CN114373245A - Intelligent inspection system based on digital power plant - Google Patents
Intelligent inspection system based on digital power plant Download PDFInfo
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Abstract
The invention belongs to the technical field of artificial intelligence application, and particularly discloses an intelligent inspection system based on a digital power plant, which comprises a coal mine complex area safety situation sensing module, an artificial intelligence technology and edge computing personnel behavior and environment detection module, a video inspection module, an infrared inspection module, an audio inspection module and a far-end upper computer or a cloud server, wherein the far-end upper computer or the cloud server is used for receiving data information and making corresponding response early warning according to comparison of preset data information. The intelligent inspection system based on the digital power plant has the beneficial effects that: utilize technologies such as infrared thermal imaging temperature measurement, intelligent video analysis, realize that key position appearance state, table meter instrument data identification, profit are revealed, detection and early warning such as overheated, flame smog, can discover equipment trouble in advance on the one hand, avoid the occurence of failure, on the other hand reduces the work load of artificially patrolling and examining, reduces the risk that patrols and examines and bring, realizes the digital safe production operation of power plant.
Description
Technical Field
The invention belongs to the technical field of artificial intelligence application, and particularly relates to an intelligent inspection system based on a digital power plant.
Background
The digital power plant is a concept of the power plant after digitization reaches a certain degree. All levels of control and management systems (including basic units such as field devices) of the power plant enter digitalization and are called digital power plants.
The digital power plant is a theory and a method for improving the value of the power plant through quantification, analysis, control and decision of the whole life cycle of the physical and working objects of the power plant. Therefore, the digitalized power plant is not a project, nor a software or system, but a theory and method, which is the object of research on the physical objects and working objects of the power plant, and the method is to research how to quantify, analyze, control and make decisions on the objects from the whole life cycle.
For water and electricity, at present, an inspection worker adopts a handheld mechanical point inspection instrument, regularly carries out equipment inspection work twice a day along a set inspection route, and realizes the recording, summarizing and analysis of inspection data in a paper recording or manual input mode. The automation, informatization and intellectualization levels of the inspection work are low, the inspection work is basically completed manually by inspection personnel, the efficiency and the quality of the inspection work are greatly influenced by human factors, and the phenomena of missing inspection, wrong inspection and the like easily occur.
For wind power and wind power daily operation maintenance, the polling work of a booster station, a fan cabin and a tower barrel still depends on manual work as required, and as a wind power plant is generally remote, the occupied area is wide, the production area in the station is large, the manual polling period is long, the frequency is low, and the difficulty is brought to the operation management, important asset guarantee of fans and the like and personal safety guarantee of operation and maintenance personnel. Important power generation components of the fan system are located in an overhead engine room with the tower drum more than 70 meters, and along with the shaking of the tower drum in the power generation process, the connection of partial components is loosened, the failure rate is increased, and overcurrent and overvoltage are possibly caused to further cause fire.
For working vehicles and operators in power plants
Therefore, based on the above problems, the present invention provides a smart inspection system based on a digital power plant.
Disclosure of Invention
The purpose of the invention is as follows: the invention aims to provide an intelligent inspection system based on a digital power plant, which solves the problems existing in the realization of the current digital power plant, realizes all-weather and real-time detection of water and electricity, wind power, working vehicles of the power plant, operating personnel and the like, improves the inspection management and control efficiency and the safety of the production operation of the power plant, and meets the management requirement of the digitization of the power plant.
The technical scheme is as follows: the intelligent inspection system based on the digital power plant comprises a coal mine complex region safety situation sensing module, an artificial intelligence technology and edge computing personnel behavior and environment detection module, a video inspection module, an infrared inspection module and an audio inspection module, wherein the coal mine complex region safety situation sensing module, the artificial intelligence technology and edge computing personnel behavior and environment detection module, the video inspection module, the infrared inspection module and the audio inspection module are respectively connected with a far-end upper computer or a cloud server; the remote upper computer or the cloud server is used for receiving the data information, and making corresponding response early warning according to comparison of preset data information, wherein the early warning includes but is not limited to sending a short message, stroboscopic lamps and voice broadcast.
According to the technical scheme, the coal mine complex area security situation sensing module collects information of multi-source heterogeneous sensors, comprehensively analyzes security situations in a factory area, carries out security assessment on states of essential elements such as personnel, vehicles, facilities and production materials in the factory area, and ensures the security of operating personnel, vehicles, facilities and production materials; fusing boundary information of an operation area (such as coal yard coal pile edge) and vehicle personnel positioning and detection information, analyzing the safety situation of personnel and vehicles in the whole operation area, predicting the movement tracks of personnel and vehicles, and giving an alarm for the dangerous state and the trend by combining the boundary information of the operation area; according to the historical track data of the personnel and the vehicles, a personnel and vehicle motion track model is established, and collision early warning is carried out; the important facility state is combined with the position information of personnel and vehicles, the influence of different facilities on the safety state of the personnel is analyzed, and safety early warning is given.
In this technical solution, the module for detecting the artificial intelligence technology, the behavior of the edge calculator and the environment includes: (1) based on the plant situation information acquisition function of the multi-source heterogeneous sensor, in order to obtain comprehensive and all-weather stable information of a plant, sensors such as a visible light camera, an infrared camera, a three-dimensional laser radar, an ultra-wideband module and a depth camera need to be comprehensively considered according to the characteristics of an application scene and the characteristics of the sensors, and comprehensive acquisition of scene information under different light conditions, at different time periods and at different angles is realized in a mode of combining a plurality of sensors; the sensor information is subjected to time alignment and space alignment in the same scene, so that the multi-sensor information describes the same scene, and the multi-sensor information is used for supporting subsequent application based on multi-source information fusion; (2) high-precision positioning based on multi-sensor fusion has complex factory operation environment, and certain potential safety risk exists in the cross operation of various vehicles and operators. For example, when a driver operates a vehicle in a cab, the visual field of the driver is limited, people around the vehicle are difficult to be completely perceived, and accidents such as collision and even rolling can occur when the driver operates the vehicle; a driver may neglect the distance between the vehicle and the edge of a specific working surface, and the vehicle may fall off a working area and turn over; the method adopts a mode of combining a plurality of sensors (cameras, laser radars, ultra wide bands and the like) and a plurality of sensors (angles and heights), and realizes high-precision real-time dynamic positioning service of plant personnel, vehicles and important equipment through multi-sensor information fusion; (3) based on the target detection and identification function of the artificial intelligence technology, in a complex factory, targets influencing safety can be roughly divided into moving targets and fixed targets, wherein the moving targets mainly comprise personnel, vehicles, safety belts, safety helmets, hung objects and the like; fixing targets such as temporary fences, hole cover plates, important equipment and the like; selecting multi-sensor information fusion according to the characteristics and application scenes of the target to be detected, and carrying out real-time detection on the target by adopting an artificial intelligence method; (4) the safety equipment detection function is worn to factory personnel abnormal, for guaranteeing the safety of operation personnel in the factory, carries out real-time supervision to the condition that the personnel wore safety equipment, and monitoring content includes: detecting the safety belt wearing condition of a high-altitude worker, detecting the connection condition of a safety belt hanging point, monitoring the wearing of a safety helmet, monitoring personnel, personnel position information and the like; monitoring the condition that the safety equipment is abnormally worn by the personnel in the plant in real time, quickly finding potential safety hazards and giving alarm information; (5) detecting unsafe behaviors of people in a dangerous area, wherein abnormal stay or walking of the people in the dangerous area is a huge potential safety hazard in the plant area operation process, for example, in an area below a hanging object, scene information is obtained according to multiple visual angles, and the abnormal stay or walking detection of the people in the dangerous area and the abnormal detection of an empty fence are realized by combining information such as a person detection result, a hanging object positioning result, a person movement trend prediction result and the like; and (3) identifying a high-altitude operator who possibly falls above the falling height reference plane by more than 2m (including 2 m).
According to the technical scheme, the video inspection module realizes intelligent reading of meter reading, state lamp identification, switch and disconnecting link station judgment and other special equipment requirements of power generation enterprises through identification of hydroelectric and wind power meters in deep learning of video inspection; for image inspection, a method of matching and comparing a real-time image of an inspection area (equipment) with a given picture of a user is adopted for realizing the image inspection, the image inspection application comprises the parts of field image acquisition, image inspection model creation, image inspection service engine, image inspection display and the like,
including but not limited to: (1) the water level of the top cover, the position of a guide vane shearing pin, the state of a pipeline valve, the position of a locking ingot and the state of a guide vane opening sensor pull rod; (2) the environment (foreign matter) of a main transformer chamber, the oil temperature and leakage of a main transformer, the oil level of an oil conservator, the pressure of an oil gas sleeve, the color change of silica gel, the position of a ground cutter and the like; (3) the method comprises the following steps of (1) indicating leakage of a main transformer cooler and an oil flow meter, cooling water pressure, an operation mode, signal lamp states of a main transformer cooler control cabinet/a main transformer water filter control cabinet and switch positions; (4) the on-off indication of the breaker/isolation disconnecting link, the operating air pressure of the breaker, the SF6 pressure, the current control cabinet light character plate, the GIS gas storage tank pressure index and the like; (5) the outgoing line platform comprises an isolation disconnecting link, a grounding disconnecting link, a voltage transformer, a lightning rod and a cable terminal; (6) the dam is provided with upper and lower reservoir bank slopes, enclosing walls, outgoing line platforms, towers, insulators, dam cracks, upper reservoir zone floaters and the like.
In the technical scheme, the infrared inspection module measures temperature through infrared thermal imaging, adopts the technology of on-site infrared temperature measurement acquisition, installs infrared temperature measurement equipment in key areas and key parts of the equipment, continuously acquires the key areas and the key parts of the equipment to form infrared temperature measurement data, transmits the infrared temperature measurement data to a background through a data line, and inspects the temperature measurement of equipment such as a transformer, a capacitor, a reactor, a lightning arrester, a disconnecting link, a wire clamp and the like,
also included, but not limited to: (1) the operation condition of the oil leakage pump, the temperature of a stator outlet copper bar, the temperature of a neutral point outlet copper bar, the temperature of a rotor excitation cable, the temperature of a carbon brush and ignition are carried out, and whether local overheating exists in a GIS equipment shell or not; (2) the outgoing line platform is provided with an isolation switch, a voltage transformer, a lightning rod, a cable terminal and the like.
According to the technical scheme, the audio inspection module performs feature extraction on audio data to obtain the optimal feature expression of the sound signal, and models the audio data by using a long and short memory neural network (LSTM), wherein the modeling includes but is not limited to waterwheel room sound and main transformer operation sound.
Compared with the prior art, the intelligent inspection system based on the digital power plant has the beneficial effects that: 1. by applying the development achievements in the fields of artificial intelligence technology, image processing technology, edge computing technology, multi-source heterogeneous information fusion technology and the like, the situation perception technology based on multi-source heterogeneous information fusion is integrated for dynamic situations of facilities, equipment, personnel, working conditions, important materials and the like in a complex factory area (such as a production area and a coal yard), the real-time high-precision perception of states of working conditions, equipment, people, vehicles, materials and the like in the complex factory area is realized based on the specific target detection technology of artificial intelligence, the high-precision positioning technology based on a sensor cluster and the like, the scientific management and scheduling of the important production materials, the key equipment and special working conditions in the factory area are realized, the possible events and dangers are predicted and early warned in time, and an actively supported technical means is provided for the safety production management of the complex factory area of a power plant; 2. utilize technologies such as infrared thermal imaging temperature measurement, intelligent video analysis, realize that key position appearance state, table meter instrument data identification, profit are revealed, detection and early warning such as overheated, flame smog, can discover equipment trouble in advance on the one hand, avoid the occurence of failure, on the other hand reduces the work load of artificially patrolling and examining, reduces the risk that patrols and examines and bring, realizes the digital safe production operation of power plant.
Drawings
FIG. 1 is a schematic diagram of analysis and safety pre-warning of a digital power plant-based intelligent inspection system of the present invention;
FIG. 2 is a combined schematic diagram of multi-source heterogeneous sensors of the intelligent patrol inspection system based on a digital power plant;
FIG. 3 is a schematic diagram of the laser radar edge detection of the intelligent patrol inspection system based on the digital power plant;
FIG. 4 is a schematic diagram of the personnel and vehicle target detection of the intelligent patrol system based on the digital power plant;
FIG. 5 is a schematic view of a laser radar installation and scanning surface of the intelligent inspection system based on a digital power plant;
FIG. 6 is a schematic diagram of the target detection based on the deep convolutional neural network of the intelligent patrol inspection system based on the digital power plant;
FIG. 7 is a model diagram of meter readings for the intelligent routing inspection system based on a digital power plant of the present invention;
FIG. 8 is a schematic diagram of the key points of meter readings of the intelligent patrol system based on a digital power plant;
FIG. 9 is a schematic audio analysis diagram of the intelligent patrol system based on a digital power plant of the present invention;
FIG. 10 is a schematic diagram of an audio analysis model of the intelligent patrol system based on a digital power plant according to the present invention;
FIG. 11 is a schematic diagram of an edge calculation application of the intelligent patrol system based on a digital power plant;
FIG. 12 is a schematic view of the Faster CNN of the intelligent patrol system based on a digital power plant of the present invention;
FIG. 13 is a schematic diagram of the personnel detection under the hoisted object of the intelligent inspection system based on the digital power plant;
FIG. 14 is a schematic diagram of a safety helmet detection network model of the intelligent patrol system based on a digital power plant;
FIG. 15 is a schematic diagram of the helmet detection of the intelligent routing inspection system based on a digital power plant of the present invention;
FIG. 16 is a schematic diagram of the high altitude seat belt detection of the intelligent patrol system based on a digital power plant of the present invention;
FIG. 17 is a schematic diagram of a deep convolutional neural network model of the intelligent patrol system based on a digital power plant.
Detailed Description
The invention is further elucidated with reference to the drawings and the embodiments.
The intelligent inspection system based on the digital power plant comprises a coal mine complex region safety situation sensing module, an artificial intelligence technology and edge computing personnel behavior and environment detection module, a video inspection module, an infrared inspection module and an audio inspection module, wherein the coal mine complex region safety situation sensing module, the artificial intelligence technology and edge computing personnel behavior and environment detection module, the video inspection module, the infrared inspection module and the audio inspection module are respectively connected with a far-end upper computer or a cloud server; the remote upper computer or the cloud server is used for receiving the data information, and making corresponding response early warning according to comparison of preset data information, wherein the early warning includes but is not limited to sending a short message, stroboscopic lamps and voice broadcast.
As shown in fig. 1, the intelligent inspection system based on the digital power plant is preferred, the security situation sensing module in the complex area of the coal mine collects information of the multi-source heterogeneous sensor, comprehensively analyzes the security situation in the plant area, and performs security assessment on the states of the essential elements such as personnel, vehicles, facilities and production materials in the plant area, so as to ensure the security of the operating personnel, the vehicles, the facilities and the production materials; fusing boundary information of an operation area (such as coal yard coal pile edge) and vehicle personnel positioning and detection information, analyzing the safety situation of personnel and vehicles in the whole operation area, predicting the movement tracks of personnel and vehicles, and giving an alarm for the dangerous state and the trend by combining the boundary information of the operation area; according to the historical track data of the personnel and the vehicles, a personnel and vehicle motion track model is established, and collision early warning is carried out; the important facility state is combined with the position information of personnel and vehicles, the influence of different facilities on the safety state of the personnel is analyzed, and safety early warning is given.
As shown in fig. 2, fig. 3 and fig. 4, the intelligent patrol inspection system based on the digital power plant preferably includes an artificial intelligence technology and edge computer personnel behavior and environment detection module, which includes: (1) based on the plant situation information acquisition function of the multi-source heterogeneous sensor, in order to obtain comprehensive and all-weather stable information of a plant, sensors such as a visible light camera, an infrared camera, a three-dimensional laser radar, an ultra-wideband module and a depth camera need to be comprehensively considered according to the characteristics of an application scene and the characteristics of the sensors, and comprehensive acquisition of scene information under different light conditions, at different time periods and at different angles is realized in a mode of combining a plurality of sensors; the sensor information is subjected to time alignment and space alignment in the same scene, so that the multi-sensor information describes the same scene, and the multi-sensor information is used for supporting subsequent application based on multi-source information fusion; (2) high-precision positioning based on multi-sensor fusion has complex factory operation environment, and certain potential safety risk exists in the cross operation of various vehicles and operators. For example, when a driver operates a vehicle in a cab, the visual field of the driver is limited, people around the vehicle are difficult to be completely perceived, and accidents such as collision and even rolling can occur when the driver operates the vehicle; a driver may neglect the distance between the vehicle and the edge of a specific working surface, and the vehicle may fall off a working area and turn over; the method adopts a mode of combining a plurality of sensors (cameras, laser radars, ultra wide bands and the like) and a plurality of sensors (angles and heights), and realizes high-precision real-time dynamic positioning service of plant personnel, vehicles and important equipment through multi-sensor information fusion; (3) based on the target detection and identification function of the artificial intelligence technology, in a complex factory, targets influencing safety can be roughly divided into moving targets and fixed targets, wherein the moving targets mainly comprise personnel, vehicles, safety belts, safety helmets, hung objects and the like; fixing targets such as temporary fences, hole cover plates, important equipment and the like; selecting multi-sensor information fusion according to the characteristics and application scenes of the target to be detected, and carrying out real-time detection on the target by adopting an artificial intelligence method; (4) the safety equipment detection function is worn to factory personnel abnormal, for guaranteeing the safety of operation personnel in the factory, carries out real-time supervision to the condition that the personnel wore safety equipment, and monitoring content includes: detecting the safety belt wearing condition of a high-altitude worker, detecting the connection condition of a safety belt hanging point, monitoring the wearing of a safety helmet, monitoring personnel, personnel position information and the like; monitoring the condition that the safety equipment is abnormally worn by the personnel in the plant in real time, quickly finding potential safety hazards and giving alarm information; (5) detecting unsafe behaviors of people in a dangerous area, wherein abnormal stay or walking of the people in the dangerous area is a huge potential safety hazard in the plant area operation process, for example, in an area below a hanging object, scene information is obtained according to multiple visual angles, and the abnormal stay or walking detection of the people in the dangerous area and the abnormal detection of an empty fence are realized by combining information such as a person detection result, a hanging object positioning result, a person movement trend prediction result and the like; and (3) identifying a high-altitude operator who possibly falls above the falling height reference plane by more than 2m (including 2 m).
The intelligent inspection system based on the digital power plant is preferred, and the video inspection module realizes intelligent reading of meter reading, state lamp identification, switch and disconnecting link station judgment and other special equipment requirements of power generation enterprises through identification of hydroelectric and wind power instruments deeply learned through video inspection; for image inspection, a method of matching and comparing a real-time image of an inspection area (equipment) with a given picture of a user is adopted for realizing the image inspection, the image inspection application comprises the parts of field image acquisition, image inspection model creation, image inspection service engine, image inspection display and the like,
including but not limited to: (1) the water level of the top cover, the position of a guide vane shearing pin, the state of a pipeline valve, the position of a locking ingot and the state of a guide vane opening sensor pull rod; (2) the environment (foreign matter) of a main transformer chamber, the oil temperature and leakage of a main transformer, the oil level of an oil conservator, the pressure of an oil gas sleeve, the color change of silica gel, the position of a ground cutter and the like; (3) the method comprises the following steps of (1) indicating leakage of a main transformer cooler and an oil flow meter, cooling water pressure, an operation mode, signal lamp states of a main transformer cooler control cabinet/a main transformer water filter control cabinet and switch positions; (4) the on-off indication of the breaker/isolation disconnecting link, the operating air pressure of the breaker, the SF6 pressure, the current control cabinet light character plate, the GIS gas storage tank pressure index and the like; (5) the outgoing line platform comprises an isolation disconnecting link, a grounding disconnecting link, a voltage transformer, a lightning rod and a cable terminal; (6) the dam is provided with upper and lower reservoir bank slopes, enclosing walls, outgoing line platforms, towers, insulators, dam cracks, upper reservoir zone floaters and the like.
The intelligent inspection system based on the digital power plant preferably has the infrared inspection module which measures temperature through infrared thermal imaging, adopts the technology of acquiring the site infrared temperature measurement, installs infrared temperature measurement equipment in key areas and key parts of the equipment, continuously acquires the key areas and the key parts of the equipment to form infrared temperature measurement data, transmits the infrared temperature measurement data to a background through a data line, and inspects the temperature measurement of equipment such as a transformer, a capacitor, a reactor, a lightning arrester, a disconnecting link, a wire clamp and the like,
also included, but not limited to: (1) the operation condition of the oil leakage pump, the temperature of a stator outlet copper bar, the temperature of a neutral point outlet copper bar, the temperature of a rotor excitation cable, the temperature of a carbon brush and ignition are carried out, and whether local overheating exists in a GIS equipment shell or not; (2) the outgoing line platform is provided with an isolation switch, a voltage transformer, a lightning rod, a cable terminal and the like.
According to the intelligent inspection system based on the digital power plant, the audio inspection module preferably extracts the characteristics of the audio data according to the audio data to obtain the optimal characteristic expression of the sound signal, and models the audio data by utilizing a long and short memory neural network (LSTM), wherein the modeling includes but is not limited to the sound of a waterwheel room and the operation sound of a main transformer.
Examples
The coal mine complex area security situation sensing module shown in fig. 5: the core function requirements of the complex area situation awareness and intelligent safety monitoring system of the power plant are to perform safety awareness on the coal yard and perform safety monitoring on operators and vehicles, accurately measure the edge position of the coal pile, detect the high-precision position information of the operators and the vehicles in the coal yard, and detect the movement tracks of the operators and the vehicles.
In order to realize accurate and reliable detection of the coal pile edge, a plurality of three-dimensional laser radars are used as basic sensors, and the coal pile edge in a local scene is detected by combining a three-dimensional point cloud data processing algorithm. The method comprises the steps that an edge calculation high-definition camera is adopted to obtain high-definition video images in a coal yard in real time, and detection of personnel and vehicles is achieved by combining a deep learning algorithm; a UWB positioning base station is arranged in the coal yard, and the operator vehicle carries a beacon, so that the high-precision positioning of the operator vehicle in the coal yard is realized.
The coal pile edge detection is to realize the three-dimensional position detection of 2 edges at the top of the coal pile in the coal yard and give the three-dimensional position information of the edge of the coal pile under the coordinate system of the coal yard;
detecting the personnel and vehicle targets, namely detecting the operators and the vehicles in the coal yard and giving position information and category information of the operators and the vehicles in the images;
high-precision positioning: three-dimensional high-precision positioning of operators and vehicles in the coal yard is realized, and three-dimensional position information of each beacon under a coal yard coordinate system is given;
two three-dimensional laser radars are arranged on two sides of each trapezoid coal pile, each four three-dimensional laser radars are used for monitoring one coal pile, the installation schematic diagram of each three-dimensional laser radar is shown in figure 5, and each scanning line is parallel to the edge of the coal pile.
(1) And (3) situation data fusion, wherein the situation data fusion refers to the fusion of data acquired by a plurality of intelligent situation sensing nodes. The situation data fusion comprises coal pile edge information, constructor vehicle detection information and high-precision positioning information which are measured by a three-dimensional laser radar. The radar coordinate system and the high-precision positioning coordinate system are unified to the positioning coordinate system through calibration, and the conversion relation between the image and the physical coordinate system can be realized by combining camera calibration parameters, so that the target detected by the image is converted into the positioning coordinate system, and the spatial alignment of the information of the three sensors is realized.
(2) And (4) early warning of motion collision of the working vehicle, establishing a vehicle motion track model according to the historical positioning information of the vehicle, and further judging the motion trend of the vehicle. The multi-vehicle collision early warning is realized by predicting the motion tracks of different vehicles and combining the spatial position information of a plurality of vehicles.
(3) A slide-off hazard event alert, which is an alert to an event that the work vehicle has moved too close to the edge of the coal pile (the lateral distance between the work vehicle and the edge of the coal pile is less than a certain value) or to a trend that may be moving too close to the edge of the coal pile within a certain time.
The video inspection module shown in fig. 6 adopts a target detection model based on a deep convolutional neural network to realize target detection and target classification in an image.
For the infrared inspection module shown in fig. 7 and 8, CV image processing is adopted for the analysis type of visible light image-meter reading, and the reading is automatically recognized by the meter combined with the key points of the meter.
As shown in fig. 9 and 10, the audio patrol module models audio data.
The artificial intelligence technique and edge calculator behavior and environment detection module as shown in fig. 11 and 13: the utilization of edge computing brings many benefits to internet of things devices, such as near zero latency, less network load, increased flexibility, reduced data exposure and lower data management cost, and in order to meet all requirements of front-end devices, edge computing and cloud computing need to work in cooperation. All data from smart devices and sensors still need to be aggregated on the cloud, more in-depth analysis in order to gain meaningful insight therefrom, and cloud computing still plays a key role in making internet of things devices more intelligent and better.
A. Abnormal state detection based on fast R-CNN: the detection of the absence of the empty balustrade needs to solve the challenges caused by various light conditions, shielding of operators and the like.
The traditional target detection method has bottlenecks in the aspects of light adaptability, detection accuracy, robustness and the like. By utilizing the device safety monitoring technology of the Faster R-CNN, the image information of the detection target under different light conditions is collected, and the real-time and accurate detection of the empty handrail loss under the complex light condition is realized by training the deep neural network. The method mainly comprises the following steps:
feature extraction: taking the whole picture as input and obtaining a characteristic layer of the picture by using CNN;
area nomination: extracting region candidate frames from an original picture by using a Selective Search method and the like, and projecting the candidate frames to a final feature layer one by one;
area normalization: carrying out RoI Pooling operation on each region candidate box on the feature layer to obtain feature representation with fixed size;
classification and regression: and then, by means of the two full connection layers, multi-classification is respectively carried out by softmax for target recognition, and fine adjustment of the position and the size of the frame is carried out by using a regression model.
B. The detection is detained to the personnel below the object of lifting by crane based on degree of depth study: for key areas in the power plant, safety risk assessment needs to be carried out on any person and object breaking the normality of the area.
The technology for detecting the abnormal retention of the personnel below the lifted object based on deep learning monitors the personnel in the scene below the lifted object, and when the personnel in the scene are abnormally retained and do not meet the specification, the system can automatically send out early warning, so that safety risk assessment is realized.
C. The wearing condition of the safety helmet and the wearing condition of the safety belt are detected based on the deep neural network: and a network model diagram 14 for detecting the wearing conditions of the safety helmet and the safety belt is realized by adopting the residual error neural network with superior performance at present.
The wearing condition of the safety helmet of an operator is detected under the conditions of complex shielding, light and deformation by utilizing the safety helmet wearing condition detection technology of the deep neural network, the detection result of the safety helmet is shown in figure 15, a green frame in the figure is a person who normally wears the safety helmet, a red frame is the condition that the safety helmet is abnormally worn, and a blue frame is the detection result of the safety helmet. And giving out risk early warning in real time according to wearing conditions of safety facilities such as safety helmets and the like.
The problem of detecting the wearing condition of the high-altitude safety belt is solved, the safety belt is easily shielded by clothes, image data of the safety belt when an operator works is collected, a residual error neural network is trained, the detection of the safety belt is realized, and the detection effect is shown in fig. 16.
The invention relates to a water and electricity meter in an intelligent inspection system based on a digital power plant, which is used for learning and realizing the extraction of classification characteristics by utilizing a multilayer convolution network and realizing the identification of targets with different scales by constructing multilayer convolution network layers with different scales for defect inspection, meter reading and pressure plate position state identification, wherein the instrument identification deep convolution neural network structure is shown in figure 17, the network is a convolution neural network based on forward propagation, the network output obtains a final prediction result through a non-maximum suppression algorithm, the defect inspection, the meter reading and the pressure plate position state identification are realized, and the offline learning is combined with the online learning. The method continuously optimizes the network structure and the network parameters and improves the identification precision.
The intelligent inspection system based on the digital power plant provides technical support and implementation means for essential safety management of human, machine, ring, pipe and other elements of a complex plant area of the power plant, comprehensively improves automation and informatization levels of plant area safety management and inspection, improves working efficiency and reduces operation accident rate.
The intelligent inspection system based on the digital power plant comprehensively introduces various technical measures such as three-dimensional virtualization, intelligent video analysis, infrared thermal imaging, machine learning, deep learning and the like into the intelligent inspection of the digital power plant according to the intelligent inspection work requirement of the digital power plant, and performs early warning analysis, comparative analysis and correlation analysis on the trend of inspection data. By utilizing a digital twin technology, the whole power plant is modeled in three dimensions, and key areas and equipment (such as GIS, a booster station, a fan cabin and the like) are modeled in a refined manner, and the key areas and equipment routing inspection data and analysis are displayed on the three-dimensional model in real time and finally displayed on a far-end upper computer or a cloud server.
The foregoing is only a preferred embodiment of this invention and it should be noted that modifications can be made by those skilled in the art without departing from the principle of the invention and these modifications should also be considered as the protection scope of the invention.
Claims (6)
1. Intelligent system of patrolling and examining based on digital power plant, its characterized in that: the system comprises a coal mine complex region security situation sensing module, an artificial intelligence technology and edge computing personnel behavior and environment detection module, a video inspection module, an infrared inspection module and an audio inspection module, wherein the coal mine complex region security situation sensing module, the artificial intelligence technology and edge computing personnel behavior and environment detection module, the video inspection module, the infrared inspection module and the audio inspection module are respectively connected with a far-end upper computer or a cloud server; the remote upper computer or the cloud server is used for receiving the data information, and making corresponding response early warning according to comparison of preset data information, wherein the early warning includes but is not limited to sending a short message, stroboscopic lamps and voice broadcast.
2. The intelligent inspection system based on digital power plant of claim 1, wherein: the coal mine complex area safety situation sensing module collects multi-source heterogeneous sensor information, comprehensively analyzes the safety situation in a factory area, and performs safety assessment on the states of personnel, vehicles, facilities and production material elements in the factory area to ensure the safety of operating personnel, vehicles, facilities and production materials;
the boundary information of the operation area, the vehicle personnel positioning and detecting information are fused, the personnel and vehicle safety situation in the whole operation area is analyzed, the movement tracks of personnel and vehicles are predicted, and the danger state and the trend are alarmed by combining the boundary information of the operation area; according to the historical track data of the personnel and the vehicles, a personnel and vehicle motion track model is established, and collision early warning is carried out;
the important facility state is combined with the position information of personnel and vehicles, the influence of different facilities on the safety state of the personnel is analyzed, and safety early warning is given.
3. The intelligent inspection system based on digital power plant of claim 1, wherein: the artificial intelligence technique and edge calculator behavior and environment detection module comprises:
(1) based on the plant situation information acquisition function of the multi-source heterogeneous sensor, in order to obtain comprehensive and all-weather stable information of a plant, sensors such as a visible light camera, an infrared camera, a three-dimensional laser radar, an ultra-wideband module and a depth camera need to be comprehensively considered according to the characteristics of an application scene and the characteristics of the sensors, and comprehensive acquisition of scene information under different light conditions, at different time periods and at different angles is realized in a mode of combining a plurality of sensors; the sensor information is subjected to time alignment and space alignment in the same scene, so that the multi-sensor information describes the same scene, and the multi-sensor information is used for supporting subsequent application based on multi-source information fusion;
(2) high-precision positioning based on multi-sensor fusion has complex factory operation environment, various vehicles and operators perform cross operation, certain potential safety risk exists, and high-precision real-time dynamic positioning service of factory personnel, vehicles and important equipment is realized by adopting a mode of combining various sensors (such as a camera, a laser radar, an ultra wide band and the like) and a plurality of sensors (such as angles and heights) through multi-sensor information fusion;
(3) based on the target detection and identification function of the artificial intelligence technology, in a complex factory, targets influencing safety can be roughly divided into moving targets and fixed targets, wherein the moving targets mainly comprise personnel, vehicles, safety belts, safety helmets, hung objects and the like; fixing targets such as temporary fences, hole cover plates and important equipment; selecting multi-sensor information fusion according to the characteristics and application scenes of the target to be detected, and carrying out real-time detection on the target by adopting an artificial intelligence method;
(4) the safety equipment detection function is worn to factory personnel abnormal, for guaranteeing the safety of operation personnel in the factory, carries out real-time supervision to the condition that the personnel wore safety equipment, and monitoring content includes: detecting the safety belt wearing condition of a high-altitude worker, detecting the connection condition of a safety belt hanging point, monitoring the wearing of a safety helmet, monitoring personnel, personnel position information and the like; monitoring the condition that the safety equipment is abnormally worn by the personnel in the plant in real time, quickly finding potential safety hazards and giving alarm information;
(5) and detecting unsafe behaviors of personnel in the dangerous area, wherein abnormal stay or walking of the personnel in the dangerous area is a huge potential safety hazard in the plant operation process, for example, in an area below the hanging object, scene information is obtained according to multiple visual angles, and the abnormal stay or walking detection of the personnel in the dangerous area is realized by combining information such as personnel detection results, hanging object positioning results, personnel movement trend prediction results and the like.
4. The intelligent inspection system based on digital power plant of claim 1, wherein: the video inspection module realizes intelligent reading of meter reading, state lamp identification, switch and disconnecting link station judgment and other special equipment requirements of power generation enterprises through identification of hydroelectric and wind power instruments subjected to video inspection deep learning;
for image inspection, a method of matching and comparing a real-time image of an inspection area (equipment) with a given picture of a user is adopted for realizing the image inspection, and an image inspection application comprises a field image acquisition part, an image inspection model creation part, an image inspection service engine and an image inspection display part.
5. The intelligent inspection system based on digital power plant of claim 1, wherein: the infrared inspection module measures temperature through infrared thermal imaging, adopts infrared temperature measurement equipment to be installed in key areas and equipment key positions for an on-site infrared temperature measurement acquisition technology, and collects the key areas and the equipment key positions uninterruptedly to form infrared temperature measurement data, and transmits the infrared temperature measurement data to a background through a data line.
6. The intelligent inspection system based on digital power plant of claim 1, wherein: the audio inspection module extracts the characteristics of the audio data to obtain the optimal characteristic expression of the sound signals, and models the audio data by utilizing the long and short memory neural network, wherein the audio data include but are not limited to the sound of a waterwheel room and the running sound of a main transformer.
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