CN114973765A - Environment self-adaptive collision alarm system - Google Patents
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- G—PHYSICS
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/165—Anti-collision systems for passive traffic, e.g. including static obstacles, trees
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- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
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- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems
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Abstract
The invention provides an environment self-adaptive collision alarm system, which comprises: the information acquisition module is used for acquiring environment change information related to the protective frame and equipment information related to the alarm equipment; the information processing module is used for carrying out standardized processing on the environment change information and the equipment information and generating detection data; the AI identification calculation module is used for judging the detection data and the alarm threshold value of the corresponding environment training model; the self-adaptive calculation module is used for analyzing the detection data, generating an alarm correction parameter and automatically correcting the alarm threshold value through the alarm correction parameter. The invention has the advantages that the AI recognition calculation module and the self-adaptive calculation module are integrated on the front-end alarm equipment, so that the generated alarm information is more and more accurate along with the detection and use of the equipment on the spot through the deep learning of the detection data by the preset environment training model even if the initial alarm threshold values of the alarm equipment leaving the factory are the same.
Description
Technical Field
The invention belongs to the technical field of security systems, and particularly relates to an environment self-adaptive collision alarm system.
Background
With the continuous emergence of high and new technologies, the collision alarm device is also continuously updated and iterated. The collision alarm device can enable a traffic department to know collision conditions of bridges and protection frames in time, evaluate collision degree, check events by combining a video system and the like. If the collision alarm device is not provided, the safe running of the railway is influenced because the railway bridge in the remote area is seriously collided, and the accident is serious, so that the irreparable economic loss and the adverse social phenomenon can be caused.
Patent CN201310032281.4 discloses a railway bridge contains limit for height anticollision frame intelligence alarm system, this patent collision sensor be used for monitoring limit for height anticollision frame and receive the violent degree of collision, the tilt sensor is used for detecting the degree of limit for height anticollision frame slope, these two sensors can trigger the alarm and report to the police when the testing result exceedes the settlement threshold value, simultaneously the sensor sends the testing result to the backstage through wireless communication module, SMS module.
Patent 202110081537.5 discloses a wireless alarm system of limit for height collision, and this patent utilizes collision detection device to detect whether protection frame is hit, utilizes gesture detection device to detect protection frame gesture condition, if equipment surpasses the threshold value and sets for, then awakens up the camera that is in low-power consumption standby state, and the camera is awaken the back and is opened the machine and shoot. And uploading the alarm event to the server and the user side.
In summary, in the above invention, the alarm threshold is preset in advance in the alarm device, the alarm value cannot be modified remotely or dynamically adjusted according to the environment, if special environments such as vegetation, wind, passing car resonance and the like are encountered, the effectiveness of the alarm cannot be accurately judged, and a higher false alarm is easily caused, thereby causing unnecessary work of the user and generating unnecessary video flow cost.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides an environment self-adaptive collision alarm system which is applied to alarm equipment arranged on a protective frame and can automatically identify the influence of environmental change on the equipment.
In order to achieve the above object, the present invention provides an environment adaptive collision warning system for an alarm device mounted on a protection frame, comprising:
the information acquisition module is used for acquiring environmental change information related to the protective frame and equipment information related to the alarm equipment;
the information processing module is connected with the information acquisition module and is used for carrying out standardized processing on the environment change information and the equipment information and generating detection data;
the AI recognition calculation module is connected with the information processing module and comprises a model base preset with a plurality of environment training models and is used for judging the detection data and the corresponding alarm threshold of the environment training models and generating corresponding alarm information if the detection data reaches the alarm threshold;
the adaptive computing module is connected with the information processing module and the AI identification computing module and is used for analyzing the detection data, generating an alarm correction parameter and automatically correcting the alarm threshold value through the alarm correction parameter;
and the communication module is connected with the AI identification and calculation module and is used for sending the alarm information to a monitoring terminal connected with the communication module.
Preferably, the system further comprises a model training module, wherein the model training module is connected with the information processing module and the AI identification calculation module, and is used for receiving the detection data, forming training data, importing the training data into the environment model for training and optimization, and storing the optimized environment model into the model library.
Preferably, the environment training model includes: the system comprises an equipment electric quantity and AI identification self-adaptive model, an event merging analysis model in a short time and a special environment preset model.
Preferably, the monitoring terminal further comprises an information compiling module, wherein the information compiling module is connected with the AI identification calculating module and the communication module and is used for compiling the alarm information and transmitting the compiled alarm information to the monitoring terminal through the communication module.
Preferably, the system further comprises a control module, wherein the control module is connected with the AI identification calculation module and the adaptive calculation module, and is used for remotely modifying the alarm threshold and the alarm correction parameter.
Preferably, the alarm device is provided with a storage battery, the storage battery is connected with a solar power supply module, and the information acquisition module is connected with the solar power supply module and is used for acquiring the power supply state of the solar power supply module and the power consumption of the alarm device.
Preferably, the information acquisition module further includes a signal conversion unit and a voltage amplification unit, the signal conversion unit is configured to convert the environment change information into a digital signal, and the voltage amplification unit is configured to amplify a voltage of the digital signal and return the voltage to the information processing module, so as to generate the detection data.
Preferably, the communication module includes at least one of a 4G module, a 5G module, a radio frequency module and a bluetooth module.
Preferably, the environment change information includes a vibration intensity signal collected by the vibration sensor and a multi-dimensional tilt angle signal collected by the tilt angle sensor.
Preferably, the device information includes a return-to-zero position, a correction offset, a device power level, and a system time.
The technical scheme of the invention has the beneficial effects that:
according to the invention, the AI recognition calculation module and the self-adaptive calculation module are integrated on the front-end alarm equipment, so that even though the initial alarm threshold values of the alarm equipment in the factory are the same, the alarm threshold values can be dynamically adjusted and automatically corrected according to different environments along with the detection and use of the equipment on the site through the deep learning of the detection data by the preset environment training model. Through the dynamic adjustment of the alarm threshold value, the interference events generated due to vegetation interference, insecure protective frames, vehicle resonance and other reasons can be effectively filtered, so that the generated alarm information is more and more accurate, the abnormal false alarm phenomenon is reduced, the accuracy of collision alarm is improved, and meanwhile, the flow cost generated by unnecessary alarm information is saved.
The invention presets a model base with a plurality of environment training models, can record detection data generated by different events through a model training module, and leads the detection data into the existing environment training model for training and continuously optimizing the model, thereby further improving the accuracy of collision alarm.
The invention remotely modifies the alarm threshold value and the alarm correction parameter through the control module, and avoids unnecessary work and unnecessary information flow cost easily caused by higher false alarm in the initial stage of model training through methods of manually adjusting the alarm threshold value, the alarm correction parameter and the like.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail exemplary embodiments thereof with reference to the attached drawings, in which like reference numerals generally represent like parts throughout.
FIG. 1 is a schematic diagram of an environmentally adaptive collision warning system according to the present invention;
fig. 2 is a schematic structural diagram of an information acquisition module of the environment adaptive collision warning system of the present invention.
Description of reference numerals:
1. an information acquisition module; 11. a signal conversion unit; 12. a voltage amplifying unit; 2. an information processing module; 3. an AI identification calculation module; 4. a self-adaptive computing module; 5. a communication module; 6. a monitoring terminal; 7. a model training module; 8. an information compiling module; 9. and a control module.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the following describes preferred embodiments of the present invention, it should be understood that the present invention may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
Referring to fig. 1, the present invention provides an environment adaptive collision alarm system, which is applied to an alarm device installed on a protection frame, and comprises:
the information acquisition module 1 is used for acquiring environmental change information related to the protective frame and equipment information related to alarm equipment;
the information processing module 2 is connected with the information acquisition module 1 and is used for carrying out standardized processing on the environment change information and the equipment information and generating detection data;
the AI recognition and calculation module 3 is connected with the information processing module 2, the AI recognition and calculation module 3 comprises a model base preset with a plurality of environment training models and is used for judging the detection data and the alarm threshold value of the corresponding environment training model, and if the detection data and the alarm threshold value are judged to be reached, corresponding alarm information is generated;
the self-adaptive computing module 4 is connected with the information processing module 2 and the AI identification computing module 3 and is used for analyzing the detection data, generating an alarm correction parameter and automatically correcting the alarm threshold value through the alarm correction parameter;
and the communication module 5 is connected with the AI identification and calculation module 3 and used for sending the alarm information to the monitoring terminal 6 connected with the communication module 5.
Specifically, the system mainly comprises an information acquisition module 1, an information processing module 2, an AI identification calculation module 3, an adaptive calculation module 4 and a communication module 5. The signal acquisition module 1 acquires environmental change information related to the protective frame and equipment information related to alarm equipment, wherein the environmental change information comprises monitoring data from equipment sensors, and the equipment information is system data of the equipment, and forms normalized detection data according to a specified protocol. The model library is a dynamic environment model library and comprises an environment characteristic signal library and a collision event signal library, data detection and collection are carried out on the field environment actually used by the alarm equipment through the information acquisition module 1, and an initial environment model is used as an alarm interpretation foundation. The environment training model carries out periodic signal feature extraction on the detection data and classification on the basis of a de-noising neural network automatic recognizer, and dynamically updates a current environment feature signal library and a collision event signal library, wherein the environment feature signal library is a set of signals generated when equipment runs on site under the condition of no serious interference; the collision event signal library is a set of characteristic signals formed by simulating collision events and real collision events occurring in the current scene. The environmental training model can carry out machine deep learning according to detection data contained in interference events or collision events which occur under different environments, when the sample size of the collected detection data is large enough and the model base is stable enough, the trained environmental training model can adjust the alarm threshold value of the current equipment according to different environments so as to realize dynamic adjustment of the alarm threshold value, and the alarm threshold value can trigger alarm information. The environmental training model that the training was accomplished can carry out effective filtration to the suspected interference incident that produces because of vegetation interference, the protection frame is insecure, driving resonance etc. reason, simultaneously, there is the detection data of suspected collision incident in the analysis interference incident, if judge and reach alarm threshold value then trigger alarm information, the equipment alarm that is in under the special interference environment triggers, reduce the emergence of wrong report incident, the readiness of reporting to the police has been promoted, and the scene has linked the camera system of collecting evidence, the monitor terminal platform software has also been linked, upload alarm information to server and user's end through communication module 5, a whole set of collision alarm system of collecting evidence has been formed.
Furthermore, the adaptive computing module 4 can analyze the detection data under different environments to generate different alarm correction parameters, and automatically correct the alarm threshold value through the alarm correction parameters, so that the environmental adaptability is realized, and the alarm use requirements of the alarm device under different environments are met.
An optional example further includes a model training module 7, where the model training module 7 is connected to the information processing module 2 and the AI identification calculation module 3, and is configured to receive the detection data, form training data, import the training data to the environment model for training optimization, and store the optimized environment model in a model library.
Specifically, the model training module 7 records and classifies detection data generated by the interference event to form different types of real training data sets, guides the real training data sets to the existing environment training model for machine deep learning, and uses the different types of real training data sets according to different environment training models. When the detected data are gradually increased, the known model is gradually optimized, and the accuracy of alarming is further improved.
An alternative example, the environmental training model includes: the system comprises an equipment electric quantity and AI identification self-adaptive model, an event merging analysis model in a short time and a special environment preset model.
Specifically, the device electric quantity and the AI identification self-adaptive model analyze the current residual electric quantity of the alarm device, the charging condition of the solar power supply module, the power consumption of the AI identification calculation module 3, the relation between the standby power consumption and the alarm power consumption of the alarm device in the detection data through the self-adaptive calculation module 4, automatically adjust the calculation frequency of the AI identification calculation module 3, automatically reduce the calculation frequency when the residual electric quantity of the alarm device cannot meet the high-frequency AI identification of the AI identification calculation module 3, provide sufficient electric quantity for providing alarm information, and automatically improve the calculation frequency when the charging energy of the solar power supply module is larger than the consumed energy. The short-time event merging analysis model analyzes detection data with small deviation value generated in a certain specific time in a plurality of interference events through the self-adaptive computing module 4, and only generates one piece of alarm information through comparison and analysis with the AI identification computing module 3. The special environment preset model mainly analyzes and filters unstable, regular and quantifiable interference factors aiming at the protective frame in the detection data through the self-adaptive computing module 4, and reduces the influence of the environment training model on the alarm result in the special environment through methods of correcting the alarm correction parameter, increasing the constant coefficient, improving the trigger threshold value of the alarm threshold value and the like. Even if the threshold values of the factory initial alarm threshold values of all alarm devices are the same, a new alarm mechanism of an environment training model is established along with the detection and the use of the devices on the spot, so that the generated alarm information is more and more accurate.
An optional example further includes an information compiling module 8, where the information compiling module 8 is connected to the AI identification calculating module 3 and the communication module 5, and is configured to compile and transmit the alarm information to the monitoring terminal 6 through the communication module 5.
Specifically, the information compiling module 8 can select a matched information transmission protocol according to alarm devices of different models, compile generated alarm information to form an alarm prompt containing various types of information, and transmit the alarm prompt to the terminal device 6 through the communication module 5 so as to be compatible with the alarm devices of different models.
An optional example further comprises a control module 9, wherein the control module 9 is connected to the AI identification calculation module 3 and the adaptive calculation module 4, and is configured to remotely modify the alarm threshold and the alarm correction parameter.
Specifically, the control module 9 is configured to remotely modify the alarm threshold and the alarm correction parameter, and is configured to manually adjust the alarm threshold and the alarm correction parameter when an optimized alarm model is not formed at an initial stage of model training, so as to avoid unnecessary work and unnecessary information flow cost due to a high false alarm.
An alternative example is that the alarm device is provided with a storage battery, the storage battery is connected with a solar power supply module, and the information acquisition module 1 is connected with the solar power supply module and is used for acquiring the power supply state of the solar power supply module and the power consumption of the alarm device.
Specifically, this alarm device adopts solar device power supply alone, need not the operation that external power supply supplied the net just can realize equipment, is convenient for be under construction and maintain, and with low costs, can real-time supervision report to the police, can realize 24 hours all-weather unmanned on duty works.
Further, the battery is the lithium cell, and solar power module includes solar panel, and solar panel is connected with the battery electricity, and solar panel is A level single crystal solar panel, and operating parameter is 60W 18V, and the lithium cell is 80AH lithium cell, the maximum output: 12V, 5A; the battery can be fully charged in about 8-16 hours.
In an optional example, the information acquisition module 1 further includes a signal conversion unit 11 and a voltage amplification unit 12, the signal conversion unit 11 is configured to convert the environment change information into a digital signal, and the voltage amplification unit 12 is configured to amplify a voltage of the digital signal and return the voltage to the information processing module 2, so as to generate the detection data.
Specifically, the information acquisition module 1 includes monitoring signals from sensors, such as monitoring signals of a vibration sensor and an inclination sensor, when the alarm device detects an impact event, the vibration sensor and the inclination sensor transmit vibration intensity signals and multidimensional inclination angle signals with millisecond-level response efficiency, the monitoring signals realize analog-to-digital conversion through an a/D conversion function, and data after a/D conversion needs to be amplified after being converted by a signal amplifier due to signal interference, unobvious weak signal characteristics and the like, and then all the monitoring data are transmitted to the information processing module. The signal distortion is avoided, and the accuracy of signal acquisition is improved.
In an alternative example, the communication module 5 includes at least one of a 4G module, a 5G module, a radio frequency module, and a bluetooth module.
Specifically, the communication module 5 transmits the alarm information to the management system and the mobile terminal through the 4G module, the 5G module, the radio frequency module or the bluetooth module. The 4G module and the 5G module are mainly used in an application environment which is good in network environment, diverse in receiving equipment terminals (a mobile phone terminal, a server, a camera and a sensing detector), unlimited in flow use and relatively low in linkage response real-time performance between equipment.
The radio frequency module is mainly used for field equipment which has no signal shielding and is relatively close to the alarm equipment in a distance range, is provided with a radio frequency receiving module and has high requirements on linkage response real-time performance among the equipment, such as a camera, a voice sound box and other detection devices provided with the radio frequency receiving module.
The Bluetooth module is mainly used for being compatible with traditional equipment, some transmission protocols of the traditional sensing equipment are Bluetooth transmission, and in order to meet the use requirement of the traditional equipment, the equipment also provides a Bluetooth wireless transmission mode.
In an alternative example, the environment change information includes a vibration intensity signal collected by a vibration sensor and a multi-dimensional tilt angle signal collected by a tilt sensor.
An alternative example, the device information includes a return-to-zero position, a correction offset, a device power level, and a system time.
Specifically, the vibration sensor can periodically sense whether the protection frame collides or not, the inclination angle sensor can sense the inclination condition of the protection frame when the protection frame is collided, and then the inclination angle of the protection frame can be judged. According to the vibration intensity signal acquired by the vibration sensor and the multi-dimensional inclination angle signal acquired by the inclination angle sensor, a training data set is formed and used for model training to judge whether the protection frame is impacted by an ultrahigh vehicle or interfered by tree vegetation beside the protection frame.
Example 1
Referring to fig. 1, the present embodiment provides an environment adaptive collision warning system, which is applied to a warning device installed on a protection rack, and includes:
the information acquisition module 1 is used for acquiring environmental change information related to the protective frame and equipment information related to alarm equipment;
the information processing module 2 is connected with the information acquisition module 1 and is used for carrying out standardized processing on the environment change information and the equipment information and generating detection data;
the AI recognition and calculation module 3 is connected with the information processing module 2, the AI recognition and calculation module 3 comprises a model base preset with a plurality of environment training models and is used for judging the detection data and the alarm threshold value of the corresponding environment training model, and if the detection data and the alarm threshold value are judged to be reached, corresponding alarm information is generated;
the self-adaptive computing module 4 is connected with the information processing module 2 and the AI identification computing module 3 and is used for analyzing the detection data, generating an alarm correction parameter and automatically correcting the alarm threshold value through the alarm correction parameter;
and the communication module 5 is connected with the AI identification and calculation module and used for sending the alarm information to the monitoring terminal 6 connected with the communication module 5.
In this embodiment, the system further includes a model training module 7, where the model training module 7 is connected to the information processing module 2 and the AI identification calculation module 3, and is configured to receive the detection data, form training data, import the training data to the environment model for training and optimization, and store the optimized environment model in a model library.
In this embodiment, the environment training model includes: the system comprises an equipment electric quantity and AI identification self-adaptive model, an event merging analysis model in a short time and a special environment preset model.
In this embodiment, the monitoring terminal further includes an information compiling module 8, where the information compiling module 8 is connected to the AI identification calculating module 3 and the communication module 5, and is configured to compile the alarm information and transmit the compiled alarm information to the monitoring terminal 6 through the communication module 5.
In this embodiment, the system further includes a control module 9, and the control module 9 is connected to the AI identification calculation module 3 and the adaptive calculation module 4, and is configured to remotely modify the alarm threshold and the alarm correction parameter.
In this embodiment, the alarm device is provided with a storage battery, the storage battery is connected with a solar power supply module, and the information acquisition module 1 is connected to the solar power supply module and is used for acquiring the power supply state of the solar power supply module and the power consumption of the alarm device.
In this embodiment, the information acquisition module 1 further includes a signal conversion unit 11 and a voltage amplification unit 12, where the signal conversion unit 11 is configured to convert the environment change information into a digital signal, and the voltage amplification unit 12 is configured to amplify a voltage of the digital signal and return the voltage to the information processing module 2, so as to generate detection data.
In this embodiment, the communication module 5 includes at least one of a 4G module, a 5G module, a radio frequency module, and a bluetooth module.
In this embodiment, the environment change information includes a vibration intensity signal collected by the vibration sensor and a multi-dimensional tilt angle signal collected by the tilt angle sensor.
In this embodiment, the device information includes a return-to-zero position, a correction offset, a device electric quantity, and a system time.
To sum up, the AI recognition computation module and the self-adaptive computation module are integrated on the front-end alarm device, so that the alarm accuracy is effectively improved, and even if the initial alarm threshold values of the alarm devices leaving the factory are the same, the device can be used for detecting and using the detection data deeply along with the detection of the device on the site, and further the generated alarm information is more and more accurate. The interference event under the special environment is effectively filtered, and the occurrence of false alarm event can be reduced by the equipment alarm. More accurate alarm information is provided for users.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments.
Claims (10)
1. An environment self-adaptive collision alarm system is applied to alarm equipment arranged on a protective frame, and is characterized by comprising:
the information acquisition module is used for acquiring environmental change information related to the protective frame and equipment information related to the alarm equipment;
the information processing module is connected with the information acquisition module and is used for carrying out standardized processing on the environment change information and the equipment information and generating detection data;
the AI recognition calculation module is connected with the information processing module and comprises a model base preset with a plurality of environment training models and is used for judging the detection data and the corresponding alarm threshold of the environment training models and generating corresponding alarm information if the detection data reaches the alarm threshold;
the adaptive computing module is connected with the information processing module and the AI identification computing module and is used for analyzing the detection data, generating an alarm correction parameter and automatically correcting the alarm threshold value through the alarm correction parameter;
and the communication module is connected with the AI identification and calculation module and is used for sending the alarm information to a monitoring terminal connected with the communication module.
2. The environment adaptive collision warning system according to claim 1, further comprising a model training module, wherein the model training module is connected to the information processing module and the AI recognition and computation module, and is configured to receive the detection data, form training data, import the training data to the environment model for training and optimization, and store the optimized environment model in the model library.
3. The environmentally adaptive collision warning system of claim 1, wherein the environmental training model comprises: the system comprises an equipment electric quantity and AI identification self-adaptive model, an event merging analysis model in a short time and a special environment preset model.
4. The environment adaptive collision warning system according to claim 1, further comprising an information compiling module, wherein the information compiling module is connected to the AI identification calculating module and the communication module, and is configured to compile the warning information and transmit the compiled warning information to the monitoring terminal through the communication module.
5. The environmentally adaptive collision warning system of claim 1, further comprising a control module coupled to the AI identification computation module and the adaptive computation module for remotely modifying the warning threshold and the warning correction parameter.
6. The environment adaptive collision warning system according to claim 1, wherein the warning device is provided with a storage battery, the storage battery is connected with a solar power supply module, and the information acquisition module is connected with the solar power supply module and is used for acquiring the power supply state of the solar power supply module and the power consumption of the warning device.
7. The environment adaptive collision warning system according to claim 1, wherein the information acquisition module further comprises a signal conversion unit and a voltage amplification unit, the signal conversion unit is configured to convert the environment change information into a digital signal, and the voltage amplification unit is configured to amplify a voltage of the digital signal and return the voltage to the information processing module to generate the detection data.
8. The environmentally adaptive collision warning system of claim 1, wherein the communication module comprises at least one of a 4G module, a 5G module, a radio frequency module, and a bluetooth module.
9. The environmentally adaptive collision warning system of claim 1, wherein the environmental change information includes a vibration intensity signal collected by a vibration sensor and a multi-dimensional tilt angle signal collected by a tilt sensor.
10. The environmentally adaptive collision warning system according to claim 1, wherein the device information includes a return-to-zero position, a correction offset, a device power level, a system time.
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