CN109278794B - Train track abnormity detection system - Google Patents
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- CN109278794B CN109278794B CN201811128846.8A CN201811128846A CN109278794B CN 109278794 B CN109278794 B CN 109278794B CN 201811128846 A CN201811128846 A CN 201811128846A CN 109278794 B CN109278794 B CN 109278794B
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- B61K9/00—Railway vehicle profile gauges; Detecting or indicating overheating of components; Apparatus on locomotives or cars to indicate bad track sections; General design of track recording vehicles
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Abstract
The invention provides a train track abnormity detection system, which comprises: the detection module is used for detecting the train track state and acquiring train track state detection data; the anomaly analysis module comprises a comparison unit and an anomaly analysis unit, wherein the comparison unit is used for comparing the train track state detection data with a calibration range; the abnormity analysis unit is used for determining that the train track state detection data is abnormal when the train track state detection data exceeds the calibration range; and the visual early warning module is used for generating alarm information and displaying the alarm information after determining that the train track state detection data is abnormal.
Description
Technical Field
The invention relates to the technical field of trains, in particular to a train track abnormity detection system.
Background
The safety and the stability of train operation can be directly influenced by the condition of the train track state, so that the regular detection of the train track state is very important in order to ensure that the train can be in a good operation state for a long time. In the prior art, a manual detection mode is adopted for track detection. However, the manual detection speed is slow, and the detection result cannot be fed back in time, so that the problem cannot be processed in real time, and the manual detection at a special position has high requirement on professional literacy of detection personnel and high labor cost.
Disclosure of Invention
In view of the above problems, the present invention provides a train track abnormality detection system.
The purpose of the invention is realized by adopting the following technical scheme:
there is provided a train track abnormality detection system including:
the detection module is used for detecting the train track state and acquiring train track state detection data;
the anomaly analysis module comprises a comparison unit and an anomaly analysis unit, wherein the comparison unit is used for comparing the train track state detection data with a calibration range; the abnormity analysis unit is used for determining that the train track state detection data is abnormal when the train track state detection data exceeds the calibration range;
and the visual early warning module is used for generating alarm information and displaying the alarm information after the train track state detection data is determined to be abnormal so that maintenance personnel can repair the abnormal train track.
The detection module comprises a sink node and a plurality of sensor nodes, the plurality of sensor nodes collect train track state detection data, and the sink node collects the train track state detection data of the plurality of sensor nodes and sends the train track state detection data to the abnormity analysis module.
Preferably, the sensor nodes comprise ultrasonic sensors and pressure sensors, and the ultrasonic sensors and the pressure sensors periodically detect the train tracks corresponding to the ultrasonic sensors and the pressure sensors.
Preferably, the comparison unit compares the ultrasonic signal acquired by the ultrasonic sensor with a first calibration range, and the first calibration range is used for expressing an ultrasonic signal attenuation range; the comparison unit is also used for comparing the pressure information acquired by the pressure sensor with a second calibration range, and the second calibration range is used for expressing the change range of the pressure information.
The invention has the beneficial effects that: the wireless sensor network is adopted to realize real-time acquisition of train track state detection data, whether the state of the train track is abnormal or not is determined by comparing the acquired train track state detection data with a calibration range, and if the train track state detection data exceeds the calibration range, the state information of the train track is determined to be abnormal, so that problems can be found in time, the real-time performance is strong, manual participation in detection is not needed, and the labor cost is low.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
Fig. 1 is a block diagram schematically illustrating a structure of a train track abnormality detection system according to an exemplary embodiment of the present invention;
fig. 2 is a block diagram schematically illustrating the structure of an anomaly analysis module according to an exemplary embodiment of the present invention.
Reference numerals:
the device comprises a detection module 1, an abnormality analysis module 2, a visual early warning module 3, a comparison unit 10 and an abnormality analysis unit 20.
Detailed Description
The invention is further described with reference to the following examples.
Referring to fig. 1 and 2, an embodiment of the present invention provides a train track abnormality detection system, including:
the detection module 1 is used for detecting the train track state and acquiring train track state detection data;
the anomaly analysis module 2 comprises a comparison unit 10 and an anomaly analysis unit 20, wherein the comparison unit 10 is used for comparing the train track state detection data with a calibration range; the anomaly analysis unit 20 is configured to determine that the train track state detection data is anomalous when the train track state detection data exceeds the calibration range;
and the visual early warning module 3 is used for generating alarm information and displaying the alarm information after the train track state detection data is determined to be abnormal so that maintenance personnel can repair the abnormal train track.
The detection module 1 comprises a sink node and a plurality of sensor nodes, the plurality of sensor nodes collect train track state detection data, and the sink node collects the train track state detection data of the plurality of sensor nodes and sends the train track state detection data to the abnormity analysis module 2. In a preferred embodiment, the sensor nodes include an ultrasonic sensor and a pressure sensor, and the ultrasonic sensor and the pressure sensor periodically detect the train track corresponding to the ultrasonic sensor and the pressure sensor. Furthermore, the comparison unit 10 compares the ultrasonic signal acquired by the ultrasonic sensor with a first calibration range, where the first calibration range is used to express an ultrasonic signal attenuation range; the comparison unit 10 further compares the pressure information collected by the pressure sensor with a second calibration range, where the second calibration range is used to express a pressure information variation range.
The embodiment of the invention adopts the wireless sensor network to realize the real-time acquisition of the train track state detection data, determines whether the state of the train track is abnormal or not by comparing the acquired train track state detection data with the calibration range, and determines that the state information of the train track is abnormal if the train track state detection data exceeds the calibration range, thereby being capable of finding problems in time, having strong real-time performance, needing no manual detection and having low labor cost.
In one embodiment, after the sensor node acquires the train track state detection data, the transmitting the train track state detection data to the sink node includes:
(1) the distance between the sensor node and the sink node does not exceed a preset lower distance limit WminWhen the system is used, the sensor node directly transmits the acquired train track state detection data to the sink node;
(2) the distance between the sensor node and the sink node exceeds the preset lower distance limit WminAnd the sensor node transmits the acquired train track state detection data to the sink node in a multi-hop mode.
In a preferred implementation manner, each sensor node acquires the identifier and the position information of a neighbor node through exchanging information, wherein the neighbor node is other sensor nodes in the transmission range of the sensor node; the sensor node transmits the acquired train track state detection data to the sink node in a multi-hop mode, and the method comprises the following steps:
(1) setting a sensor node for acquiring train track state detection data as a source node, and determining the total transmission hop number a from the source node to a sink node by the source node;
(2) a source node generates a data packet, wherein the data packet comprises an identifier of the source node, a train track state detection data packet and a hop counter, an initial value of the hop counter is a total transmission hop determined by the source node, and the train track state detection data packet comprises train track state detection data acquired by the source node;
(3) the source node randomly selects one neighbor node from the neighbor nodes as a target node of the hop, and sends the data packet to the target node of the hop;
(4) after receiving the data packet, the destination node updates the data packet, including: subtracting the value of a hop counter in the data packet by one, and storing the train track state detection data acquired by the data packet into a train track state detection data packet in the data packet;
(5) repeating the steps (3) and (4) by taking the destination node as a source node of the next hop until the value of a hop counter in the data packet received by the destination node is 1; and the destination node with the value of the hop counter in the received data packet being 1 stores the train track state detection data acquired by the destination node into the train track state detection data packet in the data packet, and then directly sends the train track state detection data packet to the sink node.
The determination formula of the total transmission hop number a is as follows:
in the formula, Wi,oDistance from source node i to sink node, Wb,oThe distance from the b-th sensor node to the sink node in the network, H is the number of the sensor nodes in the network,
for the rounding function, represent pairs
And (6) carrying out rounding.
The embodiment provides a routing mechanism for transmitting the acquired train track state detection data to the sink node in a multi-hop mode by the sensor node, wherein the routing mechanism determines the total hop number of transmission of the train track state detection data according to the distance from the source node to the sink node, and determines the destination node of the next hop in a random walk mode. The routing mechanism is used for multi-hop transmission of train track state detection data, is simple and convenient, can limit the length of a transmission path, avoids unnecessary energy consumption caused by overlong path due to a random walk mode, and saves the data acquisition cost of a train track abnormity detection system.
In one implementation, if the distance between the source node and the destination node does not exceed the lower limit W of the cooperative distancep-minWhen the data packet is received, the source node directly sends the data packet to the destination node of the hop; if the distance between the source node and the destination node exceeds the lower limit W of the cooperation distancep-minWhen the temperature of the water is higher than the set temperature,the source node compares the energy consumption of the mode of directly transmitting the data packet with the energy consumption of the mode of cooperatively transmitting the data packet, if the energy consumption of the mode of directly transmitting the data packet is the lowest, the source node directly transmits the data packet to the target node of the hop, otherwise, the source node transmits the data packet to the target node of the hop in the mode of cooperatively transmitting the data packet; when the source node sends the data packet to the destination node of the hop, the lower limit W of the cooperation distance is calculated according to the following formulap-min：
In the formula, T is the area of a monitoring area, H is the number of sensor nodes in the network, and F is the transmission radius of a source node;
in this embodiment, a specific transmission mechanism for transmitting a data packet to a destination node by a source node is set, wherein a distance from the source node to the destination node is compared with a lower limit of a cooperation distance, when the distance is greater than the lower limit of the cooperation distance, energy consumption of a direct data packet transmission mode and energy consumption of a cooperative data packet transmission mode are compared, and a transmission mode with the lowest energy consumption is always selected for transmitting the data packet.
Compared with a mode of transmitting data packets through a single transmission mode, the method can better ensure the reliability of data packet transmission, and compared with a mode of transmitting data packets only through a cooperative transmission mode, the method can further reduce the energy consumption of train track state detection data transmission because the transmission mode with the lowest energy consumption is always selected for transmitting the data packets; the embodiment further provides a calculation formula of the lower limit of the cooperation distance, and the determination of the lower limit of the cooperation distance is closer to the actual situation compared with a mode of subjectively presetting a threshold.
In an implementation mode, after acquiring the identification and position information of the neighbor nodes, the source node calculates the weight of each neighbor node, and sorts the neighbor node lists according to the sequence of the weights from large to small to construct the neighbor node list; the source node sends the data packet to the destination node of the hop in a cooperative data packet transmission mode, and the following steps are specifically executed: the source node broadcasts a cooperation message to the first 3 neighbor nodes of the neighbor node list, the first 3 neighbor nodes feed back a cooperation confirmation message to the source node after receiving the cooperation message, the source node selects the neighbor node which firstly feeds back the cooperation confirmation message as a cooperation node, a data packet is sent to the cooperation node, and the cooperation node sends the data packet to a target node of the hop;
in this embodiment, the source node broadcasts the cooperation message to the first 3 neighboring nodes in the neighboring node list, the first 3 neighboring nodes receive the cooperation message and then feed back the cooperation confirmation message to the source node, the source node selects the neighboring node that feeds back the cooperation confirmation message first as the cooperating node, and sends the data packet to the cooperating node, so that the selected cooperating node can be effectively ensured to reliably complete the task of cooperatively transmitting the train track state detection data, and compared with a mode of randomly selecting the cooperating node, the method is more beneficial to saving energy consumption of cooperatively transmitting the train track state detection data, and further saves the data collection cost of the train track abnormality detection system.
Wherein, let DijThe weight of the jth neighbor node of the source node i, DijThe calculation formula of (2) is as follows:
in the formula, GjIs the current residual energy, G, of the jth neighbor nodeminTo preset a minimum energy value, Wi,jIs the distance, s, of the source node i and its j-th neighbor node1Is a predetermined energy weight factor, s2Is a preset distance weighting factor.
In this embodiment, a weight calculation formula of the neighbor node is set, and it can be known from the calculation formula that the neighbor node with more residual energy and greater position advantage has a greater weight. The source node arranges all the neighbor nodes in advance according to the sequence of the weights from large to small, so that the time waste caused by calculation of the weights in the stage of transmitting the train track state detection data is avoided.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (3)
1. Train track anomaly detection system, characterized by includes:
the detection module is used for detecting the train track state and acquiring train track state detection data;
the anomaly analysis module comprises a comparison unit and an anomaly analysis unit, wherein the comparison unit is used for comparing the train track state detection data with a calibration range; the abnormity analysis unit is used for determining that the train track state detection data is abnormal when the train track state detection data exceeds the calibration range;
the visual early warning module is used for generating alarm information and displaying the alarm information after the train track state detection data are determined to be abnormal; the detection module comprises a sink node and a plurality of sensor nodes, the plurality of sensor nodes collect train track state detection data, and the sink node collects the train track state detection data of the plurality of sensor nodes and sends the train track state detection data to the abnormity analysis module; after the sensor node acquires the train track state detection data, the train track state detection data are transmitted to the sink node, and the method comprises the following steps:
(1) the distance between the sensor node and the sink node does not exceed a preset lower distance limit WminWhen the system is used, the sensor node directly transmits the acquired train track state detection data to the sink node;
(2) the distance between the sensor node and the sink node exceeds the preset lower distance limit WminThe sensor node transmits the acquired train track state detection data to the sink node in a multi-hop mode; each sensor node acquires the identification and position information of a neighbor node through exchanging information, wherein the neighbor node is other sensor nodes in the transmission range of the sensor node; train rail that sensor node will gatherThe method for transmitting the road state detection data to the sink node in a multi-hop mode comprises the following steps:
(1) setting a sensor node for acquiring train track state detection data as a source node, and determining the total transmission hop number a from the source node to a sink node by the source node;
(2) a source node generates a data packet, wherein the data packet comprises an identifier of the source node, a train track state detection data packet and a hop counter, an initial value of the hop counter is a total transmission hop determined by the source node, and the train track state detection data packet comprises train track state detection data acquired by the source node;
(3) the source node randomly selects one neighbor node from the neighbor nodes as a target node of the hop, and sends the data packet to the target node of the hop;
(4) after receiving the data packet, the destination node updates the data packet, including: subtracting the value of a hop counter in the data packet by one, and storing the train track state detection data acquired by the data packet into a train track state detection data packet in the data packet;
(5) repeating the steps (3) and (4) by taking the destination node as a source node of the next hop until the value of a hop counter in the data packet received by the destination node is 1; the destination node with the value of the hop counter in the received data packet being 1 stores the train track state detection data acquired by the destination node into the train track state detection data packet in the data packet, and then directly sends the train track state detection data packet to the sink node;
the determination formula of the total transmission hop number a is as follows:
in the formula, Wi,oDistance from source node i to sink node, Wb,oThe distance from the b-th sensor node to the sink node in the network, H is the number of the sensor nodes in the network,
is a function of roundingRepresents a pair
And (6) carrying out rounding.
2. The system according to claim 1, wherein the sensor node includes an ultrasonic sensor and a pressure sensor, and the ultrasonic sensor and the pressure sensor periodically detect the train track corresponding thereto.
3. The train track abnormality detection system according to claim 2, wherein the comparison unit compares the ultrasonic signal acquired by the ultrasonic sensor with a first calibration range, the first calibration range being used to express an ultrasonic signal attenuation range; the comparison unit is also used for comparing the pressure information acquired by the pressure sensor with a second calibration range, and the second calibration range is used for expressing the change range of the pressure information.
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| CN109249956A (en) * | 2018-10-17 | 2019-01-22 | 深圳众宝城贸易有限公司 | Train rail real-time radio detection system |
| CN109109912A (en) * | 2018-10-25 | 2019-01-01 | 深圳美特优科技有限公司 | Train rail intelligent detecting system |
| CN114633774A (en) * | 2022-03-30 | 2022-06-17 | 东莞理工学院 | Rail transit fault detection system based on artificial intelligence |
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