CN113203841B - Harmful gas detection system and method based on multi-sensor cooperation - Google Patents

Harmful gas detection system and method based on multi-sensor cooperation Download PDF

Info

Publication number
CN113203841B
CN113203841B CN202110687879.1A CN202110687879A CN113203841B CN 113203841 B CN113203841 B CN 113203841B CN 202110687879 A CN202110687879 A CN 202110687879A CN 113203841 B CN113203841 B CN 113203841B
Authority
CN
China
Prior art keywords
harmful gas
target area
sensor
image
topographic map
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202110687879.1A
Other languages
Chinese (zh)
Other versions
CN113203841A (en
Inventor
张文
王卫国
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Smart Park Solution Technology Co ltd
Original Assignee
Beijing Smart Park Solution Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Smart Park Solution Technology Co ltd filed Critical Beijing Smart Park Solution Technology Co ltd
Priority to CN202110687879.1A priority Critical patent/CN113203841B/en
Publication of CN113203841A publication Critical patent/CN113203841A/en
Application granted granted Critical
Publication of CN113203841B publication Critical patent/CN113203841B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004Gaseous mixtures, e.g. polluted air
    • G01N33/0009General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0027General constructional details of gas analysers, e.g. portable test equipment concerning the detector
    • G01N33/0031General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
    • G01N33/0034General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array comprising neural networks or related mathematical techniques
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004Gaseous mixtures, e.g. polluted air
    • G01N33/0009General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0062General constructional details of gas analysers, e.g. portable test equipment concerning the measuring method or the display, e.g. intermittent measurement or digital display
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/24Classification techniques
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • G06T11/001Texturing; Colouring; Generation of texture or colour
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T11/002D [Two Dimensional] image generation
    • G06T11/20Drawing from basic elements, e.g. lines or circles
    • G06T11/203Drawing of straight lines or curves
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/40Extraction of image or video features
    • G06V10/44Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00Arrangements for image or video recognition or understanding
    • G06V10/40Extraction of image or video features
    • G06V10/56Extraction of image or video features relating to colour
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Biochemistry (AREA)
  • Pathology (AREA)
  • Immunology (AREA)
  • General Health & Medical Sciences (AREA)
  • Evolutionary Computation (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
  • Combustion & Propulsion (AREA)
  • Data Mining & Analysis (AREA)
  • Food Science & Technology (AREA)
  • Medicinal Chemistry (AREA)
  • Analytical Chemistry (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Biology (AREA)
  • Mathematical Physics (AREA)
  • Emergency Alarm Devices (AREA)

Abstract

The invention relates to the technical field of environmental detection, in particular to a harmful gas detection system and a harmful gas detection method based on multi-sensor cooperation, wherein the system comprises: the sensor group comprises a plurality of sensors and is configured to sense harmful gas in a target area and acquire harmful gas sensing data in real time; the sensors in the sensor group are randomly distributed in the target area and need to meet the following requirements: in any position in the target area, the number of the sensors exceeds a set second threshold value in a circular area defined by taking the position as a circle center and taking a set first threshold value as a radius. It obtains the harmful gas data of each position of target area through sensor group is whole, carries out holistic first image analysis through drawing the harmful gas data at the harmful gas topography map of target area to judge whether need carry out the early warning, use the source of writing the longitudinal section and finding harmful gas simultaneously, have efficient and the high advantage of rate of accuracy.

Description

Harmful gas detection system and method based on multi-sensor cooperation
Technical Field
The invention belongs to the technical field of environmental detection, and particularly relates to a harmful gas detection system and method based on multi-sensor cooperation, which are particularly suitable for detecting harmful gas in the chemical industry.
Background
The harmful gas is gas which has adverse effect on the health of people or animals, or has no effect on the health of people or animals, but causes discomfort to people or animals and influences the comfort of people or animals. Such as NH3, H2S, CO, and the like.
In daily life, people not only beautify rooms, but also purify air through some plants capable of absorbing harmful gases in the air. Such as: chrysanthemum, camellia, cyclamen, violet, tuberose, morning glory, pink, gladiolus and other plants.
A gas sensor is a transducer that converts a certain gas volume fraction into a corresponding electrical signal. The probe conditions the gas sample through the gas sensor, typically including filtering out impurities and interfering gases, drying or refrigerating the instrument display.
The detection of harmful gases is a great function of gas sensors, and has two purposes, namely explosion detection and poison detection. The explosion detection is to detect the content of combustible gas in dangerous places and alarm the exceeding standard so as to avoid the occurrence of explosion accidents; the toxic gas detection is used for detecting the content of toxic gas in dangerous places and alarming in an overproof manner so as to avoid poisoning of workers.
In daily production, harmful gas is easily produced in the chemical industry, and the detection of the harmful gas in production places and the guarantee of safe production are very important problems in the field.
Patent No. CN201310692344.9A discloses a mine environment harmful gas detection system based on wireless sensor, the system is composed of multiple detection units and detection center computer, and realizes multipoint simultaneous detection and status discrimination for mine environment harmful gas, the detection units realize detection for characteristic parameters of detection point harmful gas, each detection unit realizes information interaction with the detection center computer through wireless communication module, the detection center computer is used for processing and managing detected mine environment harmful gas data, and grade discrimination is realized for detection point, detection surface and whole detection environment harmful gas status through multiunit wavelet neural network identifier. According to the invention, by researching the problems of low accuracy and poor real-time performance of the existing coal mine harmful gas detection technology, a detection system is designed, and the gas inlets can be uniformly distributed at different positions of a detected coal mine according to the actual situation of coal mine detection points, so that the actual situation of detecting the concentration of the coal mine harmful gas at multiple points simultaneously is realized.
It carries out harmful gas through the gas sensor of wide distribution and detects, rethread data processing, and multiunit wavelet neural network recognizer realizes carrying out the grade to the check point, detection surface and whole detection ring border harmful gas's situation and differentiates, although can detect regional interior multiple spot harmful gas, and carry out the early warning, nevertheless can not be based on the source that the accurate location harmful gas of testing result produced, simultaneously because it needs carry out concentration detection and judgement respectively to every point, rather than carrying out analysis and judgement from regional whole, lead to detection efficiency lower.
Disclosure of Invention
In view of the above, the main objective of the present invention is to provide a system and a method for detecting harmful gas based on multi-sensor cooperation, wherein the system and the method integrally obtain harmful gas data of each position of a target area through a sensor group, perform an integral first image analysis by drawing a topographic map of the harmful gas data in the target area to determine whether an early warning is needed, and simultaneously find a source of the harmful gas by using a sketch longitudinal section, and have the advantages of high efficiency and high accuracy.
In order to achieve the purpose, the technical scheme of the invention is realized as follows:
harmful gas detection system based on multi-sensor cooperation, the system includes:
the sensor group comprises a plurality of sensors and is configured to sense harmful gas in a target area and acquire harmful gas sensing data in real time; the sensors in the sensor group are randomly distributed in the target area and need to meet the following requirements: in any position in the target area, the position is taken as the center of a circle, the radius is taken as the first set threshold value, and the number of the sensors in a defined circular area exceeds the second set threshold value;
the target area mapping unit is configured for establishing a plane coordinate graph of the target area, and simultaneously mapping corresponding coordinate points of the sensors in the plane coordinate graph based on the positions of the sensors in the target area to map a sensor coordinate graph of the target area;
the data recording unit is configured for recording harmful gas sensing data acquired by the sensor group at the same moment to a coordinate corresponding to each sensor based on a sensor coordinate graph in a target area;
the harmful gas topographic map drawing unit is configured to draw a harmful gas topographic map according to a contour line drawing mode on the basis of the sensor coordinate map of the target area and the harmful gas sensing data recorded in each sensor coordinate;
and the image analysis unit is configured to perform first image analysis based on a harmful gas topographic map by using a preset early warning analysis model so as to judge whether early warning is needed or not, and simultaneously, perform image longitudinal section scanning analysis by using a preset positioning analysis model based on the harmful gas topographic map so as to obtain an edge profile curve of a longitudinal section, perform second image analysis on the edge profile curve of the longitudinal section, calculate the irregularity of each position in the edge profile curve of the longitudinal section, and determine a source of harmful gas generation.
Further, the method for drawing the coordinate graph of the target area sensor by the target area mapping unit comprises the following steps: generating a two-dimensional rectangular coordinate system, reducing or amplifying the target area in equal proportion, and placing the target area in the two-dimensional rectangular coordinate system; and then, based on the position of the sensor in the target area, marking the corresponding position in the two-dimensional rectangular coordinate system of the target area, and drawing a coordinate graph of the sensor in the target area.
Further, the harmful gas topographic map drawing unit, the method for drawing the harmful gas topographic map, includes: on the basis of a two-dimensional rectangular coordinate system of a target area sensor coordinate graph, adding a dimension to generate the target area sensor coordinate graph in a three-dimensional coordinate system, and drawing a harmful gas topographic map according to a mode of drawing contour lines in the added dimension on the basis of the target area sensor coordinate graph and harmful gas sensing data recorded in each sensor coordinate.
Further, the image analysis unit includes: the early warning analysis unit and the positioning analysis unit; the early warning analysis unit is configured to perform first image analysis by using a preset early warning analysis model based on a harmful gas topographic map so as to judge whether early warning is required or not; the positioning analysis unit uses a preset positioning analysis model to identify the source of harmful gas generation; the method for the early warning analysis unit to perform the first image analysis comprises the following steps: scanning a topographic map of harmful gas to obtain a topographic map scanning image, and distributing different color information to contour lines of different heights corresponding to the topographic map scanning image, wherein the color information is used for representing types of the contour lines of different heights, and the contour line type comprises a danger type or a safety type; determining a target candidate region from the scanned image according to the color information, and performing feature extraction in the target candidate region to obtain extracted image features; performing target detection based on a target detection model by using the extracted image features to obtain candidate targets; and identifying the candidate target by using the extracted image characteristics and based on a target classification model to obtain an image identification result.
Further, when different color information is allocated to the contour lines of different heights corresponding to the topographic map scanning image, the classification values of the contour lines are calculated by using the following formula:
Figure GDA0003706092280000041
wherein x is the value of the contour, and c (x) is the calculated classification value of the contour; if C (x) is more than 0.5, the contour line is judged to be a dangerous type; if C (x) is lower than 0.5, the contour is determined to be safe.
Further, the method for identifying the source of the harmful gas generation by the positioning analysis unit comprises the following steps: based on harmful gas topography, use the location analysis model to carry out image longitudinal section scanning analysis, specifically include: finding the highest point in the harmful gas topographic map to obtain a longitudinal section of the harmful gas topographic map at the point position, and performing second image analysis on an edge profile curve of the longitudinal section by using a positioning analysis model; the second image analysis process includes: and calculating the irregularity of each position in the edge profile curve of the longitudinal section by using a positioning analysis model, and if the irregularity of a certain position in the calculated edge profile curve exceeds a set third threshold value, taking the position as a source of harmful gas generation.
Further, the localization analysis model is expressed by the following formula:
Figure GDA0003706092280000051
Figure GDA0003706092280000052
wherein x is the value of the contour line at a certain position, and L isThe slope value of a certain position in the edge profile curve, M adjustment coefficient, the value range is: 1 to 1.5.
A harmful gas detection method based on multi-sensor cooperation, the method comprising the steps of: step 1: the sensors are used for sensing harmful gases in a target area and acquiring harmful gas sensing data in real time; the sensors in the sensor group are randomly distributed in the target area and need to meet the following requirements: in any position in the target area, the position is taken as the center of a circle, the radius is taken as the first set threshold value, and the number of the sensors in the defined circular area exceeds the second set threshold value; step 2: establishing a plane coordinate graph of the target area, simultaneously drawing corresponding coordinate points of the sensors in the plane coordinate graph based on the position of each sensor in the target area, and drawing a sensor coordinate graph of the target area; and step 3: harmful gas sensing data acquired by the sensor group at the same moment are recorded under the corresponding coordinates of each sensor based on a sensor coordinate graph in a target area; and 4, step 4: drawing a harmful gas topographic map according to a contour line drawing mode based on the target area sensor coordinate map and the harmful gas sensing data recorded in each sensor coordinate; and 5: based on the harmful gas topographic map, a preset early warning analysis model is used for carrying out first image analysis to judge whether early warning is needed or not, meanwhile, based on the harmful gas topographic map, a preset positioning analysis model is used for carrying out scanning analysis on the longitudinal section of the image to obtain an edge profile curve of the longitudinal section, then second image analysis is carried out on the edge profile curve of the longitudinal section, the irregularity of each position in the edge profile curve of the longitudinal section is calculated, and the source of harmful gas generation is determined.
According to the harmful gas detection system and method based on multi-sensor cooperation, the harmful gas data of each position of the target area is integrally obtained through the sensor group, the integral image analysis is carried out by drawing the harmful gas topographic map of the harmful gas data in the target area, whether early warning is needed or not is judged, meanwhile, the source of the harmful gas is found by using the sketch longitudinal section, and the harmful gas detection system and method based on multi-sensor cooperation have the advantages of being high in efficiency and accuracy. The method is mainly realized by the following steps: 1. establishing a target area sensor coordinate graph: according to the method, the sensor coordinate graph of the target area is established to realize establishment of the harmful gas detection model of the target area, the sensor coordinate graph of the target area can completely represent and simulate the condition of detecting the harmful gas of the target area through the sensor group, and through the mode, the whole method can be started more during subsequent analysis and detection, so that the efficiency is improved; 2. drawing a harmful gas topographic map: different from the prior art, the method introduces the probability of contour lines in the geography by constructing the topographic map of the harmful gas in the target area, can generate different contour lines because the numerical values of the harmful gas detected at different moments are different, identifies and judges the source of the harmful gas generation based on the distribution rule of the contour lines, and has high accuracy and efficiency; 3. and (3) a discrimination algorithm: according to the method, the irregularity of each position in the edge contour curve of the longitudinal section is calculated by using the positioning analysis model, and if the irregularity of a certain position in the calculated edge contour curve exceeds a set third threshold, the position is taken as a source for generating harmful gas, so that the calculation complexity of the process is low, but compared with the prior art that manual detection and tracing are continuously required, the method can obviously reduce the damage to a human body caused by manual operation.
Drawings
Fig. 1 is a schematic system structure diagram of a harmful gas detection system based on multi-sensor cooperation of the internet of things according to an embodiment of the invention;
FIG. 2 is a schematic flow chart of a method for detecting harmful gas based on multi-sensor cooperation according to an embodiment of the present invention;
fig. 3 is a schematic distribution diagram of sensing groups in a target area, according to the system and method for detecting harmful gas based on multi-sensor cooperation provided by the embodiment of the invention.
Detailed Description
The method of the present invention will be described in further detail with reference to the accompanying drawings and embodiments of the invention.
Example 1
As shown in fig. 1, the harmful gas detection system based on multi-sensor cooperation comprises:
the sensor group comprises a plurality of sensors and is configured to sense harmful gas in a target area and acquire harmful gas sensing data in real time; the sensors in the sensor group are randomly distributed in the target area and need to meet the following requirements: in any position in the target area, the position is taken as the center of a circle, the radius is taken as the first set threshold value, and the number of the sensors in the defined circular area exceeds the second set threshold value;
the target area mapping unit is configured for establishing a plane coordinate graph of the target area, and simultaneously mapping corresponding coordinate points of the sensors in the plane coordinate graph based on the positions of the sensors in the target area to map a sensor coordinate graph of the target area;
the data recording unit is configured for recording harmful gas sensing data acquired by the sensor group at the same moment to a coordinate corresponding to each sensor based on a sensor coordinate graph in a target area;
the harmful gas topographic map drawing unit is configured to draw a harmful gas topographic map according to a contour line drawing mode on the basis of the sensor coordinate map of the target area and the harmful gas sensing data recorded in each sensor coordinate;
and the image analysis unit is configured for carrying out first image analysis based on a harmful gas topographic map by using a preset early warning analysis model so as to judge whether early warning is needed or not, and meanwhile, carrying out image longitudinal section scanning analysis by using a preset positioning analysis model based on the harmful gas topographic map so as to obtain an edge profile curve of a longitudinal section, then carrying out second image analysis on the edge profile curve of the longitudinal section, calculating the irregularity of each position in the edge profile curve of the longitudinal section, and determining the source of harmful gas generation.
By adopting the technical scheme, the sensor group is used for integrally acquiring the harmful gas data of each position of the target area, the overall image analysis is carried out by drawing the harmful gas topographic map of the harmful gas data in the target area so as to judge whether the early warning is needed, and meanwhile, the source of the harmful gas is found by using the sketch longitudinal section, so that the method has the advantages of high efficiency and high accuracy. The method is mainly realized by the following steps: 1. establishing a target area sensor coordinate graph: according to the method, the sensor coordinate graph of the target area is established to realize establishment of the harmful gas detection model of the target area, the sensor coordinate graph of the target area can completely represent and simulate the condition of detecting the harmful gas of the target area through the sensor group, and through the mode, the whole method can be started more during subsequent analysis and detection, so that the efficiency is improved; 2. drawing a harmful gas topographic map: different from the prior art, the method introduces the probability of contour lines in the geography by constructing the topographic map of the harmful gas in the target area, can generate different contour lines because the numerical values of the harmful gas detected at different moments are different, identifies and judges the source of the harmful gas generation based on the distribution rule of the contour lines, and has high accuracy and efficiency; 3. and (3) a discrimination algorithm: according to the method, the irregularity of each position in the edge contour curve of the longitudinal section is calculated by using the positioning analysis model, and if the irregularity of a certain position in the calculated edge contour curve exceeds a set third threshold, the position is taken as a source for generating harmful gas, so that the calculation complexity of the process is low, but compared with the prior art that manual detection and tracing are continuously required, the method can obviously reduce the damage to a human body caused by manual operation.
Example 2
On the basis of the above embodiment, the method for drawing the target area sensor coordinate graph by the target area mapping unit includes: generating a two-dimensional rectangular coordinate system, reducing or amplifying the target area in equal proportion, and placing the target area in the two-dimensional rectangular coordinate system; and then, based on the position of the sensor in the target area, marking the corresponding position in the two-dimensional rectangular coordinate system of the target area, and drawing a coordinate graph of the sensor in the target area.
Example 3
On the basis of the above embodiment, the harmful gas topographic map drawing unit, the method for drawing the harmful gas topographic map includes: on the basis of a two-dimensional rectangular coordinate system of a target area sensor coordinate graph, adding a dimension to generate the target area sensor coordinate graph in a three-dimensional coordinate system, and drawing a harmful gas topographic map according to a mode of drawing contour lines in the added dimension on the basis of the target area sensor coordinate graph and harmful gas sensing data recorded in each sensor coordinate.
Example 4
On the basis of the above embodiment, the image analysis unit includes: the early warning analysis unit and the positioning analysis unit; the early warning analysis unit is configured to perform first image analysis by using a preset early warning analysis model based on a harmful gas topographic map so as to judge whether early warning is required or not; the positioning analysis unit uses a preset positioning analysis model to identify the source of harmful gas; the method for the early warning analysis unit to perform the first image analysis comprises the following steps: scanning a topographic map of harmful gas to obtain a topographic map scanning image, and distributing different color information to contour lines of different heights corresponding to the topographic map scanning image, wherein the color information is used for representing types of the contour lines of different heights, and the contour line type comprises a danger type or a safety type; determining a target candidate region from the scanned image according to the color information, and performing feature extraction in the target candidate region to obtain extracted image features; performing target detection based on a target detection model by using the extracted image features to obtain candidate targets; and identifying the candidate target by using the extracted image characteristics and based on a target classification model to obtain an image identification result.
Specifically, image analysis generally utilizes a mathematical model in combination with image processing techniques to analyze underlying features and overlying structures, thereby extracting information with some intelligence.
The technology of analyzing, describing, classifying and interpreting a scene by a mode recognition and artificial intelligence method is also called scene analysis or image understanding. Since the 60's of the 20 th century, there have been many studies on image analysis, and the development of image analysis techniques for specific problems and applications has gradually moved toward the establishment of general theories. The image analysis is closely related to the research content of image processing, computer graphics and the like, and is mutually crossed and overlapped. But image processing mainly studies image transmission, storage, enhancement and restoration; the method for representing the main points, lines, faces and volumes of computer graphics and the method for displaying visual information; the image analysis focuses on the description method for constructing the image, and more, the various images are represented by symbols, rather than the images themselves are operated, and various related knowledge is used for reasoning. Image analysis is also germane to research on human vision, where research on certain recognizable modules in human vision mechanisms may contribute to improved computer vision capabilities.
Example 5
On the basis of the above embodiment, when different color information is assigned to the contour lines of different heights corresponding to the topographic map scan image, the classification values of the contour lines are calculated using the following formula:
Figure GDA0003706092280000111
wherein x is the value of the contour, and c (x) is the calculated classification value of the contour; if C (x) is more than 0.5, the contour line is judged to be a dangerous type; if C (x) is lower than 0.5, the contour is determined to be safe.
Specifically, the contour lines in the geography are applied to the detection of the harmful gas, and the contour lines can display the distribution rules of the harmful gas in different areas, so that the source of the harmful gas is identified and judged.
Contour lines refer to closed curves formed by connecting adjacent points with equal height on a topographic map. Connecting points with the same altitude on the ground into a closed curve, vertically projecting the closed curve onto a horizontal plane, and scaling and drawing the closed curve on a drawing according to the proportion to obtain a contour line. The contour lines may also be seen as the intersection of horizontal planes of different altitude with the actual ground, so the contour lines are closed curves. The number marked on the contour is the altitude of the contour.
In contrast, in the present invention, the number marked is not the altitude but the value of the harmful gas sensed by each sensor.
Example 6
On the basis of the above embodiment, the method for identifying the source of harmful gas generation by the positioning analysis unit comprises the following steps: based on harmful gas topography, use the location analysis model to carry out image longitudinal section scanning analysis, specifically include: finding the highest point in the harmful gas topographic map to obtain a longitudinal section of the harmful gas topographic map at the point position, and performing second image analysis on an edge profile curve of the longitudinal section by using a positioning analysis model; the second image analysis process includes: and calculating the irregularity of each position in the edge profile curve of the longitudinal section by using a positioning analysis model, and if the irregularity of a certain position in the calculated edge profile curve exceeds a set third threshold value, taking the position as a source of harmful gas generation.
In particular, image recognition is an important area of artificial intelligence. In order to create a computer program that simulates human image recognition activities, different image recognition models have been proposed. Such as a template matching model. This model considers that a certain image is recognized and that it is necessary to have a memory pattern, also called template, of this image in past experience. If the current stimulus matches the template in the brain, the image is identified. For example, if there is an a-template in the brain, the letter a is recognized if its size, orientation, shape are identical to the a-template. The model is simple and clear and can be easily applied to practical use. However, this model emphasizes that the image must be completely matched with the template in the brain for recognition, and in fact, a person can recognize not only an image that is completely matched with the template in the brain, but also an image that is not completely matched with the template. For example, one can recognize not only a specific letter a, but also various letters a of a print, a script, an incorrect direction, and different sizes. At the same time, the number of images that a person can recognize is large, and it is impossible if each image recognized has a corresponding template in the brain.
Example 7
On the basis of the previous embodimentThe positioning analysis model is expressed by the following formula:
Figure GDA0003706092280000121
wherein, x is the value of the contour line of a certain position, L is the slope value of a certain position in the edge profile curve, M adjustment coefficient, the value range is: 1 to 1.5.
Example 8
A harmful gas detection method based on multi-sensor cooperation, the method comprising the steps of: step 1: the sensors sense harmful gas in a target area and acquire harmful gas sensing data in real time; the sensors in the sensor group are randomly distributed in the target area and need to meet the following requirements: in any position in the target area, the position is taken as the center of a circle, the radius is taken as the first set threshold value, and the number of the sensors in the defined circular area exceeds the second set threshold value; step 2: establishing a plane coordinate graph of the target area, simultaneously drawing corresponding coordinate points of the sensors in the plane coordinate graph based on the position of each sensor in the target area, and drawing a sensor coordinate graph of the target area; and step 3: harmful gas sensing data acquired by the sensor group at the same moment are recorded under the corresponding coordinates of each sensor based on a sensor coordinate graph in a target area; and 4, step 4: drawing a harmful gas topographic map according to a contour line drawing mode based on the target area sensor coordinate map and the harmful gas sensing data recorded in each sensor coordinate; and 5: based on the harmful gas topographic map, a preset early warning analysis model is used for carrying out first image analysis to judge whether early warning is needed or not, meanwhile, based on the harmful gas topographic map, a preset positioning analysis model is used for carrying out scanning analysis on the longitudinal section of the image to obtain an edge profile curve of the longitudinal section, then second image analysis is carried out on the edge profile curve of the longitudinal section, the irregularity of each position in the edge profile curve of the longitudinal section is calculated, and the source of harmful gas generation is determined.
In particular, gas sensors are a broad class of chemical sensors. From the working principle and characteristic analysis to the measurement technology, from the used materials to the manufacturing process, from the detection object to the application field, independent classification standards can be formed, and a numerous and complicated classification system is derived. The following is a description of the main characteristics of a gas sensor:
1. stability of
Stability refers to the stability of the sensor response over substantially the entire operating time, depending on zero drift and interval drift. Zero drift refers to the change in sensor output response over the entire operating time in the absence of the target gas. Interval drift is the change in output response of a sensor continuously placed in a target gas, manifested as a decrease in the sensor output signal over operating time. Ideally, one sensor has a zero drift of less than 10% per year under continuous operating conditions.
2. Sensitivity of the probe
Sensitivity is the ratio of the amount of change in sensor output to the amount of change in measured input, and is primarily dependent on the technology used in the sensor structure. Most gas sensors are designed on the principles of biochemistry, electrochemistry, physics, and optics. The first consideration is to choose a sensitive technique that is sufficiently sensitive to detect the percentage of the valve limit (TLV-thresh-oldlimitvalue) or the lowest explosion limit (LEL-lowerexplosivelimit) of the target gas.
3. Selectivity is
Selectivity is also referred to as cross-sensitivity. Can be determined by measuring the sensor response produced by a concentration of interfering gas. This response is equivalent to the sensor response generated by a concentration of the target gas. This characteristic is very important in applications for tracking multiple gases, since cross-sensitivity reduces the repeatability and reliability of the measurement and an ideal sensor should have high sensitivity and selectivity.
4. Corrosion resistance
Corrosion resistance refers to the ability of the sensor to be exposed to a high volume fraction of the target gas. When a large amount of gas leaks, the probe can bear 10-20 times of the expected volume fraction of the gas. The sensor drift and zero correction values should be as small as possible upon return to normal operating conditions.
The basic characteristics of a gas sensor, i.e., sensitivity, selectivity, stability, etc., are determined primarily by the choice of materials. The method selects proper materials and develops new materials to optimize the sensitivity of the gas sensor.
Example 9
On the basis of the above embodiment, the method for drawing the coordinate graph of the target area sensor by the target area mapping unit includes: generating a two-dimensional rectangular coordinate system, reducing or amplifying the target area in equal proportion, and placing the target area in the two-dimensional rectangular coordinate system; and then, based on the position of the sensor in the target area, marking the corresponding position in the two-dimensional rectangular coordinate system of the target area, and drawing a coordinate graph of the sensor in the target area.
Example 10
On the basis of the above embodiment, the harmful gas topographic map drawing unit, the method for drawing the harmful gas topographic map includes: on the basis of a two-dimensional rectangular coordinate system of a target area sensor coordinate graph, adding a dimension to generate the target area sensor coordinate graph in a three-dimensional coordinate system, and drawing a harmful gas topographic map according to a mode of drawing contour lines under the added dimension based on the target area sensor coordinate graph and harmful gas sensing data recorded in each sensor coordinate.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process and related description of the system described above may refer to the corresponding process in the foregoing method embodiments, and will not be described herein again.
It should be noted that, the system provided in the foregoing embodiment is only illustrated by dividing the functional units, and in practical applications, the functions may be distributed by different functional units according to needs, that is, the units or steps in the embodiments of the present invention are further decomposed or combined, for example, the units in the foregoing embodiment may be combined into one unit, or may be further decomposed into multiple sub-units, so as to complete all or the functions of the units described above. The names of the units and steps involved in the embodiments of the present invention are only for distinguishing the units or steps, and are not to be construed as unduly limiting the present invention.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes and related descriptions of the storage device and the processing device described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Those of skill in the art would appreciate that the various illustrative elements, method steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that programs corresponding to the elements, method steps may be located in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The terms "first," "second," and the like are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
The terms "comprises," "comprising," or any other similar term are intended to cover a non-exclusive inclusion, such that a process, method, article, or unit/apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or unit/apparatus.
So far, the technical solutions of the present invention have been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Equivalent modifications or substitutions of the related art marks may be made by those skilled in the art without departing from the principle of the present invention, and the technical solutions after such modifications or substitutions will fall within the protective scope of the present invention.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.

Claims (6)

1. Harmful gas detection system based on multi-sensor cooperation, characterized in that, the system includes:
the sensor group comprises a plurality of sensors and is configured to sense harmful gas in a target area and acquire harmful gas sensing data in real time; the sensors in the sensor group are randomly distributed in the target area and need to meet the following requirements: in any position in the target area, the position is taken as the center of a circle, the radius is taken as the first set threshold value, and the number of the sensors in a defined circular area exceeds the second set threshold value;
the target area mapping unit is configured for establishing a plane coordinate graph of the target area, and simultaneously mapping corresponding coordinate points of the sensors in the plane coordinate graph based on the positions of the sensors in the target area to map a sensor coordinate graph of the target area;
the data recording unit is configured for recording harmful gas sensing data acquired by the sensor group at the same moment to a coordinate corresponding to each sensor based on a sensor coordinate graph in a target area;
the harmful gas topographic map drawing unit is configured to draw a harmful gas topographic map according to a contour line drawing mode on the basis of the sensor coordinate map of the target area and the harmful gas sensing data recorded in each sensor coordinate;
the image analysis unit is configured for performing first image analysis by using a preset early warning analysis model based on a harmful gas topographic map so as to judge whether early warning is needed or not, and meanwhile, performing image longitudinal section scanning analysis by using a preset positioning analysis model based on the harmful gas topographic map so as to obtain an edge profile curve of a longitudinal section, performing second image analysis on the edge profile curve of the longitudinal section, calculating the irregularity of each position in the edge profile curve of the longitudinal section, and determining the source of harmful gas generation;
the method for identifying the source of harmful gas generation by the positioning analysis unit comprises the following steps: based on harmful gas topography, use the location analysis model to carry out image longitudinal section scanning analysis, specifically include: finding the highest point in the harmful gas topographic map to obtain a longitudinal section of the harmful gas topographic map at the point position, and performing second image analysis on an edge profile curve of the longitudinal section by using a positioning analysis model; the second image analysis process includes: and calculating the irregularity of each position in the edge profile curve of the longitudinal section by using a positioning analysis model, and if the irregularity of a certain position in the calculated edge profile curve exceeds a set third threshold value, taking the position as a source of harmful gas generation.
The positioning analysis model is expressed by the following formula:
Figure FDA0003737719300000021
Figure FDA0003737719300000022
wherein, x is the value of the contour line of a certain position, L is the slope value of a certain position in the edge profile curve, M adjustment coefficient, the value range is: 1 to 1.5.
2. The system of claim 1, wherein the method of the target area mapping unit drawing the target area sensor coordinates comprises: generating a two-dimensional rectangular coordinate system, reducing or amplifying the target area in equal proportion, and placing the target area in the two-dimensional rectangular coordinate system; and then, based on the position of the sensor in the target area, marking the corresponding position in the two-dimensional rectangular coordinate system of the target area, and drawing a coordinate graph of the sensor in the target area.
3. The system of claim 2, wherein the harmful gas topography drawing unit, the method of drawing a harmful gas topography includes: on the basis of a two-dimensional rectangular coordinate system of a target area sensor coordinate graph, adding a dimension to generate the target area sensor coordinate graph in a three-dimensional coordinate system, and drawing a harmful gas topographic map according to a mode of drawing contour lines under the added dimension based on the target area sensor coordinate graph and harmful gas sensing data recorded in each sensor coordinate.
4. The system of claim 3, wherein the image analysis unit comprises: the early warning analysis unit and the positioning analysis unit; the early warning analysis unit is configured to perform first image analysis by using a preset early warning analysis model based on a harmful gas topographic map so as to judge whether early warning is required or not; the positioning analysis unit uses a preset positioning analysis model to identify the source of harmful gas generation; the method for the early warning analysis unit to perform the first image analysis comprises the following steps: scanning a topographic map of harmful gas to obtain a topographic map scanning image, and distributing different color information to contour lines of different heights corresponding to the topographic map scanning image, wherein the color information is used for representing types of the contour lines of different heights, and the contour line type comprises a danger type or a safety type; determining a target candidate area from the scanned image according to the color information, and performing feature extraction in the target candidate area to obtain extracted image features; performing target detection based on a target detection model by using the extracted image features to obtain candidate targets; and identifying the candidate target by using the extracted image characteristics and based on a target classification model to obtain an image identification result.
5. The system of claim 4, wherein the contours are equal when scanning the relief image for different heights corresponding to the imagesWhen the lines are assigned different color information, the classification values of the contour lines are calculated using the following formula:
Figure FDA0003737719300000031
Figure FDA0003737719300000032
wherein x is the value of the contour, and c (x) is the calculated classification value of the contour; if C (x) is more than 0.5, the contour line is judged to be a dangerous type; if C (x) is lower than 0.5, the contour is determined to be safe.
6. A harmful gas detection method based on multi-sensor cooperation based on the system of one of claims 1 to 5, characterized in that the method performs the following steps: step 1: the sensors sense harmful gas in a target area and acquire harmful gas sensing data in real time; the sensors in the sensor group are randomly distributed in the target area and need to meet the following requirements: in any position in the target area, the position is taken as the center of a circle, the radius is taken as the first set threshold value, and the number of the sensors in the defined circular area exceeds the second set threshold value; step 2: establishing a plane coordinate graph of a target area, simultaneously drawing corresponding coordinate points of the sensors in the plane coordinate graph based on the position of each sensor in the target area, and drawing a sensor coordinate graph of the target area; and step 3: harmful gas sensing data acquired by the sensor group at the same moment are recorded under the corresponding coordinates of each sensor based on a sensor coordinate graph in a target area; and 4, step 4: drawing a harmful gas topographic map according to a contour line drawing mode based on the target area sensor coordinate map and the harmful gas sensing data recorded in each sensor coordinate; and 5: based on the harmful gas topographic map, a preset early warning analysis model is used for carrying out first image analysis to judge whether early warning is needed or not, meanwhile, based on the harmful gas topographic map, a preset positioning analysis model is used for carrying out scanning analysis on the longitudinal section of the image to obtain an edge profile curve of the longitudinal section, then second image analysis is carried out on the edge profile curve of the longitudinal section, the irregularity of each position in the edge profile curve of the longitudinal section is calculated, and the source of harmful gas generation is determined.
CN202110687879.1A 2021-06-21 2021-06-21 Harmful gas detection system and method based on multi-sensor cooperation Active CN113203841B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110687879.1A CN113203841B (en) 2021-06-21 2021-06-21 Harmful gas detection system and method based on multi-sensor cooperation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110687879.1A CN113203841B (en) 2021-06-21 2021-06-21 Harmful gas detection system and method based on multi-sensor cooperation

Publications (2)

Publication Number Publication Date
CN113203841A CN113203841A (en) 2021-08-03
CN113203841B true CN113203841B (en) 2022-08-26

Family

ID=77022437

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110687879.1A Active CN113203841B (en) 2021-06-21 2021-06-21 Harmful gas detection system and method based on multi-sensor cooperation

Country Status (1)

Country Link
CN (1) CN113203841B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114812681B (en) * 2021-11-11 2023-11-28 艾感科技(广东)有限公司 Detection system based on multi-sensor fusion under complex environment
CN118430198A (en) * 2023-09-06 2024-08-02 常州勤裕信息科技有限公司 Operating room harmful gas monitoring and alarming system and method based on Internet of things

Family Cites Families (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4041089B2 (en) * 2004-04-02 2008-01-30 日本電信電話株式会社 Chemical substance abnormal high concentration emission source identification system and method
KR100661595B1 (en) * 2004-07-01 2006-12-27 박옥현 Decision Support System for Air pollution source planning: ApspDSS
CN102103061B (en) * 2009-12-18 2014-03-19 西安费斯达自动化工程有限公司 Isoconcentration line method for atmosphere monitoring, prediction and assessment
CN103679610A (en) * 2013-12-12 2014-03-26 北京航空航天大学 Visualization system for atmospheric environmental monitoring
CN104713985B (en) * 2013-12-17 2016-08-17 淮安信息职业技术学院 A kind of Minepit environment harmful gas detection system based on wireless senser
CN109780452B (en) * 2019-01-24 2021-02-26 天津中科飞航技术有限公司 Gas leakage unmanned aerial vehicle inspection concentration inversion method based on laser remote measurement technology
CN110457829B (en) * 2019-08-15 2020-05-01 王博 Source item release inversion and diffusion prediction method based on integrated atmospheric diffusion model
CN112270112A (en) * 2020-11-19 2021-01-26 天津大学 Toxic and harmful gas leakage tracing method
CN112780344A (en) * 2021-01-26 2021-05-11 中冶赛迪工程技术股份有限公司 Method and system for monitoring invasion of dangerous area in mine

Also Published As

Publication number Publication date
CN113203841A (en) 2021-08-03

Similar Documents

Publication Publication Date Title
CN110796284B (en) Method and device for predicting pollution level of fine particulate matters and computer equipment
CN113203841B (en) Harmful gas detection system and method based on multi-sensor cooperation
Goodchild Integrating GIS and remote sensing for vegetation analysis and modeling: methodological issues
Wulder et al. Local spatial autocorrelation characteristics of remotely sensed imagery assessed with the Getis statistic
CN107478683B (en) Toxic and harmful gas detection and identification method based on machine olfaction
CN109948469A (en) The automatic detection recognition method of crusing robot instrument based on deep learning
CN112734694A (en) Water quality monitoring method based on big data
CN112504461A (en) Intelligent temperature measurement system and temperature measurement method
CN103500342A (en) Human behavior recognition method based on accelerometer
CN115659195B (en) Online identification method for atmospheric pollution
CN113487470A (en) Environmental protection hidden danger accurate monitoring and positioning method based on big data
CN113378975A (en) Method for evaluating difference of risk perception capability of electrician operating personnel
CN117171533B (en) Real-time acquisition and processing method and system for geographical mapping operation data
CN109784390A (en) A kind of artificial intelligence smell dynamic response map gas detection recognition methods
CN117079082A (en) Intelligent visual image target object detection method and device and DMC (digital media control) equipment
CN113267601A (en) Industrial production environment remote real-time monitoring cloud platform based on machine vision and data analysis
Janeczko et al. Assessment of orchid surfaces using top-down contact angle mapping
Morosi et al. A WSN for ground-level ozone monitoring based on plant electrical activity analysis
CN111832401A (en) Electronic marking recognition method
CN116912672A (en) Unmanned survey vessel-based biological integrity evaluation method for large benthonic invertebrates
CN109633113B (en) Water quality monitoring and early warning method and system based on medaka step-by-step behavior model
CN109654559A (en) A kind of healthy cooking system that can identify steam and oil smoke
CN112259172B (en) Reverse identification method for pollution source with air pollutant chemical reaction condition
Bakar et al. Development of application specific electronic nose for monitoring the atmospheric hazards in confined space
CN113129374A (en) Method and device for assisting reading and writing posture judgment by laser point cloud face recognition

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant