw of the side wall of the bottom plate, the controller is used for controlling the operation of the cameras 4, and a guiding component for displaying guiding information and a transmission component for driving the guiding component to operate are arranged at the top of the bottom plate 1.
The drive assembly includes the drive frame 5 of welding in bottom plate 1 one side, the first driving piece of bottom wall bolt fixedly connected with in the drive frame 5, the controller is used for controlling the operation of first driving piece, in this embodiment, first driving piece is preferably first step motor, first step motor output shaft coaxial fixed joint has drive gear 6, drive gear 6 meshing has first ring gear 7, first ring gear 7 normal running fit is in bottom plate 1 top, the inboard meshing of first ring gear 7 has a plurality of removal wheel 8, the inboard meshing of removal wheel 8 has second ring gear 9, second ring gear 9 and fixed plate 2 lateral wall welding.
The guide assembly comprises a movable rod 10 which is fixedly clamped with the movable wheel 8 coaxially, a plurality of guide frames 11 are arranged above the bottom plate 1, the tops of the adjacent movable rods 10 are in running fit with the bottoms of the adjacent guide frames 11, and replacement assemblies for replacing different guide modes are arranged in the guide frames 11.
The replacement assembly comprises a second driving piece fixedly connected to the inner side wall of the guide frame 11 through bolts, the controller is used for controlling the operation of the second driving piece, in the embodiment, the second driving piece is a second stepping motor, an output shaft of the second stepping motor is fixedly connected with a connecting rod 12 through coaxial bolts, and a limiting block 13 is integrally formed at the lower end of the connecting rod 12.
The guide frame 11 inside wall normal running fit has axis of rotation 14, and coaxial fixed joint has spacing wheel 15 on the axis of rotation 14, and it has a plurality of spacing grooves to open on the spacing wheel 15 equidistance, stopper 13 and the equal sliding fit in spacing groove.
The coaxial equidistant fixed joint has a plurality of first bevel gears 16 on the axis of rotation 14, and first bevel gear 16 all meshes there is second bevel gear 17, and the equal coaxial fixed joint of second bevel gear 17 has reversing lever 18, and the uniforms on the reversing lever 18 have triangular prism shaped's guide post 19, and reversing lever 18 top all rotates with guide frame 11 inner top wall cooperation, and guide post 19 lateral wall all screw fixedly connected with represents the guide board 20 of different guide strategies.
The specific implementation process is that a worker starts a first stepping motor through a controller, an output shaft of the first stepping motor rotates to drive a transmission gear 6 to rotate, the transmission gear 6 drives a first gear ring 7 meshed with the transmission gear 6 to rotate, and the first gear ring 7 drives a movable wheel 8 meshed with the first gear ring to rotate. Since the moving wheel 8 is meshed with the second gear ring 9, the second gear ring 9 is welded with the side wall of the fixed plate 2, when the moving wheel 8 rotates, the moving wheel 8 rotates around the second gear ring 9, and then the moving rod 10 is driven by the moving wheel 8 to rotate around the second gear ring 9 together. The moving rod 10 drives the adjacent guide frame 11 to move during the moving process, and the worker can adjust the position of the guide frame 11 by controlling the start and stop of the first stepping motor.
When the guiding strategy is required to be adjusted, a worker starts the second stepping motor through the controller, the output shaft of the stepping motor rotates to drive the connecting rod 12 to rotate, and the connecting rod 12 drives the limiting block 13 integrally formed with the connecting rod to rotate together. Since a plurality of limiting grooves are formed in the limiting wheel 15 at equal intervals, the limiting block 13 and the limiting grooves are in sliding fit, the limiting block 13 can stir the limiting wheel 15 in the rotating process, the limiting wheel 15 drives the rotating shaft 14 to rotate, the rotating shaft 14 drives the first bevel gear 16 to rotate, the first bevel gear 16 drives the second bevel gear 17 to rotate, the second bevel gear 17 drives the reversing rod 18 to rotate, the reversing rod 18 drives the guide post 19 integrally formed with the reversing rod to rotate, at the moment, the guide post 19 can drive the guide plates 20 to rotate together when rotating due to the fact that the guide posts 19 are fixedly connected with guide plates 20 representing different guide strategies through screws, and the staff can realize conversion of the guide strategies through rotation of the guide plates 20.
The guiding device body can collect information of the traffic flow of the current environment and the traffic flow density in unit time through the camera 4.
When the guiding device body is damaged, the alarm can give an alarm in time to inform a worker to go to maintenance.
Example 3:
as shown in fig. 2, the difference from embodiment 2 is that a plurality of universal wheels 21 are fixedly connected to the bottom of the base plate 1 by bolts.
The implementation is as follows, when the guiding device body needs to be moved, the universal wheel 21 can be designed to enable the staff to push the guiding device body easily.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050108001A1 (en) * | 2001-11-15 | 2005-05-19 | Aarskog Brit H. | Method and apparatus for textual exploration discovery |
| CN104090931A (en) * | 2014-06-25 | 2014-10-08 | 华南理工大学 | Information prediction and acquisition method based on webpage link parameter analysis |
| US20200143208A1 (en) * | 2018-11-05 | 2020-05-07 | Royal Bank Of Canada | Opponent modeling with asynchronous methods in deep rl |
| CN111175626A (en) * | 2020-03-20 | 2020-05-19 | 广东电网有限责任公司 | Abnormal detection method for insulation state of switch cabinet |
| CN111539331A (en) * | 2020-04-23 | 2020-08-14 | 西安科技大学 | Visual image reconstruction system based on brain-computer interface |
| CN111985815A (en) * | 2020-08-21 | 2020-11-24 | 国网能源研究院有限公司 | Method and device for screening energy and power operation evaluation indexes |
| CN113987158A (en) * | 2021-10-15 | 2022-01-28 | 北京搜狗科技发展有限公司 | Display method and device and display device |
| CN114040870A (en) * | 2018-09-25 | 2022-02-11 | 大众汽车股份公司 | Method and device for supporting a lane change of a vehicle |
| CN114495506A (en) * | 2022-02-23 | 2022-05-13 | 复旦大学 | Multi-intersection signal lamp control system and method based on traffic flow prediction and reinforcement learning |
| CN118982927A (en) * | 2024-10-11 | 2024-11-19 | 电子科技大学 | A community traffic flow prediction and optimization system based on artificial intelligence |
-
2025
- 2025-01-09 CN CN202510031921.2A patent/CN119445849A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050108001A1 (en) * | 2001-11-15 | 2005-05-19 | Aarskog Brit H. | Method and apparatus for textual exploration discovery |
| CN104090931A (en) * | 2014-06-25 | 2014-10-08 | 华南理工大学 | Information prediction and acquisition method based on webpage link parameter analysis |
| CN114040870A (en) * | 2018-09-25 | 2022-02-11 | 大众汽车股份公司 | Method and device for supporting a lane change of a vehicle |
| US20200143208A1 (en) * | 2018-11-05 | 2020-05-07 | Royal Bank Of Canada | Opponent modeling with asynchronous methods in deep rl |
| CN111175626A (en) * | 2020-03-20 | 2020-05-19 | 广东电网有限责任公司 | Abnormal detection method for insulation state of switch cabinet |
| CN111539331A (en) * | 2020-04-23 | 2020-08-14 | 西安科技大学 | Visual image reconstruction system based on brain-computer interface |
| CN111985815A (en) * | 2020-08-21 | 2020-11-24 | 国网能源研究院有限公司 | Method and device for screening energy and power operation evaluation indexes |
| CN113987158A (en) * | 2021-10-15 | 2022-01-28 | 北京搜狗科技发展有限公司 | Display method and device and display device |
| CN114495506A (en) * | 2022-02-23 | 2022-05-13 | 复旦大学 | Multi-intersection signal lamp control system and method based on traffic flow prediction and reinforcement learning |
| CN118982927A (en) * | 2024-10-11 | 2024-11-19 | 电子科技大学 | A community traffic flow prediction and optimization system based on artificial intelligence |
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