An optimization method for hydrogen long-distance pipeline sensors based on artificial intelligence algorithm

Abstract

The present application discloses a method for optimizing sensors for long-distance hydrogen pipelines based on an artificial intelligence algorithm, comprising the following steps: obtaining sensor data of temperature, pressure, flow rate and vibration in long-distance hydrogen pipelines, and performing data cleaning and denoising; extracting temperature, pressure and flow rate characteristics, and constructing a multivariate coupling model; inputting model characteristic parameters and multivariate coupling data sets into an adaptive compensation algorithm, obtaining a sensor optimization data sequence and performing real-time prediction analysis to generate a sensor data state prediction sequence; monitoring and analyzing the safety status of the pipeline, generating a pipeline safety assessment coefficient, and completing real-time safety monitoring and early warning feedback. The present invention realizes precise optimization of sensor data by real-time analysis and processing of multivariate data, using mathematical operation formulas and deep coupling models, which can significantly improve the data accuracy and monitoring reliability of sensors in complex environments, and solve the problem of data deviation caused by complex environmental changes.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen transmission pipeline safety, and in particular to a method for optimizing a hydrogen long-distance pipeline sensor based on an artificial intelligence algorithm. Background Art

[0002] As a clean energy, hydrogen is increasingly used in long-distance pipeline transportation. However, the physical properties of hydrogen make it very sensitive to variables such as temperature, pressure, flow rate and environmental vibration during transportation. Any abnormality may lead to safety hazards, so accurate monitoring of pipeline operation status is particularly critical. Existing technologies mainly rely on single or limited sensor data analysis methods, and fail to conduct in-depth analysis of the complex coupling relationship between multiple variables. In addition, when the data is interfered or abnormal, the sensor accuracy will be greatly reduced. Faced with the complex and dynamically changing environment of long-distance pipelines, how to optimize sensor data in real time and ensure high accuracy and high stability of data has become an urgent problem to be solved in current hydrogen pipeline monitoring.

[0003] With the development of artificial intelligence technology, deep learning models based on multivariate data have shown significant advantages in processing complex environmental data. By introducing artificial intelligence learning algorithms, multivariate data such as temperature, pressure, flow rate and vibration can be coupled and analyzed and adaptively compensated, thereby optimizing the accuracy of sensor data, evaluating the safety status of pipelines in real time, and meeting the needs of high-precision monitoring in complex environments. Therefore, the technology of optimizing hydrogen long-distance pipeline sensor data based on artificial intelligence learning algorithms has important application value in pipeline safety monitoring. Summary of the invention

[0004] The present invention aims at the deficiencies in the prior art and provides a method for optimizing hydrogen long-distance pipeline sensors based on artificial intelligence algorithms, so as to solve the problem of how to optimize the sensor data of hydrogen long-distance pipelines in real time through artificial intelligence learning algorithms based on multivariate data of temperature, pressure, flow rate and vibration, so as to ensure high-precision monitoring and safety assessment of pipeline operation status in complex environments.

[0005] In order to achieve the above object, the present invention provides a method for optimizing a hydrogen long-distance pipeline sensor based on an artificial intelligence algorithm, comprising the following steps:

[0006] S100: Acquire sensor data of temperature, pressure, flow rate and vibration in the long-distance hydrogen pipeline, perform data cleaning and denoising, and obtain a pre-processed multivariate coupling data set;

[0007] S200: extracting temperature, pressure and flow rate features from the multivariable coupling data set, constructing a matrix based on the features, constructing a deep learning model based on the matrix and performing training optimization to obtain a multivariable coupling model and its characteristic parameters;

[0008] S300: Inputting the multivariable coupling model characteristic parameters and the multivariable coupling data set into an adaptive compensation algorithm, performing real-time compensation on sensor data, obtaining a sensor optimization data sequence, performing real-time prediction analysis, and generating a sensor data state prediction sequence;

[0009] S400: Based on the sensor data state prediction sequence and the multivariable coupling model, the safety state of the pipeline is monitored and analyzed, a pipeline safety assessment coefficient is generated, and real-time safety monitoring and early warning feedback are completed according to the pipeline safety assessment coefficient.

[0010] Furthermore, the step S100 specifically includes:

[0011] S110: acquiring sensor data of temperature, pressure, flow rate and vibration in the long-distance hydrogen pipeline, and performing data standardization processing to obtain a preliminary standardized data set;

[0012] S120: performing outlier detection and denoising processing on the preliminary standardized data set to obtain a cleaned data sequence;

[0013] S130: Integrate the cleaned data sequence according to time sequence and spatial position to form the preprocessed multivariate coupling data set.

[0014] Furthermore, the step S200 specifically includes:

[0015] S210: extracting temperature, pressure and flow rate features from the multivariate coupling data set, constructing a feature extraction matrix based on the features and performing standardization processing to obtain a preliminary feature matrix;

[0016] S220: Based on the preliminary feature matrix, a deep learning model is constructed and preliminary training is performed using a deep learning algorithm to extract correlation features between variables to obtain a preliminary training model and its feature parameters;

[0017] S230: Based on the preliminary training model and the multivariable coupling data set, continue to optimize the model characteristic parameters to obtain the final multivariable coupling model and its characteristic parameters.

[0018] Furthermore, the step S220 is specifically to construct a deep learning model based on the preliminary feature matrix, use an adaptive learning rate method for initial training, dynamically adjust the training rate to capture the coupling relationship between temperature, pressure, and flow rate variables, iteratively update model parameters during training, and obtain the preliminary training model and associated feature matrix.

[0019] Furthermore, the step S230 is specifically to form an optimized training data set based on the preliminary training model and the associated feature matrix in combination with the multivariable coupling data set, and to adjust and train the parameters of the preliminary training model based on the optimized training data set to obtain the final multivariable coupling model and its characteristic parameters.

[0020] Furthermore, the step S300 specifically includes:

[0021] S310: Inputting the multivariable coupling model characteristic parameters and the multivariable coupling data set into an adaptive compensation algorithm, dynamically adjusting the sensor data, and obtaining a preliminary compensation data sequence;

[0022] S320: Based on the preliminary compensation data sequence, further adjusting the adaptive compensation algorithm parameters, performing error compensation on the real-time sensor data, and obtaining the sensor optimization data sequence;

[0023] S330: Perform real-time prediction analysis on the sensor optimization data sequence to generate the sensor data state prediction sequence.

[0024] Furthermore, the step S310 is specifically as follows: inputting the multivariable coupling model characteristic parameters and the multivariable coupling data set into an adaptive compensation algorithm to perform preliminary real-time compensation processing on the collected sensor data, and based on the multivariable coupling model characteristic parameters, calculating the preliminary compensation value of each sensor variable to generate the preliminary compensation data sequence.

[0025] Furthermore, the step S320 is specifically as follows: according to the real-time sensor data corresponding to each variable in the preliminary compensation data sequence, the current error is detected and calculated, and the error value is used to adjust the adaptive compensation algorithm parameters so that the compensated data is closer to the actual measurement value, thereby obtaining the sensor optimization data sequence.

[0026] Furthermore, the step S330 is specifically to input the sensor optimization data sequence into the state prediction model, generate state prediction values ​​of temperature, pressure, and flow rate variables based on the characteristic parameters of the state prediction model, and form the sensor data state prediction sequence as input for safety monitoring, providing data support for subsequent early warning feedback.

[0027] Furthermore, the step S400 specifically includes:

[0028] S410: Based on the sensor data state prediction sequence and the multivariable coupling model, monitor and analyze the real-time state of the pipeline to obtain a current safety state sequence of the pipeline;

[0029] S420: extracting key parameters from the safety state sequence, and calculating and generating the pipeline safety assessment coefficient;

[0030] S430: Based on the pipeline safety assessment coefficient, a safety warning signal is generated and output to complete real-time monitoring and warning feedback of the pipeline status.

[0031] The beneficial effects of this solution can be known from the description of the above solution. Compared with the prior art, it has the following beneficial effects:

[0032] (1) Through real-time analysis and processing of multivariate data such as temperature, pressure, flow rate and vibration, and using complex mathematical calculation formulas and deep coupling models, accurate optimization of sensor data is achieved. Compared with traditional methods, it can significantly improve the data accuracy and monitoring reliability of sensors in complex environments, and solve the problem of data deviation caused by complex environmental changes.

[0033] (2) A multivariate coupling model of temperature, pressure, and flow rate is constructed through deep learning algorithms, which can capture the complex correlation between multiple variables, avoid the limitations of traditional single variable analysis, and make monitoring more accurate.

[0034] (3) The sensor data is adaptively compensated using a real-time dynamic adaptive compensation algorithm combined with model characteristic parameters, which effectively reduces the errors caused by environmental changes and ensures the stability and continuity of the calculation data.

[0035] (4) Based on the calculation of sensor-optimized data sequences and safety assessment coefficients, it is possible to issue early warning feedback in real time when the safety status deviates, provide active protection for pipeline management, and improve the safety and efficiency of overall operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0037] In order to clearly illustrate the technical features of this solution, this solution is described below through a specific implementation method.

[0038] like Figure 1 As shown, this embodiment is a hydrogen long-distance pipeline sensor optimization method based on an artificial intelligence algorithm, comprising the following steps:

[0039] S100: Obtain sensor data of temperature, pressure, flow rate and vibration in the long-distance hydrogen pipeline, perform data cleaning and denoising, and obtain a preprocessed multivariate coupling data set.

[0040] Temperature (T) and pressure (P) are directly related to the physical properties of hydrogen and affect the stability and safety of transmission; velocity (V) describes the flow of hydrogen in the pipeline and is an important indicator for evaluating the transmission efficiency; vibration (A) is closely related to the operating status of the pipeline structure and changes in the external environment, and can reflect mechanical vibration and external interference. These four variables cover many aspects of pipeline operation and are basic parameters that cannot be ignored in multivariable monitoring.

[0041] Step S100 at least includes steps S110-S130:

[0042] S110: Obtain sensor data of temperature, pressure, flow rate and vibration in the hydrogen long-distance pipeline, and perform data standardization processing to obtain a preliminary standardized data set, specifically:

[0043] The sensor data of temperature (T), pressure (P), flow rate (V) and vibration (A) of the hydrogen long-distance pipeline are obtained through the sensor network to form a multivariate input data set , where i is the number of the collection point, , , , The raw temperature, pressure, flow rate and vibration sensor data collected;

[0044] The multivariate input data set X is standardized to eliminate the dimensional differences caused by different data dimensions and obtain a preliminary standardized data set , where each variable is standardized according to the following formula:

[0045]

[0046]

[0047]

[0048]

[0049] in, , , , is the mean of each variable; , , , is the standard deviation of each variable, which represents the statistical distribution characteristics of these variables and is used to measure the degree of dispersion of data;

[0050] S120: Perform outlier detection and denoising on the preliminary standardized data set to screen out high-quality data that meets the system requirements and obtain a cleaned data sequence, specifically:

[0051] Perform outlier detection on the preliminary standardized data set X' to identify extreme data outside the pipeline monitoring range; the outlier detection method is based on the range definition of the mean and standard deviation, with 3 times the standard deviation of the data as the limit:

[0052]

[0053]

[0054]

[0055]

[0056] The data that meets the above conditions are marked as outliers and recorded as the outlier set ;

[0057] The non-abnormal data is further denoised. The denoising method uses a low-pass filter to remove high-frequency noise in the sensor data to retain the core trend information of the variable and obtain the cleaned data sequence X″:

[0058] ;

[0059] S130: Integrate the cleaned data sequence according to time sequence and spatial position to form a preprocessed multivariate coupling data set, specifically:

[0060] Sort the cleaned data sequence X″ according to the acquisition time t to form a time series , where j represents the time step and m represents the time step length; this time series is used to capture the changing trends of temperature, pressure, flow rate, and vibration variables over time;

[0061] The multivariate data of each collection point are sorted according to its spatial position Arrange to form a spatial position matrix , where k represents the acquisition location and p represents the total number of spatial nodes; the integration of spatial position matrix is ​​helpful for the subsequent multivariate coupling model to analyze the correlation of data at different locations;

[0062] The time series and spatial position matrix form a preprocessed multivariate coupled data set = , X contains the temporal and spatial dynamic relationships of temperature, pressure, flow rate, and vibration variables, providing accurate data input for the multivariable coupling model;

[0063] Preprocessed multivariate coupled data set Multivariate coupling analysis of data characteristics will be achieved by further extracting key feature parameters and building a deep learning model.

[0064] S200: Extract temperature, pressure and flow rate features from the multivariable coupling data set, build a matrix based on the features, build a deep learning model based on the matrix and perform training optimization to obtain the multivariable coupling model and its characteristic parameters.

[0065] The characteristic data of temperature, pressure, and flow rate are coupled and modeled through deep learning algorithms to form a model with key variable correlation characteristics, providing core characteristic data support for subsequent real-time compensation and fault prediction. The vibration (A) variable is used for preprocessing and preliminary cleaning of sensor data in the S100 stage, because in the actual collection process, vibration data may carry information related to external disturbances or mechanical characteristics of the pipeline, which helps to improve the integrity and consistency of the data. However, after entering the S200 stage, the main focus is on building a coupling relationship model and extracting key features of temperature (T), pressure (P), and flow rate (V). At this time, the vibration data (A) is no longer used for the following reasons:

[0066] (1) Low contribution to subsequent analysis: Vibration (A) mainly describes external disturbances, while temperature, pressure and flow rate directly affect the physical properties and transmission efficiency of hydrogen inside the pipeline, so the focus is on these three variables;

[0067] (2) Low feature correlation: During the construction of the multivariate coupling model, vibration (A) did not show significant correlation with other variables (T, P, V) and was therefore not selected into the multivariate coupling data set.

[0068] Therefore, vibration (A) is used in S100 for preliminary data processing and integrity assessment to ensure the breadth and reliability of the model input data. After S200, the model focuses on variables such as temperature, pressure and flow rate that are more critical to hydrogen transmission. Vibration (A) is no longer a core variable, but its early processing still provides important support for data optimization of the overall system.

[0069] Step S200 at least includes steps S210-S230:

[0070] S210: Extract temperature, pressure and flow rate features from the multivariate coupling data set, construct a feature extraction matrix based on the features and perform standardization to obtain a preliminary feature matrix, specifically:

[0071] Based on multivariable coupled data sets ={T″( ),P″( ),V″( )} Extract key features of temperature, pressure, and flow rate; use the spatiotemporal distribution characteristics of each variable to construct a feature extraction matrix It is expressed as:

[0072]

[0073] Among them, T″( )、P″( )、V″( ) represent the key characteristic values ​​of temperature, pressure and velocity in time and space respectively; m and p represent the time step and the total number of spatial nodes respectively;

[0074] The extracted feature extraction matrix Standardization is performed to reduce the impact of numerical differences on model training and obtain a preliminary feature matrix , as input data for the preliminary training model to retain the temperature, pressure and flow velocity characteristic information of time and space location;

[0075] S220: Based on the preliminary feature matrix, a deep learning model is constructed using a deep learning algorithm and preliminary training is performed to extract correlation features between variables and obtain a preliminary training model and its feature parameters, specifically:

[0076] Based on the preliminary feature matrix , build a deep learning model The initial training uses an adaptive learning rate method to dynamically adjust the training rate to capture the coupling relationship between temperature, pressure, and flow rate variables, and iteratively update the model parameters during training. , get the initial training model And the associated feature matrix , and its calculation formula is as follows:

[0077]

[0078] in, Represents a preliminary training model Characteristic parameters Deep learning function mapping relationship; Represents the extracted correlation feature matrix between variables;

[0079] Save the initial training model Characteristic parameters And the associated feature matrix , as important reference data for further optimizing the model, so as to conduct optimization training in the S230 module;

[0080] S230: Based on the preliminary training model and the multivariable coupling data set, continue to optimize the preliminary training model characteristic parameters to obtain the final multivariable coupling model and its characteristic parameters, specifically:

[0081] Based on the initial training model and its associated feature matrix , combined with multivariate coupled data sets , forming an optimized training data set :

[0082]

[0083] Based on optimized training data set , for the initial training model Perform parameter adjustment and training to obtain the final multivariable coupling model Its characteristic parameters , the training formula is as follows:

[0084]

[0085] in, Represents the final optimized feature mapping relationship;

[0086] The resulting multivariable coupled model Its characteristic parameters , providing trained model support for real-time adaptive compensation and fault prediction.

[0087] S300: Input the multivariable coupling model characteristic parameters and the multivariable coupling data set into the adaptive compensation algorithm, perform real-time compensation on the sensor data, obtain the sensor optimization data sequence and perform real-time prediction analysis to generate the sensor data state prediction sequence.

[0088] Step S300 at least includes steps S310-S330:

[0089] S310: Input the multivariable coupling model characteristic parameters and the multivariable coupling data set into the adaptive compensation algorithm, dynamically adjust the sensor data, and obtain a preliminary compensation data sequence, specifically:

[0090] Multivariable coupled model Characteristic parameters and multivariate coupled datasets Inputting an adaptive compensation algorithm to perform preliminary real-time compensation processing on the collected sensor data;

[0091] Based on characteristic parameters , calculate the initial compensation value of each sensor variable and obtain the initial compensated data , , The calculation is as follows:

[0092]

[0093]

[0094]

[0095] in, , , The raw temperature, pressure and flow rate data collected by the sensor; Represents the final optimized feature mapping relationship;

[0096] Generate preliminary compensation data sequence Used for subsequent further error adjustment;

[0097] S320: Based on the preliminary compensation data sequence, further adjust the adaptive compensation algorithm parameters to perform error compensation on the real-time sensor data to obtain the sensor optimization data sequence, specifically:

[0098] According to the preliminary compensation data sequence The real-time sensor data corresponding to each variable in the image is used to detect and calculate the current error. , , :

[0099]

[0100]

[0101]

[0102] Using the error value, adjust the adaptive compensation algorithm parameters to make the compensated data closer to the actual measured value, and obtain the sensor optimized data sequence , the formula is as follows:

[0103]

[0104]

[0105]

[0106] in, Dynamic learning rate for adaptive compensation algorithm to control compensation intensity;

[0107] Optimized sensor data sequence It will serve as input data for the next step of real-time predictive analysis;

[0108] S330: Perform real-time prediction analysis on the sensor optimization data sequence to generate a state prediction sequence of the sensor data, specifically:

[0109] Optimize the sensor data sequence Input to the state prediction model M to generate the state prediction value of each variable in real time. The state prediction model M is a neural network model, specifically a long short-term memory network (LSTM) model, designed for the prediction of time series data. Its goal is to: use the optimized sensor data sequence (optimized data of temperature, pressure and flow rate) as input; predict the real-time state of the hydrogen long-distance pipeline based on the associated features and mapping relationships within the model; output the predicted value of each variable to form a state prediction sequence.

[0110] Based on the characteristic parameters of the state prediction model M , generate state prediction values ​​of temperature, pressure, and flow rate variables , , , the calculation formula is as follows:

[0111]

[0112]

[0113]

[0114] in, is a mapping relationship based on state prediction model parameters;

[0115] State prediction sequence for real-time prediction It will serve as input for safety monitoring and provide data support for subsequent early warning feedback.

[0116] S400: Based on the sensor data state prediction sequence and multivariable coupling model, the safety status of the pipeline is monitored and analyzed, the pipeline safety assessment coefficient is generated, and real-time safety monitoring and early warning feedback are completed based on the pipeline safety assessment coefficient.

[0117] Step S400 at least includes steps S410-S430:

[0118] S410: Based on the state prediction sequence and the multivariable coupling model, the real-time state of the pipeline is monitored and analyzed to obtain the current safety state sequence of the pipeline, specifically:

[0119] The state prediction sequence Input to multivariable coupled model In order to evaluate the operating status of sensor data in the current environment;

[0120] Based on multivariable coupling model Characteristic parameters , conduct real-time monitoring and analysis of input status data to obtain the current safety status indicators , , :

[0121]

[0122]

[0123]

[0124] in, It is the feature mapping relationship after final optimization; , , Real-time safety status indicators showing temperature, pressure and flow rate;

[0125] Collate the real-time safety status indicators of all sensors into a sequence of the current safety status of the pipeline , as basic data for subsequent safety assessment;

[0126] S420: Extract key parameters from the safety state sequence and calculate the pipeline safety assessment coefficient, which is:

[0127] From safe state sequence Extract key temperature, pressure, and flow rate parameters from the data to generate a set of characteristic indicators for computational safety assessment :

[0128]

[0129] in, , , Indicates the maximum values ​​of temperature, pressure and flow rate in the current state; , , Indicates the average value of temperature, pressure and flow rate;

[0130] According to the extracted feature index set Calculate the safety assessment factor of the pipeline , the evaluation formula is as follows:

[0131]

[0132] in, It represents the safety assessment coefficient, and its value reflects the safety status of the current pipeline;

[0133] The calculated safety assessment factor Used to determine whether the operating status of the pipeline is within an acceptable range;

[0134] S430: Based on the safety assessment coefficient, generate and output a safety warning signal to complete real-time monitoring and early warning feedback of the pipeline status, specifically:

[0135] The calculated safety assessment factor With the set safety threshold Make comparisons to determine the current safety status of the pipeline;

[0136] when Exceeding safety threshold When the corresponding safety warning signal is generated :

[0137]

[0138] in, =1 means triggering an early warning. =0 means the status is normal;

[0139] Safety warning signal The information is output to the control center to complete the real-time status monitoring and early warning feedback of the pipeline for further operation or response.

[0140] From the above description, it can be seen that the present invention includes four structural modules: data acquisition and preprocessing module, feature extraction and coupling model building module, adaptive compensation and optimization module, safety monitoring and early warning feedback module, to implement the method steps.

[0141] Technical features not described in the present invention can be achieved through or by adopting existing technologies and will not be described in detail herein. Of course, the above description is not a limitation of the present invention, and the present invention is not limited to the above examples. Changes, modifications, additions or substitutions made by ordinary technicians in this technical field within the essential scope of the present invention should also fall within the scope of protection of the present invention.

Claims

1. A hydrogen long-distance pipeline sensor optimization method based on artificial intelligence algorithm, characterized in that: The following steps are involved: S100: Acquire sensor data of temperature, pressure, flow rate and vibration in the long-distance hydrogen pipeline, perform data cleaning and denoising, and obtain a pre-processed multivariate coupling data set; S200: extracting temperature, pressure and flow rate features from the multivariable coupling data set, constructing a matrix based on the features, constructing a deep learning model based on the matrix and performing training optimization to obtain a multivariable coupling model and its characteristic parameters; S300: Inputting the multivariable coupling model characteristic parameters and the multivariable coupling data set into an adaptive compensation algorithm, performing real-time compensation on sensor data, obtaining a sensor optimization data sequence, performing real-time prediction analysis, and generating a sensor data state prediction sequence; The step S300 specifically includes: S310: Input the multivariable coupling model characteristic parameters and the multivariable coupling data set into an adaptive compensation algorithm, dynamically adjust the sensor data, and obtain a preliminary compensation data sequence; specifically: Multivariable coupled model Characteristic parameters and multivariate coupled datasets ={T″( ),P″( ),V″( )} Input an adaptive compensation algorithm to perform preliminary real-time compensation processing on the collected sensor data; Based on characteristic parameters , calculate the initial compensation value of each sensor variable and obtain the initial compensated data , , The calculation is as follows: in, , , The raw temperature, pressure and flow rate data collected by the sensor; Represents the feature mapping relationship after final optimization; T″( )、P″( )、V″( ) respectively represent time and spatial location Key characteristic values ​​of temperature, pressure and flow rate; Generate preliminary compensation data sequence Used for subsequent further error adjustment; S320: Based on the preliminary compensation data sequence, further adjust the adaptive compensation algorithm parameters to perform error compensation on the real-time sensor data to obtain the sensor optimization data sequence; specifically: According to the preliminary compensation data sequence The real-time sensor data corresponding to each variable in the image is used to detect and calculate the current error. , , : Using the error value, adjust the adaptive compensation algorithm parameters to make the compensated data closer to the actual measured value, and obtain the sensor optimized data sequence , the formula is as follows: in, Dynamic learning rate for adaptive compensation algorithm to control compensation intensity; S330: Perform real-time prediction analysis on the sensor optimization data sequence to generate the sensor data state prediction sequence; specifically: Optimize the sensor data sequence Input to the state prediction model M to generate the state prediction value of each variable in real time; the state prediction model M is a neural network model, specifically a long short-term memory network LSTM model, designed for the prediction of time series data, and its goal is to use the optimized sensor data sequence, i.e. the optimized data of temperature, pressure and flow rate as input; based on the associated features and mapping relationships within the model, predict the real-time state of the hydrogen long-distance pipeline; output the predicted value of each variable to form a state prediction sequence; Based on the characteristic parameters of the state prediction model M , generate state prediction values ​​of temperature, pressure, and flow rate variables , , , the calculation formula is as follows: in, is a mapping relationship based on state prediction model parameters; S400: Based on the sensor data state prediction sequence and the multivariable coupling model, the safety state of the pipeline is monitored and analyzed, a pipeline safety assessment coefficient is generated, and real-time safety monitoring and early warning feedback are completed according to the pipeline safety assessment coefficient.

2. According to claim 1, a hydrogen long-distance pipeline sensor optimization method based on artificial intelligence algorithm is characterized in that: The step S100 specifically includes: S110: Obtain sensor data of temperature, pressure, flow rate and vibration in the long-distance hydrogen pipeline, and perform data standardization processing to obtain a preliminary standardized data set; specifically: The sensor data of temperature (T), pressure (P), flow rate (V) and vibration (A) of the hydrogen long-distance pipeline are obtained through the sensor network to form a multivariate input data set , where i is the number of the collection point, , , , The raw temperature, pressure, flow rate and vibration sensor data collected; The multivariate input data set X is standardized to eliminate the dimensional differences caused by different data dimensions and obtain a preliminary standardized data set , where each variable is standardized according to the following formula: in, , , , is the mean of each variable; , , , is the standard deviation of each variable, which represents the statistical distribution characteristics of these variables and is used to measure the degree of dispersion of data; S120: Perform outlier detection and denoising on the preliminary standardized data set to screen out high-quality data that meets system requirements and obtain a cleaned data sequence; specifically: Perform outlier detection on the preliminary standardized data set X' to identify extreme data outside the pipeline monitoring range; the outlier detection method is based on the range definition of the mean and standard deviation, with 3 times the standard deviation of the data as the limit: The data that meets the above conditions are marked as outliers and recorded as the outlier set ; The non-abnormal data is further denoised. The denoising method uses a low-pass filter to remove high-frequency noise in the sensor data to retain the core trend information of the variable and obtain the cleaned data sequence X″: ； S130: Integrate the cleaned data sequence according to time sequence and spatial position to form the pre-processed multivariate coupling data set; specifically: Sort the cleaned data sequence X″ according to the acquisition time t to form a time series , where j represents the time step and m represents the time step length; this time series is used to capture the changing trends of temperature, pressure, flow rate, and vibration variables over time; The multivariate data of each collection point are sorted according to its spatial position Arrange to form a spatial position matrix , where k represents the acquisition location and p represents the total number of spatial nodes; the integration of spatial position matrix is ​​helpful for the subsequent multivariate coupling model to analyze the correlation of data at different locations; The time series and spatial position matrix form a preprocessed multivariate coupled data set = , X contains the temporal and spatial dynamic relationships of temperature, pressure, flow rate, and vibration variables, providing accurate data input for the multivariable coupling model; Preprocessed multivariate coupled data set Multivariate coupling analysis of data characteristics will be achieved by further extracting key feature parameters and building a deep learning model.

3. The method for optimizing hydrogen long-distance pipeline sensors based on artificial intelligence algorithm according to claim 1 is characterized in that: The step S200 specifically includes: S210: extracting temperature, pressure and flow rate features from the multivariate coupling data set, constructing a feature extraction matrix based on the features and performing standardization processing to obtain a preliminary feature matrix; specifically: Based on multivariable coupled data sets ={T″( ),P″( ),V″( )} Extract key features of temperature, pressure, and flow rate; use the spatiotemporal distribution characteristics of each variable to construct a feature extraction matrix It is expressed as: Among them, T″( )、P″( )、V″( ) represent the key characteristic values ​​of temperature, pressure and velocity in time and space respectively; m and p represent the time step and the total number of spatial nodes respectively; The extracted feature extraction matrix Standardization is performed to reduce the impact of numerical differences on model training and obtain a preliminary feature matrix , as input data for the preliminary training model to retain the temperature, pressure and flow velocity characteristic information of time and space location; S220: Based on the preliminary feature matrix, a deep learning model is constructed and preliminary training is performed using a deep learning algorithm to extract correlation features between variables to obtain a preliminary training model and its feature parameters; specifically: Based on the preliminary feature matrix , build a deep learning model The initial training uses an adaptive learning rate method to dynamically adjust the training rate to capture the coupling relationship between temperature, pressure, and flow rate variables, and iteratively update the model parameters during training. , get the initial training model And the associated feature matrix , and its calculation formula is as follows: in, Represents a preliminary training model Characteristic parameters Deep learning function mapping relationship; Represents the extracted correlation feature matrix between variables; Save the initial training model Characteristic parameters And the associated feature matrix , as important reference data for further optimizing the model; S230: Based on the preliminary training model and the multivariable coupling data set, continue to optimize the model characteristic parameters to obtain the final multivariable coupling model and its characteristic parameters; specifically: Based on the initial training model and its associated feature matrix , combined with multivariate coupled data sets , forming an optimized training data set : Based on optimized training data set , for the initial training model Perform parameter adjustment and training to obtain the final multivariable coupling model Its characteristic parameters , the training formula is as follows: in, Represents the final optimized feature mapping relationship; The resulting multivariable coupled model Its characteristic parameters , providing trained model support for real-time adaptive compensation and fault prediction.

4. The method for optimizing hydrogen long-distance pipeline sensors based on artificial intelligence algorithm according to claim 1 is characterized in that: The step S400 specifically includes: S410: Based on the sensor data state prediction sequence and the multivariable coupling model, the real-time state of the pipeline is monitored and analyzed to obtain the current safety state sequence of the pipeline; specifically: The state prediction sequence Input to multivariable coupled model In order to evaluate the operating status of sensor data in the current environment; Based on multivariable coupling model Characteristic parameters , conduct real-time monitoring and analysis of input status data to obtain the current safety status indicators , , : in, It is the feature mapping relationship after final optimization; , , Real-time safety status indicators showing temperature, pressure and flow rate; Collate the real-time safety status indicators of all sensors into a sequence of the current safety status of the pipeline ; S420: extracting key parameters from the safety status sequence and calculating and generating the pipeline safety assessment coefficient; specifically: From safe state sequence Extract key temperature, pressure, and flow rate parameters from the data to generate a set of characteristic indicators for computational safety assessment : in, , , Indicates the maximum values ​​of temperature, pressure and flow rate in the current state; , , Indicates the average value of temperature, pressure and flow rate; According to the extracted feature index set Calculate the safety assessment factor of the pipeline , the evaluation formula is as follows: in, It represents the safety assessment coefficient, and its value reflects the safety status of the current pipeline; S430: Based on the pipeline safety assessment coefficient, generate and output a safety warning signal to complete real-time monitoring and warning feedback of the pipeline status; specifically: The calculated safety assessment factor With the set safety threshold Make comparisons to determine the current safety status of the pipeline; when Exceeding safety threshold When the corresponding safety warning signal is generated : in, =1 means triggering an early warning. =0 means the status is normal; Safety warning signal The data is output to the control center to complete the real-time status monitoring and early warning feedback of the pipeline.

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