VOCs gas leakage detection system integrated with multi-terminal coordination and intelligent analysis platform
The VOCs gas leak detection system, which integrates multi-terminal coordination and intelligent analysis platform, utilizes a cooled detector and intelligent analysis platform, combined with a multispectral feature fusion model and a leak diffusion simulation engine, to solve the problems of inaccurate detection and insufficient emergency response in existing technologies, and achieves high-precision gas leak detection and path prediction.
Patent Information
- Application Number
- CN202510951331.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-10
- Publication Date
- 2025-11-07
Smart Images
Figure CN120907735A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application belongs to the technical field of VOCs gas leakage detection, and particularly relates to a VOCs gas leakage detection system integrated with a multi-terminal coordination and intelligent analysis platform. BACKGROUND
[0002] In the industrial production process, VOCs gas leakage not only causes environmental pollution, but also may cause safety accidents, threatening the safety of personnel life and property. At present, the existing VOCs gas leakage detection methods on the market mainly include point gas sensor detection and ordinary infrared imaging detection. The point gas sensor detection can only detect specific points and cannot realize large-area and rapid leakage detection, and has a detection blind area. The ordinary infrared imaging detection is easily disturbed by environmental factors such as water vapor and dust, resulting in inaccurate detection results, false positives, and missed reports. Moreover, the existing detection system lacks the ability to predict the diffusion of gas leakage and emergency response, and cannot timely and effectively respond to gas leakage accidents.
[0003] Therefore, the above problems need to be further improved. SUMMARY
[0004] The main purpose of the present application is to provide a VOCs gas leakage detection system integrated with a multi-terminal coordination and intelligent analysis platform to solve the problems of inaccurate detection, inability to predict gas diffusion path, and lack of emergency response capability in the prior art.
[0005] To achieve the above purpose, the present application provides a VOCs gas leakage detection system integrated with a multi-terminal coordination and intelligent analysis platform, comprising a data acquisition module and an intelligent analysis platform module, wherein: The data acquisition module comprises a refrigeration type detector, which is used to acquire infrared images of the leaked gas and transmit the infrared images to the intelligent analysis platform module. The intelligent analysis platform module comprises a multispectral feature fusion model and a leakage diffusion simulation engine, wherein: The multispectral feature fusion model is based on a deep learning algorithm and is used to combine gas absorption peak characteristics and environmental temperature and humidity data to distinguish between real leakage and interference signals. The leakage diffusion simulation engine uses a fluid mechanics model to predict the gas diffusion path and generate an emergency response plan.
[0006] As a further preferred technical solution of the above technical solution, the multispectral feature fusion model is specifically implemented as: First, multispectral feature extraction: let the input spectral data be Extract the absorption peak feature in the target waveband: where Conv1D captures the absorption peak details using narrow-band convolution kernels; Second, environmental feature fusion: temperature and humidity data After feature enhancement: where is a Sigmoid activation function, is a learnable weight, is a bias vector for environmental feature encoding; Third, cross-modal feature fusion: dynamically weighted spectral features using attention mechanism: ; ; where, denotes feature concatenation, denotes Hadamard product, is an attention weight vector, is a weight matrix for the attention mechanism; Fourth, leak probability prediction: the final classification layer output is: ; where, denotes the leak probability, is a weight matrix for the class layer, is a multi-layer perceptron, is a bias vector for the classification layer.
[0007] As a further preferred technical solution of the above technical solution, the leak diffusion simulation engine is specifically implemented as: First, fluid dynamics model: build a gas diffusion model based on Navier-Stokes equation: ; where, is a wind velocity vector, is a pressure, is a kinematic viscosity, is a terrain correction term, is a time vector, is a change per unit time, is a gas density, is a gravity acceleration vector; Second, gas concentration field prediction: material transport equation coupled with turbulence model: ; is a gas concentration, , is a leak source term; Third, the leakage source inversion model: based on sensor data to deduce the leakage position: ; is the leakage source position coordinate, is the sensor coefficient, is the model predicted concentration value, is the kth sensor position, is the kth sensor actual measurement value, is the regularization coefficient, is the terrain constraint function; Fourth, the risk assessment model: explosion risk probability calculation: ; is the explosion risk probability, is the evaluation time window, is the spatial integration domain, is the indicator function, taking 1 when the concentration is greater than LEL, is the lower limit of the explosion concentration.
[0008] As a further preferred technical solution of the above technical solution, it further comprises an application layer, which provides a visual interface to display a thermal imaging superimposed gas concentration cloud map, supports PC / mobile terminal multi-platform access, and can automatically generate a detection report.
[0009] As a further preferred technical solution of the above technical solution, the data transmission between the data acquisition module and the intelligent analysis platform module supports wired and wireless dual-mode communication, ensuring data real-time and reliability. BRIEF DESCRIPTION OF DRAWINGS
[0010] Figure 1 is a schematic diagram of the present application. DETAILED DESCRIPTION
[0011] The following description is used to disclose the present application so that those skilled in the art can implement the present application. The preferred embodiments in the following description are only as examples, and other obvious modifications can be thought of by those skilled in the art. The basic principles of the present application defined in the following description can be applied to other embodiments, modifications, improvements, equivalents and other technical solutions without departing from the spirit and scope of the present application.
[0012] In the preferred embodiments of the present application, those skilled in the art should note that the VOCs gas and the like involved in the present application can be regarded as prior art.
[0013] Preferred embodiments.
[0014] As Figure 1As shown, the application discloses a VOCs gas leakage detection system integrated with a multi-terminal coordination and intelligent analysis platform, comprising a data acquisition module and an intelligent analysis platform module, wherein: The data acquisition module comprises a refrigeration type detector (supporting high-sensitivity imaging in a 3.2-3.5 μm wave band), which is used for collecting infrared images of the leaked gas and transmitting the infrared images to the intelligent analysis platform module; The intelligent analysis platform module comprises a multi-spectral feature fusion model and a leakage diffusion simulation engine, wherein: The multi-spectral feature fusion model is based on a deep learning algorithm and is used for combining gas absorption peak characteristics (3.2-3.5 μm wave band) and environmental temperature and humidity data to distinguish real leakage from interference signals (such as water vapor and dust); The leakage diffusion simulation engine utilizes a fluid mechanics model to predict a gas diffusion path and generates an emergency response plan.
[0015] Specifically, the multi-spectral feature fusion model is specifically implemented as: First, multi-spectral feature extraction: let the input spectral data be (wherein is the number of wave bands, is the number of wavelength points), and the absorption peak characteristics are extracted in the target wave band (3.2-3.5 μm): wherein Conv1D uses a narrow-band convolution kernel to capture absorption peak details; Second, environmental feature fusion: temperature and humidity data are enhanced by features: wherein is a Sigmoid activation function, is a learnable weight, is a bias vector for environmental feature coding; Third, cross-modal feature fusion: an attention mechanism is adopted to dynamically weight the spectral features: ; ; wherein, denotes feature splicing, denotes Hadamard product, is an attention weight vector, is a weight matrix of the attention mechanism; Fourth, leakage probability prediction: the final classification layer outputs are: ; wherein, denotes leakage probability, a weight matrix for a class layer, a multi-layer perceptron, a bias vector for a class layer.
[0016] More specifically, for the leak diffusion simulation engine, it is implemented as: First, a fluid dynamics model: a gas diffusion model is constructed based on the Navier-Stokes equation: ; where, is the wind velocity vector, is the pressure, is the kinematic viscosity, is the terrain correction term, is the time vector, is the change per unit time, is the gas density, is the gravity acceleration vector; Second, gas concentration field prediction: material transport equation coupled with turbulence model: ; is the gas concentration, (molecular + turbulent diffusion coefficient, is the effective diffusion coefficient), is the leak source term; Third, leak source inversion model: based on sensor data to deduce the leak location: ; is the leak source location coordinate, is the sensor coefficient, is the model predicted concentration value, is the kth sensor location, is the kth sensor actual measurement value, is the regularization coefficient, is the terrain constraint function; Fourth, risk assessment model: explosion risk probability calculation: ; is the explosion risk probability, is the evaluation time window, is the spatial integration domain, is the indicator function, taking 1 when the concentration is greater than LEL, is the lower explosive limit.
[0017] Further, an application layer is further included, a visual interface is provided, a thermal imaging superimposed gas concentration cloud picture is displayed, PC / mobile terminal multi-platform access is supported, and a detection report can be automatically generated.
[0018] Further, data transmission between the data acquisition module and the intelligent analysis platform module supports wired (industrial Ethernet) and wireless (5G / LoRa) dual-mode communication, ensuring data real-time and reliability.
[0019] It is worth mentioning that the VOCs gas and other technical features involved in the present patent application should be regarded as prior art, and the specific structure, working principle and possible control mode and spatial arrangement mode of these technical features can be selected conventionally in the art, and should not be regarded as the invention point of the present patent, and the present patent will not be further expanded and detailed.
[0020] For those skilled in the art, the technical solutions recorded in the foregoing embodiments can still be modified, or some technical features can be replaced equivalently, and any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims
1. A VOCs gas leak detection system integrated with a multi-terminal coordination and intelligent analysis platform, characterized in that, Comprising a data acquisition module and an intelligent analysis platform module, wherein: The data acquisition module comprises a refrigeration-type detector, which is used to collect infrared images of the leaked gas and transmit the infrared images to the intelligent analysis platform module; The intelligent analysis platform module comprises a multispectral feature fusion model and a leakage diffusion simulation engine, wherein: The multispectral feature fusion model is based on a deep learning algorithm and is used to combine gas absorption peak characteristics and environmental temperature and humidity data to distinguish between real leaks and interference signals; The leakage diffusion simulation engine uses a fluid dynamics model to predict the gas diffusion path and generate an emergency response plan.
2. The VOCs gas leak detection system integrated with a multi-terminal coordination and intelligent analysis platform according to claim 1, wherein, For the multispectral feature fusion model, the implementation is as follows: First, multispectral feature extraction: Let the input spectral data be Extracting absorption peak features in the target waveband: where Conv1D captures the absorption peak details using narrow-band convolution kernels; Second, environmental feature fusion: temperature and humidity data Feature enhanced: wherein is a Sigmoid activation function, is a learnable weight, is a bias vector for the environmental feature encoding; Third, cross-modal feature fusion: dynamic weighting of spectral features using an attention mechanism: ； ； wherein, denotes feature concatenation, denotes Hadamard product, is an attention weight vector, is a weight matrix of the attention mechanism; Fourth, leakage probability prediction: the final classification layer outputs: ； wherein, denotes the probability of leakage, is a weight matrix of the classification layer, is a multi-layer perceptron, is a bias vector of the classification layer.
3. The VOCs gas leak detection system integrated with a multi-terminal coordination and intelligent analysis platform according to claim 2, wherein, For the leakage diffusion simulation engine, the implementation is as follows: First, fluid dynamics model: a gas diffusion model is constructed based on the Navier-Stokes equation: ； wherein is the wind velocity vector, is the pressure, is the kinematic viscosity, is the terrain correction term, is the time vector, is the change per unit time, is the gas density, is the gravity acceleration vector; Second, gas concentration field prediction: material transport equation coupled with a turbulence model: ； is the gas concentration, , is the leak source term; Third, leakage source inversion model: based on sensor data to infer the leakage location: ； for a source location coordinate, for a sensor coefficient, for a model predicted concentration value, for a kth sensor location, for a kth sensor actual measurement value, for a regularization coefficient, for a terrain constraint function; Fourth, risk assessment model: explosion risk probability calculation: ； is the explosion risk probability, is the evaluation time window, is the spatial integration domain, is the indicator function, which takes the value 1 if the concentration is greater than the LEL, is the lower explosion limit.
4. The VOCs gas leak detection system integrated with a multi-terminal coordination and intelligent analysis platform of claim 1, wherein, It also includes an application layer that provides a visual interface to display thermal imaging superimposed with gas concentration cloud maps, supports PC / mobile multi-platform access, and can automatically generate detection reports.
5. The VOCs gas leak detection system integrated with a multi-terminal coordination and intelligent analysis platform according to claim 1, wherein, The data transmission between the data acquisition module and the intelligent analysis platform module supports wired and wireless dual-mode communication to ensure data real-time and reliability.