Low-carbon type ventilation system control method based on air quality online monitoring

Abstract

The application relates to a low-carbon ventilation system control method based on air quality online monitoring, and the core is a ventilation control scheme based on air quality online monitoring. Specifically, the method comprises the following steps: monitoring the gas concentration values (for example, hydrogen sulfide concentration, ammonia concentration, oxygen concentration, carbon dioxide concentration) in the environment, temperature, humidity, negative oxygen ions and the like in real time through a gas sensor, and adopting an artificial intelligence technology based on deep learning to perform time series analysis on the gas concentration values, respectively mining the significant change rules of the respective gas concentrations in the global time domain, and then adaptively adjusting the working frequency value of the air purification equipment in the ventilation system based on the principal component time series combined features of the respective gas concentrations. In this way, the ventilation system can be intelligently regulated and controlled according to the change of the volatile gas concentration, so that the energy use efficiency is improved, the operation cost is reduced, and the purpose of energy saving and carbon reduction is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of volatile gas monitoring, and in particular to a low-carbon ventilation system control method based on online air quality monitoring. Background Art

[0002] With the increasing environmental protection requirements and the shortage of land resources, underground sewage treatment plant projects have become more and more common in recent years. Compared with above-ground sewage treatment plants, the investment and energy consumption of ventilation systems in underground sewage treatment plants have greatly increased, making it one of the design focuses for energy conservation and carbon reduction.

[0003] Research has revealed that ventilation systems in sewage treatment plants are currently primarily controlled manually and through simple threshold controls. While this approach can reduce air pollutants to a certain extent, the lack of real-time monitoring and intelligent control of actual operating conditions often prevents the ventilation system from effectively adjusting to actual needs, resulting in energy waste and increased operating costs. This problem is particularly acute in environments with large fluctuations in volatile organic compound (VOC) concentrations.

[0004] Specifically, current indoor ventilation systems are generally designed to operate at full capacity, with six air changes. In actual operation, these systems are typically either fully open or completely closed to save energy, which results in poor air quality. Some ventilation control systems also employ weakly intelligent control schemes. For example, they monitor air quality and wait until it drops below a certain level before activating the ventilation system. This results in control lags and prevents timely maintenance of air quality.

[0005] Therefore, in order to improve the intelligence level of the ventilation system, a low-carbon ventilation system control method based on online monitoring of air quality is expected. Summary of the Invention

[0006] This summary is provided to briefly introduce concepts that will be described in detail in the detailed description below. This summary is not intended to identify key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0007] In a first aspect, the present application provides a low-carbon ventilation system control method based on online air quality monitoring, the method comprising:

[0008] The time sequence of hydrogen sulfide concentration and the time sequence of ammonia concentration are collected by gas sensors;

[0009] Transmitting the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration to a central controller of an air purification device via a wireless communication module;

[0010] At the central controller of the air purification equipment, performing time series feature extraction based on a local time scale on the time queue of the hydrogen sulfide concentration and the time queue of the ammonia concentration to obtain a sequence of local time series fluctuation feature vectors of the hydrogen sulfide concentration and a sequence of local time series fluctuation feature vectors of the ammonia concentration;

[0011] Inputting the sequence of the local time series fluctuation feature vectors of the hydrogen sulfide concentration and the sequence of the local time series fluctuation feature vectors of the ammonia concentration into a feature aggregation network guided by feature significance attenuation to obtain a hydrogen sulfide concentration time series significant aggregation representation vector and an ammonia concentration time series significant aggregation representation vector;

[0012] Based on the principal component joint features of the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector, a control instruction for the operating frequency of the air purification equipment is generated.

[0013] Optionally, performing time series feature extraction based on a local time scale on the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration to obtain a sequence of local time series fluctuation feature vectors of hydrogen sulfide concentration and a sequence of local time series fluctuation feature vectors of ammonia concentration includes:

[0014] The time queue of hydrogen sulfide concentration and the time queue of ammonia concentration are divided into predetermined time scales and input into a sequence encoder based on a 1D-CNN model to obtain a sequence of local temporal fluctuation feature vectors of hydrogen sulfide concentration and a sequence of local temporal fluctuation feature vectors of ammonia concentration.

[0015] Optionally, inputting the sequence of the local time-series fluctuation feature vectors of the hydrogen sulfide concentration and the sequence of the local time-series fluctuation feature vectors of the ammonia concentration into a feature aggregation network guided by feature significance attenuation to obtain a hydrogen sulfide concentration time-series significant aggregation representation vector and an ammonia concentration time-series significant aggregation representation vector, including:

[0016] Calculating a characteristic significance description factor of each hydrogen sulfide concentration local time-series fluctuation characteristic vector in the sequence of hydrogen sulfide concentration local time-series fluctuation characteristic vectors, wherein the characteristic significance description factor is related to the mean and variance of each hydrogen sulfide concentration local time-series fluctuation characteristic vector;

[0017] Taking the last hydrogen sulfide concentration local time-series fluctuation feature vector in the sequence of hydrogen sulfide concentration local time-series fluctuation feature vectors as the current hydrogen sulfide concentration local time-series fluctuation feature vector, and constructing characteristic significance attenuation factors of each hydrogen sulfide concentration local time-series fluctuation feature vector based on the distance span between each hydrogen sulfide concentration local time-series fluctuation feature vector in the sequence of hydrogen sulfide concentration local time-series fluctuation feature vectors and the current hydrogen sulfide concentration local time-series fluctuation feature vector;

[0018] Calculating the product of the characteristic significance attenuation factor of each of the local time series fluctuation characteristic vectors of the hydrogen sulfide concentration and the characteristic significance description factor thereof to obtain a sequence of characteristic significance attenuation description factors;

[0019] Inputting the sequence of feature significance attenuation description factors into a gated mask module to obtain a sequence of feature significance attenuation weighting factors;

[0020] The sequence of the characteristic significance attenuation weight factors is used as a sequence of weights, and the weighted sum of the sequence of the local time series fluctuation characteristic vectors of the hydrogen sulfide concentration is calculated to obtain the hydrogen sulfide concentration time series significant aggregation representation vector.

[0021] Optionally, calculating a characteristic significance description factor of each hydrogen sulfide concentration local time-series fluctuation characteristic vector in the sequence of hydrogen sulfide concentration local time-series fluctuation characteristic vectors includes:

[0022] The expected value of the fourth power of the difference between each eigenvalue in the local time series fluctuation eigenvalue vector of the hydrogen sulfide concentration and its eigenmean is calculated, and the expected value is divided by the square of the eigenvariance of the local time series fluctuation eigenvalue vector of the hydrogen sulfide concentration to obtain the characteristic significance description factor.

[0023] Optionally, constructing a characteristic significance attenuation factor of each local time-series fluctuation feature vector of hydrogen sulfide concentration based on a distance span between each local time-series fluctuation feature vector of hydrogen sulfide concentration in the sequence of local time-series fluctuation feature vectors of hydrogen sulfide concentration and the current local time-series fluctuation feature vector of hydrogen sulfide concentration includes:

[0024] Calculate the difference between the maximum eigenvalue of the current local time-series fluctuation characteristic vector of hydrogen sulfide concentration and the maximum eigenvalue of each local time-series fluctuation characteristic vector of hydrogen sulfide concentration in the sequence of local time-series fluctuation characteristic vectors of hydrogen sulfide concentration, and then divide the difference by the number of eigenvectors between the two to obtain the characteristic significance attenuation factor of each local time-series fluctuation characteristic vector of hydrogen sulfide concentration.

[0025] Optionally, generating a control instruction for the operating frequency of the air purification device based on the principal component joint feature of the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector includes:

[0026] Inputting the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector into a significant fusion network based on feature principal component optimization matching to obtain a hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector;

[0027] The hydrogen sulfide concentration-ammonia concentration main component joint time series aggregation representation vector is input into a decoder-based air purification equipment control module to obtain the control instruction, which is used to represent the recommended decoded value of the operating frequency of the air purification equipment.

[0028] Optionally, the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector are input into a significant fusion network based on feature principal component optimization matching to obtain a hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector, including:

[0029] Normalizing the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector to obtain a standardized hydrogen sulfide concentration time series significant aggregation representation vector and a standardized ammonia concentration time series significant aggregation representation vector;

[0030] Calculating the sample covariance matrices of the standardized hydrogen sulfide concentration time series significant aggregate representation vector and the standardized ammonia concentration time series significant aggregate representation vector respectively to obtain a hydrogen sulfide concentration sample covariance matrix and an ammonia concentration sample covariance matrix;

[0031] Performing matrix decomposition-based eigenvector extraction on the hydrogen sulfide concentration sample covariance matrix and the ammonia concentration sample covariance matrix to obtain a set of hydrogen sulfide concentration time series principal component eigenvectors and a set of ammonia concentration time series principal component eigenvectors;

[0032] Inputting the set of principal component eigenvectors of the hydrogen sulfide concentration time series and the set of principal component eigenvectors of the ammonia concentration time series into a maximum approximation query matching network to obtain a set of optimal matching pairs of principal component eigenvectors of the hydrogen sulfide concentration time series and principal component eigenvectors of the ammonia concentration time series;

[0033] Inputting each best matching pair in the set of best matching pairs of the hydrogen sulfide concentration time series principal component feature vector and the ammonia concentration time series principal component feature vector into a semantic fine-grained gating joint module to obtain a set of hydrogen sulfide concentration-ammonia concentration principal component joint feature vectors;

[0034] The set of hydrogen sulfide concentration-ammonia concentration principal component joint feature vectors is cascaded to obtain the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector.

[0035] Optionally, inputting the set of hydrogen sulfide concentration time series principal component eigenvectors and the set of ammonia concentration time series principal component eigenvectors into a maximum approximation query matching network to obtain a set of optimal matching pairs of hydrogen sulfide concentration time series principal component eigenvectors and ammonia concentration time series principal component eigenvectors, including:

[0036] Calculating the cosine similarity between the i-th hydrogen sulfide concentration time series principal component eigenvector in the set of hydrogen sulfide concentration time series principal component eigenvectors and each ammonia concentration time series principal component eigenvector in the set of ammonia concentration time series principal component eigenvectors to obtain a set of cosine similarities;

[0037] The ammonia concentration time series principal component eigenvector corresponding to the maximum value in the cosine similarity set and the i-th hydrogen sulfide concentration time series principal component eigenvector form the best matching pair.

[0038] Optionally, each best matching pair in the set of best matching pairs of the hydrogen sulfide concentration time series principal component feature vector and the ammonia concentration time series principal component feature vector is input into a semantic fine-grained gating joint module to obtain a set of hydrogen sulfide concentration-ammonia concentration principal component joint feature vectors, including:

[0039] Calculating the position-point addition, position-point subtraction, and position-point multiplication between the hydrogen sulfide concentration time series principal component eigenvector and the ammonia concentration time series principal component eigenvector in the best matching pair to obtain a first matching fusion result, a second matching fusion result, and a third matching fusion result;

[0040] After the first matching fusion result, the second matching fusion result and the third matching fusion result are cascaded into a multi-scale fusion feature vector of the hydrogen sulfide concentration-ammonia concentration principal component, one-dimensional convolution processing and maximum pooling processing are performed on them to obtain the hydrogen sulfide concentration-ammonia concentration principal component joint feature vector.

[0041] Using this technical solution, gas sensors monitor the concentrations of hydrogen sulfide and ammonia in the environment in real time. Deep learning-based artificial intelligence (AI) technology is then used to perform time-series analysis on the ammonia and hydrogen sulfide concentration data. This technology identifies significant patterns of change in hydrogen sulfide and ammonia concentrations across the global time domain. Based on the combined time-series characteristics of the principal components of hydrogen sulfide and ammonia concentrations, the operating frequency of the current air purification equipment is adaptively adjusted. This allows for intelligent regulation of the ventilation system based on changes in volatile gas concentrations, improving energy efficiency, reducing operating costs, and ultimately achieving energy conservation and carbon reduction.

[0042] Other features and advantages of the present application will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and other features, advantages and aspects of the embodiments of the present application will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale. In the drawings:

[0044] Figure 1 The present invention is a flowchart showing a low-carbon ventilation system control method based on online air quality monitoring according to an exemplary embodiment.

[0045] Figure 2 The block diagram shows an intelligent control system based on volatile gas monitoring according to an exemplary embodiment.

[0046] Figure 3 It is a block diagram of an electronic device according to an exemplary embodiment.

[0047] Figure 4 This is an application scenario diagram of a low-carbon ventilation system control method based on online air quality monitoring according to an exemplary embodiment. DETAILED DESCRIPTION

[0048] The following describes embodiments of the present application in more detail with reference to the accompanying drawings. Although certain embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be construed as limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present application. It should be understood that the drawings and embodiments of the present application are for illustrative purposes only and are not intended to limit the scope of protection of the present application.

[0049] It should be understood that the various steps described in the method embodiments of the present application can be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present application is not limited in this respect.

[0050] As used herein, the term "including" and its variations are open-ended, i.e., "including but not limited to." The term "based on" means "based, at least in part, on." The term "one embodiment" means "at least one embodiment," the term "another embodiment" means "at least one additional embodiment," and the term "some embodiments" means "at least some embodiments." Other terms are defined in the following description.

[0051] It should be noted that the concepts of "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0052] It should be noted that the modifications of "one" and "multiple" mentioned in this application are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0053] The names of the messages or information exchanged between multiple devices in the embodiments of the present application are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0054] With the increasing environmental protection requirements and the shortage of land resources, underground sewage treatment plant projects have become more and more common in recent years. Compared with above-ground sewage treatment plants, the investment and energy consumption of ventilation systems in underground sewage treatment plants have greatly increased, making it one of the design focuses for energy conservation and carbon reduction.

[0055] After investigation and understanding, it is found that there are some common problems in the control of ventilation systems in domestic sewage treatment plants:

[0056] (1) The ventilation system is still basically manually controlled. Although it is equipped with an automatic control system, there is no clear control logic and no clear control objectives;

[0057] (2) Lack of understanding of the environmental characteristics of the space and unclear concepts of the parameters of various instruments;

[0058] (3) The ventilation system is always running 24 hours a day throughout the year or some areas are not running at all, which may lead to waste or safety hazards;

[0059] (4) In summer, the ventilation and air-conditioning systems in electrical rooms run simultaneously for a long time, wasting electricity.

[0060] From the perspective of air environment and safe operation, the control objectives of the underground sewage treatment plant ventilation system are clarified, and the energy-saving potential of the high-efficiency ventilation system is fully utilized to reduce the operating energy consumption of the ventilation system.

[0061] The intelligent environmental control system should have the following functions:

[0062] (1) Automatically adjust and control system operation according to pre-set gas concentration and time control strategy;

[0063] (2) Continuously record and display the operating parameters of each ventilation and air conditioning system;

[0064] (3) Calculate and count the continuous and cumulative operating time of the controlled equipment;

[0065] (4) Security mechanisms such as permission setting and access control are provided.

[0066] When the ventilation system also serves as a smoke exhaust system, its monitoring system should be linked with the factory area's automatic fire alarm system and be able to switch to the automatic fire alarm system control state in the event of a fire. The open / closed status of various fire dampers and smoke exhaust dampers should be displayed in the fire control room.

[0067] The specific implementation of this application is described in detail below with reference to the accompanying drawings.

[0068] Figure 1is a flow chart showing a low-carbon ventilation system control method based on online air quality monitoring according to an exemplary embodiment. Figure 1 As shown, the method includes:

[0069] Step S101: collecting a time queue of hydrogen sulfide concentration and a time queue of ammonia concentration through a gas sensor;

[0070] Step S102: transmitting the time queue of the hydrogen sulfide concentration and the time queue of the ammonia concentration to the central controller of the air purification equipment through a wireless communication module;

[0071] Step S103: At the central controller of the air purification equipment, extract time series features based on local time scales for the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration to obtain a sequence of local time series fluctuation feature vectors of hydrogen sulfide concentration and a sequence of local time series fluctuation feature vectors of ammonia concentration;

[0072] Step S104: Input the sequence of the local time-series fluctuation feature vectors of the hydrogen sulfide concentration and the sequence of the local time-series fluctuation feature vectors of the ammonia concentration into a feature aggregation network guided by feature significance attenuation to obtain a hydrogen sulfide concentration time-series significant aggregation representation vector and an ammonia concentration time-series significant aggregation representation vector;

[0073] Step S105: Generate a control instruction for the operating frequency of the air purification equipment based on the principal component joint features of the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector.

[0074] In response to the above technical problems, the technical concept of this application is to monitor the hydrogen sulfide concentration and ammonia concentration in the environment in real time through gas sensors, and use deep learning-based artificial intelligence technology to perform time series analysis on the ammonia concentration data and hydrogen sulfide concentration data, respectively, to mine the significant change patterns of hydrogen sulfide concentration and ammonia concentration in the global time domain, and then adaptively adjust the operating frequency of the current air purification equipment based on the joint time series characteristics of the main components of hydrogen sulfide concentration and ammonia concentration. In this way, the ventilation system can be intelligently controlled according to the changes in volatile gas concentration, thereby improving energy efficiency, reducing operating costs, and achieving the goal of energy conservation and carbon reduction.

[0075] Based on this, in the technical solution of the present application, first, the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration are collected by gas sensors. It should be understood that the concentration change of volatile organic compounds (VOCs) has a direct impact on the operation requirements of the ventilation system. When the VOCs concentration is high, the operating frequency of the equipment should be increased to quickly reduce the concentration; when the VOCs concentration is low, the operating frequency can be reduced to reduce energy consumption and noise. Ammonia and methane are common volatile organic compounds (VOCs). Therefore, in the technical solution of the present application, ammonia and methane are used as the main monitoring objects, and the operating frequency of the air purification equipment is adaptively adjusted based on the concentration detection of the two.

[0076] Among them, the monitoring content is divided into three types and the corresponding control strategies are shown in Table 1.

[0077] Table 1

[0078]

[0079]

[0080] Relevant detection instruments and alarm devices are installed in the underground sewage facilities, and the monitoring contents are shown in Table 2 below.

[0081] Table 2

[0082]

[0083] After collecting the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration, the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration are further transmitted to the central controller of the air purification equipment through a wireless communication module for real-time analysis and centralized processing of the data.

[0084] Next, considering that directly processing the entire time series data of hydrogen sulfide concentration and ammonia concentration may result in excessive computational complexity and loss of local detail information due to the long time span, in order to more carefully capture the local fluctuation characteristics of hydrogen sulfide concentration and ammonia concentration, in the technical solution of the present application, first, the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration are segmented according to a predetermined time scale, and the continuous data stream is divided into multiple shorter local time windows. Then, a 1D-CNN (one-dimensional convolutional neural network) model is used to perform time series encoding on the data in each local time window to capture the local correlation in the time series data and mine the time series fluctuation patterns of hydrogen sulfide concentration and ammonia concentration, thereby generating a sequence of local time series fluctuation feature vectors of hydrogen sulfide concentration and a sequence of local time series fluctuation feature vectors of ammonia concentration.

[0085] In one embodiment of the present application, time series feature extraction based on a local time scale is performed on the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration to obtain a sequence of local time series fluctuation feature vectors of hydrogen sulfide concentration and a sequence of local time series fluctuation feature vectors of ammonia concentration, including: dividing the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration according to a predetermined time scale and then inputting the resulting data into a sequence encoder based on a 1D-CNN model to obtain a sequence of local time series fluctuation feature vectors of hydrogen sulfide concentration and a sequence of local time series fluctuation feature vectors of ammonia concentration.

[0086] Then, in order to further integrate the local time series information of hydrogen sulfide concentration and ammonia concentration, and to dig out the dynamic change pattern of hydrogen sulfide concentration and ammonia concentration in the global time domain, this application introduces a feature aggregation network based on feature significance attenuation guidance to fuse the sequence of local time series fluctuation feature vectors of hydrogen sulfide concentration and the sequence of local time series fluctuation feature vectors of ammonia concentration. Specifically, the feature aggregation network first calculates the feature significance descriptor of each local time series fluctuation feature vector based on the mean and variance of each local time series fluctuation feature vector in the input sequence, and quantitatively represents the feature importance. Then, a feature significance attenuation factor is constructed based on the distance span between each local time series fluctuation feature vector and the current local time series fluctuation feature vector (the end of the time series), and its feature significance descriptor is modulated by this, so that the local time series fluctuation feature vector that is closer to the current moment in the feature space obtains a higher weight, thereby more accurately reflecting the relative importance of each local feature in the overall sequence. Then, the modulated descriptive factors are further screened using activation functions and gating mechanisms, and the input sequence is weightedly summed with the screened descriptive factors as weights to integrate the feature information of each local time domain in the input sequence, emphasize the key local feature information, improve the significance and relevance of the feature representation, and generate a significant aggregation representation vector of the hydrogen sulfide concentration time series and a significant aggregation representation vector of the ammonia concentration time series.

[0087] In one embodiment of the present application, the sequence of the local time-series fluctuation feature vectors of the hydrogen sulfide concentration and the sequence of the local time-series fluctuation feature vectors of the ammonia concentration are input into a feature aggregation network based on feature significance attenuation guidance to obtain the hydrogen sulfide concentration time-series significant aggregation representation vector and the ammonia concentration time-series significant aggregation representation vector, including: calculating the feature significance description factor of each local time-series fluctuation feature vector of the hydrogen sulfide concentration in the sequence of the local time-series fluctuation feature vector of the hydrogen sulfide concentration, wherein the feature significance description factor is related to the mean and variance of each local time-series fluctuation feature vector of the hydrogen sulfide concentration; taking the last local time-series fluctuation feature vector of the hydrogen sulfide concentration in the sequence of the local time-series fluctuation feature vector of the hydrogen sulfide concentration as the current local time-series fluctuation feature vector of the hydrogen sulfide concentration, based on the hydrogen sulfide concentration; The characteristic significance attenuation factor of each hydrogen sulfide concentration local time series fluctuation characteristic vector in the sequence of concentration local time series fluctuation characteristic vectors is constructed based on the distance span between each hydrogen sulfide concentration local time series fluctuation characteristic vector and the current hydrogen sulfide concentration local time series fluctuation characteristic vector; the product between the characteristic significance attenuation factor of each hydrogen sulfide concentration local time series fluctuation characteristic vector and its characteristic significance description factor is calculated to obtain a sequence of characteristic significance attenuation description factors; the sequence of characteristic significance attenuation description factors is input into the gated mask module to obtain a sequence of characteristic significance attenuation weight factors; the sequence of characteristic significance attenuation weight factors is used as the sequence of weights to calculate the weighted sum of the sequence of hydrogen sulfide concentration local time series fluctuation characteristic vectors to obtain the hydrogen sulfide concentration time series significant aggregation representation vector.

[0088] Furthermore, in one embodiment of the present application, the characteristic significance description factor of each local time-series fluctuation characteristic vector of hydrogen sulfide concentration in the sequence of local time-series fluctuation characteristic vectors of hydrogen sulfide concentration is calculated, including: calculating the expected value of the fourth power of the difference between each eigenvalue in the local time-series fluctuation characteristic vector of hydrogen sulfide concentration and its characteristic mean, and dividing the expected value by the square of the characteristic variance of the local time-series fluctuation characteristic vector of hydrogen sulfide concentration to obtain the characteristic significance description factor.

[0089] Furthermore, in one embodiment of the present application, based on the distance span between each local time-series fluctuation characteristic vector of hydrogen sulfide concentration in the sequence of local time-series fluctuation characteristic vectors of hydrogen sulfide concentration and the current local time-series fluctuation characteristic vector of hydrogen sulfide concentration, a characteristic significance attenuation factor of each local time-series fluctuation characteristic vector of hydrogen sulfide concentration is constructed, including: calculating the difference between the maximum eigenvalue of the current local time-series fluctuation characteristic vector of hydrogen sulfide concentration and the maximum eigenvalue of each local time-series fluctuation characteristic vector of hydrogen sulfide concentration in the sequence of local time-series fluctuation characteristic vectors of hydrogen sulfide concentration, and then dividing the difference by the number of eigenvectors between the two to obtain the characteristic significance attenuation factor of each local time-series fluctuation characteristic vector of hydrogen sulfide concentration.

[0090] Specifically, the sequence of the local time series fluctuation feature vectors of the hydrogen sulfide concentration is processed using the following feature significance attenuation guided fusion formula to obtain the hydrogen sulfide concentration time series significant aggregation representation vector, wherein the feature significance attenuation guided fusion formula is:

[0091] X={x1,x2,...,x k ,...,x n}

[0092]

[0093] ws i =mask(w i )

[0094]

[0095] Wherein, X represents the sequence of the characteristic vectors of the local time series fluctuation of the hydrogen sulfide concentration, the value of n is the number of characteristic vectors in the sequence of the characteristic vectors of the local time series fluctuation of the hydrogen sulfide concentration, and x1, x2, x i 、x k and x n represent the first, second, i-th, k-th and current local time series fluctuation feature vectors of hydrogen sulfide concentration in the sequence of local time series fluctuation feature vectors of hydrogen sulfide concentration, max(·) represents the maximum eigenvalue of the feature vector, x i (j) represents the jth eigenvalue of the i-th local time series fluctuation eigenvector of hydrogen sulfide concentration, α i and β i 4 They represent the characteristic mean and square of the characteristic variance of the ith local time series fluctuation characteristic vector of hydrogen sulfide concentration, E{[x i (j)-μ i ] 4} represents the fourth-order central moment of the ith local time-series fluctuation characteristic vector of hydrogen sulfide concentration, that is, the expected value of the fourth power of the difference between each eigenvalue in the ith local time-series fluctuation characteristic vector of hydrogen sulfide concentration and its characteristic mean, the value range of i is 1 to n-1, τ i represents the characteristic significance attenuation factor of the i-th local time series fluctuation characteristic vector of hydrogen sulfide concentration, k i represents the descriptive factor of the i-th feature significance attenuation, e is a natural constant, w i represents the descriptive factor of the ith normalized feature significance attenuation, ws i represents the i-th feature significance attenuation weight factor, θ is a preset threshold, mask(·) represents masking, and V1 represents the hydrogen sulfide concentration time series significant aggregate representation vector. It should be understood that the process of inputting the sequence of ammonia concentration local time series fluctuation feature vectors into the feature aggregation network based on feature significance attenuation guidance to obtain the ammonia concentration time series significant aggregate representation vector can refer to the above-mentioned feature significance attenuation guided fusion formula.

[0096] In one embodiment of the present application, based on the principal component joint features of the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector, a control instruction for the operating frequency of the air purification equipment is generated, including: inputting the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector into a significant fusion network based on feature principal component optimization matching to obtain a hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector; inputting the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector into an air purification equipment control module based on a decoder to obtain the control instruction, which is used to represent a recommended decoding value of the operating frequency of the air purification equipment.

[0097] Furthermore, in order to more accurately describe the air quality status, it is necessary to further jointly analyze the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector, and mine the joint change pattern of hydrogen sulfide concentration and ammonia concentration in the global time domain to achieve effective integration of multi-source data information. Based on this, the present application introduces a significant fusion network based on feature principal component optimization matching to achieve this goal. Specifically, the significant fusion network first standardizes the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector to ensure that the data is distributed on a zero-mean scale and has unit variance, eliminating the impact of different dimensions and magnitudes. Then, the principal component analysis algorithm is used to extract the principal component features of the standardized hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector, and the main change features therein are mined, thereby obtaining a set of hydrogen sulfide concentration time series principal component feature vectors and a set of ammonia concentration time series principal component feature vectors. Furthermore, based on the maximum approximate query matching between feature vectors, the best matching pair is found by calculating and comparing the similarity between the two principal component feature vector sets. Next, a fine-grained semantic union is performed on each best matching pair, and the feature fusion results of each best matching pair are cascaded to aggregate the core components of hydrogen sulfide and ammonia concentrations in the global time domain, generating a joint temporal aggregate representation vector of the hydrogen sulfide and ammonia concentration principal components.

[0098] In one embodiment of the present application, the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector are input into a significant fusion network based on feature principal component optimization matching to obtain a hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector, including: normalizing the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector to obtain a standardized hydrogen sulfide concentration time series significant aggregation representation vector and a standardized ammonia concentration time series significant aggregation representation vector; calculating the sample covariance matrices of the standardized hydrogen sulfide concentration time series significant aggregation representation vector and the standardized ammonia concentration time series significant aggregation representation vector respectively to obtain a hydrogen sulfide concentration sample covariance matrix and an ammonia concentration sample covariance matrix; performing a sparse fusion on the hydrogen sulfide concentration sample covariance matrix and the ammonia concentration sample covariance matrix. The invention further comprises the following steps: performing matrix decomposition-based feature vector extraction to obtain a set of hydrogen sulfide concentration time series principal component feature vectors and a set of ammonia concentration time series principal component feature vectors; inputting the set of hydrogen sulfide concentration time series principal component feature vectors and the set of ammonia concentration time series principal component feature vectors into a maximum approximation query matching network to obtain a set of best matching pairs of hydrogen sulfide concentration time series principal component feature vectors and ammonia concentration time series principal component feature vectors; inputting each best matching pair in the set of best matching pairs of hydrogen sulfide concentration time series principal component feature vectors and ammonia concentration time series principal component feature vectors into a semantic fine-grained gating joint module to obtain a set of hydrogen sulfide concentration-ammonia concentration main component joint feature vectors; and cascading the set of hydrogen sulfide concentration-ammonia concentration main component joint feature vectors to obtain the hydrogen sulfide concentration-ammonia concentration main component joint time series aggregation representation vector.

[0099] Furthermore, in one embodiment of the present application, the set of hydrogen sulfide concentration time series principal component eigenvectors and the set of ammonia concentration time series principal component eigenvectors are input into a maximum approximation query matching network to obtain a set of optimal matching pairs of hydrogen sulfide concentration time series principal component eigenvectors and ammonia concentration time series principal component eigenvectors, including: calculating the cosine similarity between the i-th hydrogen sulfide concentration time series principal component eigenvector in the set of hydrogen sulfide concentration time series principal component eigenvectors and each ammonia concentration time series principal component eigenvector in the set of ammonia concentration time series principal component eigenvectors to obtain a set of cosine similarities; and forming the optimal matching pair with the ammonia concentration time series principal component eigenvector corresponding to the maximum value in the set of cosine similarities.

[0100] Furthermore, in one embodiment of the present application, each best matching pair in the set of best matching pairs of the hydrogen sulfide concentration time series principal component feature vector and the ammonia concentration time series principal component feature vector is input into a semantic fine-grained gated joint module to obtain a set of hydrogen sulfide concentration-ammonia concentration principal component joint feature vectors, including: calculating the position point addition, position point subtraction and position point multiplication between the hydrogen sulfide concentration time series principal component feature vector and the ammonia concentration time series principal component feature vector in the best matching pair to obtain a first matching fusion result, a second matching fusion result and a third matching fusion result; after cascading the first matching fusion result, the second matching fusion result and the third matching fusion result into a hydrogen sulfide concentration-ammonia concentration principal component multi-scale fusion feature vector, one-dimensional convolution processing and maximum pooling processing are performed on it to obtain the hydrogen sulfide concentration-ammonia concentration principal component joint feature vector.

[0101] Specifically, the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector are fused using the following principal component query matching formula to obtain the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector, wherein the principal component query matching formula is:

[0102]

[0103] C1=U1Λ1U1 T

[0104] C2=U2Λ2U2 T

[0105] U1=[v 11 ,v 12 ,…,v 1m ]

[0106] A1=diag(λ 11 ,λ 12 ,…,λ 1m )

[0107] U2=[v 21 ,v 22 ,…,v 2m ]

[0108] Λ2=diag(λ 21 ,λ 22 ,…,λ 2m )

[0109]

[0110] v f =[v p1 ;v p2 ;...vpm ]

[0111] Wherein, V1 represents the significant aggregation representation vector of the hydrogen sulfide concentration time series, μ1 and σ1 represent the mean and standard deviation of the significant aggregation representation vector of the hydrogen sulfide concentration time series, respectively. 1norm represents the time series significant aggregation representation vector of the standardized hydrogen sulfide concentration, V2 represents the time series significant aggregation representation vector of the ammonia concentration, μ2 and σ2 represent the mean and standard deviation of the time series significant aggregation representation vector of the ammonia concentration, respectively, 2norm represents the standardized ammonia concentration time series significant aggregation representation vector, the value of n1 is the length of the hydrogen sulfide concentration time series significant aggregation representation vector, the value of n2 is the length of the ammonia concentration time series significant aggregation representation vector, C1 represents the hydrogen sulfide concentration sample covariance matrix, C2 represents the ammonia concentration sample covariance matrix, U1 represents the matrix consisting of the set of principal component eigenvectors of the hydrogen sulfide concentration time series obtained by performing eigenvalue decomposition on the hydrogen sulfide concentration sample covariance matrix, Λ1 represents the diagonal matrix consisting of the set of principal component eigenvalues ​​of the hydrogen sulfide concentration time series obtained by performing eigenvalue decomposition on the hydrogen sulfide concentration sample covariance matrix, and v 11 、v 12 、v 1i and v 1m represent the first, second, i-th and m-th principal component eigenvectors of the hydrogen sulfide concentration time series in the set of principal component eigenvectors of the hydrogen sulfide concentration time series, respectively, 11 ,λ 12 and λ 1m represents the first, second and mth principal component eigenvalues ​​of the hydrogen sulfide concentration time series in the set of principal component eigenvalues ​​of the hydrogen sulfide concentration time series, respectively, where m is the number of principal component eigenvalues ​​of the hydrogen sulfide concentration time series, U2 represents a matrix consisting of a set of principal component eigenvectors of the ammonia concentration time series obtained by performing eigenvalue decomposition on the ammonia concentration sample covariance matrix, Λ2 represents a diagonal matrix consisting of a set of principal component eigenvalues ​​of the ammonia concentration time series obtained by performing eigenvalue decomposition on the ammonia concentration sample covariance matrix, and v 21 、v 22 、v 2j 、v 2k and v 2m represent the first, second, jth, kth and mth ammonia concentration time series principal component eigenvectors in the set of ammonia concentration time series principal component eigenvectors, λ 21 ,λ 22 and λ 2mrepresents the first, second and mth ammonia concentration time series principal component eigenvalues ​​in the set of ammonia concentration time series principal component eigenvalues, respectively. diag is the diag function used to construct a diagonal matrix. <·> represents the inner product. ||·|| represents the modulus of the eigenvector. argmax(·) represents the index corresponding to the maximum value. k represents the index of the ammonia concentration time series principal component eigenvector that forms the best matching pair with the i-th hydrogen sulfide concentration time series principal component eigenvector. [·,·] represents a cascade operation. MaxPool(·) represents a maximum pooling operation. conv1D(·) represents a one-dimensional convolution operation. ⊙ represents a dot product. Indicates point addition, Indicates point minus, v pi represents the joint eigenvector of the principal component of hydrogen sulfide concentration and ammonia concentration, v f A vector representing the principal component joint time series aggregation of the hydrogen sulfide concentration and the ammonia concentration.

[0112] Next, the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector is input into a decoder-based air purification equipment control module to obtain a control instruction, which is used to represent a recommended decoded value of the operating frequency of the air purification equipment.

[0113] In a preferred example, it is considered that the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector respectively represent the full-time domain time series aggregation feature of the local time series fluctuation feature of the hydrogen sulfide concentration based on the node feature significance attenuation guidance and the full-time domain time series aggregation feature of the local time series fluctuation feature of the ammonia concentration based on the node feature significance attenuation guidance. Therefore, when performing significant fusion based on feature principal component optimization matching, due to the time series correlation feature distribution difference of the source time series distribution difference and the node feature significance attenuation difference superimposed, the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector will also have a time series feature diversified set expression distribution. Therefore, it is expected to improve the balance between the regression mapping accuracy and completeness of the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector when inputting the decoder-based air purification equipment control module for decoding regression, thereby improving the accuracy of the obtained control instructions.

[0114] Based on this, in the preferred example, inputting the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector into a decoder-based air purification equipment control module to obtain a control instruction specifically includes:

[0115] Calculating the sum of the absolute values ​​of each eigenvalue of the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector to obtain a first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation spatial structure value, and calculating the square root of the sum of the squares of each eigenvalue of the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector to obtain a second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation spatial structure value;

[0116] Multiplying each eigenvalue of the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector by the first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation space structure value and the second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation space structure value respectively to obtain a first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation structure reference value and a second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation structure reference value corresponding to each eigenvalue;

[0117] Multiplying each eigenvalue of the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector by the length of the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector and the square root of the length to obtain a first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation scale transformation value and a second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation scale transformation value corresponding to each eigenvalue;

[0118] Dividing the first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation structure reference value by the difference between the first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation space structure value and the first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation scale transformation value to obtain the first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation transformation adjustment value;

[0119] Dividing the second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation structure reference value by the difference between the second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation spatial structure value and the second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation scale transformation value to obtain a second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation transformation adjustment value;

[0120] Calculating a weighted sum of the first hydrogen sulfide concentration-ammonia concentration principal component joint time-series aggregation transformation adjustment value and the second hydrogen sulfide concentration-ammonia concentration principal component joint time-series aggregation transformation adjustment value to obtain each eigenvalue of the optimized hydrogen sulfide concentration-ammonia concentration principal component joint time-series aggregation representation vector;

[0121] The optimized hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate representation vector is input into a decoder-based target object position changer to obtain a target object position change decoding value.

[0122] Here, the optimized hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate representation vector, denoted as V, is expressed as:

[0123]

[0124] v 4i = (α × v i ) / (α - L × v i )

[0125]

[0126] vx∈V∈R 1×L

[0127] v 3i ∈V3∈R 1×L

[0128] v 4i ∈V4∈R 1×L

[0129] where V is the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate representation vector, R represents the set of real numbers, v i represents the eigenvalue of the i-th position of the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate representation vector, L represents the length of the hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate representation vector, α represents a first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate spatial structure value, β represents a second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate spatial structure value, and represents a point-by-position multiplication, represents a point-by-position addition, V3 represents a second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate transformation adjustment vector, v 3i represents a second hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate transformation adjustment value, V4 represents a first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate transformation adjustment vector, v 4i represents a first hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate transformation adjustment value, ω represents a weighting hyperparameter, and V' represents an optimized hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregate representation vector.

[0130] Therefore, in the preferred example, for the spatial structure information of the feature set of the hydrogen sulfide concentration-ammonia concentration principal component joint temporal aggregation representation vector in the high-dimensional space, the scale-based frame transformation of each eigenvalue of the hydrogen sulfide concentration-ammonia concentration principal component joint temporal aggregation representation vector is performed by using the class-norm space structured representation of the hydrogen sulfide concentration-ammonia concentration principal component joint temporal aggregation representation vector as a reference window, and the spatial structure-based frame attention weight adjustment of each eigenvalue of the hydrogen sulfide concentration-ammonia concentration principal component joint temporal aggregation representation vector is realized to ensure the spatial transformation (translation, scaling and rotation) invariance of the hydrogen sulfide concentration-ammonia concentration principal component joint temporal aggregation representation vector under feature space interaction, so as to achieve a balanced executable performance between mapping accuracy and mapping completeness in the decoding regression process based on the discretized feature distribution of the hydrogen sulfide concentration-ammonia concentration principal component joint temporal aggregation representation vector, thereby improving the accuracy of the control instructions obtained by the air purification equipment control module based on the decoder input of the hydrogen sulfide concentration-ammonia concentration principal component joint temporal aggregation representation vector.

[0131] In summary, the above solution uses gas sensors to monitor the concentrations of hydrogen sulfide and ammonia in the environment in real time. Deep learning-based artificial intelligence technology is then used to perform time-series analysis on the ammonia and hydrogen sulfide concentration data. This allows the system to identify significant patterns of change in hydrogen sulfide and ammonia concentrations across the global time domain. Based on the combined time-series characteristics of the principal components of hydrogen sulfide and ammonia concentrations, the operating frequency of the current air purification equipment is adaptively adjusted. This allows the ventilation system to be intelligently controlled based on changes in volatile gas concentrations, thereby improving energy efficiency, reducing operating costs, and achieving energy conservation and carbon reduction.

[0132] Figure 2 The block diagram shows an intelligent control system based on volatile gas monitoring according to an exemplary embodiment.

[0133] like Figure 2 As shown, the intelligent control system 200 includes:

[0134] The gas concentration collection module 201 is used to collect the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration through a gas sensor;

[0135] The gas concentration value transmission module 202 is used to transmit the time queue of the hydrogen sulfide concentration and the time queue of the ammonia concentration to the central controller of the air purification equipment through the wireless communication module;

[0136] A time series feature extraction module 203 is configured to perform time series feature extraction based on a local time scale on the time queue of the hydrogen sulfide concentration and the time queue of the ammonia concentration in the central controller of the air purification equipment to obtain a sequence of local time series fluctuation feature vectors of the hydrogen sulfide concentration and a sequence of local time series fluctuation feature vectors of the ammonia concentration;

[0137] A feature aggregation module 204 is configured to input the sequence of the local time-series fluctuation feature vectors of the hydrogen sulfide concentration and the sequence of the local time-series fluctuation feature vectors of the ammonia concentration into a feature aggregation network guided by feature significance attenuation to obtain a hydrogen sulfide concentration time-series significant aggregation representation vector and an ammonia concentration time-series significant aggregation representation vector;

[0138] The control instruction generation module 205 is used to generate a control instruction for the operating frequency of the air purification equipment based on the principal component joint features of the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector.

[0139] Reference below Figure 3 , which shows a schematic structural diagram of an electronic device 600 suitable for implementing an embodiment of the present application. The terminal device in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0140] like Figure 3 As shown, the electronic device 600 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the electronic device 600 are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0141] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 608 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device 600 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 600 is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0142] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 609, or installed from the storage device 608, or installed from the ROM 602. When the computer program is executed by the processing device 601, the above-mentioned functions defined in the method of the embodiment of the present application are performed.

[0143] It should be noted that the computer-readable medium in the above application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, device or apparatus. In this application, a computer-readable signal medium can include a data signal carried in a baseband or as a carrier wave in a propagated data signal, which carries computer-readable program code. Such a propagated data signal can take many forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium that is not a storage medium and that can transmit, propagate or transport program for use by or in connection with an instruction execution system, device or apparatus. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wire, cable, fiber optic, RF (radio frequency), or any suitable combination of the above.

[0144] In some embodiments, the client, server can communicate using any current known or future developed network protocol, such as HTTP (HyperText Transfer Protocol), and can be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet, and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any current known or future developed network.

[0145] The above computer-readable medium can be contained in the above electronic device; or can exist separately without being assembled into the electronic device.

[0146] Computer program code for performing the operations of the present application can be written in one or more programming languages, or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0147] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0148] The modules described in the embodiments of this application may be implemented in software or hardware. In some cases, the name of a module does not necessarily limit the module itself. For example, a test parameter acquisition module may also be described as a "module for acquiring device test parameters corresponding to a target device."

[0149] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0150] In the context of the present application, a machine-readable medium can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0151] Figure 4 FIG is an application scenario diagram of a low-carbon ventilation system control method based on online air quality monitoring according to an exemplary embodiment. Figure 4 As shown, in this application scenario, first, the time queue of ammonia concentration is collected by the gas sensor (for example, Figure 4 C1) and the time series of hydrogen sulfide concentrations (e.g., Figure 4 Then, the obtained time queue of hydrogen sulfide concentration and the time queue of ammonia concentration are input to a server (for example, Figure 4 In the S) shown in , the server can process the time queue of the hydrogen sulfide concentration and the time queue of the ammonia concentration based on the intelligent control algorithm of volatile gas monitoring to generate control instructions for the operating frequency of the air purification equipment.

[0152] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

[0153] In addition, although adopting specific order to describe each operation, this should not be interpreted as requiring these operations to be executed in the specific order shown or in sequential order.Under certain environment, multitasking and parallel processing may be advantageous.Similarly, although comprising some specific implementation details in the above discussion, these should not be interpreted as limiting the scope of the application.Some features described in the context of separate embodiment can also be implemented in a single embodiment in combination.On the contrary, the various features described in the context of a single embodiment also can be implemented in multiple embodiments individually or in the mode of any suitable subcombination.

[0154] Although the subject matter has been described using language specific to structural features and / or method logic, it should be understood that the subject matter as defined is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely exemplary implementations. Regarding the apparatus in the above-described embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method and will not be further elaborated here.

Claims

1. A low-carbon ventilation system control method based on online air quality monitoring, characterized in that: include: The time sequence of hydrogen sulfide concentration and the time sequence of ammonia concentration are collected by gas sensors; Transmitting the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration to a central controller of an air purification device via a wireless communication module; At the central controller of the air purification equipment, performing time series feature extraction based on a local time scale on the time queue of the hydrogen sulfide concentration and the time queue of the ammonia concentration to obtain a sequence of local time series fluctuation feature vectors of the hydrogen sulfide concentration and a sequence of local time series fluctuation feature vectors of the ammonia concentration; Inputting the sequence of the local time series fluctuation feature vectors of the hydrogen sulfide concentration and the sequence of the local time series fluctuation feature vectors of the ammonia concentration into a feature aggregation network guided by feature significance attenuation to obtain a hydrogen sulfide concentration time series significant aggregation representation vector and an ammonia concentration time series significant aggregation representation vector; Generating a control instruction for the operating frequency of the air purification equipment based on the principal component joint features of the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector, including: Normalizing the hydrogen sulfide concentration time series significant aggregation representation vector and the ammonia concentration time series significant aggregation representation vector to obtain a standardized hydrogen sulfide concentration time series significant aggregation representation vector and a standardized ammonia concentration time series significant aggregation representation vector; Calculate the sample covariance matrices of the standardized hydrogen sulfide concentration time series significant aggregate representation vector and the standardized ammonia concentration time series significant aggregate representation vector respectively to obtain the hydrogen sulfide concentration sample covariance matrix and the ammonia concentration sample covariance matrix; Performing matrix decomposition-based eigenvector extraction on the hydrogen sulfide concentration sample covariance matrix and the ammonia concentration sample covariance matrix to obtain a set of principal component eigenvectors of the hydrogen sulfide concentration time series and a set of principal component eigenvectors of the ammonia concentration time series; Calculating the cosine similarity between the i-th hydrogen sulfide concentration time series principal component eigenvector in the set of hydrogen sulfide concentration time series principal component eigenvectors and each ammonia concentration time series principal component eigenvector in the set of ammonia concentration time series principal component eigenvectors to obtain a set of cosine similarities; forming an optimal matching pair with the i-th hydrogen sulfide concentration time series principal component eigenvector corresponding to the maximum value in the set of cosine similarities; Calculate the position point addition, position point subtraction, and position point multiplication between the principal component eigenvector of the hydrogen sulfide concentration time series and the principal component eigenvector of the ammonia concentration time series in the best matching pair to obtain a first matching fusion result, a second matching fusion result, and a third matching fusion result; After concatenating the first matching fusion result, the second matching fusion result, and the third matching fusion result into a multi-scale fusion feature vector of the hydrogen sulfide concentration-ammonia concentration principal component, one-dimensional convolution processing and maximum pooling processing are performed on the vector to obtain a hydrogen sulfide concentration-ammonia concentration principal component joint feature vector; The set of hydrogen sulfide concentration-ammonia concentration principal component joint feature vectors is cascaded to obtain a hydrogen sulfide concentration-ammonia concentration principal component joint time series aggregation representation vector; The principal component joint time series aggregation representation vector of hydrogen sulfide concentration and ammonia concentration is input into the decoder-based air purification equipment control module to obtain a control instruction, which is used to represent the recommended decoded value of the operating frequency of the air purification equipment.

2. The low-carbon ventilation system control method based on online air quality monitoring according to claim 1 is characterized in that: Performing time series feature extraction based on a local time scale on the time queue of hydrogen sulfide concentration and the time queue of ammonia concentration to obtain a sequence of local time series fluctuation feature vectors of hydrogen sulfide concentration and a sequence of local time series fluctuation feature vectors of ammonia concentration, including: The time queue of hydrogen sulfide concentration and the time queue of ammonia concentration are divided into predetermined time scales and input into a sequence encoder based on a 1D-CNN model to obtain a sequence of local temporal fluctuation feature vectors of hydrogen sulfide concentration and a sequence of local temporal fluctuation feature vectors of ammonia concentration.

3. The low-carbon ventilation system control method based on online air quality monitoring according to claim 2 is characterized in that: Inputting the sequence of the local time series fluctuation feature vectors of the hydrogen sulfide concentration and the sequence of the local time series fluctuation feature vectors of the ammonia concentration into a feature aggregation network guided by feature significance attenuation to obtain a hydrogen sulfide concentration time series significant aggregation representation vector and an ammonia concentration time series significant aggregation representation vector, including: Calculating a characteristic significance description factor of each hydrogen sulfide concentration local time-series fluctuation characteristic vector in the sequence of hydrogen sulfide concentration local time-series fluctuation characteristic vectors, wherein the characteristic significance description factor is related to the mean and variance of each hydrogen sulfide concentration local time-series fluctuation characteristic vector; Taking the last hydrogen sulfide concentration local time-series fluctuation feature vector in the sequence of hydrogen sulfide concentration local time-series fluctuation feature vectors as the current hydrogen sulfide concentration local time-series fluctuation feature vector, and constructing characteristic significance attenuation factors of each hydrogen sulfide concentration local time-series fluctuation feature vector based on the distance span between each hydrogen sulfide concentration local time-series fluctuation feature vector in the sequence of hydrogen sulfide concentration local time-series fluctuation feature vectors and the current hydrogen sulfide concentration local time-series fluctuation feature vector; Calculating the product of the characteristic significance attenuation factor of each of the local time series fluctuation characteristic vectors of the hydrogen sulfide concentration and the characteristic significance description factor thereof to obtain a sequence of characteristic significance attenuation description factors; Inputting the sequence of feature significance attenuation description factors into a gated mask module to obtain a sequence of feature significance attenuation weighting factors; The sequence of the characteristic significance attenuation weight factors is used as a sequence of weights, and the weighted sum of the sequence of the local time series fluctuation characteristic vectors of the hydrogen sulfide concentration is calculated to obtain the hydrogen sulfide concentration time series significant aggregation representation vector.

4. The low-carbon ventilation system control method based on online air quality monitoring according to claim 3 is characterized in that: Calculating the characteristic significance description factor of each hydrogen sulfide concentration local time series fluctuation characteristic vector in the sequence of hydrogen sulfide concentration local time series fluctuation characteristic vectors includes: The expected value of the fourth power of the difference between each eigenvalue in the local time series fluctuation eigenvalue vector of the hydrogen sulfide concentration and its eigenmean is calculated, and the expected value is divided by the square of the eigenvariance of the local time series fluctuation eigenvalue vector of the hydrogen sulfide concentration to obtain the characteristic significance description factor.

5. The low-carbon ventilation system control method based on online air quality monitoring according to claim 4 is characterized in that: Constructing a characteristic significance attenuation factor of each hydrogen sulfide concentration local time-series fluctuation feature vector based on a distance span between each hydrogen sulfide concentration local time-series fluctuation feature vector in the sequence of hydrogen sulfide concentration local time-series fluctuation feature vectors and the current hydrogen sulfide concentration local time-series fluctuation feature vector, including: Calculate the difference between the maximum eigenvalue of the current local time-series fluctuation characteristic vector of hydrogen sulfide concentration and the maximum eigenvalue of each local time-series fluctuation characteristic vector of hydrogen sulfide concentration in the sequence of local time-series fluctuation characteristic vectors of hydrogen sulfide concentration, and then divide the difference by the number of eigenvectors between the two to obtain the characteristic significance attenuation factor of each local time-series fluctuation characteristic vector of hydrogen sulfide concentration.

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