Early warning system for gas equipment

Abstract

The invention provides an early warning system for gas equipment, and relates to the technical field of data processing, and the system comprises an equipment information management module which is used for obtaining the basic information of the gas equipment, generating a two-dimensional code label corresponding to each piece of equipment, and scanning the two-dimensional code labels to obtain the basic information of the gas equipment; basic information, maintenance records, remote transmission information and flowmeter maintenance records of the equipment are checked, and maintenance operation is executed; and the data analysis and prediction module is used for analyzing the operation data and the historical maintenance data of the gas equipment by establishing a prediction model, predicting the future operation state and the potential fault risk of the gas equipment, and generating corresponding early warning information and maintenance suggestions according to the prediction result. By integrating the modules, the efficiency of maintenance personnel is improved, the equipment maintenance cost is reduced, the equipment operation stability is enhanced, one-stop service is provided for equipment managers, and the safety monitoring and operation and maintenance management efficiency of the gas equipment is comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an early warning system for gas equipment. Background Art

[0002] In the current field of gas equipment management, the traditional offline paper records or no-record methods for equipment maintenance, although they can meet basic maintenance needs to a certain extent, have some shortcomings. These shortcomings are not completely insurmountable, but they do bring certain troubles in actual operations.

[0003] On the one hand, traditional maintenance record keeping methods may result in incomplete information or difficulty in tracing. For example, some maintenance personnel may forget to record the details of a maintenance, or make a clerical error when recording, which will make it difficult to accurately trace and analyze subsequent maintenance work. For another example, since paper records are inconvenient to save and query, when you need to understand the maintenance history of a certain device, you may need to spend a lot of time and energy to review and organize the information.

[0004] On the other hand, the traditional maintenance method lacks a scientific and systematic management method, making it difficult to ensure the efficiency and effectiveness of maintenance work. For example, the setting of the maintenance cycle may rely too much on personal experience and lack scientific basis, resulting in some equipment being over-maintained and others being under-maintained. For another example, due to the lack of support from information technology, maintenance personnel may not be able to understand the operating status and maintenance needs of the equipment in a timely manner, thus missing the best maintenance opportunity. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide an early warning system for gas equipment to achieve scientific, efficient and safe management of the gas equipment.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows: In a first aspect, an early warning system for gas equipment comprises: The equipment information management module is used to obtain the basic information of the gas equipment, generate a QR code label corresponding to each device, and view the basic information, maintenance records, remote transmission information and flow meter maintenance records of the equipment by scanning the QR code label, and perform maintenance operations; The data analysis and prediction module is used to analyze the operating data and historical maintenance data of gas equipment by establishing a prediction model, predict the future operating status and potential failure risks of gas equipment, and generate corresponding early warning information and maintenance suggestions based on the prediction results; The gas leakage warning module is used to train the gas leakage warning model based on fuzzy logic and random forest algorithm, using historical gas leakage data and real-time environmental data to monitor the leakage risk of gas equipment in real time; The video monitoring module is used to collect video information of the surrounding environment of the gas equipment, automatically identify abnormal situations and trigger early warnings through intelligent video analysis, and transmit the video to the cloud platform module to achieve remote monitoring and real-time recording of the operating status of the gas equipment; The cloud platform module is used to receive data from the equipment information management module, data analysis and prediction module, gas leak warning module and video monitoring module to realize data storage, processing and display.

[0007] Furthermore, by establishing a prediction model to analyze the operating data and historical maintenance data of the gas equipment, the future operating status and potential failure risks of the gas equipment are predicted, and corresponding early warning information and maintenance suggestions are generated based on the prediction results, including: Continuously collect the operating data of gas equipment, including pressure, flow, temperature sensor data, the switch status of gas equipment, and operating parameters of working mode, and collect the historical maintenance data of gas equipment, including maintenance time, maintenance content, maintenance results and maintenance personnel information; According to the operating characteristics and failure modes of gas equipment, the features related to the operating status and failure risk of gas equipment are determined, and the operating data and historical maintenance data of gas equipment are reduced in dimension and feature extracted through principal component analysis to obtain a feature data set after dimension reduction; According to the dimension of the feature data set and the prediction target, the structure of the neural network is set, including the number of nodes in the input layer, hidden layer, and output layer, and the prediction model is constructed and trained using the set neural network structure and initial parameters; During the training process, the prediction model is evaluated and optimized through the whale optimization algorithm to obtain the trained prediction model; Input the real-time gas equipment operation data into the trained prediction model to predict the operation status and potential failure risks to obtain the prediction results; According to the prediction results, the operating status of the gas equipment is evaluated to determine whether it is in a normal, abnormal or potential fault state. When the operating status of the gas equipment is in an abnormal or faulty state, a quantitative analysis of the abnormality or potential fault is performed to determine the type, severity and impact range of the fault; Based on the prediction results, fault type, severity and impact scope, corresponding warning information is generated, including warning level, warning content and recommended measures. In combination with the equipment's maintenance cycle and historical maintenance records, maintenance recommendations are generated, including maintenance time, maintenance content and maintenance personnel information.

[0008] Furthermore, according to the operating characteristics and failure modes of the gas equipment, the features related to the operating status and failure risk of the gas equipment are determined, and the gas equipment operating data and historical maintenance data are reduced in dimension and feature extracted through principal component analysis to obtain a feature data set after dimension reduction, including: According to the operating characteristics and failure modes of gas equipment, determine the characteristics related to the operating status and failure risks of gas equipment, including operating time, operating frequency, pressure fluctuation range, flow rate change rate, and collect operating data and historical maintenance data of gas equipment; Construct a feature matrix based on the features related to the operating status and failure risk of gas equipment ,In the feature matrix, each row represents a data sample and each column represents a feature; Calculate the covariance matrix of the feature matrix to reflect the correlation and change trend between the features, and perform eigenvalue decomposition on the covariance matrix to obtain a set of eigenvalues ​​and corresponding eigenvectors; According to the eigenvalues, the number of principal components is determined, and the features related to the operating status and failure risk of the gas equipment are projected onto the principal components to obtain a feature data set after dimensionality reduction.

[0009] Furthermore, during the training process, the prediction model is evaluated and optimized by the whale optimization algorithm to obtain a trained prediction model, including: Initialize the parameters of the whale optimization algorithm, including the number of whale populations, the maximum number of iterations, and the search space, and randomly generate an initial whale population. Each individual whale represents a set of prediction model parameters. For each individual whale, the prediction model is trained using the prediction model parameters represented by each individual whale, and the prediction model is tested using the evaluation indicators to calculate the performance score of the individual whale; According to the update strategy of the whale optimization algorithm, including prey search, bubble net attack, and prey search behavior, the whale individuals are updated to obtain new whale individuals, where each new individual represents a new set of prediction model parameters; Repeat the process of training and evaluating the prediction model and updating individual whales until the maximum number of iterations is reached, and obtain the iteratively optimized whale population and final whale individuals; Use the prediction model parameters of the final whale individual representative to build a post-training prediction model.

[0010] Furthermore, the calculation formula for the performance score of individual whales is: ; in, represents the performance score of an individual whale; , , represents the weight coefficient; Indicates the number of samples correctly predicted as positive by the prediction model; Indicates the number of samples correctly predicted as negative by the prediction model; represents the total sample size; Indicates the number of positive samples; Represents the scaling parameter.

[0011] Furthermore, based on fuzzy logic and random forest algorithms, the gas leakage warning model is trained using historical gas leakage data and real-time environmental data to monitor the leakage risk of gas equipment in real time, including: Obtain historical gas leak data, including the time, location, and amount of leaks, and collect real-time environmental data; Based on historical gas leakage data and real-time environmental data, determine the characteristics related to gas leakage risk, including the time distribution of leakage events, location characteristics, leakage volume, and real-time environmental factors such as ambient temperature, humidity, and wind speed; Based on the characteristics related to gas leakage risk, fuzzy logic rules are defined to preliminarily describe the relationship between gas leakage risk and environmental factors; The features related to gas leakage risk are used as input data and leakage risk is used as output data to build the framework of random forest model; The random forest model is trained using historical gas leakage data, and the performance of the random forest model is optimized by adjusting the parameters of the random forest model, including the number and depth of trees, to obtain a trained random forest model; The trained random forest model is combined with fuzzy logic rules to form a hybrid early warning model, in which fuzzy logic is used for preliminary screening and assessment of leakage risks; Input the real-time acquired environmental data into the trained hybrid early warning model to conduct real-time monitoring and processing of gas leakage risks, so as to obtain real-time leakage risk assessment results; According to the real-time leakage risk assessment results, the leakage risk level of the current gas equipment is assessed to monitor the leakage risk of the gas equipment in real time.

[0012] Furthermore, the environmental data acquired in real time is input into the trained hybrid early warning model to conduct real-time monitoring and processing of gas leakage risks, so as to obtain real-time leakage risk assessment results, including: In the hybrid model, according to the fuzzy logic rules, the scope and possibility of gas leakage risk under the current environmental conditions are preliminarily judged to obtain the preliminary screened data; The data after preliminary screening is input into the random forest model, and the gas leakage risk is evaluated through integrated learning of multiple decision trees to obtain leakage risk level and leakage probability indicators; According to the leakage risk level and leakage probability, the gas leakage diffusion situation is predicted and the potential leakage impact range is calculated; The results of the preliminary judgment of fuzzy logic, the evaluation results of the random forest model and the prediction of the potential leakage impact range are integrated to generate real-time leakage risk assessment results, including leakage risk level, leakage probability and potential impact range information.

[0013] Furthermore, the video information of the surrounding environment of the gas equipment is collected, and the abnormal situation is automatically identified and an early warning is triggered through intelligent analysis of the video. At the same time, the video is transmitted to the cloud platform module to realize remote monitoring and real-time recording of the operation status of the gas equipment, including: Obtain video information of the surrounding environment of gas equipment in real time and extract key features, including pedestrian activities and vehicle traffic around gas equipment; Initialize the parameters of the firefly algorithm, including the number of fireflies, attraction, and step factor, and use the firefly algorithm to select and optimize key features to determine the final feature combination; Define the fitness function and evaluate the accuracy of each feature combination for anomaly detection to obtain the evaluation results; Each firefly searches and updates by adjusting its own brightness and position according to the fitness function and the current feature combination, and selects fireflies other than itself to move according to the attractiveness to obtain the optimized feature combination; Using the optimized feature combination, the video is intelligently analyzed to automatically identify abnormal situations. When an abnormal situation is detected, including gas leakage and illegal intrusion, an early warning is immediately triggered, and the early warning information and the video of the surrounding environment of the gas equipment are transmitted to the cloud platform module, realizing remote monitoring and real-time recording of the operating status of the gas equipment.

[0014] In a second aspect, a computing device includes: one or more processors; The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the system.

[0015] According to a third aspect, a computer-readable storage medium stores a program, and the program implements the system when executed by a processor.

[0016] The above solution of the present invention includes at least the following beneficial effects: The equipment information management module simplifies the maintenance process by generating equipment QR code labels. Maintenance personnel only need to scan the QR code to quickly obtain key data such as basic equipment information and maintenance records, thereby reducing the time for query and recording and improving the efficiency and accuracy of maintenance operations.

[0017] The data analysis and prediction module uses advanced prediction models to analyze the operating data and historical maintenance data of gas equipment, and can accurately predict the future operating status and potential failure risks of the equipment. This not only helps maintenance personnel to formulate maintenance plans in advance, but also effectively avoids safety risks caused by sudden equipment failures and extends the service life of the equipment.

[0018] The gas leak warning module uses fuzzy logic and random forest algorithms, combined with historical gas leak data and real-time environmental data, to train a high-precision gas leak warning model. This module can monitor the leakage risk of gas equipment in real time and immediately generate warning information when potential leaks are found, providing strong support for timely response measures.

[0019] The video monitoring module uses intelligent analysis technology to automatically identify abnormal conditions in the surrounding environment of gas equipment, such as intrusion, flames, etc., and trigger early warnings. At the same time, the module can also transmit video information to the cloud platform module in real time to achieve remote monitoring and real-time recording, providing an important basis for accident tracing.

[0020] As the data center of the system, the cloud platform module is responsible for receiving and processing data from various modules. Through the cloud platform module, managers can view key data such as the operating status, maintenance records, and early warning information of the equipment anytime and anywhere, and realize comprehensive monitoring and management of gas equipment. This not only improves management efficiency, but also provides strong support for scientific decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a schematic diagram of an early warning system for gas equipment provided by an embodiment of the present invention.

[0022] Figure 2 It is a process diagram that analyzes the operating data and historical maintenance data of gas equipment by establishing a prediction model, predicts the future operating status and potential failure risks of gas equipment, and generates corresponding early warning information and maintenance suggestions based on the prediction results. DETAILED DESCRIPTION

[0023] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0024] like Figure 1 As shown, an embodiment of the present invention provides an early warning system for gas equipment, comprising: The equipment information management module 11 is used to obtain the basic information of the gas equipment, generate a QR code label corresponding to each equipment, and view the basic information, maintenance records, remote transmission information and flow meter maintenance records of the equipment by scanning the QR code label, and perform maintenance operations; The data analysis and prediction module 12 is used to analyze the operation data and historical maintenance data of the gas equipment by establishing a prediction model, predict the future operation status and potential failure risks of the gas equipment, and generate corresponding early warning information and maintenance suggestions based on the prediction results; The gas leakage warning module 13 is used to train a gas leakage warning model based on fuzzy logic and random forest algorithm, using historical gas leakage data and real-time environmental data to monitor the leakage risk of gas equipment in real time; The video monitoring module 14 is used to collect video information of the surrounding environment of the gas equipment, automatically identify abnormal situations and trigger early warnings through intelligent analysis of the video, and transmit the video to the cloud platform module to realize remote monitoring and real-time recording of the operating status of the gas equipment; The cloud platform module 15 is used to receive data from the equipment information management module, the data analysis and prediction module, the gas leakage warning module and the video monitoring module, and realize data storage, processing and display.

[0025] In the embodiment of the present invention, by obtaining the basic information of the gas equipment and generating the corresponding QR code label, the equipment information is quickly and accurately identified. The maintenance personnel only need to scan the QR code to easily view the basic information, maintenance records, remote transmission information and flow meter maintenance records of the equipment, which greatly improves the work efficiency. The use of QR code labels also simplifies the recording and management process of equipment information, reduces human errors, and ensures the accuracy and integrity of the data.

[0026] The data analysis and prediction module 12 can accurately predict the future operating status and potential failure risks of the equipment by establishing a prediction model to analyze the operating data and historical maintenance data of the gas equipment. The timeliness and accuracy of the prediction results provide maintenance personnel with a scientific basis for decision-making, enabling them to formulate maintenance plans in advance and avoid safety risks and economic losses caused by sudden equipment failures. The generated early warning information and maintenance suggestions help maintenance personnel take timely measures to extend the service life of the equipment and improve the reliability of the equipment.

[0027] The gas leak warning module 13 uses fuzzy logic and random forest algorithms, combined with historical gas leak data and real-time environmental data, to train a high-precision gas leak warning model. It monitors the leakage risk of gas equipment in real time and generates warning information immediately when potential leaks are found, providing strong support for timely response measures. The accuracy and real-time performance of the gas leak warning module 13 help reduce the occurrence of gas leak accidents and protect the safety of life and property.

[0028] The video monitoring module 14 can automatically identify abnormal situations and trigger early warnings by collecting video information of the surrounding environment of the gas equipment and performing intelligent analysis of the video. The real-time transmission of video information to the cloud platform module enables remote monitoring and real-time recording of the operating status of the gas equipment, providing an important basis for accident tracing. The intelligent and remote features of the video monitoring module 14 improve monitoring efficiency, reduce labor costs, and enhance the safety management level of gas equipment.

[0029] The cloud platform module 15 serves as the data center of the system. The cloud platform module 15 is responsible for receiving and processing data from the equipment information management module, data analysis and prediction module, gas leak warning module and video monitoring module. It realizes the centralized storage, processing and display of data, provides comprehensive and accurate data support for managers, and improves management efficiency. The scalability and customizability of the cloud platform module 15 enable the system to be flexibly adjusted and optimized according to actual needs to meet the application needs in different scenarios.

[0030] In a preferred embodiment of the present invention, a prediction model is established to analyze the operation data and historical maintenance data of the gas equipment, predict the future operation status and potential failure risk of the gas equipment, and generate corresponding early warning information and maintenance suggestions based on the prediction results, which may include: Continuously collect the operating data of gas equipment, including pressure, flow, temperature sensor data, the switch status of gas equipment, and operating parameters of working mode, and collect the historical maintenance data of gas equipment, including maintenance time, maintenance content, maintenance results and maintenance personnel information; According to the operating characteristics and failure modes of gas equipment, the features related to the operating status and failure risk of gas equipment are determined, and the operating data and historical maintenance data of gas equipment are reduced in dimension and feature extracted through principal component analysis to obtain a feature data set after dimension reduction; According to the dimension of the feature data set and the prediction target, the structure of the neural network is set, including the number of nodes in the input layer, hidden layer, and output layer, and the prediction model is constructed and trained using the set neural network structure and initial parameters; During the training process, the prediction model is evaluated and optimized through the whale optimization algorithm to obtain the trained prediction model; Input the real-time gas equipment operation data into the trained prediction model to predict the operation status and potential failure risks to obtain the prediction results; According to the prediction results, the operating status of the gas equipment is evaluated to determine whether it is in a normal, abnormal or potential fault state. When the operating status of the gas equipment is in an abnormal or faulty state, a quantitative analysis of the abnormality or potential fault is performed to determine the type, severity and impact range of the fault; Based on the prediction results, fault type, severity and impact scope, corresponding warning information is generated, including warning level, warning content and recommended measures. In combination with the equipment's maintenance cycle and historical maintenance records, maintenance recommendations are generated, including maintenance time, maintenance content and maintenance personnel information.

[0031] In the embodiment of the present invention, the interface is connected to the gas equipment to read the operation data of the equipment in real time. The operation data includes data collected by pressure sensors, flow sensors, temperature sensors, etc., as well as operation parameters such as the switch status and working mode of the equipment. The historical maintenance records of the gas equipment are extracted from the maintenance management system. The maintenance data includes maintenance time, maintenance content, maintenance results, and information of maintenance personnel. These data are also stored in the database.

[0032] Analyze the operation data and maintenance data according to the operation characteristics and known failure modes of gas equipment. Determine the features that are closely related to the operation status and failure risk of the equipment through statistical analysis, correlation analysis and other methods. These features include pressure fluctuation range, flow rate change rate, temperature anomaly duration, etc. Use principal component analysis (PCA) to reduce the dimension of the operation data and historical maintenance data of gas equipment. PCA maps the high-dimensional feature space of the original data to the low-dimensional feature space through linear transformation while retaining the main information of the data. Select an appropriate number of principal components to obtain a feature data set after dimensionality reduction.

[0033] According to the dimension of the feature data set after dimensionality reduction and the prediction target, the structure of the neural network is set, including determining the number of nodes in the input layer, hidden layer, and output layer, and selecting hyperparameters such as activation function and optimizer. Use the set neural network structure and initial parameters to build a prediction model. Use the whale optimization algorithm (WOA) to evaluate and optimize the prediction model. WOA searches for the final model parameter combination by simulating the foraging behavior of whale groups. During the training process, the parameters of the model are continuously adjusted to minimize the prediction error. The real-time gas equipment operation data is input into the trained prediction model. The operating status and potential failure risk of the gas equipment are predicted based on the input data. The prediction results include whether the equipment is in normal state, abnormal state or potential failure state, as well as the severity and impact range of the abnormality or failure.

[0034] Assume that there is a gas turbine, and its operating data is continuously collected, including sensor data such as pressure, flow, temperature, and operating parameters such as the switch status and working mode of the equipment. At the same time, the historical maintenance data of the gas turbine is also extracted from the maintenance management system.

[0035] The data were analyzed to identify features related to the gas turbine operating status and failure risk, such as pressure fluctuation range, flow rate change rate, etc. Then, the PCA method was used to reduce the dimension of the data to obtain a feature data set after dimensionality reduction. The structure of the neural network was set, including hyperparameters such as the number of nodes in the input layer, hidden layer, and output layer, and a prediction model was built using these structures and initial parameters. During the training process, the model was evaluated and optimized using the whale optimization algorithm to improve the prediction accuracy of the model.

[0036] When real-time operating data is input into the trained prediction model, the model predicts the operating status and potential failure risk of the gas turbine based on the input data. For example, the model predicts that the pressure fluctuation range of the gas turbine exceeds the normal range and the flow rate change rate is abnormal, and determines that the gas turbine may be in a potential failure state.

[0037] Further quantitative analysis is performed on abnormalities or potential faults to determine the type of fault (such as pressure sensor failure, flow control valve failure, etc.), severity (such as minor failure, major failure, etc.) and scope of impact (such as impact on gas turbine performance, possible shutdown, etc.).

[0038] By continuously collecting and analyzing the operating data and historical maintenance data of gas equipment, the operating status and potential failure risks of the equipment can be predicted more accurately. Using advanced algorithms such as PCA and neural networks, the main features of the data can be extracted and a high-precision prediction model can be built to improve the accuracy of the prediction. When an abnormality or potential failure occurs in the gas equipment, early warning information can be generated in time, including the warning level, warning content and recommended measures, which helps the operation and maintenance personnel to respond and take measures in time to avoid production interruptions and safety hazards caused by equipment failure and reduce losses. Maintenance suggestions can be generated based on the prediction results and the maintenance cycle and historical maintenance records of the equipment, which helps the operation and maintenance personnel to formulate a more reasonable maintenance plan, improve maintenance efficiency, reduce maintenance costs, and extend the service life of the equipment. By continuously monitoring and predicting the operating status and potential failure risks of gas equipment, it can help the operation and maintenance personnel to discover and solve problems in a timely manner, which helps to reduce the occurrence of equipment failures, improve the reliability of equipment, and ensure the safety and stability of production.

[0039] In another preferred embodiment of the present invention, according to the operating characteristics and failure modes of the gas equipment, the features related to the operating status and failure risk of the gas equipment are determined, and the gas equipment operating data and historical maintenance data are reduced in dimension and feature extracted by principal component analysis to obtain a feature data set after dimension reduction, which may include: According to the operating characteristics and failure modes of gas equipment, determine the characteristics related to the operating status and failure risks of gas equipment, including operating time, operating frequency, pressure fluctuation range, flow rate change rate, and collect operating data and historical maintenance data of gas equipment; Construct a feature matrix based on the features related to the operating status and failure risk of gas equipment ,In the feature matrix, each row represents a data sample and each column represents a feature; Calculate the covariance matrix of the feature matrix to reflect the correlation and change trend between the features, and perform eigenvalue decomposition on the covariance matrix to obtain a set of eigenvalues ​​and corresponding eigenvectors; According to the eigenvalues, the number of principal components is determined, and the features related to the operating status and failure risk of the gas equipment are projected onto the principal components to obtain a feature data set after dimensionality reduction.

[0040] In the embodiment of the present invention, by analyzing the operating characteristics and known failure modes of the gas equipment, the features closely related to the operating status and failure risk of the equipment are determined. These features include operating time, operating frequency, pressure fluctuation range, flow rate change rate, etc. The operating data of the gas equipment is continuously collected through the preset sensors, including the above-mentioned identified features. At the same time, the historical maintenance data of the gas equipment is extracted from the maintenance management system. These data include the equipment's maintenance records, replacement parts, maintenance time and other information. The relevant features in the collected operating data and maintenance data are sorted out to construct a feature matrix ;in, represents the number of data samples, Represents the number of features. Each row represents a data sample and each column represents a feature. Covariance matrix It is used to reflect the correlation and change trend between features. The calculation formula of the covariance matrix is: ;in, Shidi data samples, Represents the mean vector of all samples; represents the transpose of the covariance matrix.

[0041] Covariance matrix Perform eigenvalue decomposition to obtain a set of eigenvalues and the corresponding eigenvector The formula for eigenvalue decomposition is ;in, , Represents an index variable; Indicates the number of features. According to the size of the eigenvalue, the number of principal components is determined . Select the first principal components to retain the main information of the data. Determined by a preset threshold or ratio, such as selecting the top number whose sum of eigenvalues ​​accounts for a certain ratio (such as 95%) of the sum of the total eigenvalues. principal components.

[0042] The feature matrix Projection to front The feature vectors corresponding to the principal components are used to obtain the feature data set after dimensionality reduction. The projection formula is ;in, It is before The matrix consists of the eigenvectors corresponding to the principal components.

[0043] Assume that there is a gas boiler, and its operating data is continuously collected through sensors, including operating time, operating frequency, pressure fluctuation range, flow rate change rate and other characteristics. At the same time, the historical maintenance data of the gas boiler is also extracted from the maintenance management system. Based on these characteristics, the feature matrix is ​​constructed. , where each row represents the operation data at a time point or a maintenance record, and each column represents a feature. Then, calculate the feature matrix The covariance matrix of , and perform eigenvalue decomposition to obtain a set of eigenvalues ​​and corresponding eigenvectors.

[0044] According to the size of the eigenvalue, the first three principal components are selected to retain the main information of the data. Project it onto the eigenvectors corresponding to the first three principal components to obtain the reduced-dimensional feature data set .Now, It contains the main information about the gas boiler’s operating status and failure risk, but at a lower dimensionality.

[0045] By reducing the dimension of gas equipment operation data and historical maintenance data through principal component analysis, the dimension and complexity of the data can be significantly reduced, thereby improving the efficiency of data processing and analysis. Principal component analysis can retain the main information and change trends of the data while removing redundant and noisy information, which helps to more accurately evaluate the operating status and failure risk of gas equipment. Feature data set after dimension reduction It can be used to train more efficient prediction models or classification models. The dimension of the model is lower, and the speed and accuracy of model training may be improved. Dimensionality reduction can reduce the storage requirements of data, thereby reducing storage costs.

[0046] Through dimensionality reduction and feature extraction, features closely related to the operating status and failure risk of gas equipment can be more accurately identified. This helps to warn of potential failures in advance and reduce production interruptions and safety hazards.

[0047] In another preferred embodiment of the present invention, evaluating and optimizing the prediction model by a whale optimization algorithm to obtain a trained prediction model may include: Initialize the parameters of the whale optimization algorithm, including the number of whale populations, the maximum number of iterations, and the search space, and randomly generate an initial whale population. Each individual whale represents a set of prediction model parameters. For each individual whale, the prediction model is trained using the prediction model parameters represented by each individual whale, and the prediction model is tested using the evaluation indicators to calculate the performance score of the individual whale; According to the update strategy of the whale optimization algorithm, including prey search, bubble net attack, and prey search behavior, the whale individuals are updated to obtain new whale individuals, where each new individual represents a new set of prediction model parameters; Repeat the process of training and evaluating the prediction model and updating individual whales until the maximum number of iterations is reached, and obtain the iteratively optimized whale population and final whale individuals; Use the prediction model parameters of the final whale individual representative to build a post-training prediction model.

[0048] In an embodiment of the present invention, the key parameters of the whale optimization algorithm are determined, including the number of whale populations (i.e., the number of whale individuals that simultaneously explore the solution space), the maximum number of iterations (i.e., the maximum number of cycles for the algorithm to run), and the search space (i.e., the possible range of values ​​of the prediction model parameters). Subsequently, an initial whale population is randomly generated in the specified search space, and each whale individual represents a set of prediction model parameters. These parameters may include model weights, biases, learning rates, regularization coefficients, etc. For each whale individual, the prediction model is trained using the prediction model parameters it represents, which involves dividing the data set into a training set and a validation set, using the training set for model training, and using the validation set to evaluate the performance of the model. After the training is completed, the prediction model is tested using predefined evaluation indicators (such as accuracy, precision, recall, F1 score, or mean square error, etc.), and the performance score of the whale individual is calculated. This score reflects the prediction ability of the model under the current parameter settings.

[0049] Whale individuals are updated according to the update strategies of the whale optimization algorithm, which include searching around prey (simulating whales swimming around food groups to find the final location), bubble net attack (simulating whales catching prey through bubble nets), and searching for prey behavior (simulating whales searching for food in a wider area). Under the guidance of these strategies, new whale individuals are generated, and each new individual represents a new set of prediction model parameters. These new parameters are obtained by moving whale individuals in the search space and applying certain randomness and heuristic rules.

[0050] Repeat the above process of training and evaluating the prediction model and updating the whale individuals until the maximum number of iterations is reached. In each iteration, the whale population is updated. Finally, the iteratively optimized whale population and the final whale individuals are obtained. The prediction model parameters represented by the final whale individuals are the final solution obtained after multiple iterative optimizations. Use the prediction model parameters represented by the final whale individuals to build the trained prediction model, which has been parameterized by the whale optimization algorithm and is ready for actual prediction tasks.

[0051] Assume that a linear regression model for predicting house prices is being optimized, and the parameters of the whale optimization algorithm have been initialized, including the number of whale populations, the maximum number of iterations, and the search space (e.g., the range of values ​​for the model's weights and biases). In the first iteration, the initial whale population is randomly generated, and each individual whale represents a set of parameters for the linear regression model. For each individual whale, the linear regression model is trained using the parameters it represents, and the test data set is used to evaluate the performance of the model (e.g., calculate the mean squared error).

[0052] According to the update strategy of the whale optimization algorithm, the whale individuals are updated to generate new parameter combinations. These new parameters are obtained by searching around the current final solution, performing bubble net attacks, or searching for prey more widely. Repeat the above process, updating the whale population in each iteration and retaining the individuals with the final performance. After multiple iterations, the final whale individual is obtained, and the parameter combination represented by it is the optimized linear regression model parameters. Finally, these optimized parameters are used to build the trained linear regression model and prepare it for the actual house price prediction task.

[0053] The prediction performance of the model can be improved by tuning the parameters of the prediction model through the whale optimization algorithm. The optimized model performs better than the unoptimized model on the test data set. The whale optimization algorithm provides an automated parameter tuning method to find the final parameter combination without manual intervention, which saves time and labor costs and improves the efficiency of model development. The optimized model has better generalization ability, that is, it also performs better on new data sets. The whale optimization algorithm can be applied to a variety of prediction models, including linear regression, support vector machine, neural network, etc. This makes the algorithm widely applicable in the field of prediction model optimization. The whale optimization algorithm can be easily integrated into the existing prediction system and seamlessly connects with the existing data processing and model training processes, which helps to improve the overall performance and intelligence level of the system.

[0054] In another preferred embodiment of the present invention, the calculation formula for the performance score of an individual whale is: ; in, represents the performance score of an individual whale; , , represents the weight coefficient; Indicates the number of samples correctly predicted as positive by the prediction model; Indicates the number of samples correctly predicted as negative by the prediction model; represents the total sample size; Indicates the number of positive samples; Represents the scaling parameter.

[0055] In the embodiment of the present invention, it is assumed that the weight coefficient of the accuracy is = 0.4, weight coefficient of accuracy: = 0.3, weighting factor for the harmonic mean of accuracy and precision (a variant of the F1 score) = 0.2, nonlinear adjustment coefficient =0.05, smoothing factor of accuracy = 0.05, scaling parameter = 10. Count and calculate the key elements in the confusion matrix: True class ( )、True Negative( ), total sample size ( ) and the number of positive samples ( ). According to the calculation results of the confusion matrix, the accuracy is calculated and precision These performance indicators are the basis for calculating the performance score of individual whales. Substitute the calculated accuracy and precision into the performance score formula. According to the set weight coefficient , , and scaling parameters The value of , calculates the performance score of the individual whale . Each term in the formula represents a different performance indicator and tuning strategy, such as the weight of accuracy, the weight of precision, the harmonic mean of the two (a variant of the F1 score), nonlinear adjustment, and logarithmic function smoothing of accuracy. For each individual whale, repeat the above steps to calculate its performance score. According to the update strategy of the whale optimization algorithm, the whale individuals are updated to generate new parameter combinations. In each iteration, the whale individual with the highest performance score is retained as the current solution. After multiple iterations, the whale individual with the highest performance score is selected as the final optimization result, and the prediction model parameters represented by the whale individual and the corresponding performance score are output.

[0056] The performance scoring formula comprehensively considers multiple performance indicators such as accuracy and precision, as well as the balance between them, which helps to find a prediction model that is both accurate and precise during the optimization process. By introducing sine and logarithmic functions, the performance scoring formula can handle nonlinear relationships and smooth the accuracy. This helps to avoid falling into local solutions during the optimization process and improve the global search capability. The weight coefficients and scaling parameters can be adjusted according to specific tasks and data sets, which makes the performance scoring formula highly flexible and adjustable, and can adapt to different prediction models and application scenarios. By combining the whale optimization algorithm with the performance scoring formula, the optimal combination of prediction model parameters can be efficiently searched, which helps to reduce the time and cost of model training and evaluation and improve the efficiency of model development. The optimized prediction model has better generalization ability, that is, it also performs better on new data sets, because the performance scoring formula considers the global performance and stability of the model during the optimization process.

[0057] In a preferred embodiment of the present invention, according to fuzzy logic and random forest algorithm, using historical gas leakage data and real-time environmental data, a gas leakage warning model is trained to monitor the leakage risk of gas equipment in real time, which may include: Obtain historical gas leak data, including the time, location, and amount of leaks, and collect real-time environmental data; Based on historical gas leakage data and real-time environmental data, determine the characteristics related to gas leakage risk, including the time distribution of leakage events, location characteristics, leakage volume, and real-time environmental factors such as ambient temperature, humidity, and wind speed; Based on the characteristics related to gas leakage risk, fuzzy logic rules are defined to preliminarily describe the relationship between gas leakage risk and environmental factors; The features related to gas leakage risk are used as input data and leakage risk is used as output data to build the framework of random forest model; The random forest model is trained using historical gas leakage data, and the performance of the random forest model is optimized by adjusting the parameters of the random forest model, including the number and depth of trees, to obtain a trained random forest model; The trained random forest model is combined with fuzzy logic rules to form a hybrid early warning model, in which fuzzy logic is used for preliminary screening and assessment of leakage risks; Input the real-time acquired environmental data into the trained hybrid early warning model to conduct real-time monitoring and processing of gas leakage risks, so as to obtain real-time leakage risk assessment results, including leakage risk level, leakage probability, and potential impact range information; According to the real-time leakage risk assessment results, the leakage risk level of the current gas equipment is assessed to monitor the leakage risk of the gas equipment in real time.

[0058] In the embodiment of the present invention, historical gas leakage data is obtained from a database and historical records, including the timestamp, geographic location (latitude and longitude or address), and leakage volume (such as cubic meters or percentage) of the leakage event.

[0059] Deploy temperature sensors, humidity sensors, anemometers, etc. around gas equipment and read data from sensors regularly (e.g. every minute), including ambient temperature, humidity, wind speed, etc. Transmit real-time data wirelessly to data centers or edge computing devices. Analyze the time distribution of historical leakage events, identify the peak leakage periods, and analyze the impact of terrain, soil type, surrounding facilities, etc. on leakage risks based on the leakage location.

[0060] Count the consequences of different leakage amounts, identify the range of high-risk leakage amounts, and analyze the impact of real-time ambient temperature, humidity, and wind speed on leakage diffusion and detection.

[0061] Define fuzzy sets (such as "high", "medium", "low") for features such as time, location, leakage volume, ambient temperature, humidity, and wind speed. Based on historical data analysis, formulate fuzzy logic rules, such as "If the ambient temperature is high and the humidity is low, the leakage risk is high", and store all rules in the rule base. Select the most valuable features for leakage risk prediction from the relevant features, initialize the model using the random forest algorithm, and set the initial parameters (such as the number and depth of trees). Build a framework for model training, validation, and testing, prepare data segmentation and cross-validation, and divide historical data into training sets, validation sets, and test sets. Use the training set data to train the random forest model and adjust the model parameters to optimize performance. Find the final parameter combination through the cross-validation method. Use fuzzy logic rules to preliminarily screen and evaluate the real-time environmental data, input the screened data into the trained random forest model, perform detailed risk prediction, and combine fuzzy logic and random forest models to form a hybrid early warning model.

[0062] Assume that the following real-time environmental data is received: Time: 14:30; Location: Urban industrial area; Ambient temperature: 35°C; Humidity: 40%; Wind speed: 3m / s.

[0063] According to the fuzzy logic rules, the current ambient temperature is judged to be "high", the humidity is "low", and the wind speed is "medium". The rules in the matching rule base are such as "If the ambient temperature is high and the humidity is low, the risk of leakage is high". The preliminary assessment of the leakage risk is "high". The data after preliminary screening (including time, location, ambient temperature, humidity, wind speed) is input into the trained random forest model. The model predicts the specific level, probability and potential impact range of the current leakage risk based on the patterns learned from historical data. The leakage risk level is "Level II" (high risk), the probability of leakage is 85%, and the potential impact range is 500 meters.

[0064] Combining fuzzy logic and random forest models, historical data can be fully utilized to improve the accuracy of early warning. Environmental data is obtained in real time, and quickly processed and analyzed to ensure the timeliness of early warning information. False alarms and missed alarms are effectively reduced through preliminary screening with fuzzy logic and fine prediction with random forest models. Comprehensive assessment information such as leakage risk level, probability and potential impact range is output to support decision-making. The model framework is flexible and it is easy to add new features or update model parameters to adapt to the changing environment and gas leakage risks. Timely detection and early warning of gas leakage risks will help take preventive measures, reduce the possibility of accidents, and ensure the safety of personnel and the environment.

[0065] In another preferred embodiment of the present invention, the environmental data acquired in real time is input into the trained hybrid early warning model to perform real-time monitoring and processing of gas leakage risk to obtain real-time leakage risk assessment results, including leakage risk level, leakage probability, and potential impact range information, which may include: In the hybrid model, according to the fuzzy logic rules, the scope and possibility of gas leakage risk under the current environmental conditions are preliminarily judged to obtain the preliminary screened data; The data after preliminary screening is input into the random forest model, and the gas leakage risk is evaluated through integrated learning of multiple decision trees to obtain leakage risk level and leakage probability indicators; According to the leakage risk level and leakage probability, the gas leakage diffusion situation is predicted and the potential leakage impact range is calculated; The results of the preliminary judgment of fuzzy logic, the evaluation results of the random forest model and the prediction of the potential leakage impact range are integrated to generate real-time leakage risk assessment results, including leakage risk level, leakage probability and potential impact range information.

[0066] In the embodiment of the present invention, real-time environmental data such as ambient temperature, humidity, wind speed, etc. are received from sensors. The received environmental data are input into a predefined fuzzy logic rule set. These rules are formulated based on expert knowledge or historical data analysis to describe the relationship between environmental data and gas leakage risk.

[0067] Reasoning on environmental data based on fuzzy logic rules can yield a preliminary judgment on the scope and possibility of gas leakage risk under current environmental conditions. For example, if the ambient temperature is high and the humidity is low, it may be judged that the leakage risk is high. Integrate the preliminary judgment results and related environmental data to generate a preliminary screened data set. Perform necessary preprocessing on the preliminary screened data, such as standardization and normalization, to ensure that the data is suitable for input into the random forest model. Input the preprocessed data into the trained random forest model. The random forest model consists of multiple decision trees, each of which makes decisions independently based on the input data.

[0068] Through ensemble learning, the output results of multiple decision trees are combined to assess the risk of gas leakage. The output of each decision tree is ( =1, 2, ..., ), the final output of the random forest model can be obtained by integrating the output results of all decision trees, using the average value. According to the training results and parameter settings of the model, the leakage risk level and leakage probability are obtained. The leakage risk level is a discrete category label (such as high, medium, and low), while the leakage probability is a continuous value (such as a percentage).

[0069] Select an appropriate gas leak diffusion model based on the leak risk level and leak probability. Input the leak risk level, leak probability and related environmental data (such as wind speed, wind direction, etc.) as input parameters into the diffusion model. The model calculates the diffusion of the gas leak based on these parameters, including the concentration distribution of the leaked gas, the diffusion rate, etc. Predict the potential leak impact range based on the calculation results of the diffusion model. Integrate the results of the fuzzy logic preliminary judgment, the evaluation results of the random forest model and the prediction of the potential leak impact range, which is achieved through data merging. Generate a real-time leak risk assessment report based on the integrated data, which includes the leak risk level (such as high, medium, low), the leak probability (such as percentage) and the potential leak impact range (such as a specific geographical area). Output the assessment report to the specified display device.

[0070] Assume that the following real-time environmental data is received: ambient temperature 28°C, humidity 60%, wind speed 5m / s, and historical leakage data indicate that the leakage risk is high under the environmental conditions. According to the fuzzy logic rules, the gas leakage risk under the current environmental conditions is judged to be "medium to high". Generate a preliminary screened data set, including ambient temperature, humidity, wind speed, and the preliminary judgment results of fuzzy logic. Input the preliminary screened data set into the trained random forest model. The random forest model evaluates the gas leakage risk level as "high" and the leakage probability as 75% through ensemble learning of multiple decision trees. According to the leakage risk level and leakage probability, select an appropriate gas leakage diffusion model. Input relevant parameters into the diffusion model and calculate the potential leakage impact range as a circular area with a radius of 300 meters. Integrate the results of the preliminary judgment of fuzzy logic, the evaluation results of the random forest model, and the prediction of the potential leakage impact range to generate a real-time leakage risk assessment report, including the leakage risk level of "high", the leakage probability of 75%, and the potential impact range as a circular area with a radius of 300 meters. Output the assessment report to the display device for relevant personnel to view and make decisions.

[0071] By combining fuzzy logic and random forest models, it is possible to more accurately assess the risk of gas leakage and issue early warnings in a timely manner, which helps reduce the possibility of accidents. The generated assessment report includes comprehensive information such as leakage risk level, leakage probability, and potential leakage impact range, which helps relevant personnel to fully understand the risk situation and take appropriate measures. Real-time leakage risk assessment results provide strong decision-making support for relevant personnel, helping them to make correct decisions quickly and reduce losses. Sending the assessment results to the relevant emergency response system helps to initiate emergency response procedures in a timely manner and reduce the damage caused by accidents. Through the analysis of historical leakage data and the continuous optimization of the model, it can help enterprises continuously improve safety management measures and improve the overall safety management level.

[0072] In a preferred embodiment of the present invention, video information of the surrounding environment of the gas equipment is collected, abnormal conditions are automatically identified and early warnings are triggered by intelligent analysis of the video, and the video is transmitted to the cloud platform module to realize remote monitoring and real-time recording of the operating status of the gas equipment, which may include: Obtain video information of the surrounding environment of gas equipment in real time and extract key features, including pedestrian activities and vehicle traffic around gas equipment; Initialize the parameters of the firefly algorithm, including the number of fireflies, attraction, and step factor, and use the firefly algorithm to select and optimize key features to determine the final feature combination; Define the fitness function and evaluate the accuracy of each feature combination for anomaly detection to obtain the evaluation results; Each firefly searches and updates by adjusting its own brightness and position according to the fitness function and the current feature combination, and selects fireflies other than itself to move according to the attractiveness to obtain the optimized feature combination; Using the optimized feature combination, the video is intelligently analyzed to automatically identify abnormal situations. When an abnormal situation is detected, including gas leakage and illegal intrusion, an early warning is immediately triggered, and the early warning information and the video of the surrounding environment of the gas equipment are transmitted to the cloud platform module, realizing remote monitoring and real-time recording of the operating status of the gas equipment.

[0073] In the embodiment of the present invention, a high-definition camera is installed around the gas equipment to ensure coverage of key areas. A video capture card or a network camera driver is used to transmit the video stream to a computer in real time, and the video stream is pre-processed by decoding, denoising, color correction, etc. The background subtraction method is used to detect pedestrians in the video, track the movement trajectory of pedestrians, and calculate the speed, direction and other features. The background subtraction method is also used to detect vehicles, identify vehicle types (such as cars and trucks), and calculate vehicle flow, speed and other features.

[0074] The number of fireflies is determined based on the computing resources and problem complexity, and the attraction function is defined, which is inversely proportional to the distance between fireflies and controls the speed and range of firefly movement. Each firefly represents a feature combination and its position is randomly initialized. For each firefly, the fitness function is used to evaluate the accuracy of the feature combination it represents for anomaly detection.

[0075] The calculation formula of the fitness function is: ;in, , , , represents the weight coefficient; Indicates the number of abnormalities; Indicates the number of anomalies that were falsely detected; represents the average detection time; represents the number of samples; Represents an index variable; Indicates The detection time of an event; represents the false alarm rate; represents the cost of misreporting; Indicates the importance weight of the event; represents the adjustment factor; represents the total number of all events; represents the number of noise events; Indicates the number of key anomalies; represents the criticality coefficient; Indicates the number of anomalies that were falsely detected.

[0076] , , , are weight coefficients, which are set as =0.5, =0.3, =0.1, =0.1, which is used to adjust the relative importance of each feature in the fitness function. It is the ratio of the number of anomalies to the number of falsely detected anomalies, reflecting the accuracy of the feature combination in detecting anomalies. is the mean detection time, which represents the average time required for the system to detect an event.

[0077] is the standard deviation of the detection time, which indicates the dispersion of the detection time around the mean detection time. is the false alarm rate, set to =0.05, which means the proportion of normal events that are falsely reported as abnormal by the system to the total detected events. is the false alarm cost, set to =100 (the unit can be defined according to actual conditions, such as yuan, cents, etc.), indicating the economic cost caused by false reporting. is the event importance weight, set to =1.5 (for particularly important events, the weight can be higher), which is used to indicate the importance of different events in anomaly detection. is the adjustment factor, set to =0.8, used to adjust the event importance weight. is the total number of all events, including normal events and abnormal events. is the number of noise events, that is, the number of events that are recognized by the system as noise or insignificant. The number of critical anomalies refers to the number of abnormal events that are particularly important or require special attention identified during system monitoring. is the criticality coefficient, set to = 2.0, used to emphasize the importance of key anomalies in the fitness function.

[0078] Move the fireflies, update their positions, and use the fitness function to evaluate the feature combination represented by each firefly. Repeat the moving and evaluating steps until the stopping condition is met (such as reaching the maximum number of iterations), and output the final feature combination and its corresponding fitness value for the configuration and optimization of the anomaly detection system. Adjust the position and brightness of the fireflies according to the fitness function and the current feature combination. Fireflies select fireflies other than themselves based on their attractiveness to move, tending to fireflies with higher brightness. The fitness function should be able to accurately evaluate the accuracy of the feature combination for anomaly detection.

[0079] Use historical data or simulated data as training sets to mark anomalies. Use accuracy, recall, F1 score and other indicators to evaluate the performance of feature combinations. Use optimized feature combinations to train anomaly detection models. Analyze video streams in real time to detect anomalies (such as gas leaks, illegal intrusions). When an anomaly is detected, immediately trigger an early warning signal (such as sound alarm, SMS notification), and transmit the warning information and video stream to the cloud platform module.

[0080] Assume that a gas equipment is installed in a corner of a factory. Cameras are installed around it. The camera captures a video stream of the factory corner, which contains gas equipment, pedestrians, vehicles, etc. A worker is detected approaching the gas equipment and staying for a while. A truck is detected entering the factory and stopping near the gas equipment. A group of fireflies is initialized, and each firefly represents a feature combination (such as pedestrian stay time, vehicle type, etc.). By calculating the fitness function, it is found that certain feature combinations (such as pedestrians staying for a long time and the appearance of a specific type of vehicle) are highly correlated with abnormal situations. The anomaly detection model is trained using the optimized feature combination. When analyzing the video stream in real time, it is found that the worker stays too long and the type of truck is different from the past, triggering anomaly detection. The warning signal is triggered to notify relevant personnel to check the gas equipment, and the warning information and video stream are transmitted to the cloud platform for remote monitoring and recording.

[0081] Real-time detection of abnormal situations such as gas leaks and illegal intrusions, timely triggering of warnings, and effective prevention of accidents. Remote monitoring and real-time recording of gas equipment operation status are achieved through the cloud platform, allowing managers to understand the equipment status at any time. The firefly algorithm is used to optimize feature combinations, improve the accuracy of anomaly detection, reduce false positives and false negatives, automate the analysis process, reduce manual intervention, and improve work efficiency. The number and types of cameras can be expanded according to actual needs to adapt to different scenarios and environments. The cloud platform supports multi-user access and permission management, making it convenient for managers at different levels to view and control equipment. The cloud platform stores a large amount of historical data, supports data analysis and mining, and provides decision support for equipment maintenance and management.

[0082] In a preferred embodiment of the present invention, receiving data from the equipment information management module, the data analysis and prediction module, the gas leakage warning module and the video monitoring module to store, process and display data may include: In the embodiment of the present invention, the gas sensor data is received in real time and sent via a WebSocket request.

[0083] It stores basic information of the device (ID, type, location, etc.), historical sensor data (timestamp, sensor ID, value, etc.), and metadata of the video stream (timestamp, camera ID, video file path, etc.).

[0084] Store processed data into the database regularly and cache real-time data (such as Redis) to improve access speed. Use prediction models (such as LSTM) to predict leaks based on historical data and real-time data, set thresholds, and trigger warnings. When sensor data exceeds the preset threshold, a warning is triggered and relevant personnel are notified via SMS, email, or system notification. Record the time, location, reason, and processing results of each warning. Use OpenCV to process video streams in real time (such as motion detection and face recognition), display the processed video streams in real time, and record the video metadata for retrieval.

[0085] The embodiment of the present invention further provides a computing device, including: a processor, a memory storing a computer program, and when the computer program is executed by the processor, the system described above is executed. All implementation methods in the above system embodiment are applicable to this embodiment and can achieve the same technical effect.

[0086] The embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the system described above. All implementations in the above system embodiment are applicable to this embodiment and can achieve the same technical effect.

[0087] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An early warning system for gas equipment, characterized in that: include: The equipment information management module is used to obtain the basic information of the gas equipment, generate a QR code label corresponding to each device, and scan the QR code label to view the basic information, maintenance records, remote transmission information and flow meter maintenance records of the equipment to perform maintenance operations; The data analysis and prediction module is used to analyze the operating data and historical maintenance data of gas equipment by establishing a prediction model, predict the future operating status and potential failure risks of gas equipment, and generate corresponding early warning information and maintenance suggestions based on the prediction results; The gas leakage warning module is used to train the gas leakage warning model based on fuzzy logic and random forest algorithm, using historical gas leakage data and real-time environmental data to monitor the leakage risk of gas equipment in real time; The video monitoring module is used to collect video information of the surrounding environment of the gas equipment, automatically identify abnormal situations and trigger early warnings through intelligent video analysis, and transmit the video to the cloud platform module to achieve remote monitoring and real-time recording of the operating status of the gas equipment; The cloud platform module is used to receive data from the equipment information management module, data analysis and prediction module, gas leak warning module and video monitoring module to realize data storage, processing and display.

2. The early warning system for gas equipment according to claim 1, characterized in that: By establishing a prediction model to analyze the operating data and historical maintenance data of gas equipment, the future operating status and potential failure risks of gas equipment are predicted, and corresponding early warning information and maintenance suggestions are generated based on the prediction results, including: Continuously collect the operating data of gas equipment, including pressure, flow, temperature sensor data, the switch status of gas equipment, and operating parameters of working mode, and collect the historical maintenance data of gas equipment, including maintenance time, maintenance content, maintenance results and maintenance personnel information; According to the operating characteristics and failure modes of gas equipment, the features related to the operating status and failure risk of gas equipment are determined, and the operating data and historical maintenance data of gas equipment are reduced in dimension and feature extracted through principal component analysis to obtain a feature data set after dimension reduction; According to the dimension of the feature data set and the prediction target, the structure of the neural network is set, including the number of nodes in the input layer, hidden layer, and output layer, and the prediction model is constructed and trained using the set neural network structure and initial parameters; During the training process, the prediction model is evaluated and optimized through the whale optimization algorithm to obtain the trained prediction model; Input the real-time gas equipment operation data into the trained prediction model to predict the operation status and potential failure risks to obtain prediction results; According to the prediction results, the operating status of the gas equipment is evaluated to determine whether it is in a normal, abnormal or potential fault state. When the operating status of the gas equipment is in an abnormal or faulty state, a quantitative analysis of the abnormality or potential fault is performed to determine the type, severity and impact range of the fault; Based on the prediction results, fault type, severity and impact scope, corresponding warning information is generated, including warning level, warning content and recommended measures. In combination with the equipment's maintenance cycle and historical maintenance records, maintenance recommendations are generated, including maintenance time, maintenance content and maintenance personnel information.

3. The early warning system for gas equipment according to claim 2, characterized in that: According to the operating characteristics and failure modes of gas equipment, the features related to the operating status and failure risk of gas equipment are determined, and the gas equipment operating data and historical maintenance data are reduced in dimension and feature extracted through principal component analysis to obtain a feature data set after dimension reduction, including: According to the operating characteristics and failure modes of gas equipment, determine the characteristics related to the operating status and failure risks of gas equipment, including operating time, operating frequency, pressure fluctuation range, flow rate change rate, and collect operating data and historical maintenance data of gas equipment; Construct a feature matrix based on the features related to the operating status and failure risk of gas equipment ,In the feature matrix, each row represents a data sample and each column represents a feature; Calculate the covariance matrix of the feature matrix to reflect the correlation and change trend between the features, and perform eigenvalue decomposition on the covariance matrix to obtain a set of eigenvalues ​​and corresponding eigenvectors; According to the eigenvalues, the number of principal components is determined, and the features related to the operating status and failure risk of the gas equipment are projected onto the principal components to obtain a feature data set after dimensionality reduction.

4. The early warning system for gas equipment according to claim 3, characterized in that: During the training process, the prediction model is evaluated and optimized through the whale optimization algorithm to obtain the trained prediction model, including: Initialize the parameters of the whale optimization algorithm, including the number of whale populations, the maximum number of iterations, and the search space, and randomly generate an initial whale population. Each individual whale represents a set of prediction model parameters. For each individual whale, the prediction model is trained using the prediction model parameters represented by each individual whale, and the prediction model is tested using the evaluation indicators to calculate the performance score of the individual whale; According to the update strategy of the whale optimization algorithm, including prey search, bubble net attack, and prey search behavior, the whale individuals are updated to obtain new whale individuals, where each new individual represents a new set of prediction model parameters; Repeat the process of training and evaluating the prediction model and updating individual whales until the maximum number of iterations is reached, and obtain the iteratively optimized whale population and final whale individuals; Use the prediction model parameters of the final whale individual representative to build a post-training prediction model.

5. The early warning system for gas equipment according to claim 4, characterized in that: The performance score of an individual whale is calculated as: ； in, represents the performance score of an individual whale; , , represents the weight coefficient; Indicates the number of samples correctly predicted as positive by the prediction model; Indicates the number of samples correctly predicted as negative by the prediction model; represents the total sample size; Indicates the number of positive samples; Represents the scaling parameter.

6. The early warning system for gas equipment according to claim 5, characterized in that: Based on fuzzy logic and random forest algorithms, the gas leakage warning model is trained using historical gas leakage data and real-time environmental data to monitor the leakage risk of gas equipment in real time, including: Obtain historical gas leak data, including the time, location, and amount of leaks, and collect real-time environmental data; Based on historical gas leakage data and real-time environmental data, determine the characteristics related to gas leakage risk, including the time distribution of leakage events, location characteristics, leakage volume, and real-time environmental factors such as ambient temperature, humidity, and wind speed; Based on the characteristics related to gas leakage risk, fuzzy logic rules are defined to preliminarily describe the relationship between gas leakage risk and environmental factors; The features related to gas leakage risk are used as input data and leakage risk is used as output data to build the framework of random forest model; The random forest model is trained using historical gas leakage data, and the performance of the random forest model is optimized by adjusting the parameters of the random forest model, including the number and depth of trees, to obtain a trained random forest model; The trained random forest model is combined with fuzzy logic rules to form a hybrid early warning model, in which fuzzy logic is used for preliminary screening and assessment of leakage risks; Input the real-time acquired environmental data into the trained hybrid early warning model to conduct real-time monitoring and processing of gas leakage risks, so as to obtain real-time leakage risk assessment results; According to the real-time leakage risk assessment results, the leakage risk level of the current gas equipment is assessed to monitor the leakage risk of the gas equipment in real time.

7. The early warning system for gas equipment according to claim 6, characterized in that: The real-time environmental data is input into the trained hybrid early warning model to monitor and process the gas leakage risk in real time to obtain real-time leakage risk assessment results, including: In the hybrid model, according to the fuzzy logic rules, the scope and possibility of gas leakage risk under the current environmental conditions are preliminarily judged to obtain the preliminary screened data; The data after preliminary screening is input into the random forest model, and the gas leakage risk is evaluated through integrated learning of multiple decision trees to obtain leakage risk level and leakage probability indicators; According to the leakage risk level and leakage probability, the gas leakage diffusion situation is predicted and the potential leakage impact range is calculated; The results of the preliminary judgment of fuzzy logic, the evaluation results of the random forest model and the prediction of the potential leakage impact range are integrated to generate real-time leakage risk assessment results, including leakage risk level, leakage probability and potential impact range information.

8. The early warning system for gas equipment according to claim 7, characterized in that: Collect video information of the surrounding environment of gas equipment, analyze the video intelligently, automatically identify abnormal situations and trigger warnings, and transmit the video to the cloud platform module to achieve remote monitoring and real-time recording of the operating status of gas equipment, including: Obtain video information of the surrounding environment of gas equipment in real time and extract key features, including pedestrian activities and vehicle traffic around gas equipment; Initialize the parameters of the firefly algorithm, including the number of fireflies, attraction, and step factor, and use the firefly algorithm to select and optimize key features to determine the final feature combination; Define the fitness function and evaluate the accuracy of each feature combination for anomaly detection to obtain the evaluation results; Each firefly searches and updates by adjusting its own brightness and position according to the fitness function and the current feature combination, and selects fireflies other than itself to move according to the attractiveness to obtain the optimized feature combination; Using the optimized feature combination, the video is intelligently analyzed to automatically identify abnormal situations. When an abnormal situation is detected, including gas leakage and illegal intrusion, an early warning is immediately triggered, and the early warning information and the video of the surrounding environment of the gas equipment are transmitted to the cloud platform module, realizing remote monitoring and real-time recording of the operating status of the gas equipment.

9. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the system according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, and when the program is executed by a processor, the system according to any one of claims 1 to 8 is implemented.

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