CN117793308A - Artificial intelligent safety protection device for railway line and unmanned crossing - Google Patents
Artificial intelligent safety protection device for railway line and unmanned crossing Download PDFInfo
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Abstract
The invention discloses an artificial intelligent safety protection device along a railway and an unmanned crossing. The device comprises an embedded artificial intelligent processing unit consisting of an embedded microprocessor, a WIFI module and a power management chip, and further comprises a LoRa wireless transmission unit, a 4G wireless transmission unit, a network camera, an audible and visual alarm and a solar power supply unit. The device adopts a general microprocessor which is specially applied to the fields of artificial intelligence and machine vision, uses a built-in NPU (neutral point unit), carries out artificial intelligent target detection on video images locally, and avoids the defects of large transmission data, long transmission time delay and unstable transmission caused by the traditional adoption of a public network for transmitting images; the wireless transmission scheme based on the LoRa technology has the characteristics of long transmission distance, low working power consumption, more networking nodes, strong anti-interference performance, low cost and the like, and is suitable for reliable transmission in complex wireless scenes such as a weak field, a road embedding, a tunnel and the like of a railway.
Description
Technical Field
The invention relates to artificial intelligent terminal deployment realization, in particular to an artificial intelligent safety protection device along a railway and at an unmanned crossing, which is oriented to artificial intelligent model industry landing.
Background
The distribution of the railway unmanned crossing in China is very wide. The unmanned level crossing is mostly distributed at urban and rural junctions, rural highway intersections and the like along the railway. The railway operation speed reaches 120 km, and the full closure of the railway is realized, but a large number of local railways and freight railways with the operation speed lower than 120 km still exist, and the local railways and freight railways are not closed. The intersections of railways, roads, waterways and the like are places where accidents are easy to occur, and illegal behaviors such as running of vehicles and pedestrians to make red light and crossing can be easily caused. The method has the advantages that the method is specially managed at the railway open crossing with the person on duty, and the passing vehicles and pedestrians are managed and guided, so that if the management is bad or the management personnel are not in place, the risk of traffic accidents can be increased. In the unattended open line and road opening, more people depend on the consciousness of pedestrians and vehicles and the safety condition of drivers looking at the front road opening, and the road condition of the front road opening cannot be seen clearly due to the fact that the road is bent or the sight is shielded by woods and crops; even in plain areas, the eye's line of sight is limited, and evading measures are taken too late after dangerous situations are found. Thus greatly influencing the driving safety of trains and causing unnecessary life and property loss to drivers, pedestrians and railway companies.
At present, cameras are installed on part of road sections along the railway, and are mainly used for monitoring train running conditions, and the cameras can monitor the train running conditions along the railway in real time and discover abnormal conditions such as derailment, accidents and the like of the train in time; the camera is also used for maintaining railway facilities, and can monitor the running condition of the railway facilities and timely find abnormal conditions, such as signal lamp damage, road opening abnormality and the like; the camera is also used for preventing crimes, and the camera can monitor the safety condition along the railway and prevent criminal behaviors such as theft, damage and the like. In the mode, video images are required to be collected to a monitoring center or a station duty room, and are manually stared or intelligently processed in the center; a large amount of bandwidth is required to be occupied, reliable and stable transmission is required, and some road sections and road crossings do not have the conditions; meanwhile, the system lacks an acousto-optic warning function for pedestrians and vehicles at the crossing and cannot warn trains entering along the road.
Disclosure of Invention
In view of the potential safety hazards of the open line and the unmanned crossing along the railway, the invention provides an artificial intelligent safety protection device for the railway along the railway and the unmanned crossing. The device is deployed along the railway and at an open crossing, and images of the line or the crossing are observed in real time through a network camera; after the original image information is processed by artificial intelligence at the side of the local terminal equipment, useful information is extracted, wherein the useful information comprises information of the running direction of trains along the railway, information of invasion of livestock and foreign matters, and information of vehicles, pedestrians, livestock and the like at the crossing; extracting the information to form alarm information; the generated alarm information is sent to the adjacent railway line and unmanned road junction artificial intelligent safety protection device through the LoRa, and can also be sent to the remote railway line and unmanned road junction artificial intelligent safety protection device which cannot be covered by the LoRa wireless transmission unit through the 4G wireless transmission unit; the artificial intelligent safety protection devices along the railway and the unmanned road junction receive the alarm information, drive the audible and visual alarm to carry out alarm prompt according to the alarm content, and relay and forward the alarm information to the artificial intelligent safety protection devices along the adjacent railway and the unmanned road junction.
The technical scheme adopted by the invention is as follows: the artificial intelligent safety protection device comprises an embedded artificial intelligent processing unit consisting of an embedded microprocessor, a WIFI module and a power management chip, and further comprises a LoRa wireless transmission unit, a 4G wireless transmission unit, a network camera, an audible and visual alarm and a solar power supply unit; wherein the embedded microprocessor model is RV1126; the model number of the LoRa wireless transmission unit is E22-400T30S; the model number of the 4G wireless transmission unit is EC20-CE; the embedded microprocessor comprises a locally generated alarm information processing flow and a received alarm information processing flow; the embedded microprocessor is connected with the LoRa wireless transmission unit through a serial port 1, is connected with the 4G wireless transmission unit through a Mini PCIe port, is connected with the network camera through an Ethernet port, is connected with the WIFI module through an SDIO port, is connected with the power management chip through an I2C port, is connected with the audible and visual alarm through a serial port 2, and is connected with the solar power supply unit through the power management chip.
The network camera of the device acquires real-time digital video information along the railway and at the unmanned crossing; the embedded artificial intelligence processing unit receives video information of the network camera, analyzes the picture, identifies a specific target object, judges the running direction of the driven train and forms alarm information; the low-power consumption long-distance LoRa wireless transmission unit provides low-power consumption wireless transmission; the 4G wireless transmission unit provides ultra-long distance wireless coverage, remote configuration and management functions and provides standard time and reference clock signals for the device; the road mouth audible and visual alarm provides a safety warning function for the received warning information by emitting a loud warning sound and flashing light; the solar power supply unit provides external power supply for the device.
Compared with the prior art, the invention has the beneficial effects that: the method has the advantages that the general microprocessor RV1126 which is specially applied to the fields of artificial intelligence and machine vision is adopted, the built-in NPU is used for carrying out artificial intelligence target detection on the video image locally, and the defects of large transmission data, long transmission time delay and unstable transmission caused by the traditional transmission of the image by adopting a public network are overcome; the wireless transmission scheme based on the LoRa technology has the characteristics of long transmission distance, low working power consumption, more networking nodes, strong anti-interference performance, low cost and the like, and is suitable for reliable transmission in complex wireless scenes such as a weak field, a road embedding, a tunnel and the like of a railway. The 4G public network transmission unit can realize ultra-long-distance connection of the device, can also be used as a transmission unit of the Internet of things, and has good expansibility.
Drawings
FIG. 1 is a block diagram of the hardware components of an embodiment of the present invention;
FIG. 2 is a schematic diagram of an exemplary application scenario of the present invention;
FIG. 3 is a flow chart of a process for locally generating alert information in accordance with the present invention;
FIG. 4 is a flow chart of the process of receiving alarm information according to the present invention.
Detailed Description
The invention is further described below with reference to the drawings and examples.
As shown in fig. 1, the artificial intelligent safety protection device along the railway and at the unmanned level crossing comprises an embedded artificial intelligent processing unit consisting of an embedded microprocessor, a WIFI module and a power management chip, and also comprises a loRa wireless transmission unit, a 4G wireless transmission unit, a network camera, an audible and visual alarm and a solar power supply unit; wherein the embedded microprocessor model is RV1126; the model number of the LoRa wireless transmission unit is E22-400T30S; the model number of the 4G wireless transmission unit is EC20-CE; the embedded microprocessor comprises a locally generated alarm information processing flow and a received alarm information processing flow; the embedded microprocessor is connected with the LoRa wireless transmission unit through a serial port 1, is connected with the 4G wireless transmission unit through a Mini PCIe port, is connected with the network camera through an Ethernet port, is connected with the WIFI module through an SDIO port, is connected with the power management chip through an I2C port, is connected with the audible and visual alarm through a serial port 2, and is connected with the solar power supply unit through the power management chip.
As shown in fig. 1 and 2, the embedded artificial intelligence processing unit, the LoRa transmission unit and the 4G wireless transmission unit are placed in an outdoor communication box (the solar panel, the webcam, the antenna and the like are exposed); the outdoor communication box, the audible and visual alarm, the network camera and the solar power supply unit are sequentially fixed on the communication rod from bottom to top.
A typical application scene is shown in fig. 2, and in a monitoring area, communication rods can be installed at intervals of 1-10 km according to installation environments and protection requirements. According to the requirement, the network cameras on the communication rod along the line need to irradiate the rail, the network cameras on the road junction need to be compatible with the rail and the road junction, and the number of the network cameras can be not more than 2 (namely, 2 paths are supported maximally) so as to be observed in the uplink and downlink directions.
The network camera of the device can adopt a fixed network camera for video monitoring and has digital code stream RTSP output. According to the requirements of the typical application scene, a Kangwei 200-thousand starlight barrel type network camera is selected, and the model is DS-2CD2T26WDV3-I8. And accessing the single-path or double-path network camera according to the site installation and deployment conditions. In the implementation, a two-way network camera is adopted, and the network camera 1 and the network camera 2 are respectively connected into an Ethernet port 0 and an Ethernet port 1 of the embedded microprocessor through network cables.
The 4G wireless transmission unit of the device selects an LTE Cat 4 wireless module specially designed for M2M and IoT application in mobile communication, and the model is LTE EC20-CE. The network system is covered on the whole, and a high-precision positioning GNSS (GPS/GLONASS/BDS/Galileo/QZSS) receiver is built in the network system, so that time service and positioning functions can be provided.
The LoRa wireless transmission unit of the device selects a LoRa wireless serial port module of the easy-to-be-Baite electronic technology, has the model of E22-400T30S, has various transmission modes, supports the air speed and the configurable transmission power, meets the transmission requirement of 1-10 km of single point, supports the relay automatic networking, and can be used for long-distance transmission.
The audible and visual alarm of the device selects mature industrial voice audible and visual alarm horn, adopts direct current 12V power supply, supports IP65 through serial port control, and the loudness can reach 120 dB.
The solar power supply unit of the device consists of a solar panel and a solar controller, and is prepared from the existing products in the market, and can meet the requirement of 12V5A direct current output.
The artificial intelligence processing unit of the device is mainly based on a Rayleigh core micro RV1126 processor and has four-core CPU@1.5GHz and NPU@2Tops AI edge computing capability. On-board 1G DDR3 memory and 8G EMMC storage. The software is developed based on embedded Linux. Mainly accomplish with network camera, 4G wireless transmission unit, loRa wireless transmission unit, audible and visual alarm's data interaction, mainly include:
1. the state of each unit in the device is monitored, including power supply voltage, unit connection condition and transmission unit communication condition. And storing and retrieving parameters and historical data.
2. And receiving a digital video RTSP code stream from the network camera, decoding the digital video RTSP code stream into continuous image frames, and performing artificial intelligent processing on the images, wherein the artificial intelligent processing comprises target detection, movement detection and train running judgment. And forming alarm information according to preset conditions. And carrying out local acousto-optic alarm on the alarm information, and sending the alarm information to an artificial intelligent safety protection device adjacent to the railway line and the unmanned road junction through a LoRa or 4G wireless transmission unit.
3. And receiving alarm information received from the LoRa wireless transmission unit and the 4G wireless transmission unit, analyzing and processing the alarm information, and carrying out local audible and visual alarm or relay forwarding.
As shown in fig. 3, the local generation alert information processing flow of the present apparatus performs the following operations:
a1, initializing hardware and software configuration of the embedded artificial intelligence processing unit, and respectively establishing connection with the network camera, the LoRa wireless transmission unit, the 4G wireless transmission unit and the audible and visual alarm.
A2, reading the image data of the network camera and preprocessing. And acquiring a real-time image of the network camera through an RTSP, adjusting the size of the image, and outputting the image to be processed.
A3, artificial intelligent target detection: performing target detection operation on an image acquired by a network camera by adopting a YoLo target detection algorithm based on deep learning, judging whether a target is detected, recording a detected result in post-processing if the target is detected, and then entering a train running direction judging operation; if no target is detected, the mobile detection operation is entered.
The detected results are recorded in a post-processing, which include the type of detectable target (cow, horse, sheep, dog, car, truck, tricycle, train, pedestrian), position coordinates in the image (upper left corner, lower right corner coordinates), confidence information.
A4, train running direction judging operation: if the train is monitored, the position coordinates of the train in the image are recorded, the position coordinates of the train in the image are compared with the position coordinates of the train stored in the history, whether the current horizontal position coordinates of the train change is judged according to the comparison result, if the current horizontal position coordinates of the train are judged to be decreased, the train is descending, if the current horizontal position coordinates of the train are judged to be increased, the train is ascending, if the current horizontal position coordinates of the train are judged to be unchanged, the train is stationary, and after the running direction of the train is processed, the detection result processing operation is carried out.
The running direction of the train is judged by judging the change rule of the horizontal position of the train, and the information of the moment, the coordinates and the direction is recorded in the post-processing so as to facilitate the subsequent processing.
A5, motion detection operation: when no target is detected, an image sensing algorithm (pHash) is adopted for the image to be processed, the pHash value of the current image is calculated and cached, the Hamming distance calculation is carried out between the pHash value of the current image and the pHash value of the historical image, the similarity of the images is judged according to the Hamming distance, if the similarity difference is large and exceeds a threshold value, the images are determined to have abnormality (such as unknown objects such as falling rocks) and the like), and the detection result processing operation is carried out after the movement detection post-processing; if the threshold value is not exceeded, the detection result processing operation is directly entered.
The abnormal time, the abnormal type and the abnormal pHash value are recorded in the post-processing stage so as to facilitate the subsequent processing.
A6, processing a detection result: analyzing and filtering artificial intelligent target detection, train running direction judgment and movement detection processing results, combining the results and combining the number, position and time information of the device to form alarm information and storing the alarm information; and the audible and visual alarm is used for carrying out local alarm prompt, and the local alarm prompt is sent to the adjacent railway line and the artificial intelligent safety protection device of the unmanned road junction through the LoRa wireless transmission unit or the 4G wireless transmission unit.
A7, returning to the step A2 to continue to execute the processing flow operation of the locally generated alarm information.
As shown in fig. 4, the processing flow of the alarm receiving information of the present apparatus performs the following operations:
b1, initializing hardware and software configuration.
B2, firstly judging whether alarm information data from the LoRa transmission unit is received or not, and entering alarm information processing operation if the alarm information data are received; if not, then judging whether alarm information data from the 4G wireless transmission unit is received, if so, entering alarm information processing operation; if not, step B2 is re-executed.
B3, alarm information processing: analyzing the alarm information, and extracting the sending time, the sender and the sending content; and analyzing the alarm information, determining whether to execute the alarm of the audible and visual alarm according to the alarm information, and determining whether to execute relay forwarding through the LoRa transmission unit and the 4G wireless transmission unit according to the alarm information.
And B4, returning to the step B2 to continue to execute the processing flow operation of receiving the alarm information.
The following describes, taking fig. 2 as an example, a functional implementation of a typical application scenario of the present apparatus:
the train passes through the rail from left to right, the device A, the device B and the device C are deployed along the railway, and an open crossing exists between the devices B, C. The device observes the railway line and the road junction in real time through the network camera, and recognizes common targets (cattle, horses, sheep, dogs, automobiles, trucks, tricycles, trains and pedestrians) through an embedded artificial intelligent target recognition algorithm; judging whether foreign matters exist or not by matching the similarity of images of unidentified objects such as falling rocks and the like through a motion detection algorithm; the train target is judged to have a running direction according to the change rule of the positions in the continuous images. As shown in fig. 2, the image of the network camera in the device a contains information that there is a cow on the rail and a train is running in, and the artificial intelligence processing unit in the device a detects the information of the cow and the train and judges the running direction of the train through continuous images. The artificial intelligence processing unit of the device B detects that falling rocks exist on the left side of the crossing, and pedestrians and vehicles pass through the open crossing. The device A and the device B respectively send the alarm information of the device A and the device B, simultaneously receive the alarm information from other devices, judge the number, time and position information of a sender on the received alarm information, and determine whether to forward or not. Aiming at the scene of fig. 2, the device A sends out sound and light warning by using sound and light alarms in the device to drive away livestock and inform pedestrians of the information of livestock in the monitoring area; at the same time, the device A receives the alarm (falling stone) information from the device B, performs sound and light alarm locally and can inform the driving-in train driver. The device B informs pedestrians and vehicles at the open road junction of receiving the warning information with the train approaching sent by the device A by using a local audible and visual warning indication, and sends the warning information with the train approaching to the adjacent device C through the LoRa wireless transmission unit or the 4G wireless transmission unit according to the position relation.
The device fully utilizes the characteristics of LoRa and 4G public network transmission according to the requirements of transmission on the rate and the power consumption, adopts a spread spectrum technology by using the LoRa, has strong anti-interference capability, and can reliably transmit data with extremely low power consumption in a complex wireless environment; the 4G public network transmission is used, so that the coverage blind area can be made up, the system can be used as a configuration and management node at a place with sufficient power supply, and an upper management platform can be accessed.
Claims (3)
1. The artificial intelligence safety protection device for the railway line and the unmanned road junction is characterized by comprising an embedded artificial intelligence processing unit consisting of an embedded microprocessor, a WIFI module and a power management chip, and further comprising a LoRa wireless transmission unit, a 4G wireless transmission unit, a network camera, an audible and visual alarm and a solar power supply unit;
wherein the embedded microprocessor model is RV1126; the model number of the LoRa wireless transmission unit is E22-400T30S; the model number of the 4G wireless transmission unit is EC20-CE; the embedded microprocessor comprises a locally generated alarm information processing flow and a received alarm information processing flow; the embedded microprocessor is connected with the LoRa wireless transmission unit through a serial port 1, is connected with the 4G wireless transmission unit through a Mini PCIe port, is connected with the network camera through an Ethernet port, is connected with the WIFI module through an SDIO port, is connected with the power management chip through an I2C port, is connected with the audible and visual alarm through a serial port 2, and is connected with the solar power supply unit through the power management chip.
2. The artificial intelligence safety device along railway and unmanned crossing of claim 1, wherein the embedded artificial intelligence processing unit, the LoRa transmission unit, the 4G wireless transmission unit are placed in an outdoor communication box; the outdoor communication box, the audible and visual alarm, the network camera and the solar power supply unit are sequentially fixed on the communication rod.
3. The artificial intelligence safety apparatus for a railway line and an unmanned crossing according to claim 2, wherein the locally generated warning information processing flow performs the following operations:
a1, initializing hardware and software configuration of an embedded artificial intelligence processing unit, and respectively establishing connection with a network camera, a LoRa wireless transmission unit, a 4G wireless transmission unit and an audible and visual alarm;
a2, reading image data of the network camera and preprocessing the image data;
a3, artificial intelligent target detection: performing target detection operation on an image acquired by a network camera by adopting a YoLo target detection algorithm based on deep learning, judging whether a target is detected, recording a detected result in post-processing if the target is detected, and then entering a train running direction judging operation; if the target is not detected, entering a mobile detection operation;
a4, train running direction judging operation: if the train is monitored, the position coordinates of the train in the image are recorded, the position coordinates of the train are compared with the position coordinates of the train stored in the history, whether the current horizontal position coordinates of the train change or not is judged according to the comparison result, if the current horizontal position coordinates of the train are judged to be decreased, the train is descending, if the current horizontal position coordinates of the train are judged to be increased, the train is ascending, if the current horizontal position coordinates of the train are judged to be unchanged, the train is stationary, and after the running direction of the train is processed, the detection result processing operation is carried out;
a5, motion detection operation: when no target is detected, an image sensing pHash algorithm is adopted for the image to be processed, the pHash value of the current image is calculated and cached, hamming distance calculation is carried out on the pHash value of the historical image, the similarity of the image is judged according to the Hamming distance, if the similarity difference is large and exceeds a threshold value, the image is determined to be abnormal, and the detection result processing operation is carried out after the movement detection post-processing; if the threshold value is not exceeded, directly entering a detection result processing operation;
a6, processing a detection result: analyzing and filtering artificial intelligent target detection, train running direction judgment and movement detection processing results, combining the processing results, generating alarm information and storing the alarm information; the local alarm prompt is carried out by using an audible and visual alarm, and the local alarm prompt is sent to the adjacent railway line and the artificial intelligent safety protection device of the unmanned road junction through a LoRa wireless transmission unit or a 4G wireless transmission unit;
a7, returning to the step A2 to continuously execute the processing flow operation of the locally generated alarm information;
the process flow of receiving the alarm information performs the following operations:
b1, initializing hardware and software configuration;
b2, firstly judging whether alarm information data from the LoRa transmission unit is received or not, and entering alarm information processing operation if the alarm information data are received; if not, then judging whether alarm information data from the 4G wireless transmission unit is received, if so, entering alarm information processing operation; if not, re-executing the step B2;
b3, alarm information processing: analyzing the alarm information, and extracting the sending time, the sender and the sending content; analyzing the alarm information, determining whether to execute the alarm of the audible and visual alarm according to the alarm information, and determining whether to execute relay forwarding through the LoRa wireless transmission unit and the 4G wireless transmission unit according to the alarm information;
and B4, returning to the step B2 to continue to execute the processing flow operation of receiving the alarm information.
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