CN108391086B - Industrial video linkage analysis method and system integrating event perception and position sensing - Google Patents
Industrial video linkage analysis method and system integrating event perception and position sensing Download PDFInfo
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Abstract
The invention discloses an industrial video linkage analysis method and system integrating event perception and position sensing, which comprises the following steps: the system comprises a cloud server and a plurality of equipment terminals communicated with the cloud server; the equipment terminal acquires surrounding environment information, uploads the acquired information to the cloud server side, and acquires a processing result and an instruction of the cloud server side; the cloud server end is used for realizing personnel positioning through linkage with the personnel positioning module according to data sent by each equipment terminal, realizing rescue guiding through linkage with the rescue guiding module, realizing early warning analysis through linkage with the early warning analysis module, realizing industrial video playing and permanent storage through linkage with the industrial video storage module and feeding generated data back to the equipment terminal; the invention realizes the omnibearing display of the factory monitoring video, carries out early warning and processing on industrial emergencies through a plurality of layers and different angles, generates a danger avoiding and escaping route and simultaneously displays a real-time picture when the emergencies occur.
Description
Technical Field
The invention relates to the technical field of industrial video big data, in particular to an industrial video linkage analysis method and system integrating event perception and position sensing.
Background
In the field of industrial security, the main data source is video, and unlike structured data in other industries, video is unstructured data and cannot be directly processed or analyzed by a computer. Therefore, for the industry to apply large video data, firstly, the intelligent analysis technology is used to convert the unstructured video data into the structured information that can be recognized and processed by the computer, so that the videos can be searched, compared, analyzed, etc. by the computer. However, early industrial enterprises did not have an effective technical means to expand this work.
In addition to the unstructured video data, the big data of the industrial security industry has some typical features compared with other industries, and the features put higher demands on the storage system. For example, the data volume of video is particularly large, and is increasing continuously, the storage system is required to be able to be conveniently expanded; the video has low value density, is real-time and continuous, is evanescent and cannot be lost, and requires high reliability of storage; the hard disk of a PB-level storage system has thousands or even tens of thousands of blocks, so that the hard disks can be damaged every day, and if the hard disks are damaged, the hard disks need to be replaced very inconveniently, thereby providing a maintainability requirement for the storage; since it is an industrial big data video, it is stated that there are various users accessing the video data, so there are higher requirements for concurrent access, and so on. To meet the requirements, the traditional storage mode has no power, and only cloud storage can support the traditional storage mode.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides the industrial video linkage analysis method and system based on event perception and position sensing, which are simple and convenient to operate, accurate in data and convenient to display.
In order to achieve the purpose, the invention adopts the following technical scheme:
an industrial video linkage analysis system integrating event perception and position sensing comprises: the system comprises a cloud server and a plurality of equipment terminals communicated with the cloud server; wherein the content of the first and second substances,
the method comprises the steps that an equipment terminal collects surrounding environment information, uploads the collected information to a cloud server side, and obtains a processing result and an instruction of the cloud server side;
and the cloud server side realizes personnel positioning through linkage with the personnel positioning module according to data sent by each equipment terminal, realizes rescue guiding through linkage with the rescue guiding module, realizes early warning analysis through linkage with the early warning analysis module, realizes industrial video playing and permanent storage through linkage with the industrial video storage module, and feeds generated data back to the equipment terminal.
The cloud server side comprises: the encryption and decryption module is respectively communicated with the personnel positioning module, the industrial video storage module, the early warning analysis module, the rescue guide module and the geographic space management module, and is also communicated with the equipment terminal.
The device terminal includes: the cloud client is communicated with the encryption and decryption module of the cloud server through the communication module, and is also communicated with the identity recognition module, the positioning module, the human-computer interaction module, the sensor module, the energy supply control module and the data bus interface respectively, and the identity recognition module is connected with the identity recognition card; the data bus interface is respectively connected with the personnel management module and the industrial equipment management module; the energy supply control module is connected with the energy supply circuit, and the equipment terminal reminds the mobile terminal of the relevant leader of the equipment with the sudden abnormality for the first time and processes and reports the mobile terminal to the cloud server.
The sensor module includes: water level sensor, smoke sensor, temperature sensor, humidity sensor and vibration sensor.
The encryption and decryption module decrypts and analyzes the received data and encrypts and packages the transmitted data at the same time;
the personnel positioning module receives the position information positioned by the constructor and stores the position coordinates;
the industrial video storage module is used for storing the real-time video of the abnormal event recorded by the equipment terminal and the historical abnormal video downloaded in the disk array.
The early warning analysis module is used for training the neural network model by using historical data of the equipment, analyzing and judging current real-time data by using the trained neural network model, calculating a deviation value between a current value and a neural network model expected value, indicating that the equipment has a certain fault head or degradation trend when the deviation value is larger than a set range or the deviation value has a continuously amplified trend, finding out potential abnormalities in industrial equipment and a construction process by combining video monitoring and video recording, and providing a solution;
the neural network model consists of an input layer, a process neuron hidden layer and a process neuron output layer, wherein the input layer is used for completing the input of signals, the process neuron hidden layer is used for completing the spatial weighting aggregation and excitation operation of the input signals, and simultaneously, the output signals are transmitted to the output layer and the weights are fed back to the input layer; the output layer is used for completing spatial weighted aggregation and time aggregation operation of output signals of the hidden layer of the process neuron and system output.
The rescue guiding module is used for positioning constructors and industrial equipment for fault alarm, guiding a nearest rescue vehicle to carry out rescue, generating a danger-avoiding escape route, recording and displaying real-time monitoring pictures on the route, and storing the recorded videos into the industrial video storage module.
The geographic space management module is used for positioning buildings, industrial equipment and cameras of the industrial plant area, storing position information of the buildings, the industrial equipment and the cameras of the industrial plant area, generating an interactive map for a user, and providing basic data for the rescue guide module to generate a danger avoiding and escaping route display.
The communication module adopts at least one communication mode of GPRS, EDGE, CDMA, 3G and 4G, WIFI to complete data interaction between the cloud client and the cloud server;
the water level sensor is connected with the cloud client in a system bus, serial port or Modbus mode, is installed at a plurality of different positions of the industrial equipment, and measures the water level height in a set range near the industrial equipment.
The smoke sensor is connected with the cloud client in a system bus, serial port or Modbus mode, is installed at a plurality of different positions of the industrial equipment, and measures the smoke concentration in a set range near the industrial equipment.
The temperature sensor is connected with the cloud client in a system bus, serial port or Modbus mode, is installed at a plurality of different positions of the industrial equipment, and measures the temperature in a set range near the industrial equipment.
The humidity sensor is connected with the cloud client in a system bus, serial port or Modbus mode, is installed at a plurality of different positions of the industrial equipment, and measures the humidity in a set range near the industrial equipment.
The vibration sensor is connected with the cloud client in a system bus, serial port or Modbus mode, is installed at a plurality of different positions of the industrial equipment, and measures the vibration frequency in a set range near the industrial equipment.
The energy supply control module is used for completing the connection and disconnection of energy supply of industrial equipment;
the identity recognition module is connected with the identity recognition card in a non-contact mode to acquire information in the identity recognition card; the identity identification card is any one of the following cards: IC cards, RFID cards, smart cards, infrared read-write cards, and other cards for storing information;
the identity recognition module is connected with the cloud client in a serial port mode;
the positioning module is connected with the cloud client through a system bus, and the current position information of the industrial constructors is collected in real time.
The man-machine interaction module comprises: the system comprises a camera, a liquid crystal display, a touch screen, a keyboard, an audio playing unit, a cloud server and a cloud client, wherein the camera, the liquid crystal display, the touch screen, the keyboard and the audio playing unit are used for finishing information interaction between a user and an equipment terminal, receiving operation and instructions of the user, and displaying current industrial equipment information, sensor data and data returned to the cloud client by the cloud server to the user;
the data bus interface is connected with the personnel management module and the industrial equipment management module, and acquires constructor information data and current information data of industrial equipment in real time. The data bus interface supports, but is not limited to, CAN, Modbus, 485, 232 bus interfaces.
The linkage analysis method of the industrial video integrating event perception and position sensing comprises the following steps:
step (1): the cloud client acquires industrial environment data and displays the industrial environment data through the man-machine interaction module;
step (2): the cloud client performs preliminary analysis and judgment on the acquired data, alarms if the data are judged to be abnormal, cuts off energy supply of equipment through an energy supply control module if the abnormal data exceed a set threshold value, sends alarm information to a mobile terminal of a department leader, and then packages and encrypts the acquired data through an encryption and decryption module and uploads the encrypted data to a cloud server;
and (3): after receiving the data uploaded by the cloud client, the cloud server firstly judges whether the data is abnormal data again,
if the data is abnormal data, the rescue guide module generates a danger-avoiding escape route to guide rescue, takes a camera real-time picture of the danger-avoiding escape route, and records and saves abnormal event records;
if the data is not abnormal data, the early warning analysis module carries out early warning analysis at the cloud server end; the early warning analysis of the cloud server side is to store data of information in a data packet uploaded by the cloud client side, perform data mining by combining historical data and perform comprehensive early warning analysis in all directions.
And (4): the cloud server side transmits the data subjected to early warning analysis by the early warning analysis module back to the cloud client side; the cloud client displays the received early warning analysis data to a user;
and (5): on the basis of the step (3), permanently storing the abnormal event video file in an industrial video storage module, calling the detailed condition of the abnormal video in a set time period in the industrial video storage module, and playing back the industrial abnormal real-time video or historical abnormal video for analyzing the reason of the abnormal generation; in event playback, the corresponding time is directly located according to the recorded event start time without watching the entire video.
In the step (1), the cloud client acquires data of the smoke sensor, the temperature sensor, the humidity sensor, the vibration sensor, the water level sensor, the identity recognition module, the positioning module, the human-computer interaction module, the personnel management module and the industrial equipment management module at regular time.
In the step (2), the cloud client judges the acquired data; if the smoke concentration, temperature, humidity, vibration frequency or water level information exceeds a set threshold value, the energy supply control module cuts off energy supply of industrial equipment, meanwhile, an alarm is given out and a mobile terminal of a department leader is informed through the communication module, the cloud client side packs and encapsulates the acquired abnormal data, packs and encrypts the abnormal data through the encryption and decryption module and uploads the abnormal data to the cloud server side;
if the alarm threshold is not exceeded, the cloud client packs and encapsulates the acquired data, encrypts the data through the encryption and decryption module and uploads the data to the cloud server;
in the step (3), the encryption and decryption module decrypts the data uploaded by the cloud client, analyzes the data, the cloud server judges the data type,
if the cloud client uploads the abnormal data to the cloud server, the cloud server uses a pre-trained neural network model to judge, if the judgment result is still the abnormal data, the rescue is guided by the rescue guide module according to the position information in the data, a danger-avoiding escape route is generated to guide the rescue, a camera of the danger-avoiding escape route is taken to track a real-time picture, a video is stored in the industrial video storage module, and the abnormal event information is recorded to the relational database;
the rescue guide module receives geographic information and position data information of construction personnel and rescue vehicles in real time and stores the geographic information and the position data information in a database for positioning the construction personnel and the rescue vehicles in real time, once the rescue guide module receives a signal of an abnormal event, the position information is read from the database for positioning the construction personnel and the rescue vehicles in real time, all paths of the construction personnel leaving the abnormal event occurrence place are calculated according to the current positions of the construction personnel, the shortest escape route is obtained through comparison, the shortest route is sent to the rescue vehicle closest to the current position of the construction personnel to implement rescue, all real-time monitoring pictures in the route and the escape route are displayed in the rescue guide module, real-time recording of monitoring videos of the abnormal event is achieved, and the recorded videos are stored in the industrial video storage module.
If the cloud client uploads the normal data to the cloud server, the cloud server uses a pre-trained neural network model to judge, if the judgment result is abnormal data, the early warning analysis module sends out early warning to inform a department-led mobile terminal, calls a real-time picture of a camera to send to the department-led mobile terminal, records the video and stores the video in an industrial video storage module, and records the abnormal event information to a relational database as the data called when the abnormal video is played back; and if no abnormity exists, the cloud client receives the result returned by the cloud server and stops alarming.
In the step (4), the cloud server side encrypts the data after the early warning analysis through the data encryption and decryption module and then transmits the data back to the cloud client side; and after the cloud client calls the data encryption and decryption module to decrypt, the received data is displayed to the user through the man-machine interaction module.
In the step (5), the abnormal event video file is permanently stored in an industrial video storage module,
for real-time video recording, recording the real-time video into an MP4 format file through a real-time streaming protocol (RTSP), storing information into a relational database, simultaneously forwarding the information to a distributed file system (HDFS) of an industrial video storage module, and storing the recorded real-time video into the distributed file system (HDFS) of the industrial video storage module to realize permanent storage of the recorded real-time video of the abnormal event;
for abnormal historical video extraction, acquiring time and a camera corresponding to an abnormal video by reading abnormal event information stored in a relational database, extracting an abnormal video file from a disk array, forwarding the abnormal video file to a distributed file system (HDFS), and storing the video into an industrial video storage module (HDFS), so that the historical abnormal video extracted from the disk array is permanently stored;
calling the detailed condition of the abnormal video in a certain period of time in the industrial video storage module, playing back the industrial abnormal real-time video or historical abnormal video, and directly positioning to the corresponding time according to the recorded event starting time in the event playback without watching the whole video.
In event video storage, the real-time video recording and historical data playback are based on a storage mechanism consisting of an HDFS (Hadoop distributed File System), a relational database and a disk array.
In the linkage of abnormal event detection and video, the event needs to be recorded into a uniform readable relational database after being processed, and then the data extraction system of the industrial video system updates the abnormal event information into the system at regular time for event playback.
The storage mechanism and the video positioning playing technology are composed of the HDFS, the relational database and the disk array.
Compared with the prior art, the invention has the beneficial effects that:
the invention realizes the detection of various information such as smoke, humidity, temperature, vibration, water level and the like around the industrial equipment, can carry out all-around detection and early warning, data mining analysis, rescue guidance and accident early warning by linking with other systems, and can carry out early warning and analysis on the safety of the industrial equipment and constructors so as to support production decision.
The invention provides the industrial video linkage analysis method and system based on event perception and position sensing, which are simple and convenient to operate, accurate in data and convenient to display. The research idea of the industrial video technology is applied to enterprises to construct an industrial video enterprise network, and the establishment of the industrial video linkage analysis method and system fusing event perception and position sensing has important significance in the industrial digital enterprise era.
The industrial video linkage completes real-time playing of industrial videos and abnormal video data analysis and acquisition through a real-time stream data analysis processing technology, comprises associated information of a personnel positioning system, an industrial video storage system, an early warning analysis system, a rescue guide system and the like, thoroughly solves the problem of data sharing between a monitoring system and other systems, can realize full utilization of a monitoring camera, realizes permanent storage of key videos by utilizing a big data technology, and solves two core problems of 'unavailable storage' and 'unavailable finding' of the monitoring videos.
The method and the system have the following advantages:
(1) the method is simple and convenient to operate, the camera pictures can be viewed according to regions by clicking the map, the whole industrial plant area is subjected to visual processing by adopting the thought of the intelligent building, and the monitoring video is designed to be displayed in an all-round mode.
(2) The method realizes the permanent storage of the industrial video, and can quickly display the permanently stored real-time recorded video or historical abnormal video. Meanwhile, the waste of storage space is greatly reduced, and if all data are stored permanently, the hard disk is high in cost and difficult to manage. The invention only aims at the abnormal data for permanent storage, saves cost, is simple to realize and convenient to manage, but only stores partial videos.
(3) The invention detects industrial equipment in all directions, and carries out early warning and processing on the safety of the industrial equipment through a plurality of layers and different angles, thereby ensuring the safety of personnel and the use safety of the industrial equipment and prolonging the service life of the industrial equipment.
(4) The server end of the industrial video linkage analysis system has strong performance, the computing capability, the analysis capability and the judgment capability are far greater than the performance of the equipment terminal, the system has higher efficiency, and can judge and analyze more quickly and accurately, and the comprehensiveness, the reliability and the integrity of data analysis are ensured.
(5) The safety early warning protection server provides the most reliable and safe data storage, avoids the problems of data loss, virus invasion and the like, and provides powerful basis for early warning analysis.
(6) The early warning analysis module utilizes a neural network model to realize a system for early intelligent early warning of the fault states of equipment and systems of a factory. Compared with the traditional alarm system based on the predefined limit value, the early warning analysis module can learn and train the filing historical data through a neural network algorithm to form a normal operation model of the industrial equipment or the production system, compares the normal operation model with the real-time operation state, calculates the deviation between the current value and the expected value calculated by the model, and provides an early warning function of the fault and the degradation trend so as to reduce the risk of equipment fault and improve the reliability of equipment operation.
The early warning analysis module does not independently judge a single industrial equipment measuring point, but integrally analyzes all related signals of certain industrial equipment or a production process, so as to judge whether the equipment is in fault propagation. The traditional monitoring system only generates an alarm based on a fixed limit value, and the early warning analysis module can analyze all operation modes in real time and provide early warning according to the deviation between the current state and the model operation state. The early warning analysis module has a simple engineering configuration method, is very simple for modeling any industrial equipment and industrial process, has no any physical model or mathematical model, and only adds related measuring points into a model list. The early warning analysis module has a simple modeling function, can be used for modeling various different devices or subsystems, and even can be used for modeling the whole plant device. The early warning analysis module has a self-defined warning rule function, all available signals can be based on a predefined logic judgment rule, so that corresponding rules can be predefined for some typical defects or faults of the equipment, once the rules are triggered, the fact that certain defects or faults exist definitely is meant, and therefore the accuracy of defect and fault judgment is improved. The early warning analysis module may not only monitor the expected value and residual (difference between the expected value and the current value) of the signal, but may also provide a prediction value and state quantity output indicating whether the signal will have a deviation within a specified time interval or at what time the deviation is predicted to occur.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
Fig. 1 is a diagram of a cloud server side structure of an industrial video linkage analysis system of the present invention;
FIG. 2 is a terminal structure diagram of the industrial video linkage analysis system apparatus of the present invention;
FIG. 3 is a flow chart of a data processing method of the industrial video linkage analysis system of the present invention;
FIG. 4 is a schematic diagram illustrating a danger avoiding and escaping route of the industrial video linkage analysis system of the present invention;
FIG. 5 is a flow chart of the industrial video linkage analysis system device terminal data determination of the present invention;
FIG. 6 is a flow chart of data determination at the cloud server side of the industrial video linkage analysis system according to the present invention;
fig. 7 is a schematic view of a video storage structure of a cloud server side of the industrial video linkage analysis system according to the present invention.
Wherein: 10, a device terminal; 20, a cloud server side; 30, an identification card; 40, a personnel management module; 50, an industrial equipment management module; 60, an energy supply circuit;
101, a cloud client; 102, a communication module; 103, a positioning module; 104, a human-computer interaction module; 105, a water level sensor; 106, a smoke sensor; 107, a temperature sensor; 108, a humidity sensor; 109, a vibration sensor; 110, an identity recognition module; 111, a data bus interface; 112, an energy supply control module;
201, an encryption and decryption module; 202, a personnel location module; 203, an industrial video storage module; 204, an early warning analysis module; 205, a rescue guidance module; 206, geospatial management module.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
Fig. 1 is a structure diagram of a cloud server end of an industrial video linkage analysis system according to the present invention, and as shown in fig. 1, the industrial video linkage analysis system based on fusion of event awareness and location sensing according to the present invention includes: the device terminal 10 and the cloud server 20.
The cloud server 20 includes: the system comprises an encryption and decryption module 201, a personnel positioning module 202, an industrial video storage module 203, an early warning analysis module 204, a rescue guide module 205 and a geographic space management module 206.
The encryption and decryption module 201 decrypts and analyzes the received data, and encrypts and packages the transmitted data.
The personnel positioning module 202 stores the position information of the industrial personnel received in real time, associates the latest camera information, and prepares for rescue of emergency safety events.
The industrial video storage module 203 stores the real-time video of the abnormal event recorded by the industrial video linkage analysis system and the historical abnormal video downloaded in the disk array.
The early warning analysis module 204 performs mining analysis on historical data and current data of the equipment, combines video monitoring and recording to find out possible abnormalities in industrial equipment and a construction process and provide solutions;
the rescue guiding module 205 positions constructors and industrial equipment for fault alarm, guides the nearest rescue vehicle to carry out rescue, generates a danger-avoiding escape route, records and displays real-time monitoring pictures on the route, and stores the recorded videos into an industrial video storage module.
The rescue guidance module 205 includes: the system comprises a database for real-time positioning of constructors and rescue vehicles, a danger avoiding and escaping route generating submodule, a real-time video playing submodule and a real-time video recording submodule.
A database for real-time positioning of constructors and rescue vehicles: and receiving and storing the geographic information and position data information of the constructors and the rescue vehicles in real time.
An danger avoiding and escaping route generating submodule: once the rescue guidance module 205 receives the abnormal event occurrence information signal, it reads the position information from the database for real-time positioning of the constructor and the rescue vehicle, calculates the path from the abnormal event occurrence location according to the position of the constructor, obtains the shortest escape route by comparison, sends the shortest route information to the nearest rescue vehicle to perform rescue, and displays all real-time monitoring pictures in the route and the escape route in the rescue guidance system, as shown in fig. 4.
A real-time video playing submodule: and the real-time playing of the monitoring video of the abnormal event is realized.
A real-time video recording submodule: the real-time recording of the monitoring video of the abnormal event is realized, and the recorded video is stored in the industrial video storage module 203.
The geospatial management module 206 positions the buildings, the industrial equipment and the cameras of the industrial plant area, stores the position information of the buildings, the industrial equipment and the cameras of the industrial plant area, generates an interactive map for a user, and provides basic data for the rescue guidance module to generate a danger avoiding and escaping route display.
The geospatial management module 206 is a server for positioning the buildings, the industrial equipment and the cameras of the industrial plant and storing the position information of the buildings, the industrial equipment and the cameras of the industrial plant.
The servers are connected through a computer network.
As shown in fig. 2, the device terminal 10 includes a cloud client 101, a communication module 102, a positioning module 103, a human-computer interaction module 104, a water level sensor 105, a smoke sensor 106, a temperature sensor 107, a humidity sensor 108, a vibration sensor 109, an identity recognition module 110, a data bus interface 111, and an energy supply control module 112;
the cloud client 101 collects data of the positioning module 103, the human-computer interaction module 104, the water level sensor 105, the smoke sensor 106, the temperature sensor 107, the humidity sensor 108, the vibration sensor 109, the identity recognition module 110 and the data bus interface 111, performs data interaction with the cloud server 20 through the communication module 102, sends the collected data to the cloud server 20, submits a processing application, and obtains a processing result and an instruction of the cloud server 20.
The communication module 102 completes data interaction between the cloud client 101 and the cloud server 20, and completes data interaction between the cloud client 101 and the cloud server by adopting at least one communication mode including GPRS, EDGE, CDMA, 3G, and 4G, WIFI;
the positioning module 103 is connected with the cloud client 101 through a system bus, collects current position information of industrial constructors in real time, and requests relevant industrial equipment and personnel position information data from the geographic space management module.
The human-computer interaction module 104 comprises a liquid crystal display, a touch screen, a keyboard and an audio playing unit, completes information interaction between a user and the equipment terminal 10, receives operation and instructions of the user, and displays current industrial equipment information, sensor data and data returned to the cloud client 101 by the cloud server 20 to the user;
the water level sensor 105 is connected with the cloud client 101 in a system bus, serial port or Modbus mode, and is installed at a plurality of different positions in the industrial equipment to measure the water level height around the industrial equipment.
The smoke sensor 106 is connected with the cloud client 101 in a system bus, serial port or Modbus mode, is installed at a plurality of different positions in the industrial equipment, and measures the smoke concentration around the industrial equipment.
The temperature sensor 107 is connected with the cloud client 101 in a system bus, serial port or Modbus mode, and is installed at a plurality of different positions in the industrial equipment to measure the ambient temperature of the industrial equipment.
The humidity sensors 108 are connected with the cloud client 101 in a system bus, serial port or Modbus manner, and are installed at a plurality of different positions in the industrial equipment to measure the humidity around the industrial equipment.
The vibration sensor 109 is connected with the cloud client 101 in a system bus, serial port or Modbus mode, and is installed at a plurality of different positions in the industrial equipment to measure the vibration frequency around the industrial equipment.
The identity recognition module 110 is connected with the identity recognition card 30 in a non-contact mode to acquire information in the identity recognition card; the identity recognition module 110 is connected with the cloud client 101 in a serial port mode;
the identification card 30 is any one of the following: IC cards, RFID cards, smart cards, infrared read-write cards, and other cards for storing information;
the energy supply control module 112 is connected with the energy supply circuit 60, and the on/off of the industrial equipment energy supply circuit 60 is completed through internal control;
the data bus interface 111 is connected to the personnel management module 40 and the industrial equipment management module 50, and acquires the information data of the constructors and the current information data of the industrial equipment in real time and sends the information data to the cloud client 101. The data bus interface 111 is connected to the cloud client 101 via a system bus. The data bus interface supports, but is not limited to, CAN, Modbus, 485, 232 bus interfaces.
The data processing method of the industrial video linkage analysis system is shown in fig. 3:
the device terminal 10 collects data of the positioning module 103, the human-computer interaction module 104, the water level sensor 105, the smoke sensor 106, the temperature sensor 107, the humidity sensor 108, the vibration sensor 109, the identity recognition module 110 and the data bus interface 111, including user identity information, human-computer interaction information, water level height, smoke concentration, temperature, humidity, vibration frequency and personnel positioning information.
The data collected by the cloud client 101 are displayed to the user through the human-computer interaction module 104. The cloud client 101 judges the acquired data, alarms and notifies relevant departments of leadership if the data are abnormal, uploads the data to the industrial video linkage system for analysis if the data are normal, a rescue guide module 205 of the industrial video linkage analysis system generates a danger avoiding and escaping route to guide rescue after judgment, takes a camera real-time picture of the danger avoiding and escaping route, records abnormal events and saves the recorded abnormal events to an industrial video storage module 203; if the data is not abnormal, the cloud server 20 returns the data after the early warning analysis to the equipment terminal 10;
the rescue guidance module 205 retrieves the camera real-time picture of the danger avoiding and escaping route and plays the real-time situation of the whole route, as shown in fig. 4. And the user can master all video information in time and can modify the existing route.
As shown in fig. 5, the device terminal data determination process is that the cloud client 101 determines the acquired data of each module; if the smoke concentration, temperature, humidity, vibration frequency and water level information exceed the energy supply cut-off threshold value, the energy supply of the industrial equipment is cut off, meanwhile, an alarm is given out and a communication module 102 informs a relevant department to lead, the cloud client 101 packages the acquired abnormal data, packages and encrypts the abnormal data through an encryption and decryption module 201 and uploads the abnormal data to the cloud server 20; if the alarm threshold is not exceeded, the cloud client packs and encapsulates the acquired data, packs and encrypts the data through the encryption and decryption module 201, and uploads the data to the cloud server 20;
as shown in fig. 6, in the data determination process at the cloud server side of the industrial video linkage analysis system, after the encryption and decryption module 201 decrypts the data uploaded by the cloud client 101, data analysis is performed, and the cloud server side 20 determines the data type.
If the abnormal data is uploaded by the cloud client 101 and is still abnormal data after being mined and comprehensively analyzed by combining historical data, the rescue is guided by the rescue guiding module 205 according to the position information in the data to generate a danger avoiding and escaping route for guiding the rescue, a camera of the danger avoiding and escaping route is taken to track a real-time picture, and a video is stored in the industrial video storage module 203 and the abnormal event information is recorded to the relational database;
if the data uploaded by the cloud client 101 is normal, if the analysis result is judged to be abnormal data, the industrial video linkage analysis system early warning analysis module 204 sends out early warning to inform the leaders of relevant departments, calls the real-time pictures of the leaders provided for the relevant departments, records the video and stores the video into the industrial video storage module 203, and records the abnormal event information to the relational database as the data called when the abnormal video is played back; if there is no abnormality, the cloud client 101 receives a result returned by the cloud server 20 and stops the alarm;
as shown in fig. 7, the persistent storage mechanism composed of HDFS, relational database, and disk array according to the present invention is shown. Permanently storing the abnormal event video file in an industrial video storage module 203, recording the real-time video into an MP4 format file through RTSP for real-time video recording, storing the related information into a relational database, simultaneously forwarding the related information to a distributed file system of the industrial video storage module, and storing the recorded real-time video into an HDFS of the industrial video storage module 203 to realize the permanent storage of the real-time video;
for abnormal historical video extraction, the time and the camera corresponding to the abnormal video are obtained by reading abnormal event information stored in a relational database, a video file is extracted from a disk array by an abnormal video extraction program and is forwarded to an HDFS storage program, and the HDFS storage program stores the video into an industrial video storage module 203HDFS, so that the historical abnormal video extracted from the disk array is permanently stored;
the industrial video linkage analysis system calls the detailed conditions of the abnormal videos in a certain period of time in the industrial video storage module 203, plays back the industrial abnormal real-time videos or historical abnormal videos, and in event playback, the video positioning and playing technology directly positions the corresponding time according to the recorded event starting time without watching the whole videos.
The design of such a memory structure has the following advantages:
1) the original application is changed little. The video is firstly stored in the disk array, and the original video receiving program does not need to be modified. If the disk array is abandoned and the HDFS storage is adopted, the original video receiving program needs to be modified, and some current applications using video files also need to be modified.
2) And the cost is saved. According to the research situation, the video data generated by the camera every month is estimated to be about 140T. To ensure that data is not lost, the data should be in at least three copies. Therefore, at least 420T of space should be occupied by the generated video each month. Currently, most hard disks have a storage space of 2T, 1T or 500G (in terms of storage, 1T is 1000G). If all data are stored permanently, the hard disk is high in cost and difficult to manage. In the scheme, only abnormal data is permanently stored, so that the cost is saved, the implementation is simple, the management is convenient, and only part of videos are saved.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (2)
1. An industrial video linkage analysis system integrating event perception and position sensing is characterized by comprising: the system comprises a cloud server and a plurality of equipment terminals communicated with the cloud server; wherein the content of the first and second substances,
the method comprises the steps that an equipment terminal collects surrounding environment information, uploads the collected information to a cloud server side, and obtains a processing result and an instruction of the cloud server side;
the cloud server end is used for realizing personnel positioning through linkage with the personnel positioning module according to data sent by each equipment terminal, realizing rescue guiding through linkage with the rescue guiding module, realizing early warning analysis through linkage with the early warning analysis module, realizing industrial video playing and permanent storage of abnormal videos through linkage with the industrial video storage module and feeding generated data back to the equipment terminal;
for real-time video recording, the industrial video storage module records the real-time video into an MP4 format file through a real-time streaming protocol (RTSP), stores information into a relational database, simultaneously forwards the information to a distributed file system (HDFS) of the industrial video storage module, stores the recorded real-time video into the distributed file system (HDFS) of the industrial video storage module, and realizes permanent storage of the real-time video recorded for abnormal events;
for abnormal historical video extraction, acquiring time and a camera corresponding to an abnormal video by reading abnormal event information stored in a relational database, extracting an abnormal video file from a disk array, forwarding the abnormal video file to a distributed file system (HDFS), and storing the video into an industrial video storage module (HDFS), so that the historical abnormal video extracted from the disk array is permanently stored;
calling the detailed condition of the abnormal video in a certain period of time in the industrial video storage module, playing back the industrial abnormal real-time video or historical abnormal video, and directly positioning to the corresponding time according to the recorded event starting time without watching the whole video in the event playback;
the early warning analysis module does not independently judge a single industrial equipment measuring point, but integrally analyzes all related signals of certain industrial equipment or a production process;
the personnel positioning module stores the position information of the industrial personnel received in real time, associates the latest camera information and prepares for the rescue of emergency safety events;
the industrial video storage module is used for storing the real-time video of the abnormal event recorded by the equipment terminal and the historical abnormal video downloaded in the disk array;
the early warning analysis module is used for training the neural network model by using historical data of the equipment, analyzing and judging current real-time data by using the trained neural network model, calculating a deviation value between a current value and a neural network model expected value, indicating that the equipment has a certain fault head or degradation trend when the deviation value is larger than a set range or the deviation value has a continuously amplified trend, finding out potential abnormalities in industrial equipment and a construction process by combining video monitoring and video recording, and providing a solution;
the rescue guide module receives geographic information position data information of construction personnel and rescue vehicles in real time and stores the geographic information position data information in a database for positioning the construction personnel and the rescue vehicles in real time, once the rescue guide module receives a signal of an abnormal event, the position information is read from the database for positioning the construction personnel and the rescue vehicles in real time, all paths of the construction personnel away from the occurrence place of the abnormal event are calculated according to the current positions of the construction personnel, the shortest escape route is obtained through comparison, the shortest route is sent to the rescue vehicle closest to the current position of the construction personnel to implement rescue, all real-time monitoring pictures in the route and the escape route are displayed in the rescue guide module, real-time recording of monitoring videos of the abnormal event is realized, and the recorded videos are stored in the industrial video storage module;
the cloud server side comprises: the encryption and decryption module is respectively communicated with the personnel positioning module, the industrial video storage module, the early warning analysis module, the rescue guide module and the geographic space management module, and is also communicated with the equipment terminal;
the device terminal includes: the cloud client is communicated with the encryption and decryption module of the cloud server through the communication module, and is also communicated with the identity recognition module, the positioning module, the human-computer interaction module, the sensor module, the energy supply control module and the data bus interface respectively, and the identity recognition module is connected with the identity recognition card; the data bus interface is respectively connected with the personnel management module and the industrial equipment management module; the energy supply control module is connected with the energy supply circuit, and the equipment terminal reminds the mobile terminal of the relevant leader of the equipment with the sudden abnormality for the first time and processes and reports the mobile terminal to the cloud server;
the sensor module includes: a water level sensor, a smoke sensor, a temperature sensor, a humidity sensor and a vibration sensor;
the encryption and decryption module decrypts and analyzes the received data and encrypts and packages the transmitted data at the same time;
the geographic space management module is used for positioning buildings, industrial equipment and cameras of the industrial plant area, storing position information of the buildings, the industrial equipment and the cameras of the industrial plant area, generating an interactive map for a user, and providing basic data for the rescue guide module to generate a danger avoiding and escaping route display;
the neural network model consists of an input layer, a process neuron hidden layer and a process neuron output layer, wherein the input layer is used for completing the input of signals, the process neuron hidden layer is used for completing the spatial weighting aggregation and excitation operation of the input signals, and simultaneously, the output signals are transmitted to the output layer and the weights are fed back to the input layer; the output layer is used for finishing the spatial weighted aggregation and the time aggregation operation of the output signals of the hidden layer of the process neuron and the system output;
the communication module adopts at least one communication mode of GPRS, EDGE, CDMA, 3G and 4G, WIFI to complete data interaction between the cloud client and the cloud server;
the energy supply control module is used for completing the connection and disconnection of energy supply of industrial equipment;
the identity recognition module is connected with the identity recognition card in a non-contact mode to acquire information in the identity recognition card; the identity identification card is any one of the following cards: IC cards, RFID cards, smart cards, infrared read-write cards, and other cards for storing information;
the identity recognition module is connected with the cloud client in a serial port mode;
the positioning module is connected with the cloud client through a system bus, and collects the current position information of industrial constructors in real time;
the man-machine interaction module comprises: the system comprises a camera, a liquid crystal display, a touch screen, a keyboard, an audio playing unit, a cloud server and a cloud client, wherein the camera, the liquid crystal display, the touch screen, the keyboard and the audio playing unit are used for finishing information interaction between a user and an equipment terminal, receiving operation and instructions of the user, and displaying current industrial equipment information, sensor data and data returned to the cloud client by the cloud server to the user;
the data bus interface is connected with the personnel management module and the industrial equipment management module, and acquires constructor information data and current information data of industrial equipment in real time.
2. The method for the industrial video linkage analysis system integrating event awareness and position sensing as claimed in claim 1, wherein the steps are as follows:
step (1): the cloud client acquires industrial environment data and displays the industrial environment data through the man-machine interaction module;
in the step (1), the cloud client regularly collects data of the smoke sensor, the temperature sensor, the humidity sensor, the vibration sensor, the water level sensor, the identity recognition module, the positioning module, the human-computer interaction module, the personnel management module and the industrial equipment management module;
step (2): the cloud client performs preliminary analysis and judgment on the acquired data, alarms if the data are judged to be abnormal, cuts off energy supply of equipment through an energy supply control module if the abnormal data exceed a set threshold value, sends alarm information to a mobile terminal of a department leader, and then packages and encrypts the acquired data through an encryption and decryption module and uploads the encrypted data to a cloud server;
in the step (2), the cloud client judges the acquired data; if the smoke concentration, temperature, humidity, vibration frequency or water level information exceeds a set threshold value, the energy supply control module cuts off energy supply of industrial equipment, meanwhile, an alarm is given out and a mobile terminal of a department leader is informed through the communication module, the cloud client side packs and encapsulates the acquired abnormal data, packs and encrypts the abnormal data through the encryption and decryption module and uploads the abnormal data to the cloud server side;
if the alarm threshold is not exceeded, the cloud client packs and encapsulates the acquired data, encrypts the data through the encryption and decryption module and uploads the data to the cloud server;
and (3): after receiving the data uploaded by the cloud client, the cloud server firstly judges whether the data is abnormal data again,
if the data is abnormal data, the rescue guide module generates a danger-avoiding escape route to guide rescue, takes a camera real-time picture of the danger-avoiding escape route, and records and saves abnormal event records;
if the data is not abnormal data, the early warning analysis module carries out early warning analysis at the cloud server end; the early warning analysis of the cloud server side is to store data of information in a data packet uploaded by a cloud client side, perform data mining by combining historical data and perform comprehensive early warning analysis in all directions;
in the step (3), the encryption and decryption module decrypts the data uploaded by the cloud client and analyzes the data;
the early warning analysis module is used for training the neural network model by using historical data of the equipment, analyzing and judging current real-time data by using the trained neural network model, calculating a deviation value between a current value and a neural network model expected value, indicating that the equipment has a certain fault head or degradation trend when the deviation value is larger than a set range or the deviation value has a continuously amplified trend, finding out potential abnormalities in industrial equipment and a construction process by combining video monitoring and video recording, and providing a solution;
the rescue guide module receives geographic information position data information of construction personnel and rescue vehicles in real time and stores the geographic information position data information in a database for positioning the construction personnel and the rescue vehicles in real time, once the rescue guide module receives a signal of an abnormal event, the position information is read from the database for positioning the construction personnel and the rescue vehicles in real time, all paths of the construction personnel away from the occurrence place of the abnormal event are calculated according to the current positions of the construction personnel, the shortest escape route is obtained through comparison, the shortest route is sent to the rescue vehicle closest to the current position of the construction personnel to implement rescue, all real-time monitoring pictures in the route and the escape route are displayed in the rescue guide module, real-time recording of monitoring videos of the abnormal event is realized, and the recorded videos are stored in the industrial video storage module;
and (4): the cloud server side transmits the data subjected to early warning analysis by the early warning analysis module back to the cloud client side; the cloud client displays the received early warning analysis data to a user;
and (5): on the basis of the step (3), permanently storing the abnormal event video file in an industrial video storage module, calling the detailed condition of the abnormal video in a set time period in the industrial video storage module, and playing back the industrial abnormal real-time video or historical abnormal video for analyzing the reason of the abnormal generation; in event playback, the corresponding time is directly positioned according to the recorded event starting time without watching the whole video;
in the step (5), the abnormal event video file is permanently stored in an industrial video storage module,
for real-time video recording, recording the real-time video into an MP4 format file through a real-time streaming protocol (RTSP), storing information into a relational database, simultaneously forwarding the information to a distributed file system (HDFS) of an industrial video storage module, and storing the recorded real-time video into the distributed file system (HDFS) of the industrial video storage module to realize permanent storage of the recorded real-time video of the abnormal event;
for abnormal historical video extraction, acquiring time and a camera corresponding to an abnormal video by reading abnormal event information stored in a relational database, extracting an abnormal video file from a disk array, forwarding the abnormal video file to a distributed file system (HDFS), and storing the video into an industrial video storage module (HDFS), so that the historical abnormal video extracted from the disk array is permanently stored;
calling the detailed condition of the abnormal video in a certain period of time in the industrial video storage module, playing back the industrial abnormal real-time video or historical abnormal video, and directly positioning to the corresponding time according to the recorded event starting time without watching the whole video in the event playback;
the early warning analysis module does not independently judge a single industrial equipment measuring point, but integrally analyzes all related signals of certain industrial equipment or a production process.
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