Intelligent environment monitoring method, system and device and readable storage medium

Abstract

The invention discloses an intelligent environment monitoring method, system and device and a readable storage medium, and the method comprises the steps: obtaining the current environment data of a versatile laboratory, carrying out the intelligent data processing of the current environment data, obtaining the accurate current environment data, carrying out the intelligent analysis of the accurate current environment data, and obtaining a current analysis result; acquiring historical environment data of the versatile laboratory, performing optimization processing on the historical environment data, and analyzing the optimized historical environment data and the accurate current environment data by adopting a versatile laboratory environment prediction model to obtain a predictive analysis result; based on the current analysis result and the predictive analysis result, the test environment state of the versatile laboratory is evaluated, and environment monitoring of the versatile laboratory is achieved. The problems that the current environment monitoring technology cannot capture the subtle change of the environment in real time, the prediction capability on the future environment condition change is lacked, and the accuracy of the generated environment management decision is low are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental monitoring, and in particular relates to an intelligent environmental monitoring method, system, device and readable storage medium. Background Art

[0002] In today's all-powerful laboratory environment, accurate environmental monitoring and management are critical. Environmental parameters have a significant impact on the accuracy and reliability of experimental results. Therefore, laboratories need an efficient and accurate system to monitor these environmental parameters to ensure optimal experimental conditions. With the advancement of technology, especially in the fields of Internet of Things (IoT) technology and machine learning, the methods of laboratory environmental monitoring are also constantly evolving and improving. Traditional monitoring methods rely on simple sensor networks and manual adjustments, which often fail to capture subtle changes in the environment in real time and lack the ability to predict future changes in environmental conditions. In addition, problems such as data inconsistency, missing values, and noise often interfere with the accuracy of data analysis, leading to inaccurate environmental management decisions.

[0003] The Chinese patent with publication number CN116451865A discloses an intelligent environmental monitoring method based on deep learning, including obtaining historical monitoring data of pollution points; building a prediction and early warning model; inputting the historical monitoring data into the prediction and early warning model for training to obtain environmental prediction results; and formulating a governance plan based on the environmental prediction results. This invention does not use a special data processing method when extracting data during the environmental monitoring process, and is unable to fully express the characteristic information of the data. At the same time, it only uses a deep learning network model to analyze historical monitoring data, which makes it difficult to fully capture the complex relationships contained in the data, resulting in limited analysis results.

[0004] The Chinese patent with announcement number CN116485202B discloses a real-time monitoring method and system for industrial pollution based on the Internet of Things, including: obtaining environmental pollution information within a preset area, determining pollution sources and pollution monitoring indicators based on the environmental pollution information; generating a sensor deployment plan within a preset area based on the pollution monitoring indicators and pollution source information, obtaining pollution monitoring information collected by different sensors, extracting features from multi-source pollution monitoring information, building a multimodal feature fusion model for pollution monitoring information, obtaining correlation features of different pollution monitoring information, using the correlation features to monitor the industrial pollution status within a preset area, analyzing the pollution development trend to generate pollution prediction information for pollution warning. This invention uses a genetic algorithm to optimize the sensor deployment positions within the monitoring range of each pollution source in turn, and performs conventional preprocessing such as data cleaning on the pollution monitoring information collected by the sensor, but it also cannot completely retain valid data information. Summary of the invention

[0005] The present invention provides an intelligent environmental monitoring method, system, device and readable storage medium, aiming to solve the problems that current environmental monitoring technology cannot capture subtle changes in the environment in real time, lacks the ability to predict future changes in environmental conditions, and generates environmental management decisions with low accuracy.

[0006] In order to solve the above technical problems, the present invention provides an intelligent environment monitoring method, comprising the following steps: The current environmental data of the universal laboratory is obtained, and the current environmental data is intelligently processed to obtain accurate current environmental data. The accurate current environmental data is intelligently analyzed using the SVC algorithm to obtain the current analysis result. The current analysis result is an environmental quantitative level parameter, which is used to represent the current test environment status of the universal laboratory.

[0007] The historical environmental data of the universal laboratory is obtained, and the historical environmental data is optimized. The optimized historical environmental data and the accurate current environmental data are subjected to predictive analysis using the universal laboratory environmental prediction model constructed based on the LSTM network to obtain predictive analysis results, which are the changing trends of future environmental parameters.

[0008] Based on the current analysis results and predictive analysis results, the test environment status of the universal laboratory is evaluated to achieve environmental monitoring of the universal laboratory.

[0009] Preferably, the intelligent data processing of the current environmental data is specifically as follows: preprocessing the current environmental data, extracting the current environmental data features from the preprocessed current environmental data using feature engineering to obtain a multivariate data feature set, and performing data feature fusion processing based on the multivariate data feature set using a multivariate data fusion algorithm.

[0010] Preferably, the preprocessing includes noise removal, outlier correction, data normalization and data completion fusion algorithm, wherein the data completion fusion algorithm is used to complete the current environment data specifically as follows: For each missing data point, find its nearest neighbor data point in the current environment data set by calculating the Euclidean distance, and select the nearest neighbor data points and the nearest neighbor Assign weights to data points , weight The specific calculation formula is: ; In the formula, It is The local gradient rate of the nearest neighbor data point relative to the missing data point; is a regulating factor to balance the effects of distance and local gradient rate; It is The distance between the nearest neighbor data point and the missing data point; is the parameter that adjusts the effect of distance; .

[0011] Use the weighted average formula to calculate the interpolation value of the missing data point to obtain the interpolated data point. The specific formula is: ; In the formula, It is The interpolated values ​​for missing data points, is the missing data point nearest neighbor data points.

[0012] The interpolation values ​​of missing data points are adjusted and enhanced for data consistency. The specific formula is: ; In the formula, It is a nonlinear adjustment index, which is used to control the intensity and direction of adjustment to adapt to different data distribution characteristics; , is the variance and mean of the overall current environment data; It is the first data points; is the nearest neighbor The mean of the data points; is the nearest neighbor The variance of the data points.

[0013] Preferably, the data feature fusion processing is performed based on the multivariate data feature set using a multivariate data fusion algorithm as follows: The multivariate data feature set is standardized to obtain the standardized multivariate data feature set, and then The multivariate data feature set after the standardized processing of samples constitutes the multidimensional data feature set .

[0014] The polynomial kernel and radial basis kernel are combined to perform nonlinear expansion on each feature in the multidimensional data feature set. The specific calculation formula is: ; In the formula, It is the multidimensional data feature set The sample in Feature data on the extended feature dimension; It is the multidimensional data feature set samples; It is the multidimensional data feature set samples; is the kernel function; is the contribution coefficient in the polynomial kernel function, which is used to adjust the The contribution of each feature dimension in the extended feature; is the degree of the polynomial; is the characteristic dimension; is the parameter in the radial basis kernel function; It is a sample and The square of the Euclidean distance between Constructing feature data on the extended feature dimension .

[0015] The feature data on the extended feature dimension is combined through the multi-core function Mapping is performed to obtain the feature data after hybrid kernel mapping. The specific mapping function is: ; In the formula, is the feature data after mixed kernel mapping, express; It is Kernel function; It is The weight coefficients of the kernel functions are obtained through Lagrangian optimization; is the number of kernel functions.

[0016] The mapped feature data is subjected to wavelet transformation, and collaborative feature interaction fusion is performed according to the wavelet coefficients. By analyzing the interaction between different features, an interaction matrix is ​​constructed to generate the final fusion feature set. The calculation formula of the interaction matrix is: ; In the formula, is the feature after mixed kernel mapping and The interaction matrix elements between represent the interaction strength between features; It is a feature No. The wavelet coefficients of the layer; It is a feature No. The wavelet coefficients of the layer; The strength of the interaction between features is adjusted through periodic changes; is the number of wavelet coefficient layers.

[0017] Finally, the covariance matrix of the feature data is calculated for the standardized multidimensional data feature set, and the eigenvalues ​​and corresponding eigenvectors of the covariance feature matrix are calculated. According to the size of the eigenvalue, the previous The eigenvector corresponding to the largest eigenvalue is the selected eigenvector as the main feature. Based on the selected main feature matrix , and get the final fusion feature representation: ; in, It is the final fusion feature, i.e., accurate environmental parameter data; is through The interaction matrix is ​​composed of is the matrix consisting of the selected main features.

[0018] Preferably, the optimization processing of historical environmental data is specifically as follows: performing data processing on the historical environmental data including filling missing values, removing noise, correcting outliers, and normalizing the data; after the data processing, reconstructing and optimizing the historical environmental data using a dynamic periodic capture algorithm; and constructing a historical data feature set representing the historical environmental data using feature engineering on the optimized historical environmental data.

[0019] Preferably, after data processing, the historical environmental data is reconstructed and optimized using a dynamic periodic capture algorithm, specifically as follows: Fourier transform is used to decompose historical and current environmental data at different time scales into components of different frequencies.

[0020] The periodicity score of each frequency component is determined by calculating the autocorrelation of the frequency component and combining it with its amplitude, specifically: ; In the formula, It is The periodicity score of the frequency components, It is The frequency components are delayed The autocorrelation function of the time delay is the time interval used in the autocorrelation calculation, indicating the delay; is the number of delays; It is The amplitude of a frequency component; , is the total number of decomposed frequency components.

[0021] The weights are dynamically assigned using the Softmax function according to the periodicity score of each frequency component.

[0022] The time series is reconstructed using weighted frequency components, and the decomposed and weighted frequency components are recombined to form optimized historical current environmental data. The specific formula is: ; In the formula, It is the optimized historical current environment data; It is The weight of the frequency components; It is The frequency of the frequency component; It’s time; It is The phase of a frequency component.

[0023] Preferably, the test environment status of the universal laboratory is evaluated based on the current analysis results and the predictive analysis results, and the environmental monitoring of the universal laboratory is realized specifically as follows: For the current analysis results, according to the statistical analysis method, the environmental quantitative level of the current universal laboratory is obtained; for the predictive analysis results, according to the time series analysis technology, the predictive analysis results are used to analyze the changing trends of future environmental parameters and identify the rising and falling fluctuation range of future trends.

[0024] Using the decision tree analysis method, operational suggestions are provided based on the current environmental quantitative level of the universal laboratory and the changing trends of future environmental parameters, thereby realizing environmental monitoring of the universal laboratory.

[0025] On the other hand, the present invention provides an intelligent environment monitoring system, which adopts an intelligent environment monitoring method as described in any embodiment of the present invention to perform environment monitoring, including a current environment analysis module, a historical environment analysis module and an environment monitoring evaluation module.

[0026] The current environment analysis module is used to obtain the current environment data of the universal laboratory, perform intelligent data processing on the current environment data to obtain accurate current environment data, and use the SVC algorithm to perform intelligent analysis on the accurate current environment data to obtain the current analysis result, which is the environmental quantitative level parameter; The historical environment analysis module is used to obtain the historical environment data of the universal laboratory, optimize the historical environment data, and perform predictive analysis on the optimized historical environment data and the accurate current environment data using the universal laboratory environment prediction model built based on the LSTM network to obtain predictive analysis results. The predictive analysis results are the changing trends of future environmental parameters.

[0027] The environmental monitoring and evaluation module is used to evaluate the test environment status of the universal laboratory based on the current analysis results and predictive analysis results, so as to realize the environmental monitoring of the universal laboratory.

[0028] On the other hand, the present invention also provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, an intelligent environment monitoring method as described in any embodiment of the present invention is implemented.

[0029] On the other hand, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, an intelligent environment monitoring method as described in any embodiment of the present invention is implemented.

[0030] Compared with the prior art, the present invention has the following technical effects: 1. The present invention can effectively deal with the problem of missing values ​​and data inconsistency by introducing a data completion and fusion algorithm, thereby ensuring the integrity and uniformity of the data set and providing accurate data for subsequent data analysis and model training; by calculating the covariance matrix, eigenvalues, eigenvectors, and selecting main features for data conversion, the reduction of data dimension and the effective fusion of information are achieved, the complexity of subsequent processing is reduced, and the representation capability of the most important information in the data set is enhanced; the whole process provides a systematic method for processing and analyzing current environmental data, adjusting and optimizing according to different environmental monitoring requirements, and improving the adaptability of the processing flow.

[0031] 2. The present invention uses SVM technology to perform intelligent analysis on the fused data feature set, which can accurately classify the current environmental status of the universal laboratory and express it with environmental quantitative level parameters, thereby improving the accuracy of environmental status monitoring; the LSTM network is used to analyze historical environmental data, and combined with the dynamic periodic capture algorithm, it can accurately capture the nonlinear trends and periodic changes in the current environmental data. The enhanced prediction capability allows laboratory managers to make adjustments in advance to adapt to expected environmental changes and optimize the laboratory's operating efficiency; by performing Fourier transform decomposition on historical current environmental data and reconstructing the time series using the dynamic periodic capture algorithm, it can deeply understand and reveal the multi-level periodic characteristics in the environmental data. A high level of understanding helps to predict and respond to complex environmental changes and ensure that laboratory conditions are always in the optimal state. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is an overall flow chart of an intelligent environment monitoring method described in the present invention. DETAILED DESCRIPTION

[0033] In order to make the objectives, technical solutions and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in combination with specific embodiments of the present application and with reference to the accompanying drawings.

[0034] Embodiment 1 This embodiment provides an intelligent environment monitoring method. Figure 1 As shown, the following steps are included: The current environmental data of the omnipotence laboratory is obtained. The current environmental data obtained is the current environmental data. The collection time range of the current data depends on the frequency and demand of monitoring. Further, the current environmental data is obtained using a sensor. The sensor may include a temperature sensor, a humidity sensor, an air pressure sensor, etc.; the current environmental data is intelligently processed to obtain accurate current environmental data. The accurate current environmental data is intelligently analyzed using an SVC algorithm to obtain a current analysis result. The current analysis result uses an environmental quantification level parameter to represent the current test environment state of the omnipotence laboratory. Specifically, it can be label parameter data obtained according to different defined numerical ranges based on various data such as temperature, humidity, and air pressure corresponding to the output sensor detection data.

[0035] Obtain the historical environmental data of the universal laboratory, that is, obtain all data collected before the current environmental data currently collected; optimize the historical environmental data, and use the universal laboratory environmental prediction model constructed based on the LSTM (Long Short-Term Memory) network to perform predictive analysis on the optimized historical environmental data and the accurate current environmental data to obtain predictive analysis results. The predictive analysis results are the changing trends of future environmental parameters, which can be the predicted values ​​of future environmental parameters and the changing trends of environmental parameters, and are expressed in the form of a prediction report.

[0036] Based on the current analysis results and predictive analysis results, the test environment status of the universal laboratory is evaluated to achieve environmental monitoring of the universal laboratory.

[0037] As a preferred implementation mode of this embodiment, the intelligent data processing of the current environment data is specifically as follows: preprocessing the current environment data, extracting the current environment data features from the preprocessed current environment data using feature engineering such as statistical analysis methods, time series feature extraction technology, frequency domain feature extraction technology, etc., the current environment data features such as the statistical features of the current environment data (mean, standard deviation, etc.), time series features, etc.; obtaining a multivariate data feature set, and performing data feature fusion processing based on the multivariate data feature set using a multivariate data fusion algorithm.

[0038] As a preferred implementation of this embodiment, preprocessing includes noise removal, correction of outliers, data normalization and data completion fusion algorithm. The data completion fusion algorithm is used to avoid data inconsistency and missing values ​​from interfering with the accuracy of subsequent analysis. The data completion fusion algorithm is used to complete the current environment data specifically as follows: For each missing data point, find its nearest neighbor data point in the current environment data set by calculating the Euclidean distance, and select the nearest neighbor data points, in this embodiment is a parameter pre-set according to the empirical method and is the nearest neighbor Assign weights to data points , weight The specific calculation formula is: ; In the formula, It is The local gradient rate of the nearest neighbor data point relative to the missing data point is the local gradient adjustment factor; is a regulating factor to balance the effects of distance and local gradient rate; It is The distance between the nearest neighbor data point and the missing data point; It is a parameter that adjusts the influence of distance, which is used to control how distance affects the distribution of weights; .

[0039] Further, The local gradient rate of the nearest neighbor data point relative to the missing data point The specific calculation formula is: ; In the formula, is the adjustment factor, It is The local density of the nearest neighbor data points, is a small positive number set to avoid division by zero.

[0040] Use the weighted average formula to calculate the interpolation value of the missing data point to obtain the interpolated data point. The specific formula is: ; In the formula, It is The interpolated values ​​for missing data points, is the missing data point nearest neighbor data points.

[0041] The interpolation values ​​of missing data points are adjusted and enhanced for data consistency. The specific formula is: ; In the formula, It is a nonlinear adjustment index, which is used to control the intensity and direction of adjustment to adapt to different data distribution characteristics; , is the variance and mean of the overall current environment data; It is the first data points; is the nearest neighbor The mean of the data points is used to reflect the central trend of the local data; is the nearest neighbor The variance of the data points is used to measure the dispersion of local data point values.

[0042] As a preferred implementation of this embodiment, the data feature fusion processing is performed based on the multivariate data feature set using the multivariate data fusion algorithm as follows: The multivariate data feature set is standardized to obtain the standardized multivariate data feature set, and then The multivariate data feature set after the standardized processing of samples constitutes the multidimensional data feature set .

[0043] In order to expand the original feature space so that the feature data can show richer relationships in the nonlinear space, a combination of polynomial kernel and radial basis kernel is used to perform nonlinear expansion on each feature in the multidimensional data feature set. The specific calculation formula is: ; In the formula, It is the multidimensional data feature set The sample in Feature data on the extended feature dimension; It is the multidimensional data feature set samples; It is the multidimensional data feature set samples; is the kernel function; is the contribution coefficient in the polynomial kernel function, which is used to adjust the The contribution of each feature dimension in the extended feature; is the order of the polynomial, which determines the polynomial complexity when the feature is expanded; is the characteristic dimension; It is a parameter in the radial basis kernel function, which controls the sensitivity of the distance between samples in the kernel function calculation; It is a sample and The square of the Euclidean distance between them represents the similarity of the two samples in the original feature space; finally, Constructing feature data on the extended feature dimension .

[0044] On the basis of nonlinear feature expansion, a hybrid kernel mapping and optimization process is introduced. By combining multiple kernel functions, the correlation between features is further optimized, making the features more closely related in the new high-dimensional space. The specific operation is to pass the feature data on the expanded feature dimension through a multi-kernel combination function Mapping is performed to obtain feature data after hybrid kernel mapping; the kernel weight coefficient in the mapping process is obtained through Lagrangian optimization. In the Lagrangian optimization process, the maximization of the correlation between features and the avoidance of overfitting are considered. The mapping function is specifically: ; In the formula, is the feature data after mixed kernel mapping, express; It is The kernel function can be selected by professional technicians according to the actual application scenario, which will not be described in detail here; It is The weight coefficients of the kernel functions are obtained through Lagrangian optimization; is the number of kernel functions.

[0045] After hybrid kernel mapping and optimization, the obtained high-dimensional feature space still contains a lot of redundant information. Multi-scale feature selection is introduced to extract the features with the maximum amount of information at each scale by performing multi-scale analysis on the features. The specific operation is to perform wavelet transform on the mapped features and decompose the features into different scales to capture the feature information at different scales. The formula of wavelet transform is: ; in, It is The wavelet coefficients of the layer represent the feature data after mixed kernel mapping In the The characteristic information at each scale is obtained through wavelet transform, which reflects the characteristic performance of data at different scales. It is The wavelet function of the layer is used to decompose the characteristic signal; is the wavelet function exist Layers and Positions The values ​​at are used to calculate the signal components at different scales; is the number of decomposition positions in the wavelet transform; is the sample size.

[0046] The collaborative feature interaction fusion is performed according to the wavelet coefficients. By analyzing the interaction between different features, an interaction matrix is ​​constructed to generate the final fusion feature set. The calculation formula of the interaction matrix is: ; In the formula, is the feature after mixed kernel mapping and The interaction matrix elements between represent the interaction strength between features, through Constructing the interaction matrix ; It is a feature No. The wavelet coefficients of the layer represent the features after mixed kernel mapping Performance at different scales; It is a feature No. The wavelet coefficients of the layer represent the features after mixed kernel mapping Performance at different scales; The strength of the interaction between features is adjusted through periodic changes; is the number of wavelet coefficient layers.

[0047] Finally, the covariance matrix of the feature data is calculated for the standardized multidimensional data feature set, and the eigenvalues ​​and corresponding eigenvectors of the covariance feature matrix are calculated. According to the size of the eigenvalue, the previous The eigenvector corresponding to the largest eigenvalue is the selected eigenvector as the main feature. Based on the selected main feature matrix , and get the final fusion feature representation: ; in, It is the final fusion feature, i.e., accurate environmental parameter data; is through The interaction matrix is ​​composed of is the matrix consisting of the selected main features.

[0048] As a preferred implementation of this embodiment, the SVC algorithm is used to perform intelligent analysis on the accurate current environmental data, and the current analysis results are specifically as follows: Firstly, the accurate current environment data, that is, the fused data feature set, is standardized to obtain a standardized data feature set.

[0049] The existing SVC algorithm is used for classification to obtain an analysis result based on the fused data feature set, and the analysis result uses the environmental quantitative level parameter to represent the current all-round laboratory environment state. In machine learning, SVC (Support Vector Classification) is an application form of support vector machine (SVM) for classification tasks. The SVC algorithm achieves accurate division of different categories by finding the optimal interface between data points.

[0050] The working principle of the SVC algorithm is as follows: First, use the selected kernel function and configured hyperparameters to train the SVC model using the training data set. This usually involves optimizing an objective function, which attempts to find a hyperplane that maximizes the interval between different categories, obtains a trained SVC model, and inputs the standardized data feature set into the trained SVC model. The model will output a category label for each input sample, which represents the environmental state category predicted for each sample.

[0051] As a preferred implementation mode of this embodiment, the optimization processing of historical environmental data is specifically as follows: data processing including filling missing values, removing noise, correcting outliers, and data normalization is performed on the historical environmental data; after the data processing, the historical environmental data is reconstructed and optimized using a dynamic periodic capture algorithm; and feature engineering is used to construct a historical data feature set representing the historical environmental data for the optimized historical environmental data.

[0052] As a preferred implementation of this embodiment, in order to more accurately capture the nonlinear trends and periodic changes in the historical environmental data, the historical environmental data is reconstructed and optimized using a dynamic periodic capture algorithm after data processing. Specifically, the following steps are performed: Fourier transform of historical and current environmental data Decompose at different time scales into components of different frequencies to reveal the multi-level periodic characteristics in the data. The specific calculation formula is: ; In the formula, It is The amplitude of a frequency component; It is The frequency of the frequency component; It’s time; It is The phase of the frequency components; is the total number of decomposed frequency components.

[0053] For each frequency component, its importance and periodicity strength in the historical current environmental data series are evaluated to identify the component that contributes most to the change in the historical current environmental data. The periodicity score of each frequency component is determined by calculating the autocorrelation of the frequency component and combining its amplitude. The frequency components with high autocorrelation show strong periodicity, specifically: ; In the formula, It is The periodicity score of the frequency components, It is The frequency components are delayed The autocorrelation function of the time delay It is the time interval used in the autocorrelation calculation, representing the time difference considered in the autocorrelation calculation, and is used to analyze the self-similarity of the signal after this time difference. delay; is the number of delays; It is The amplitude of a frequency component; , is the total number of decomposed frequency components.

[0054] The Softmax function is used to dynamically assign weights based on the periodicity score of each frequency component to emphasize the periodic component that contributes most to the change in the time series. The specific calculation formula is: ; In the formula, It is The weights of the frequency components are set to ensure that the sum of all weights is 1.

[0055] The time series is reconstructed using weighted frequency components to better capture the periodic characteristics and trends of the data. The decomposed and weighted frequency components are recombined to form optimized historical current environmental data. The specific formula is: ; In the formula, It is the optimized historical current environment data; It is The weight of the frequency components; It is The frequency of the frequency component; It’s time; It is The phase of a frequency component.

[0056] As a preferred implementation mode of this embodiment, a predictive analysis is performed on the optimized historical environmental data and the accurate current environmental data using an all-round laboratory environment prediction model constructed based on an LSTM network. Specifically, the feature set is divided into a training set and a test set, and a model is designed using an LSTM network to obtain a all-round laboratory environment prediction model. The model is trained using the training set and tested using the test set to obtain the final all-round laboratory environment prediction model. The accurate current environmental data is then used as input, and the trained model is used to predict future environmental parameters to obtain predictive analysis results.

[0057] As a preferred implementation of this embodiment, the test environment status of the universal laboratory is evaluated based on the current analysis results and the predictive analysis results, and the environmental monitoring of the universal laboratory is realized as follows: For the current analysis results, the analysis results are analyzed in depth according to the statistical analysis method to obtain the current environmental quantitative level of the universal laboratory. The quantitative level labeling can be: the original current analysis results will be converted into specific quantitative levels, such as labeling the environmental status as "suitable", "warning" or "unsuitable". For the predictive analysis results, according to the time series analysis technology, the predictive analysis results are used to analyze the changing trend of future environmental parameters and identify the fluctuation range of future trends. For example, the predictive analysis results will be analyzed in detail as specific future trend changes, such as the expected increase or decrease in temperature in the next week. Specifically, the statistical analysis methods that can be used include descriptive statistical analysis, box plot analysis, skewness and kurtosis analysis; the time series analysis techniques that can be used include moving average and exponential smoothing, autoregressive moving average, and Fourier transform. Compared with the classification output directly obtained from the SVC algorithm, the above statistical analysis methods can provide more levels and more detailed information. The SVC algorithm provides direct classification results based on the demarcation lines learned by the model, which are mainly manifested as discrete category labels. Statistical analysis and time series analysis provide deeper data insights, including: Deeper data understanding: Statistical analysis methods such as descriptive statistics and box plots can provide a detailed understanding of the distribution of data and potential outliers, which may affect the setting of quantitative levels of environmental parameters.

[0058] More accurate trend forecasting: Time series analysis can not only predict future trends, but also assess the stability of trends and possible fluctuation ranges, providing more reliable support for future environmental management.

[0059] Furthermore, the comprehensive analysis results can be applied to the laboratory environment monitoring system using real-time data monitoring technology to update the environmental status display in real time: the current environmental status and predicted trends can be displayed in real time on the monitoring dashboard using Internet of Things (IoT) technology and real-time data visualization tools (such as Grafana).

[0060] Furthermore, a decision tree analysis method is used to provide operational suggestions based on the obtained environmental quantitative level parameters of the current universal laboratory and the change trends of future environmental parameters, so as to realize environmental monitoring of the universal laboratory. Specifically, the current environmental quantitative level parameters of the universal laboratory and the change trends of future environmental parameters are used as inputs, and the decision tree model is used to output specific operational suggestions for the current environmental parameters and the predicted future trends. For example, if the ambient temperature is predicted to rise, the decision tree output will recommend lowering the air conditioning temperature setting.

[0061] As a preferred implementation mode of this embodiment, the accuracy of environmental monitoring strategies and prediction models can be regularly evaluated according to the performance evaluation standards formulated according to the expert experience method, and a performance report can be obtained by comparing the prediction results with the actual observations; according to the machine learning feedback adjustment mechanism, the SVC and the universal laboratory environment prediction model can be adjusted and retrained according to the performance monitoring results to improve the prediction accuracy and classification efficiency.

[0062] As a preferred implementation of this embodiment, the data collection frequency, prediction model input characteristics, etc. can be adjusted according to the model optimization results and laboratory needs to optimize the environmental monitoring strategy.

[0063] As a preferred implementation of this embodiment, specific measures such as air quality improvement, temperature and humidity control can be implemented according to the environmental management protocol formulated according to the empirical method and the operational suggestions provided to ensure that the laboratory environment reaches the optimal state. At the same time, according to the experimental planning guidelines formulated according to the empirical method, the environmental trend prediction information is used to adjust the experimental plan and schedule to avoid conducting sensitivity experiments under unsuitable environmental conditions.

[0064] Embodiment 2 Accordingly, this embodiment provides an intelligent environment monitoring system, which uses the intelligent environment monitoring method as described in any embodiment of the present invention to perform environment monitoring, including a current environment analysis module, a historical environment analysis module and an environment monitoring evaluation module.

[0065] The current environment analysis module is used to obtain the current environment data of the universal laboratory, perform intelligent data processing on the current environment data to obtain accurate current environment data, and use the SVC algorithm to perform intelligent analysis on the accurate current environment data to obtain the current analysis result, which is the environmental quantitative level parameter; The historical environment analysis module is used to obtain the historical environment data of the universal laboratory, optimize the historical environment data, and perform predictive analysis on the optimized historical environment data and the accurate current environment data using the universal laboratory environment prediction model built based on the LSTM network to obtain predictive analysis results. The predictive analysis results are the changing trends of future environmental parameters.

[0066] The environmental monitoring and evaluation module is used to evaluate the test environment status of the universal laboratory based on the current analysis results and predictive analysis results, so as to realize the environmental monitoring of the universal laboratory.

[0067] Embodiment 3 This embodiment provides an electronic device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the intelligent environment monitoring method as described in any embodiment of the present invention is implemented.

[0068] Embodiment 4 This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the intelligent environment monitoring method as described in any embodiment of the present invention is implemented.

[0069] In the embodiments of the present application, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, c can be single or multiple.

[0070] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0071] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0072] In several embodiments provided in the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as: ROM), random access memory (Random Access Memory; hereinafter referred to as: RAM), disk or optical disk, and other media that can store program codes.

[0073] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. An intelligent environment monitoring method, characterized in that: The following steps are involved: Acquire the current environmental data of the universal laboratory, perform intelligent data processing on the current environmental data to obtain accurate current environmental data, use the SVC algorithm to perform intelligent analysis on the accurate current environmental data to obtain the current analysis result, which is an environmental quantitative level parameter used to represent the current test environment status of the universal laboratory; Obtain the historical environmental data of the universal laboratory, optimize the historical environmental data, and use the universal laboratory environmental prediction model built based on the LSTM network to perform predictive analysis on the optimized historical environmental data and the accurate current environmental data to obtain the predictive analysis results, which are the changing trends of future environmental parameters; Based on the current analysis results and predictive analysis results, the test environment status of the universal laboratory is evaluated to achieve environmental monitoring of the universal laboratory.

2. The intelligent environment monitoring method according to claim 1, characterized in that: The specific method of intelligent data processing of current environmental data is as follows: preprocessing the current environmental data, extracting the features of the current environmental data from the preprocessed current environmental data by feature engineering, obtaining a multivariate data feature set, and performing data feature fusion processing by using a multivariate data fusion algorithm based on the multivariate data feature set.

3. The intelligent environment monitoring method according to claim 2, characterized in that: Preprocessing includes noise removal, outlier correction, data normalization and data completion fusion algorithm. The data completion fusion algorithm is used to complete the current environment data as follows: For each missing data point, find its nearest neighbor data point in the current environment data set by calculating the Euclidean distance, and select the nearest neighbor data points and the nearest neighbor Assign weights to data points , weight The specific calculation formula is: ； In the formula, It is The local gradient rate of the nearest neighbor data point relative to the missing data point; is a regulating factor to balance the effects of distance and local gradient rate; It is The distance between the nearest neighbor data point and the missing data point; is the parameter that adjusts the effect of distance; ; Use the weighted average formula to calculate the interpolation value of the missing data point to obtain the interpolated data point. The specific formula is: ； In the formula, It is The interpolated values ​​for missing data points, is the missing data point nearest neighbor data points; The interpolation values ​​of missing data points are adjusted and enhanced for data consistency. The specific formula is: ； In the formula, It is a nonlinear adjustment index, which is used to control the intensity and direction of adjustment to adapt to different data distribution characteristics; , is the variance and mean of the overall current environment data; It is the first data points; is the nearest neighbor The mean of the data points; is the nearest neighbor The variance of the data points.

4. The intelligent environment monitoring method according to claim 2, characterized in that: The data feature fusion processing based on the multivariate data feature set using the multivariate data fusion algorithm is as follows: The multivariate data feature set is standardized to obtain the standardized multivariate data feature set, and then The multivariate data feature set after the standardized processing of samples constitutes the multidimensional data feature set ; The polynomial kernel and radial basis kernel are combined to perform nonlinear expansion on each feature in the multidimensional data feature set. The specific calculation formula is: ； In the formula, It is the multidimensional data feature set The sample in Feature data on the extended feature dimension; It is the multidimensional data feature set samples; It is the multidimensional data feature set samples; is the kernel function; is the contribution coefficient in the polynomial kernel function, which is used to adjust the The contribution of each feature dimension in the extended feature; is the degree of the polynomial; is the characteristic dimension; is the parameter in the radial basis kernel function; It is a sample and The square of the Euclidean distance between Constructing feature data on the extended feature dimension ; The feature data on the extended feature dimension is combined through the multi-core function Mapping is performed to obtain the feature data after hybrid kernel mapping. The specific mapping function is: ； In the formula, is the feature data after mixed kernel mapping, express; It is Kernel function; It is The weight coefficients of the kernel functions are obtained through Lagrangian optimization; is the number of kernel functions; The mapped feature data is subjected to wavelet transformation, and collaborative feature interaction fusion is performed according to the wavelet coefficients. By analyzing the interaction between different features, an interaction matrix is ​​constructed to generate the final fusion feature set. The calculation formula of the interaction matrix is: ； In the formula, is the feature after mixed kernel mapping and The interaction matrix elements between represent the interaction strength between features; It is a feature No. The wavelet coefficients of the layer; It is a feature No. The wavelet coefficients of the layer; The strength of the interaction between features is adjusted through periodic changes; is the number of wavelet coefficient layers; Finally, the covariance matrix of the feature data is calculated for the standardized multidimensional data feature set, and the eigenvalues ​​and corresponding eigenvectors of the covariance feature matrix are calculated. According to the size of the eigenvalue, the previous The eigenvector corresponding to the largest eigenvalue is the selected eigenvector as the main feature. Based on the selected main feature matrix , and get the final fusion feature representation: ； in, It is the final fusion feature, i.e., accurate environmental parameter data; is through The interaction matrix is ​​composed of is the matrix consisting of the selected main features.

5. The intelligent environment monitoring method according to claim 1, characterized in that: The specific optimization processing of historical environmental data includes: performing data processing on historical environmental data including filling missing values, removing noise, correcting outliers, and normalizing data; after data processing, reconstructing and optimizing the historical environmental data using a dynamic periodic capture algorithm; and constructing a historical data feature set representing the historical environmental data using feature engineering for the optimized historical environmental data.

6. The intelligent environment monitoring method according to claim 5, characterized in that: After data processing, the historical environmental data is reconstructed and optimized using a dynamic periodic capture algorithm. Specifically: Fourier transform is used to decompose historical and current environmental data at different time scales into components of different frequencies; The periodicity score of each frequency component is determined by calculating the autocorrelation of the frequency component and combining it with its amplitude, specifically: ； In the formula, It is The periodicity score of the frequency components, It is The frequency components are delayed The autocorrelation function of the time delay is the time interval used in the autocorrelation calculation, indicating the delay; is the number of delays; It is The amplitude of a frequency component; , is the total number of decomposed frequency components; Dynamically assign weights using the Softmax function based on the periodicity score of each frequency component; The time series is reconstructed using weighted frequency components, and the decomposed and weighted frequency components are recombined to form optimized historical current environmental data. The specific formula is: ； In the formula, It is the optimized historical current environment data; It is The weight of the frequency components; It is The frequency of the frequency component; It’s time; It is The phase of a frequency component.

7. The intelligent environment monitoring method according to claim 1, characterized in that: Based on the current analysis results and predictive analysis results, the test environment status of the universal laboratory is evaluated to achieve environmental monitoring of the universal laboratory as follows: According to the current analysis results, the current environmental quantitative level of the universal laboratory is obtained according to the statistical analysis method; according to the predictive analysis results, the change trend of future environmental parameters is obtained by using the predictive analysis results according to the time series analysis technology, and the fluctuation range of future trends is identified; Using the decision tree analysis method, operational suggestions are provided based on the current environmental quantitative level of the universal laboratory and the changing trends of future environmental parameters, thereby realizing environmental monitoring of the universal laboratory.

8. An intelligent environment monitoring system, characterized in that: The system adopts the intelligent environmental monitoring method according to any one of claims 1 to 7 to perform environmental monitoring, including a current environmental analysis module, a historical environmental analysis module and an environmental monitoring and evaluation module; The current environment analysis module is used to obtain the current environment data of the universal laboratory, perform intelligent data processing on the current environment data to obtain accurate current environment data, and use the SVC algorithm to perform intelligent analysis on the accurate current environment data to obtain the current analysis result, which is the environmental quantitative level parameter; The historical environment analysis module is used to obtain the historical environment data of the universal laboratory, optimize the historical environment data, and use the universal laboratory environment prediction model built based on the LSTM network to perform predictive analysis on the optimized historical environment data and the accurate current environment data to obtain the predictive analysis results. The predictive analysis results are the changing trends of future environmental parameters. The environmental monitoring and evaluation module is used to evaluate the test environment status of the universal laboratory based on the current analysis results and predictive analysis results, so as to realize the environmental monitoring of the universal laboratory.

9. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the intelligent environment monitoring method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the intelligent environment monitoring method according to any one of claims 1 to 7 is implemented.

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