CN114495485A - Prediction-based sleep method and sleep control device of intelligent road sensor - Google Patents
Prediction-based sleep method and sleep control device of intelligent road sensor Download PDFInfo
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Abstract
The invention discloses a prediction-based intelligent road sensor dormancy method, which relates to the technical field of intelligent roads and comprises the steps of obtaining map data information; dividing sensors deployed on a road section into a road section entrance sensor and a non-road section entrance sensor; controlling the non-road entrance sensor to be switched to a dormant state when the non-road entrance sensor does not detect the vehicle within the time T1, and controlling the road entrance sensor to be in a working state; controlling the road section entrance sensor and the non-road section entrance sensor in a working state to monitor vehicles in a visual field range and predicting the driving track of the vehicles; and predicting the time required by the vehicle to reach the next position and enter the detection range of the sensor at the next position based on the running track of the vehicle, and controlling the sensor to be switched to the working state in the time. The invention can greatly reduce the number of sensors in working state when the road is idle, on one hand, the service life of the sensors is prolonged, and on the other hand, the power consumption is reduced.
Description
Technical Field
The invention relates to the technical field of intelligent roads, in particular to a sleep method and a sleep control device of an intelligent road sensor based on prediction.
Background
A large number of sensors such as laser radars, cameras, millimeter wave radars and the like need to be deployed on the roadside to build the intelligent road. In current wisdom road, these sensor equipment need 24 hours uninterrupted duty all the year around, and real-time supervision road conditions provides the road side for the vehicle and drives auxiliary information. However, the service life of these devices is limited, and taking a mechanical laser radar as an example, the current theoretical life is about 3 ten thousand hours, that is, the devices can continuously work for 3 to 4 years. The equipment replacement cost on later stage wisdom road is higher.
Disclosure of Invention
The present invention is directed to overcome the shortcomings of the prior art, and provides a sleep method and a sleep control device for an intelligent road sensor based on prediction.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a method for intelligent road sensor sleep based on prediction includes,
acquiring map data information;
dividing sensors deployed on each road segment into a road segment entrance sensor and a non-road segment entrance sensor based on the map data information;
controlling the non-road entrance sensor to be switched from a working state to a dormant state when the non-road entrance sensor does not detect the vehicle within the time T1, and controlling the road entrance sensor to be in the working state all the time;
controlling the road section entrance sensor and the non-road section entrance sensor in a working state to monitor vehicles in a visual field range, and predicting the driving track of the vehicles based on the acquired vehicle parameter information;
and predicting the time T2 required by the vehicle to arrive at the next point and enter the detection range of the sensor at the next point based on the driving track of the vehicle and the map data information, and controlling the sensor at the next point to be switched to the working state within the time T2.
As a preferable aspect of the prediction-based sleep method of the intelligent road sensor of the present invention, wherein: the predicting a time T2 required for the vehicle to arrive at a next spot and enter into a detection range of a sensor located at the next spot based on the travel track of the vehicle and the map data information and controlling the sensor located at the next spot to be switched to the operating state for a time T2 includes,
predicting a time T2 required for the vehicle to arrive at a next point and enter a detection range of a sensor located at the next point based on the travel track of the vehicle and the map data information;
judging whether a sensor located at the next point position is a non-road section entrance sensor or not; if yes, carrying out the next step, and if not, ending;
and judging whether the non-road section entrance sensor positioned at the next point location is in a dormant state, if so, controlling the non-road section entrance sensor to be switched to a working state, and if not, ending.
As a preferable aspect of the prediction-based sleep method of the intelligent road sensor of the present invention, wherein: the acquired vehicle parameter information comprises the real-time position, the vehicle speed, the course angle and the lane where the vehicle is located.
As a preferable aspect of the prediction-based sleep method of the intelligent road sensor of the present invention, wherein: the time T1 is 10 min.
The invention also discloses a prediction-based sleep control device of the intelligent road sensor, which comprises a sensor module, an edge computing node module and a cloud platform module,
the sensor module comprises a plurality of sensors deployed at the roadside;
the edge computing node module is used for receiving the data transmitted by the sensor module, extracting vehicle parameter information from the data, predicting the running track of a vehicle based on the vehicle parameter information, uploading the prediction result to the cloud platform module, receiving a dormancy awakening instruction transmitted by the cloud platform module, and controlling the switching between the working state and the dormant state of the sensor module;
the cloud platform module is used for acquiring map data, dividing the sensors deployed on each road section into a road section entrance sensor and a non-road section entrance sensor based on map data information, receiving data uploaded by the edge computing node module, and sending a dormancy awakening instruction to the edge computing node module.
As a preferable aspect of the intelligent road sensor sleep control device based on prediction according to the present invention, wherein: the cloud platform module comprises a cloud platform module and a cloud platform module,
the map module is used for acquiring map data information;
the dividing module is used for dividing the sensors deployed on each road section into a road section entrance sensor and a non-road section entrance sensor based on the map data information;
and the communication module is used for communicating with the edge computing node module, receiving the data uploaded by the edge computing node module and sending a dormancy awakening instruction to the edge computing node module.
And the prediction module is used for predicting the time T2 required by the vehicle to arrive at the next point and enter the detection range of the sensor positioned at the next point based on the map data information and the driving track of the vehicle uploaded by the edge computing node module, and sending a wake-up instruction to the edge computing node module corresponding to the point within the time T2.
As a preferable aspect of the intelligent road sensor sleep control device based on prediction according to the present invention, wherein: the edge-computing-node module includes,
the sensing module is used for controlling the road section entrance sensor and the non-road section entrance sensor in a working state to monitor vehicles in a visual field range and predicting the driving track of the vehicles based on the acquired vehicle parameter information;
the communication module is used for communicating with the cloud platform module, uploading data to the cloud platform module and receiving a control instruction sent by the cloud platform module;
the control module is used for controlling the non-road section entrance sensor of the corresponding point position to be switched to a dormant state from a working state when the non-road section entrance sensor does not detect a vehicle within time T1 when a wake-up instruction sent by the cloud platform module is not received, and controlling the road section entrance sensor to be in the working state all the time; and when receiving a wake-up instruction sent by the cloud platform module, controlling the non-road section entrance sensor of the corresponding point position to be switched from a dormant state to a working state.
As a preferable aspect of the intelligent road sensor sleep control device based on prediction according to the present invention, wherein: the time T1 is 10 min.
The invention also discloses computer equipment which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor executes the program to realize the intelligent road sensor sleep method based on prediction according to any scheme.
The invention also discloses a computer readable storage medium on which a computer program is stored, which when executed by a processor implements a method of sleep for a prediction-based intelligent road sensor according to any of the above aspects.
The invention has the beneficial effects that:
the invention can allow part of sensors on the road section to be switched to the dormant state when the road is idle, only the sensors at the entrance of each road section are kept to work, and the information of the position, the speed, the course angle, the lane where the sensor is located and the like of the vehicle can be obtained according to the sensor in the working state, so that the subsequent driving track of the vehicle and the time required for entering the detection range of the next sensor are predicted, and the next sensor is awakened in the time to be switched to the working state, thereby greatly reducing the number of the sensors in the working state when the road is idle, prolonging the service life of the sensors on one hand, and reducing the power consumption on the other hand.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flow chart illustrating a method for providing smart road sensor sleep based on prediction according to the present invention;
FIG. 2 is a flowchart illustrating a step S105 of the intelligent road sensor sleep method based on prediction according to the present invention;
FIG. 3 is a schematic structural diagram of a sleep control apparatus for an intelligent road sensor based on prediction according to the present invention;
fig. 4 is a schematic structural diagram of a cloud platform module in the sleep control device for smart road sensors based on prediction according to the present invention;
FIG. 5 is a schematic diagram illustrating an edge computing node module in the sleep control apparatus for an intelligent road sensor based on prediction according to the present invention;
FIG. 6 is a diagram showing the status of each position sensor when the road is busy (more vehicles);
FIG. 7 is a diagram illustrating the status of each position sensor when the road is idle (no vehicle);
fig. 8 is a schematic diagram showing the state of each position sensor when the road portion is idle (few vehicles).
Detailed Description
In order that the manner in which the present invention is attained and can be more readily understood, a more particular description of the invention briefly summarized above may be had by reference to the embodiments thereof which are illustrated in the appended drawings.
Referring to fig. 1, a flow chart of a sleep method for an intelligent road sensor based on prediction according to the present embodiment is shown, the method includes steps S101 to S105, and the specific steps are as follows:
step S101: map data information is acquired.
Specifically, a high-precision map in the corresponding area is acquired.
Step S102: the sensors deployed on each road segment are divided into road segment entry sensors and non-road segment entry sensors based on map data information.
Specifically, according to the obtained high-precision map in the corresponding area, the road in the corresponding area is divided into a plurality of road sections, and the sensor deployed on each road section can be divided into a road section entrance sensor and a non-road section entrance sensor according to the position of the sensor, namely, the road section entrance sensor is positioned at the entrance of the road section. It will be appreciated that there are only two route segment entries per route segment. When the vehicles run along the opposite direction, the vehicles can respectively drive into the road section from the two road section inlets. The non-road section entrance sensor is positioned in the middle of the road section.
Step S103: and controlling the non-road entrance sensor to be switched from the working state to the dormant state when the non-road entrance sensor does not detect the vehicle within the time T1, and controlling the road entrance sensor to be in the working state all the time.
In particular, in consideration of the continuity of the movement of the vehicle, the vehicle can move from one sensor point to an adjacent sensor point on a certain road section, and cannot jump to a non-adjacent sensor point, so that the driving route of the vehicle has predictability. In the case that no vehicle enters all the road section entrances of a certain road section, the number of vehicles in the road section cannot be increased, and in this case, if no vehicle is detected in the road section for a continuous period of time, all the non-road section entrance sensors in the road section can be dormant without worrying about the condition that the vehicle is missed. Conversely, if there is a new vehicle in the road section, it is inevitable that the vehicle enters from the road section entrance and is detected by the road section entrance sensor.
Thus, when the non-link entry sensor does not detect a vehicle for a continuous period of time T1, the non-link entry sensor is controlled to switch from the operating state to the sleep state. For the road section entrance sensor, whether a vehicle enters a corresponding road section needs to be detected, so that the road section entrance sensor needs to continuously work and cannot sleep, and the road section entrance sensor is controlled to be in a working state all the time. See fig. 7, which is a schematic diagram of the states of the position sensors when the road is idle (no vehicle).
In this embodiment, the time T1 is 10 min.
It can be understood that the sensor is in a working state, that is, the sensor is in a normal working mode, can sense roadside information in real time, and uploads the acquired data. And the sensor is in a dormant state, which means that the sensor still maintains power supply but is in a standby state in a low power consumption mode. Taking the mechanical lidar as an example, when the mechanical lidar enters a dormant state, both the motor and the laser transmitter stop working.
Step S104: and controlling the road section entrance sensor and the non-road section entrance sensor in the working state to monitor the vehicles in the visual field range, and predicting the driving track of the vehicles based on the acquired vehicle parameter information.
Specifically, the road section entrance sensor and the non-road section entrance sensor in the working state monitor the vehicles in the visual field range in real time to obtain the parameter information of the vehicles. The parameter information of the vehicle includes, but is not limited to: the real-time position, speed, course angle and lane of the vehicle. According to the parameter information of the vehicle, a subsequent section of driving track of the vehicle can be predicted.
Step S105: and predicting the time T2 required by the vehicle to reach the next point and enter the detection range of the sensor at the next point based on the running track of the vehicle, and controlling the sensor at the next point to be switched to the working state in the time T2.
Specifically, if the time T2 required for the vehicle to reach the next sensor position and enter the detection range of the next sensor position is predicted based on the predicted travel trajectory of the vehicle, the sensor at the next position needs to be waken up within the time T2 to enter the operating state.
It is understood that there are various cases depending on the kind of the next point position sensor to be reached by the vehicle and the state of the point position sensor. The method specifically comprises the following steps:
step S105 a: the time T2 required for the vehicle to reach the next position and enter the detection range of the sensor located at the next position is predicted based on the travel track of the vehicle.
Step S105 b: judging whether a sensor located at the next point position is a non-road section entrance sensor or not; if so, the next step is carried out, if not, the next sensor is the road section entrance sensor, and the road section entrance sensor is always in a working state, so that the control instruction does not need to be transmitted to the road section entrance sensor, and the process is finished.
Step S105 c: and judging whether the non-road section inlet sensor at the next point is in a dormant state or not, if so, controlling the non-road section inlet sensor to be switched to a working state, otherwise, indicating that the non-road section inlet sensor at the next point is in the working state, and not transmitting a control instruction to the non-road section inlet sensor, thereby finishing the process.
Where time T2 is related to the vehicle speed and the spacing between adjacent points. In this embodiment, the distance between adjacent points is 200m, the vehicle speed is 10-20 m/s, and the value range of the time T2 is 10-20 s.
Fig. 6 is a schematic diagram showing the status of each position sensor when the road is busy (more vehicles). The number of vehicles in the road section is large, and the vehicles alternately pass through the road section, so that the non-road section entrance sensor cannot detect the vehicles continuously for a period of time, and all the non-road section entrance sensors of the road section are in a working state.
See fig. 8, which is a schematic diagram of the states of the position sensors when the road is partially idle (few vehicles). And the vehicle enters the road section from the road section entrance, the road section entrance sensor is in a working state, and the non-road section entrance sensor at the next point where the vehicle is about to arrive also enters the working state to wait for monitoring the vehicle.
Therefore, the method can greatly reduce the number of the sensors in the working state in the idle period of the road, not only can prolong the service life of the sensors, but also greatly reduce the power consumption.
Referring to fig. 3, a schematic structural diagram of a sleep control apparatus for an intelligent road sensor based on prediction according to this embodiment is provided. The dormancy control device comprises a sensor module, an edge computing node module and a cloud platform module.
Specifically, the sensor module includes a number of sensors deployed on the roadside. Such sensors include, but are not limited to: laser radar, camera, millimeter wave radar. These sensors are used to sense and monitor road information in real time. The general configuration of wisdom road is disposed one sensor point position along road every 200m, deploys 2 ~ 4 sensor point positions at the intersection. 1-2 laser radars and 1-3 cameras are deployed at each sensor point position, and the monitoring distance is calculated according to 150 m.
The number of the edge computing node modules is consistent with the number of the sensor point positions deployed on the road side, and the edge computing node modules correspond to the sensor point positions one by one. Each edge computing node module is used for receiving data transmitted by the sensor of the corresponding point location, extracting monitored vehicle parameter information from the data, predicting a subsequent section of driving track of the vehicle based on the vehicle parameter information and map data information, and uploading a prediction result to the cloud platform module. In addition, each edge computing node module is further configured to receive a control instruction transmitted by the cloud platform module, and control the sensor at the corresponding point to switch between the working state and the sleep state.
The cloud platform module is used for acquiring map data and dividing the sensors deployed on each road section into a road section entrance sensor and a non-road section entrance sensor based on the map data information. Meanwhile, the cloud platform module is also used for communicating with the edge computing node module in a wired or wireless mode, receiving data transmitted by the edge computing node module, and transmitting a control instruction to the corresponding edge computing node module, so that the corresponding edge computing node module controls the sensor of the corresponding point to switch between a working state and a dormant state.
Specifically, the cloud platform module comprises a map module, a dividing module, a communication module and a prediction module. See fig. 4.
The map module is used for acquiring map data information.
The dividing module is used for dividing the sensors deployed on each road section into a road section entrance sensor and a non-road section entrance sensor based on the map data information.
The communication module is used for communicating with the edge computing node module, receiving data uploaded by the edge computing node module and sending a dormancy awakening instruction to the edge computing node module.
The prediction module is used for predicting the time T2 required by the vehicle to arrive at the next point and enter the detection range of the sensor at the next point based on the map data information and the driving track of the vehicle uploaded by the edge computing node module, and sending a wake-up instruction to the edge computing node module corresponding to the point within the time T2.
The edge computing node module includes a sensing module, a communication module, and a control module, see fig. 5.
The sensing module is used for controlling the road section entrance sensor and the non-road section entrance sensor in a working state to monitor the vehicles in a visual field range, and predicting the driving track of the vehicles based on the acquired vehicle parameter information.
The communication module is used for communicating with the cloud platform module, uploading data to the cloud platform module and receiving a control instruction sent by the cloud platform module.
The control module is used for controlling the non-road section entrance sensor of the corresponding point position to be switched to the dormant state from the working state when the non-road section entrance sensor does not detect the vehicle within the time T1 when the wake-up instruction sent by the cloud platform module is not received, and controlling the road section entrance sensor to be in the working state all the time. And when a wake-up instruction sent by the cloud platform module is received, the non-road section entrance sensor of the corresponding point location is controlled to be switched from the dormant state to the working state.
In addition, the present embodiment also provides a computer device, and the components of the computer device may include but are not limited to: one or more processors or processing units, a system memory, and a bus connecting the various system components (including the system memory and the processing units).
A bus represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
The computer system/server typically includes a variety of computer system readable media. Such media may be any available media that is accessible by the computer system/server and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) and/or cache memory. The computer device may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system may be used to read from and write to non-removable, nonvolatile magnetic media. A magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus by one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility having a set (at least one) of program modules may be stored, for example, in the memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination may comprise an implementation of a network environment. The program modules generally perform the functions and/or methodologies of the described embodiments of the invention.
The computer device may also communicate with one or more external devices such as a keyboard, pointing device, display, etc.). Such communication may be through an input/output (I/O) interface. Also, the computer device may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) through a network adapter.
The processing unit executes programs stored in the system memory to perform the functions and/or methods of the described embodiments of the present invention.
The computer program described above may be provided in a computer storage medium encoded with a computer program that, when executed by one or more computers, causes the one or more computers to perform the method flows and/or apparatus operations shown in the above-described embodiments of the invention.
With the development of time and technology, the meaning of media is more and more extensive, and the propagation path of computer programs is not limited to tangible media any more, and can also be downloaded from a network directly and the like. Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
In addition to the above embodiments, the present invention may have other embodiments; all technical solutions formed by adopting equivalent substitutions or equivalent transformations fall within the protection scope of the claims of the present invention.
Claims (10)
1. A smart road sensor sleep method based on prediction is characterized in that: comprises the steps of (a) preparing a substrate,
acquiring map data information;
dividing sensors deployed on each road segment into a road segment entrance sensor and a non-road segment entrance sensor based on the map data information;
controlling the non-road entrance sensor to be switched from a working state to a dormant state when the non-road entrance sensor does not detect the vehicle within the time T1, and controlling the road entrance sensor to be in the working state all the time;
controlling the road section entrance sensor and the non-road section entrance sensor in a working state to monitor vehicles in a visual field range, and predicting the driving track of the vehicles based on the acquired vehicle parameter information;
and predicting the time T2 required by the vehicle to arrive at the next point and enter the detection range of the sensor at the next point based on the driving track of the vehicle and the map data information, and controlling the sensor at the next point to be switched to the working state within the time T2.
2. The smart road sensor sleep method based on prediction as claimed in claim 1, characterized in that: the predicting a time T2 required for the vehicle to arrive at a next spot and enter into a detection range of a sensor located at the next spot based on the travel track of the vehicle and the map data information and controlling the sensor located at the next spot to be switched to the operating state for a time T2 includes,
predicting a time T2 required for the vehicle to arrive at a next position and enter a detection range of a sensor located at the next position based on the travel track of the vehicle and the map data information;
judging whether a sensor located at the next point position is a non-road section entrance sensor or not; if yes, carrying out the next step, and if not, ending;
and judging whether the non-road section inlet sensor located at the next point position is in a dormant state, if so, controlling the non-road section inlet sensor to be switched to a working state, and if not, ending.
3. The intelligent prediction-based road sensor hibernation method according to claim 1, further comprising: the acquired vehicle parameter information comprises the real-time position, the vehicle speed, the course angle and the lane where the vehicle is located.
4. The smart road sensor sleep method based on prediction as claimed in claim 1, characterized in that: the time T1 is 10 min.
5. The utility model provides a dormancy controlling means of wisdom road sensor based on prediction which characterized in that: comprises a sensor module, an edge computing node module and a cloud platform module,
the sensor module comprises a plurality of sensors deployed at the roadside;
the edge computing node module is used for receiving the data transmitted by the sensor module, extracting vehicle parameter information from the data, predicting the running track of a vehicle based on the vehicle parameter information, uploading the prediction result to the cloud platform module, receiving a dormancy awakening instruction transmitted by the cloud platform module, and controlling the switching between the working state and the dormant state of the sensor module;
the cloud platform module is used for acquiring map data, dividing the sensors deployed on each road section into a road section entrance sensor and a non-road section entrance sensor based on map data information, receiving data uploaded by the edge computing node module, and sending a dormancy awakening instruction to the edge computing node module.
6. The intelligent prediction-based sleep control device for road sensors of claim 5, wherein: the cloud platform module comprises a cloud platform module and a cloud platform module,
the map module is used for acquiring map data information;
the dividing module is used for dividing the sensors deployed on each road section into a road section entrance sensor and a non-road section entrance sensor based on the map data information;
and the communication module is used for communicating with the edge computing node module, receiving the data uploaded by the edge computing node module and sending a dormancy awakening instruction to the edge computing node module.
And the prediction module is used for predicting the time T2 required by the vehicle to arrive at the next point and enter the detection range of the sensor positioned at the next point based on the map data information and the driving track of the vehicle uploaded by the edge computing node module, and sending a wake-up instruction to the edge computing node module corresponding to the point within the time T2.
7. The intelligent predictive-based sleep control for road sensors as claimed in claim 6, wherein: the edge-computing-node module includes,
the sensing module is used for controlling the road section entrance sensor and the non-road section entrance sensor in a working state to monitor vehicles in a visual field range and predicting the driving track of the vehicles based on the acquired vehicle parameter information;
the communication module is used for communicating with the cloud platform module, uploading data to the cloud platform module and receiving a control instruction sent by the cloud platform module;
the control module is used for controlling the non-road section entrance sensor of the corresponding point position to be switched to a dormant state from a working state when the non-road section entrance sensor does not detect a vehicle within time T1 when a wake-up instruction sent by the cloud platform module is not received, and controlling the road section entrance sensor to be in the working state all the time; and when receiving a wake-up instruction sent by the cloud platform module, controlling the non-road section entrance sensor of the corresponding point position to be switched from a dormant state to a working state.
8. The intelligent predictive-based sleep control for road sensors as claimed in claim 7, wherein: the time T1 is 10 min.
9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that: the processor, when executing the program, implements the method of any of claims 1-4.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that: the program when executed by a processor implementing the method of any one of claims 1 to 4.
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