CN114495485B - Dormancy method and dormancy control device of intelligent road sensor based on prediction - Google Patents
Dormancy method and dormancy control device of intelligent road sensor based on prediction Download PDFInfo
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Abstract
The invention discloses a dormancy method of an intelligent road sensor based on prediction, which relates to the technical field of intelligent roads and comprises the steps of obtaining map data information; dividing sensors deployed on a road segment into a road segment inlet sensor and a non-road segment inlet sensor; the non-road section entrance sensor is controlled to switch to a dormant state when the vehicle is not detected within the time T1, and the road section entrance sensor is controlled 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 running track of the vehicles; the next point reached by the vehicle and the time required for entering the detection range of the sensor located at the next point are predicted based on the travel locus of the vehicle, and the sensor is controlled to switch to the operating state during the time. The invention can greatly reduce the number of the sensors in the 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 dormancy method and dormancy control device of an intelligent road sensor based on prediction.
Background
The construction of intelligent roads requires the deployment of a large number of sensors, such as lidar, cameras, millimeter wave radar, etc. In the current intelligent roads, the sensor devices need to work continuously for 24 hours all the year round, monitor road conditions in real time and provide road side driving auxiliary information for vehicles. However, the service life of these devices is limited, for example, a mechanical lidar, and the current theoretical life is about 3 ten thousand hours, i.e., continuous operation for 3 to 4 years. The equipment replacement cost of later-stage wisdom road is higher.
Disclosure of Invention
The invention aims to solve the technical problem of overcoming the defects of the prior art and providing a dormancy method and a dormancy control device of an intelligent road sensor based on prediction.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a sleep method of intelligent road sensor 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;
the non-road section entrance sensor is controlled to be switched from a working state to a dormant state when a vehicle is not detected within time T1, and the road section entrance sensor is controlled to be always in the 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 running track of the vehicles based on the acquired vehicle parameter information;
and predicting the next point to be reached by the vehicle and time T2 required for entering the detection range of the sensor positioned at the next point based on the running track of the vehicle and the map data information, and controlling the sensor positioned at the next point to switch to the working state within the time T2.
As a preferable scheme of the sleep method of the intelligent road sensor based on prediction, the invention comprises the following steps: the predicting the next point where the vehicle is about to arrive and the time T2 required for entering the detection range of the sensor positioned at the next point based on the running track of the vehicle and the map data information, and controlling the sensor positioned at the next point to switch to the working state within the time T2 includes,
predicting a next point to be reached by the vehicle and a time T2 required for entering a detection range of a sensor located at the next point based on a travel track of the vehicle and the map data information;
judging whether the sensor positioned at the next point is a non-road section inlet sensor or not; if yes, the next step is carried out, if not, the process is finished;
judging whether the non-road section entrance sensor positioned at the next point is in a dormant state, if so, controlling the non-road section entrance sensor to switch to a working state, and if not, ending.
As a preferable scheme of the sleep method of the intelligent road sensor based on prediction, the invention comprises the following steps: 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 scheme of the sleep method of the intelligent road sensor based on prediction, the invention comprises the following steps: the time T1 is 10min.
The invention also discloses a dormancy control device of the intelligent road sensor based on prediction, which comprises a sensor module, an edge computing node module and a cloud platform module,
the sensor module comprises a plurality of sensors deployed on the road side;
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 the vehicle based on the vehicle parameter information, uploading a prediction result to the cloud platform module, receiving a dormancy wakeup instruction transmitted by the cloud platform module, and controlling the sensor module to work and switch dormancy states;
the cloud platform module is used for acquiring map data, dividing 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 wakeup instruction to the edge computing node module.
As a preferable mode of the sleep control device of the prediction-based intelligent road sensor of the present invention, wherein: the cloud platform module comprises a plurality of cloud platform modules,
the map module is used for acquiring map data information;
the division 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 wakeup instruction to the edge computing node module.
The prediction module is used for predicting the next point position where the vehicle is about to arrive and the time T2 required for entering the detection range of the sensor positioned at the next point position based on the map data information and the running 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 position within the time T2.
As a preferable mode of the sleep control device of the prediction-based intelligent road sensor of the present invention, wherein: the edge computing node module comprises a module for computing,
the sensing module is used for controlling the road section entrance sensor and the non-road section entrance sensor which are in a working state to monitor vehicles in a visual field range and predicting the running track of the vehicle 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 inlet sensor at the corresponding point position to switch from a working state to a dormant state when the vehicle is not detected within the time T1 when a wake-up instruction sent by the cloud platform module is not received, and controlling the road section inlet sensor to be always in the working state; and when receiving a wake-up instruction sent by the cloud platform module, controlling the non-road section entry sensor at the corresponding point position to be switched from a dormant state to a working state.
As a preferable mode of the sleep control device of the prediction-based intelligent road sensor of the present invention, wherein: the time T1 is 10min.
The invention also discloses a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor realizes the sleep method of the intelligent road sensor based on prediction according to any scheme when executing the program.
The invention also discloses a computer readable storage medium, on which a computer program is stored, which when being executed by a processor, implements a method for dormancy of a predictive-based intelligent road sensor according to any one of the above aspects.
The beneficial effects of the invention are as follows:
the invention can allow partial sensors on the road section to switch to a dormant state when the road is idle, only keeps the sensors at the entrance of each road section to work, and can acquire the information of the position, speed, course angle, lane where the vehicle is positioned and the like according to the sensors in the working state, so as to predict the subsequent running track of the vehicle and the time required for entering the detection range of the next sensor, and wake up the next sensor in the time to switch the next sensor 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.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a sleep method of a predictive-based intelligent road sensor according to the present invention;
FIG. 2 is a flowchart of step S105 in the sleep method of the predictive intelligent road sensor according to the present invention;
fig. 3 is a schematic structural diagram of a sleep control device of a prediction-based intelligent road sensor according to the present invention;
fig. 4 is a schematic structural diagram of a cloud platform module in a sleep control device of a prediction-based intelligent road sensor according to the present invention;
FIG. 5 is a schematic diagram of an edge computation node module in a sleep control device of a predictive intelligent road sensor according to the present invention;
FIG. 6 is a schematic diagram showing the state of each position sensor when the road is busy (more vehicles);
FIG. 7 is a schematic diagram of the status of each point sensor when the road is idle (no vehicle);
fig. 8 is a schematic diagram of the state of each point position sensor when the road section is idle (less vehicles).
Detailed Description
In order that the invention may be more readily understood, a more particular description thereof will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
Referring to fig. 1, a flow chart of a sleep method of a smart road sensor based on prediction according to the present embodiment is provided, and the method includes steps S101 to S105, specifically the following steps are described:
step S101: map data information is acquired.
Specifically, a high-precision map in the corresponding region is acquired.
Step S102: the sensors disposed on each road segment are divided into a road segment entrance sensor and a non-road segment entrance sensor based on the map data information.
Specifically, according to the obtained high-precision map in the corresponding area, the roads in the corresponding area are 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 road segment inlets per road segment. When the vehicle is traveling in opposite directions, it can be driven into the road from the two road entrances. The non-road section entrance sensor is located in the middle of the road section.
Step S103: and controlling the non-road-section entrance sensor to switch from the working state to the dormant state when the vehicle is not detected within the time T1, and controlling the road-section entrance sensor to be always in the working state.
In particular, considering continuity of vehicle movement, the vehicle can only move from one sensor point location to an adjacent sensor point location on a certain road section, and can not jump to a non-adjacent point location, so that the driving route of the vehicle has predictability. When no vehicles enter all road segment entrances of a certain road segment, the number of vehicles in the road segment cannot be increased, and in this case, if no vehicles are detected for a continuous period of time in the road segment, all non-road segment entrance sensors in the road segment can sleep without worrying about the condition of missed detection of the vehicles. Conversely, if there is a new vehicle in the road segment, it must enter from the road segment entrance and will be detected by the road segment entrance sensor.
Thus, when the non-road-section entry sensor does not detect the vehicle for a continuous period of time T1, the non-road-section entry sensor is controlled to switch from the operating state to the sleep state. For the road section entrance sensor, because whether a vehicle enters a corresponding road section needs to be detected, the road section entrance sensor needs to work continuously and cannot sleep, and the road section entrance sensor is controlled to be in a working state all the time. Referring to fig. 7, a schematic diagram of the state of each point sensor when the road is idle (no vehicle) is shown.
In this embodiment, the time T1 takes a value of 10min.
It can be understood that the sensor being in the working state means that the sensor is in the normal working mode, and can sense the road side information in real time and upload the acquired data. While the sensor is in the sleep mode means that the sensor remains powered but is in a standby state in a low power mode. Taking a mechanical laser radar as an example, when the laser radar enters a dormant state, 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 running 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 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: real-time position, speed, heading angle, lane in which the vehicle is located. According to the parameter information of the vehicle, a subsequent section of running track of the vehicle can be predicted.
Step S105: and predicting the next point reached by the vehicle and the time T2 required for entering the detection range of the sensor positioned at the next point based on the running track of the vehicle, and controlling the sensor positioned at the next point to switch to the working state within the time T2.
Specifically, according to the predicted running track of the vehicle, the next sensor point position to be reached by the corresponding vehicle and the time T2 required for entering the detection range of the next sensor point position can be predicted, and the sensor positioned at the next sensor point position needs to be awakened within the time T2 to enter the working state.
It will be appreciated that the various conditions may be divided according to the type of next point sensor that the vehicle is about to reach and the state of that point sensor. The method specifically comprises the following steps:
step S105a: the next point reached by the vehicle and the time T2 required for entering the detection range of the sensor located at the next point are predicted based on the travel locus of the vehicle.
Step S105b: judging whether the sensor positioned at the next point is a non-road section inlet sensor or not; if yes, the next step is carried out, if no, the sensor indicating the next point is a road section entrance sensor, and the road section entrance sensor is always in a working state, so that a control instruction is not required to be transmitted to the road section entrance sensor, and the process is finished.
Step S105c: judging whether the non-road section entrance sensor positioned at the next point is in a dormant state, if so, controlling the non-road section entrance sensor positioned at the next point to switch to a working state, otherwise, indicating that the non-road section entrance sensor positioned at the next point is in the working state, and not transmitting a control instruction to the non-road section entrance sensor, thus ending the flow.
Wherein, time T2 is related to the vehicle speed and the distance between adjacent points. In the 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.
Referring to fig. 6, a schematic diagram of the status of each point sensor when the road is busy (more vehicles) is shown. The vehicles in the road section are more, and the road section is alternately passed through, so that the non-road section entrance sensor cannot detect the vehicles for a period of time continuously, and therefore, the non-road section entrance sensor of the road section is also all in an operating state.
Referring to fig. 8, a schematic diagram of the state of each point sensor when the road is partially idle (less vehicles) is shown. 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 of 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 reduces the power consumption.
Referring to fig. 3, a schematic structural diagram of a sleep control device of a smart road sensor based on prediction is provided in this embodiment. 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 at the roadside. The sensor includes, but is not limited to: laser radar, camera, millimeter wave radar. These sensors are used to sense and monitor road information in real time. The configuration of a general intelligent road is that one sensor point is deployed every 200m along the road, and 2-4 sensor points are deployed at an intersection. 1-2 laser radars and 1-3 cameras are deployed at each sensor point, 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 are in one-to-one correspondence. 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 running track of the vehicle based on the vehicle parameter information and the map data information, and uploading a prediction result to the cloud platform module. In addition, each edge computing node module is also used for receiving a control instruction transmitted by the cloud platform module and controlling the sensor at the corresponding point location to switch between working and dormant states.
The cloud platform module is used for acquiring map data and dividing sensors deployed on each road section into road section entrance sensors and non-road section entrance sensors based on 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 location to switch between working and dormant states.
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 division 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 wakeup instruction to the edge computing node module.
The prediction module is used for predicting the next point position where the vehicle is about to arrive and the time T2 required for entering the detection range of the sensor positioned at the next point position based on the map data information and the running 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 position within the time T2.
The edge computing node module comprises 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 the working state to monitor vehicles in the visual field range and predicting the running 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 at the corresponding point position to switch from the working state to the dormant state when the vehicle is not detected in 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 always in the working state. And when receiving a wake-up instruction sent by the cloud platform module, controlling the non-road section entry sensor at the corresponding point position to switch from the dormant state to the working state.
In addition, the embodiment also provides a computer device, and 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 that connects the different system components (including the system memory and the processing units).
Bus means one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include 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.
Computer systems/servers typically include a variety of computer system readable media. Such media can be any available media that can be accessed 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, a 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 such as a CD-ROM, DVD-ROM, or other optical media may be provided. In these cases, each drive may be coupled to the bus through one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 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 a memory, such program modules including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules typically carry out the functions and/or methods of the embodiments described herein.
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. Moreover, the computer device may also communicate with one or more networks such as 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 performs the functions and/or methods of the described embodiments of the invention by running programs stored in the system memory.
The computer program described above may be provided in a computer storage medium, i.e. the computer storage medium is encoded with a computer program which, 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 media has a wider meaning, and the propagation path of the computer program is not limited to a tangible medium any more, and can be directly downloaded from a network, etc. 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. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any 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 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.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either 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 of the foregoing. 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 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 ++ 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected 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 schemes formed by equivalent substitution or equivalent transformation fall within the protection scope of the invention.
Claims (9)
1. A dormancy method of intelligent road sensor based on prediction is characterized in that: comprising the steps of (a) a step of,
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;
the non-road section entrance sensor is controlled to be switched from a working state to a dormant state when a vehicle is not detected within time T1, and the road section entrance sensor is controlled to be always in the 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 running track of the vehicles based on the acquired vehicle parameter information;
and predicting the next point to be reached by the vehicle and time T2 required for entering the detection range of the sensor positioned at the next point based on the running track of the vehicle and the map data information, and controlling the sensor positioned at the next point to switch to the working state within the time T2.
2. The prediction-based sleep method for intelligent road sensors as set forth in claim 1, wherein: the predicting the next point where the vehicle is about to arrive and the time T2 required for entering the detection range of the sensor positioned at the next point based on the running track of the vehicle and the map data information, and controlling the sensor positioned at the next point to switch to the working state within the time T2 includes,
predicting a next point to be reached by the vehicle and a time T2 required for entering a detection range of a sensor located at the next point based on a travel track of the vehicle and the map data information;
judging whether the sensor positioned at the next point is a non-road section inlet sensor or not; if yes, the next step is carried out, if not, the process is finished;
judging whether the non-road section entrance sensor positioned at the next point is in a dormant state, if so, controlling the non-road section entrance sensor to switch to a working state, and if not, ending.
3. The prediction-based sleep method for intelligent road sensors as set forth in claim 1, 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.
4. The prediction-based sleep method for intelligent road sensors as set forth in claim 1, wherein: the time T1 is 10min.
5. A sleep control device of wisdom road sensor based on prediction, its 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 on the road side;
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 the vehicle based on the vehicle parameter information, uploading a prediction result to the cloud platform module, receiving a dormancy wakeup instruction transmitted by the cloud platform module, and controlling the sensor module to work and switch dormancy states;
the cloud platform module is used for acquiring map data, dividing sensors deployed on each road section into a road section inlet sensor and a non-road section inlet sensor based on map data information, receiving data uploaded by the edge computing node module, and sending a dormancy wakeup instruction to the edge computing node module; the cloud platform module includes,
the map module is used for acquiring map data information;
the division 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 the data uploaded by the edge computing node module and sending a dormancy wakeup instruction to the edge computing node module;
the prediction module is used for predicting the next point position where the vehicle is about to arrive and the time T2 required for entering the detection range of the sensor positioned at the next point position based on the map data information and the running 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 position within the time T2.
6. The predictive-based intelligent road sensor sleep control apparatus as set forth in claim 5, wherein: the edge computing node module comprises a module for computing,
the sensing module is used for controlling the road section entrance sensor and the non-road section entrance sensor which are in a working state to monitor vehicles in a visual field range and predicting the running track of the vehicle 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 inlet sensor at the corresponding point position to switch from a working state to a dormant state when the vehicle is not detected within the time T1 when a wake-up instruction sent by the cloud platform module is not received, and controlling the road section inlet sensor to be always in the working state; and when receiving a wake-up instruction sent by the cloud platform module, controlling the non-road section entry sensor at the corresponding point position to be switched from a dormant state to a working state.
7. The predictive-based intelligent road sensor sleep control apparatus as set forth in claim 6, wherein: the time T1 is 10min.
8. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized by: the processor, when executing the program, implements the method of any one of claims 1-4.
9. A computer-readable storage medium having stored thereon a computer program, characterized by: the program, when executed by a processor, implements the method of any one of claims 1 to 4.
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