A method for outdoor environmental health monitoring based on olfactory chip

Abstract

The present invention discloses an outdoor environmental health monitoring method based on an olfactory chip, which relates to the technical field of environmental health monitoring. The present invention adopts traditional sensors and olfactory chips at the monitoring nodes at the same time, realizing the synchronous collection of temperature, humidity, gas concentration and complex odor fingerprints, fundamentally making up for the limitation of a single data source in identifying multi-component pollutants in the complex environment of industrial areas; traditional methods are difficult to distinguish the cross-interference of various types of gases, while the present invention uses a deep neural network combined with an attention mechanism to extract the implicit features of the sensor data, and realizes data cross-correction through spatiotemporal correlation analysis, which significantly improves the accuracy of pollutant identification; further introduces a graph neural network to model the spatiotemporal data of each monitoring node, captures the spatial structure information of the entire monitoring network, and forms a fusion feature vector with high distinguishing ability; effectively solves the problem of signal cross-interference when multiple malodorous gases are mixed in industrial areas.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental health monitoring, and in particular to an outdoor environmental health monitoring method based on an olfactory chip. Background Art

[0002] At present, outdoor environmental health monitoring generally uses gas sensors and spectrometers to conduct fixed-point detection of harmful gases in the air, reflecting environmental quality through regular sampling. This type of method can effectively capture temperature, humidity and concentration data of specific pollutants in stable areas or when monitoring fixed emission sources. However, in actual applications, the monitoring effect on industrial areas is not good.

[0003] There are many types of gases in industrial environments. The odorous substances emitted include not only conventional gases such as sulfur dioxide and hydrogen sulfide, but also various volatile organic compounds, which form cross-interference. A single sensor array has difficulty distinguishing the subtle differences between these gases during detection, resulting in data confusion, which in turn affects the accurate assessment of the degree of pollution. Secondly, environmental changes are highly real-time, equipment in industrial areas operates frequently, and processes fluctuate greatly. Traditional monitoring methods are difficult to respond quickly to sudden pollution incidents, and early warnings lag behind the actual spread of pollution.

[0004] To address this shortcoming, some traditional solutions have introduced multi-sensor combinations to pre-process and linearly fuse data from different types of sensors. However, this method still has difficulty effectively extracting the characteristic signals of each pollutant in complex mixed gases. Another solution uses gas chromatography-mass spectrometry (GC-MS) technology. Although this can improve detection accuracy, the equipment is bulky and costly, making it difficult to achieve wide-area real-time monitoring and rapid on-site response. In this context, how to more accurately distinguish complex gas mixtures has become a key issue that needs to be addressed urgently. Summary of the Invention

[0005] In view of the above existing problems, the present invention is proposed.

[0006] The present invention provides an outdoor environmental health monitoring method based on an olfactory chip to solve the problem that industrial areas emit a wide variety of odorous substances, often resulting in cross-responses, making it difficult for traditional single sensor arrays to distinguish complex gas mixtures.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] The embodiment of the present invention provides an outdoor environmental health monitoring method based on an olfactory chip, which includes:

[0009] Step S1: In the area to be monitored, the area is divided into regions according to a preset grid, and monitoring nodes are deployed in each area. Each monitoring node includes a sensor unit and an olfactory chip sensor module;

[0010] Step S2: filtering, standardizing and denoising the raw data collected by each monitoring node to generate pre-processed data;

[0011] Step S3, integrating the pre-processed data in step S2 using a multimodal data fusion algorithm to generate a fused feature vector;

[0012] Step S4, based on historical monitoring data, using a supervised learning method to perform model training on the fused feature vector of step S3 to establish a multi-component gas recognition model;

[0013] Step S5: Deploy the trained recognition model on the edge computing platform and analyze the fused feature vectors collected in real time:

[0014] When the identification result shows that the pollution index of a monitoring node exceeds the preset threshold, the local alarm device is immediately activated;

[0015] Step S6: After the local alarm device is activated, the real-time data and alarm information of each monitoring node are transmitted to the control console via the wireless network. The control console stores, displays and compares the real-time data and alarm information with historical data.

[0016] As a preferred solution of the outdoor environmental health monitoring method based on the olfactory chip described in the present invention, the sensor unit is used to collect temperature, humidity and gas concentration data;

[0017] The olfactory chip sensing module is an electronic nose device composed of multiple micro odor sensing elements, which is used to capture multi-component odor fingerprints in the air in real time.

[0018] As a preferred solution of the outdoor environmental health monitoring method based on an olfactory chip described in the present invention, the preprocessed data includes traditional sensor data and transient odor response data output by the olfactory chip module.

[0019] As a preferred solution of the outdoor environmental health monitoring method based on an olfactory chip described in the present invention, in which: in step S3, the multimodal data fusion algorithm includes a deep neural network structure, which is used to extract the characteristics of the data of each sensor module, and use spatiotemporal correlation analysis to realize data cross-correction and separate the mutual interference information between multi-component gases.

[0020] As a preferred solution of the outdoor environmental health monitoring method based on olfactory chip described in the present invention, in the data fusion step of step S3, a graph neural network is also used to model and analyze the spatiotemporal data of each monitoring node to identify multi-component cross-interference signals.

[0021] As a preferred solution of the outdoor environmental health monitoring method based on an olfactory chip described in the present invention, in step S3, the step of integrating using a multimodal data fusion algorithm to generate a fusion feature vector is as follows:

[0022] A deep neural network is used to process the raw data, and the processing formula is:

[0023] F i =σ(W2(σ(W1X i +b1))+b2),

[0024] Among them, X i represents the raw data collected by the i-th sensor module, W1 represents the first-layer weight matrix, b1 represents the first-layer bias vector, W2 represents the second-layer weight matrix, b2 represents the second-layer bias vector, σ represents the activation function, F i Represents the extracted deep features;

[0025] The similarity function is defined to capture the spatiotemporal correlation between data. The function formula is:

[0026]

[0027] Among them, F i,t represents the characteristics of node i at time t, F j,t represents the characteristics of node j at time t, W a Represents the similarity weight matrix, ReLU represents the rectified linear unit function, φ(F i,t ,F j,t ) indicates the similarity between the two;

[0028] Based on the similarity, the attention weight is calculated using the following formula:

[0029]

[0030] Among them, α ij represents the attention weight of node i to neighboring node j, represents the neighborhood set of node i, exp represents the exponential function, F l,t represents the characteristics of node l at time t;

[0031] Weighted aggregation of neighborhood features to obtain G i,t :

[0032]

[0033] Among them, G i,t Represents the weighted aggregation features of node i at time t.

[0034] As a preferred solution of the outdoor environmental health monitoring method based on an olfactory chip described in the present invention, in step S3, the step of integrating using a multimodal data fusion algorithm to generate a fusion feature vector further includes:

[0035] The graph neural network is used to perform graph convolution processing on each monitoring node. The convolution formula is:

[0036]

[0037] Among them, H i Represents the features of node i after graph convolution processing, represents node i and its neighborhood, c ij represents the normalization factor,

[0038] W g represents the graph convolution weight matrix, σ represents the activation function as before,

[0039] The weighted aggregation features and graph convolution features are cascaded to form a fusion feature vector, which can be expressed as:

[0040] Z i =[G i,t ;H i ],

[0041] Among them, Z i Represents the fusion feature vector of node i, [;] represents G i,t With H i Cascade by column.

[0042] As a preferred embodiment of the outdoor environmental health monitoring method based on an olfactory chip of the present invention, the recognition model is used to automatically determine the concentration levels of various malodorous gases and other harmful components;

[0043] The recognition model is trained using back propagation and gradient descent algorithms, and dynamic parameter adjustment is performed according to the actual monitoring environment.

[0044] As a preferred solution of the outdoor environmental health monitoring method based on an olfactory chip described in the present invention, in step S4, the step of using a supervised learning method to perform model training on the fused feature vector of step S3 to establish a multi-component gas recognition model is as follows:

[0045] Use the fused feature vector Z generated in step S3 i A multi-component gas identification model is established, and its output is defined as:

[0046]

[0047] in, represents the model prediction output of node i, g(·;ω) represents the recognition model function, ω represents the model parameters,

[0048] Construct the loss function, the function formula is:

[0049]

[0050] Among them, L represents the total loss, N represents the total number of samples, and y i represents the true label of node i, l(·) represents the cross entropy loss function;

[0051] Gradient descent is used to update the parameters, and the update formula is:

[0052]

[0053] Among them, η represents the learning rate, represents the gradient of the loss function with respect to the parameter ω.

[0054] As a preferred solution of the outdoor environmental health monitoring method based on an olfactory chip described in the present invention, in step S5, the step of deploying the trained recognition model on the edge computing platform and analyzing the fused feature vector collected in real time is as follows:

[0055] The trained recognition model is deployed on the edge computing platform to process the fused feature vectors collected in real time. The real-time output is expressed as:

[0056]

[0057] Set the alarm conditions as:

[0058] like Then the alarm device of node i is triggered;

[0059] Wherein, τ represents the preset alarm threshold.

[0060] The beneficial effects of the present invention are as follows: the present invention adopts traditional sensors and olfactory chips at the monitoring nodes at the same time, realizing the synchronous collection of temperature, humidity, gas concentration and complex odor fingerprints, fundamentally making up for the limitations of a single data source in identifying multi-component pollutants in complex industrial environments; traditional methods are difficult to distinguish the cross-interference of various gases, while the present invention uses deep neural networks combined with attention mechanisms to extract the implicit features of sensor data, and realizes data cross-correction through spatiotemporal correlation analysis, which significantly improves the accuracy of pollutant identification.

[0061] The present invention further introduces a graph neural network to model the spatiotemporal data of each monitoring node, captures the spatial structure information of the entire monitoring network, and forms a fusion feature vector with high discrimination ability; this multimodal data fusion method effectively solves the problem of signal cross-interference when multiple odorous gases are mixed in industrial areas, enables the data of each node to complement each other, and greatly improves the overall monitoring effect.

[0062] Furthermore, the supervised learning model constructed by this invention uses cross-entropy loss and gradient descent to achieve efficient training of fused feature vectors. The trained model is capable of automatically determining the concentrations of various pollutants and dynamically adjusting parameters based on the actual environment, adapting to complex and ever-changing industrial scenarios. Once deployed on an edge computing platform, it can analyze collected data in real time, rapidly respond to anomalies, trigger local alarms, and transmit anomaly information to a central console for centralized management and display.

[0063] In summary, the present invention forms a closed-loop control system from data acquisition and feature extraction to model training and real-time early warning, which not only improves the monitoring accuracy and response speed, but also effectively overcomes the shortcomings of traditional environmental monitoring methods in dealing with multi-component cross-interference and real-time data integration, providing reliable technical support and data guarantee for industrial zone environmental governance. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0065] Figure 1 This is a flow chart of the outdoor environmental health monitoring method based on the olfactory chip of the present invention. DETAILED DESCRIPTION

[0066] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0067] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0068] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0069] Example 1, reference Figure 1 This embodiment provides an outdoor environmental health monitoring method based on an olfactory chip, comprising:

[0070] Step S1: In the area to be monitored, the area is divided into regions according to a preset grid, and monitoring nodes are deployed in each area. Each monitoring node includes a sensor unit and an olfactory chip sensor module;

[0071] The sensor unit is used to collect temperature, humidity and gas concentration data;

[0072] The olfactory chip sensor module is an electronic nose device composed of multiple micro odor sensing elements, which is used to capture the multi-component odor fingerprint in the air in real time;

[0073] Step S2: filtering, standardizing and denoising the raw data collected by each monitoring node to generate pre-processed data;

[0074] The preprocessed data includes traditional sensor data and transient odor response data output by the olfactory chip module;

[0075] Step S3, integrating the pre-processed data in step S2 using a multimodal data fusion algorithm to generate a fused feature vector;

[0076] In step S3, the multimodal data fusion algorithm includes a deep neural network structure to extract the characteristics of the data from each sensor module, and uses spatiotemporal correlation analysis to achieve data cross-correction and separate the mutual interference information between multi-component gases;

[0077] In the data fusion step of step S3, a graph neural network is also used to model and analyze the spatiotemporal data of each monitoring node to identify multi-component cross-interference signals;

[0078] In step S3, the multimodal data fusion algorithm is used to integrate and generate the fusion feature vector.

[0079] A deep neural network is used to process the raw data, and the processing formula is:

[0080] F i =σ(W2(σ(W1X i +b1))+b2),

[0081] Among them, X irepresents the raw data collected by the i-th sensor module, W1 represents the first-layer weight matrix, b1 represents the first-layer bias vector, W2 represents the second-layer weight matrix, b2 represents the second-layer bias vector, σ represents the activation function, F i Represents the extracted deep features;

[0082] The similarity function is defined to capture the spatiotemporal correlation between data. The function formula is:

[0083]

[0084] Among them, F i,t represents the characteristics of node i at time t, F j,t represents the characteristics of node j at time t, W a Represents the similarity weight matrix, ReLU represents the rectified linear unit function, φ(F i,t ,F j,t ) indicates the similarity between the two;

[0085] Based on the similarity, the attention weight is calculated using the following formula:

[0086]

[0087] Among them, α ij represents the attention weight of node i to neighboring node j, represents the neighborhood set of node i, exp represents the exponential function, F l,t represents the characteristics of node l at time t;

[0088] Weighted aggregation of neighborhood features to obtain G i,t :

[0089]

[0090] Among them, G i,t Represents the features of node i after weighted aggregation at time t;

[0091] In step S3, the multimodal data fusion algorithm is used for integration, and the step of generating a fusion feature vector also includes:

[0092] The graph neural network is used to perform graph convolution processing on each monitoring node. The convolution formula is:

[0093]

[0094] Among them, H i Represents the features of node i after graph convolution processing, represents node i and its neighborhood, c ij represents the normalization factor,

[0095] Wg represents the graph convolution weight matrix, σ represents the activation function as before,

[0096] The weighted aggregation features and graph convolution features are cascaded to form a fusion feature vector, which can be expressed as:

[0097] Z i =[G i,t ;H i ],

[0098] Among them, Z i Represents the fusion feature vector of node i, [;] represents G i,t With H i Cascade by column;

[0099] Specifically, a deep neural network is used here to extract implicit features from the original data, and an attention mechanism is used to perform weighted aggregation on node data at different times. Graph convolution is used to capture the spatial correlation between each monitoring node. The fused feature vector formed by the cascade of the two not only retains the local response information but also reflects the overall spatiotemporal structure.

[0100] Step S4: Based on historical monitoring data, a supervised learning method is used to perform model training on the fused feature vector of step S3 to establish a multi-component gas recognition model;

[0101] The recognition model is used to automatically determine the concentration levels of various malodorous gases and other harmful components;

[0102] The recognition model is trained using back propagation and gradient descent algorithms, and dynamic parameter adjustments are performed based on the actual monitoring environment;

[0103] In step S4, the fusion feature vector of step S3 is trained using a supervised learning method to establish a multi-component gas recognition model.

[0104] Use the fused feature vector Z generated in step S3 i A multi-component gas identification model is established, and its output is defined as:

[0105]

[0106] in, represents the model prediction output of node i, g(·;ω) represents the recognition model function, ω represents the model parameters,

[0107] Construct the loss function, the function formula is:

[0108]

[0109] Among them, L represents the total loss, N represents the total number of samples, and y irepresents the true label of node i, l(·) represents the cross entropy loss function;

[0110] Gradient descent is used to update the parameters, and the update formula is:

[0111]

[0112] Among them, η represents the learning rate, represents the gradient of the loss function with respect to the parameter ω;

[0113] Specifically, a supervised learning framework is constructed here to input the fused feature vector into the recognition model, and a loss function is defined to measure the difference between the model output and the true label. The gradient descent algorithm is used to update the parameters so that the model can continuously adapt to the actual monitoring data.

[0114] Step S5: Deploy the trained recognition model on the edge computing platform and analyze the fused feature vectors collected in real time:

[0115] When the identification result shows that the pollution index of a monitoring node exceeds the preset threshold, the local alarm device is immediately activated;

[0116] In step S5, the trained recognition model is deployed on the edge computing platform, and the steps of analyzing the fusion feature vector collected in real time are as follows:

[0117] The trained recognition model is deployed on the edge computing platform to process the fused feature vectors collected in real time. The real-time output is expressed as:

[0118]

[0119] Set the alarm conditions as:

[0120] like Then the alarm device of node i is triggered;

[0121] Wherein, τ represents the preset alarm threshold;

[0122] Specifically, by deploying a trained recognition model on the edge platform, real-time collected data is quickly analyzed and the model prediction output is compared with the preset threshold. If the threshold is exceeded, the alarm device is automatically triggered. At the same time, the abnormal information is transmitted to the console via the wireless network for storage and display, which helps to respond to environmental abnormalities in a timely manner.

[0123] Step S6: After the local alarm device is activated, the real-time data and alarm information of each monitoring node are transmitted to the control console via the wireless network. The control console stores, displays and compares the real-time data and alarm information with historical data.

[0124] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for outdoor environmental health monitoring based on an olfactory chip, characterized by: include, Step S1: In the area to be monitored, the area is divided into regions according to a preset grid, and monitoring nodes are deployed in each area. Each monitoring node includes a sensor unit and an olfactory chip sensor module; Step S2: filtering, standardizing and denoising the raw data collected by each monitoring node to generate pre-processed data; Step S3, integrating the pre-processed data in step S2 using a multimodal data fusion algorithm to generate a fused feature vector; Step S4, based on historical monitoring data, using a supervised learning method to perform model training on the fused feature vector of step S3 to establish a multi-component gas recognition model; Step S5: Deploy the trained recognition model on the edge computing platform and analyze the fused feature vectors collected in real time: When the identification result shows that the pollution index of a monitoring node exceeds the preset threshold, the local alarm device is immediately activated; Step S6: After the local alarm device is activated, the real-time data and alarm information of each monitoring node are transmitted to the control console via the wireless network. The control console stores, displays and compares the real-time data and alarm information with historical data. In step S3, the multimodal data fusion algorithm includes a deep neural network structure for extracting the characteristics of the data from each sensor module, and using spatiotemporal correlation analysis to achieve data cross-correction and separate the mutual interference information between the multi-component gases; In the data fusion step of step S3, a graph neural network is also used to model and analyze the spatiotemporal data of each monitoring node to identify multi-component cross-interference signals; The recognition model is used to automatically determine the concentration levels of various malodorous gases and other harmful components; The recognition model is trained using back propagation and gradient descent algorithms, and dynamic parameter adjustment is performed according to the actual monitoring environment.

2. The outdoor environmental health monitoring method based on an olfactory chip according to claim 1, characterized in that: The sensor unit is used to collect temperature, humidity and gas concentration data; The olfactory chip sensing module is an electronic nose device composed of multiple micro odor sensing elements, which is used to capture multi-component odor fingerprints in the air in real time.

3. The outdoor environmental health monitoring method based on an olfactory chip according to claim 2, characterized in that: The preprocessed data includes traditional sensor data and transient odor response data output by the olfactory chip module.

4. The outdoor environmental health monitoring method based on an olfactory chip according to claim 3, characterized in that: In step S3, the step of integrating the multimodal data using the fusion algorithm to generate the fusion feature vector is as follows: A deep neural network is used to process the raw data, and the processing formula is: F i =σ(W2(σ(W1X i +b1))+b2), Among them, X i represents the raw data collected by the i-th sensor module, W1 represents the first-layer weight matrix, b1 represents the first-layer bias vector, W2 represents the second-layer weight matrix, b2 represents the second-layer bias vector, σ represents the activation function, F i Represents the extracted deep features; The similarity function is defined to capture the spatiotemporal correlation between data. The function formula is: Among them, F i,t represents the characteristics of node i at time t, F j,t represents the characteristics of node j at time t, W a Represents the similarity weight matrix, ReLU represents the rectified linear unit function, φ(F i,t ,F j,t ) indicates the similarity between the two; Based on the similarity, the attention weight is calculated using the following formula: Among them, α ij represents the attention weight of node i to neighboring node j, represents the neighborhood set of node i, exp represents the exponential function, F l,t represents the characteristics of node l at time t; Weighted aggregation of neighborhood features to obtain G i,t : Among them, G i,t Represents the weighted aggregation features of node i at time t.

5. The outdoor environmental health monitoring method based on an olfactory chip according to claim 4, characterized in that: In step S3, the step of integrating using a multimodal data fusion algorithm to generate a fusion feature vector further includes: The graph neural network is used to perform graph convolution processing on each monitoring node. The convolution formula is: Among them, H i Represents the features of node i after graph convolution processing, represents node i and its neighborhood, c ij represents the normalization factor, W g represents the graph convolution weight matrix, σ represents the activation function as before, The weighted aggregation features and graph convolution features are cascaded to form a fusion feature vector, which is expressed as: Z i =[G i,t ;H i ], Among them, Z i Represents the fusion feature vector of node i, [;] represents G i,t With H i Cascade by column.

6. The outdoor environmental health monitoring method based on an olfactory chip according to claim 5, characterized in that: In step S4, the supervised learning method is used to perform model training on the fused feature vector of step S3 to establish a multi-component gas recognition model. Use the fused feature vector Z generated in step S3 i A multi-component gas identification model is established, and its output is defined as: in, represents the model prediction output of node i, g(·;ω) represents the recognition model function, ω represents the model parameters, Construct the loss function, the function formula is: Among them, L represents the total loss, N represents the total number of samples, and y i represents the true label of node i, l(·) represents the cross entropy loss function; Gradient descent is used to update the parameters, and the update formula is: Among them, η represents the learning rate, represents the gradient of the loss function with respect to the parameter ω.

7. The outdoor environmental health monitoring method based on an olfactory chip according to claim 6, characterized in that: In step S5, the trained recognition model is deployed on the edge computing platform, and the step of analyzing the fusion feature vector collected in real time is as follows: The trained recognition model is deployed on the edge computing platform to process the fused feature vectors collected in real time. The real-time output is expressed as: Set the alarm conditions as: like Then the alarm device of node i is triggered; Wherein, τ represents the preset alarm threshold.

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