Gas transmission station equipment sub-transmission evaluation method, device and storage medium

Abstract

The application relates to a gas transmission station equipment sub-transmission evaluation method and device and a storage medium, relates to the gas transmission station monitoring technical field, and aims to improve the accuracy of the equipment sub-transmission evaluation result. The method comprises the following steps: in the case that the gas transmission station equipment is a fault equipment, obtaining equipment state data corresponding to the gas transmission station, the gas transmission station comprising a plurality of gas transmission station equipments, and the equipment state data comprising state data corresponding to each gas transmission station equipment; inputting the equipment state data into an equipment evaluation model, performing fuzzy clustering processing on the equipment state data through the equipment evaluation model, and obtaining the health degree of a target equipment group; the target equipment group comprising the fault equipment and associated equipment of the fault equipment; the associated equipment being a gas transmission station equipment in the gas transmission station which has data interaction and / or control linkage with the fault equipment; and determining a sub-transmission evaluation result of the fault equipment according to the state data corresponding to the fault equipment and the health degree of the target equipment group.

Description

Technical Field

[0001] This application relates to the field of gas transmission station monitoring technology, and in particular to a method, equipment and storage medium for evaluating the distribution of gas transmission station equipment. Background Technology

[0002] As the core hub of the natural gas pipeline transportation system, gas transmission stations are like the "heart" and "nerve center" of the entire pipeline network, playing an irreplaceable and crucial role in the energy transportation chain. Because gas transmission stations involve high-pressure, flammable, and explosive natural gas, and involve a wide variety of equipment and complex processes, ensuring their safe and stable operation is of paramount importance. A safety accident could trigger catastrophic consequences such as fires and explosions, causing significant casualties and economic losses. Therefore, risk monitoring of gas transmission stations is essential.

[0003] In related technologies, risk monitoring of equipment at gas transmission stations is conducted based on Analytic Network Process (ANP) models to obtain risk assessment results (health status) for the gas transmission stations, and then the distribution assessment results of the equipment are determined based on the risk assessment results. However, the equipment status data and health status of gas transmission stations are not linearly related, and the ANP model has difficulty handling nonlinear relationships, resulting in inaccurate distribution assessment results obtained using this method. Summary of the Invention

[0004] The purpose of this application is to provide a method, equipment, and storage medium for evaluating the distribution of gas transmission station equipment, in order to solve the problem of inaccurate distribution evaluation results.

[0005] Firstly, a method for assessing the distribution of gas transmission station equipment is provided, comprising: when the gas transmission station equipment is faulty, acquiring equipment status data corresponding to the gas transmission station, wherein the gas transmission station includes multiple gas transmission station equipment, and the equipment status data includes status data corresponding to each gas transmission station equipment; inputting the equipment status data into an equipment assessment model, and performing fuzzy clustering processing on the equipment status data through the equipment assessment model to obtain the health status of a target equipment group; the target equipment group includes the faulty equipment and its associated equipment; the associated equipment is gas transmission station equipment in the gas transmission station that has data interaction and / or control linkage with the faulty equipment; and determining the distribution assessment result of the faulty equipment based on the status data corresponding to the faulty equipment and the health status of the target equipment group, wherein the distribution assessment result is used to indicate the impact of the faulty equipment on the distribution process.

[0006] The beneficial effects of this application are as follows: By acquiring equipment status data from gas transmission stations, and then inputting this data into an equipment evaluation model for fuzzy clustering processing, the health status of the target equipment group is obtained. Finally, based on the health status and equipment status data of the target equipment group, the distribution evaluation results corresponding to the faulty equipment are output. The equipment evaluation model incorporates a fuzzy clustering algorithm, which can fuse equipment status data from multiple gas transmission stations, avoiding the limitations of single equipment status data and improving the accuracy of health assessment. Furthermore, the fuzzy clustering algorithm does not rely on the linear structure between data points and can handle data with various complex relationships, thereby improving the accuracy of the distribution evaluation results for faulty equipment.

[0007] In some embodiments, the equipment evaluation model includes: an input layer, an evidence processing layer, and a combination layer; the input layer is used to perform dimensionality reduction processing on the input equipment status data to obtain dimensionality-reduced equipment status data; the evidence processing layer is used to extract features from the dimensionality-reduced equipment status data to obtain feature extraction results, and assign confidence scores to each gas transmission station equipment based on the feature extraction results to obtain the confidence score of each gas transmission station equipment; the combination layer is used to perform correlation analysis on the confidence scores of faulty equipment and related equipment based on a fuzzy clustering algorithm to determine the health of the target equipment group.

[0008] In some embodiments, the evidence processing layer is specifically used to perform multi-level fuzzy comprehensive evaluation processing on the feature extraction results to obtain the membership degree corresponding to the equipment status data; and to perform basic probability allocation on each gas transmission station equipment based on the membership degree to obtain the confidence degree of each gas transmission station equipment.

[0009] In some embodiments, the fuzzy clustering algorithm uses Gaussian noise as a perturbation variable.

[0010] In some embodiments, determining the distribution assessment result of the faulty device based on the status data corresponding to the faulty device and the health of the target device group includes: performing a correlation analysis on the status data corresponding to the faulty device and the health of the target device group to determine the distribution assessment result of the faulty device.

[0011] In some embodiments, inputting device status data into a device evaluation model includes: performing moving average and wavelet transform processing on the device status data to obtain a first processing result; performing signal decomposition on the first processing result to obtain a second processing result; performing iterative update processing on the second processing result to obtain processed device status data; and inputting the processed device status data into the device evaluation model.

[0012] In some embodiments, the equipment evaluation model is trained by: acquiring a training dataset, which includes: sample equipment status data of gas transmission station equipment and target health values ​​corresponding to the sample equipment status data of gas transmission station equipment; inputting the sample equipment status data into an initial equipment evaluation model, performing fuzzy clustering processing on the sample equipment status data through the initial equipment evaluation model to obtain the predicted health values ​​of the target equipment group; and training the initial equipment evaluation model based on the predicted health values ​​and target health values ​​to obtain a trained equipment evaluation model.

[0013] Secondly, a gas transmission station equipment distribution assessment device is also provided, comprising: an acquisition unit, used to acquire equipment status data corresponding to the gas transmission station when the gas transmission station equipment is faulty, wherein the gas transmission station includes multiple gas transmission station equipment, and the equipment status data includes status data corresponding to each gas transmission station equipment; a processing unit, used to input the equipment status data into an equipment assessment model, and perform fuzzy clustering processing on the equipment status data through the equipment assessment model to obtain the health status of a target equipment group; the target equipment group includes the faulty equipment and the associated equipment of the faulty equipment; the associated equipment is the gas transmission station equipment in the gas transmission station that has data interaction and / or control linkage with the faulty equipment; and determining the distribution assessment result of the faulty equipment based on the status data corresponding to the faulty equipment and the health status of the target equipment group, wherein the distribution assessment result is used to indicate the impact of the faulty equipment on the distribution process.

[0014] In some embodiments, the equipment evaluation model includes: an input layer, an evidence processing layer, and a combination layer; the input layer is used to perform dimensionality reduction processing on the input equipment status data to obtain dimensionality-reduced equipment status data; the evidence processing layer is used to extract features from the dimensionality-reduced equipment status data to obtain feature extraction results, and assign confidence scores to each gas transmission station equipment based on the feature extraction results to obtain the confidence score of each gas transmission station equipment; the combination layer is used to perform correlation analysis on the confidence scores of faulty equipment and related equipment based on a fuzzy clustering algorithm to determine the health of the target equipment group.

[0015] In some embodiments, the evidence processing layer is specifically used to perform multi-level fuzzy comprehensive evaluation processing on the feature extraction results to obtain the membership degree corresponding to the equipment status data; and to perform basic probability allocation on each gas transmission station equipment based on the membership degree to obtain the confidence degree of each gas transmission station equipment.

[0016] In some embodiments, the fuzzy clustering algorithm uses Gaussian noise as a perturbation variable.

[0017] In some embodiments, the processing unit is specifically used to perform correlation analysis on the status data corresponding to the faulty device and the health of the target device group to determine the distribution evaluation result of the faulty device.

[0018] In some embodiments, the processing unit is specifically configured to perform moving average and wavelet transform processing on the device status data to obtain a first processing result; perform signal decomposition on the first processing result to obtain a second processing result; perform iterative update processing on the second processing result to obtain processed device status data; and input the processed device status data into the device evaluation model.

[0019] In some embodiments, the processing unit is further configured to acquire a training dataset, which includes: sample equipment status data of gas transmission station equipment and target health values ​​corresponding to the sample equipment status data of gas transmission station equipment; input the sample equipment status data into an initial equipment evaluation model; perform fuzzy clustering processing on the sample equipment status data through the initial equipment evaluation model to obtain the predicted health values ​​of the target equipment group; and train the initial equipment evaluation model based on the predicted health values ​​and target health values ​​to obtain a trained equipment evaluation model.

[0020] Thirdly, an electronic device is also provided, comprising: a processor and a memory configured to store processor-executable instructions; wherein the processor is configured to execute the instructions to implement any of the alternative methods of the first aspect described above.

[0021] Fourthly, this application provides a computer-readable storage medium storing instructions that, when executed by a device, enable the device to perform any of the optional methods described in the first aspect.

[0022] Fifthly, this application provides a computer program product including computer instructions that, when executed on a processor of a device, enable the device to perform any of the optional methods described in the first aspect above.

[0023] It should be noted that the technical effects of any of the implementation methods in aspects two through five can be found in the technical effects of the corresponding implementation methods in aspect one, and will not be repeated here.

[0024] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1A flowchart illustrating a gas transmission station equipment distribution evaluation method provided in this application embodiment;

[0027] Figure 2 A flowchart illustrating another gas transmission station equipment distribution evaluation method provided in this application embodiment;

[0028] Figure 3 A schematic diagram of the structure of a gas transmission station equipment distribution evaluation device provided in this application embodiment;

[0029] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0030] In the embodiments of this application, the terms "first," "second," "third," "fourth," "fifth," and "sixth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," "third," "fourth," "fifth," and "sixth" may explicitly or implicitly include one or more of that feature.

[0031] In embodiments of this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0032] "A and / or B" includes the following three combinations: A only, B only, and a combination of A and B.

[0033] As the core hub of the natural gas pipeline transportation system, gas transmission stations are like the "heart" and "nerve center" of the entire pipeline network, playing an irreplaceable and crucial role in the energy transportation chain. Because gas transmission stations involve high-pressure, flammable, and explosive natural gas, and involve a wide variety of equipment and complex processes, ensuring their safe and stable operation is of paramount importance. A safety accident could trigger catastrophic consequences such as fires and explosions, causing significant casualties and economic losses. Therefore, risk monitoring of gas transmission stations is essential.

[0034] In related technologies, risk monitoring of equipment at gas transmission stations is conducted based on the ANP model to obtain risk assessment results (health status) for the gas transmission stations, and then the distribution assessment results of the equipment are determined based on the risk assessment results. However, the equipment status data and health status of gas transmission stations are not linearly related, and the ANP model has difficulty handling nonlinear relationships, resulting in inaccurate distribution assessment results obtained using this method.

[0035] Based on this, this application proposes a method, device, and storage medium for assessing the distribution of gas transmission station equipment. The method includes: when a gas transmission station equipment is faulty, acquiring the corresponding equipment status data within the gas transmission station, where the gas transmission station includes multiple pieces of equipment, and the equipment status data includes the status data corresponding to each piece of equipment; inputting the equipment status data into an equipment assessment model, and performing fuzzy clustering processing on the equipment status data through the equipment assessment model to obtain the health status of a target equipment group; the target equipment group includes the faulty equipment and its associated equipment; the associated equipment is gas transmission station equipment that has data interaction and / or control linkage with the faulty equipment; and determining the distribution assessment result of the faulty equipment based on the status data corresponding to the faulty equipment and the health status of the target equipment group. The distribution assessment result is used to indicate the impact of the faulty equipment on the distribution process. By acquiring the equipment status data of the gas transmission station, inputting the equipment status data into the equipment assessment model for fuzzy clustering processing to obtain the health status of the target equipment group, and finally outputting the distribution assessment result corresponding to the faulty equipment based on the health status and equipment status data of the target equipment group. The equipment evaluation model incorporates a fuzzy clustering algorithm, which can fuse equipment status data from multiple gas transmission stations, avoiding the limitations of single equipment status data. At the same time, the fuzzy clustering algorithm does not rely on the linear structure between data and can handle data with various complex relationships, improving the accuracy of health assessment and thus improving the accuracy of the distribution assessment results of faulty equipment.

[0036] In some embodiments, the execution subject of the gas transmission station equipment distribution assessment method of this application may be a server cluster composed of multiple servers, a single server, a computer, or any device or equipment with existing distribution assessment functions, such as a processor or processing chip in a server or computer. This application does not limit this.

[0037] The following description, in conjunction with the accompanying drawings, introduces the gas transmission station equipment distribution assessment method, equipment, and storage medium of this application.

[0038] like Figure 1 As shown, the gas transmission station equipment distribution evaluation method of this application embodiment includes the following steps:

[0039] S101. When the equipment at the gas transmission station is faulty, obtain the equipment status data corresponding to the gas transmission station.

[0040] Among them, the gas transmission station includes multiple gas transmission station equipment, and the equipment status data includes the status data corresponding to each gas transmission station equipment.

[0041] In some embodiments, the gas transmission station equipment includes at least one of the following: air compressor, air cooler, pump equipment, fan, motor, station solenoid valve, and differential pressure transmitter.

[0042] In some embodiments, the equipment status data of the faulty equipment includes: the fault type corresponding to the gas transmission station equipment, the alarm type of the equipment, and the alarm level of the equipment.

[0043] The types of faults corresponding to the equipment in the gas transmission station include: deviation faults, drift faults, open circuit faults, periodic interference faults, blower imbalance, loose fastening bolts, bearing wear, loose or detached belts, motor faults, flames appearing between key equipment cabinets, heat and smoke alarms, gas leak alarms (point type, ultrasonic), and alarms linked to laser PTZ and video monitoring, etc.

[0044] The alarm types for equipment status data include: UPS alarm, inverter alarm, compressor unit alarm, integrated protection system alarm, host computer alarm, perimeter security alarm, diagnostic alarm, etc.

[0045] Equipment status alarm levels are categorized into three levels: Level 1, Level 2, and Level 3. Level 1 alarms are the most severe, significantly impacting production. For example, a process system malfunction can severely affect mainline gas supply or downstream user distribution. Level 2 alarms are also serious, such as alarms indicating low or high pressure at distribution stations. Low pressure at distribution stations may adversely affect gas distribution or equipment for downstream users. Level 3 alarms indicate abnormal changes during operation that may affect pipeline safety; these alarms require immediate attention and are considered general event alarms.

[0046] It should be noted that equipment status data also includes: the frequency of failure and the duration of failure. The frequency of failure refers to the number of times a failure occurs within a certain time frame. The duration of failure indicates the length of time from the start to the end of the failure.

[0047] Analyzing the frequency of occurrence allows us to understand the patterns of various situations that arise during equipment operation, thereby predicting the likelihood of future equipment failures. Duration is crucial for assessing equipment health. Shorter failure durations may have less impact on equipment; for example, brief voltage fluctuations that quickly return to normal may not cause substantial damage. However, longer durations, such as a gas pipeline blockage lasting for several hours, can severely impact pipeline efficiency and potentially damage the pipeline and related equipment. Therefore, incorporating occurrence frequency and duration into equipment status data provides richer and more accurate information for predicting equipment health.

[0048] S102. Input the equipment status data into the equipment evaluation model, and perform fuzzy clustering on the equipment status data through the equipment evaluation model to obtain the health status of the target equipment group.

[0049] The target equipment group includes faulty equipment and associated equipment of the faulty equipment; associated equipment refers to gas transmission station equipment that has data interaction and / or control linkage with the faulty equipment.

[0050] In some embodiments, before inputting the device status data into the device evaluation model, the device status data can be processed for outliers and missing values, and then processed by moving average and wavelet transform to obtain a first processing result; the first processing result can be decomposed to obtain a second processing result; the second processing result can be iteratively updated to obtain processed device status data; and the processed device status data can be input into the device evaluation model.

[0051] In one possible implementation, the wavelet transform function can be expressed as:

[0052]

[0053] Where, x t This is the input equipment status data for the gas transmission station facilities. Let θ(x) be the average value of the device status data, θ(x) be the moving average function, and B be the wavelet transform coefficient. In this embodiment, the wavelet transform coefficient can be 0.1 or 0.2.

[0054]

[0055] Wherein, θ(x) is the moving average function, and α is the scaling parameter of the moving average function. In this embodiment, the scaling parameter of the moving average function can be 0.02-0.05.

[0056] Then, based on singular spectral decomposition, the first processing result after moving average and wavelet transform can be decomposed into a second processing result, which includes trend components, periodic components, noise components, etc. The alternating direction multiplier method is used to iteratively update the second processing result after signal decomposition, completing the data fusion preprocessing.

[0057]

[0058] Among them, u n+1 t Let u(x) represent the frequency domain modes after iterative updates, and u(x) be the signal decomposition result. n+1 The result is the signal decomposition result after the next iteration, β is the Lagrange multiplier, which is 1 or 2, and τ is the noise tolerance. In this embodiment, the noise tolerance can be 0.005.

[0059] It should be understood that the alternating direction multiplier method is used to iteratively update the signal decomposition results. This method continuously optimizes the feature representation in each iteration, making the features more consistent with the true distribution of the data. During the iteration process, the model can gradually adjust the emphasis on different features, finding better feature combinations. This fine-tuning of features helps the model maintain good performance on different datasets because it is not based on fixed, potentially inaccurate feature representations, but rather on iteratively approaching the optimal, thereby improving generalization performance.

[0060] The iterative update process is also a process of the model adapting to changes in data. Equipment status data can undergo various changes, such as variations in data distribution due to equipment aging or environmental factors. The iterative update using the alternating direction multiplier method allows the model to dynamically adjust based on new data conditions, maintaining sensitivity to data changes. This adaptability enables the model to effectively predict and analyze new data, rather than being limited to specific features of the training data, thereby improving generalization performance.

[0061] One possible approach is to input the preprocessed equipment status data into the equipment evaluation model and use a fuzzy clustering algorithm to perform cluster analysis on the data. This yields the membership degree of each data point in the equipment status data to its respective cluster. After clustering, the equipment status data is divided into different clusters, each representing a health state of the equipment. Based on the membership degree and health score criteria, the health score of the target equipment group is calculated.

[0062] For example, one cluster might represent equipment in good working order, while another cluster might represent equipment at potential failure risk. Based on the clustering results, the level of health status represented by each cluster is determined.

[0063] S103. Determine the distribution assessment result of the faulty equipment based on the status data corresponding to the faulty equipment and the health status of the target equipment group.

[0064] Among them, the distribution assessment results are used to indicate the impact of faulty equipment on the distribution process.

[0065] As one possible approach, correlation analysis is performed on the status data corresponding to the faulty equipment and the health status of the target equipment group to determine the distribution assessment result of the faulty equipment.

[0066] In one possible implementation, after the equipment evaluation model calculates the health of the target equipment group based on real-time equipment status data, key features are extracted from the status data and health of the target equipment group. These key features are then detected using the Isolation Forest algorithm to identify possible relationships between faulty equipment and related equipment. Based on these relationships, the fault is assessed for distribution, and the distribution assessment result of the faulty equipment is obtained.

[0067] It should be understood that when processing large-scale datasets, the Isolation Forest algorithm only needs to operate on a randomly selected subset of samples, without traversing the entire dataset, greatly reducing the computational load. Therefore, when calculating large amounts of device status data, the Isolation Forest algorithm can quickly provide detection results, saving time and computing resources.

[0068] In some embodiments, the health status and distribution assessment results of the equipment can be displayed on a diagnostic screen. By displaying the health status and distribution assessment results of the equipment on the diagnostic screen, the station control personnel can improve their ability to control the healthy operation of the equipment, thereby ensuring the stable operation of the equipment.

[0069] Therefore, by acquiring equipment status data from gas transmission stations and then inputting this data into an equipment evaluation model for fuzzy clustering, the health status of the target equipment group is obtained. Finally, based on the health status and equipment status data of the target equipment group, the corresponding distribution evaluation result for the faulty equipment is output. The equipment evaluation model incorporates a fuzzy clustering algorithm, which can fuse equipment status data from multiple gas transmission stations, avoiding the limitations of single equipment status data, improving the accuracy of health assessment, and thus improving the accuracy of the distribution evaluation result for the faulty equipment.

[0070] In some embodiments, the device evaluation model includes an input layer, an evidence processing layer, and a combination layer. The input layer is used to perform dimensionality reduction processing on the input device state data to obtain dimensionality-reduced device state data.

[0071] As one possible approach, dimensionality reduction of equipment status data can be achieved using methods such as principal component analysis or autoencoders.

[0072] The evidence processing layer is used to extract features from the dimensionality-reduced equipment status data, obtain feature extraction results, and assign confidence scores to each gas transmission station equipment based on the feature extraction results, thereby obtaining the confidence score of each gas transmission station equipment.

[0073] As one possible implementation, the feature extraction result can be obtained according to the following formula:

[0074]

[0075] Among them, u pos For the feature extraction results, T u Let be the feature set consisting of feature j, Grad(u) be the feature mapping function, and t be the feature classification coefficient. In this embodiment, the feature classification coefficient can be between 0.5 and 0.8. argsort(u) j The feature is the index position of the real-time alarm data.

[0076]

[0077] Among them, u j It is the input vector representation of device status data.

[0078] In some embodiments, the evidence processing layer is specifically used to perform multi-level fuzzy comprehensive evaluation processing on the feature extraction results to obtain the membership degree corresponding to the equipment status data, and to perform basic probability allocation on each gas transmission station equipment based on the membership degree to obtain the confidence degree of each gas transmission station equipment.

[0079] For example, if a device is evaluated as having a "low risk of failure" with a membership degree of 0.8, this membership degree can be transformed into the basic probability assignment of the corresponding evidence source according to a preset rule, so that the evidence theory (Dempster–Shafer, DS) algorithm can process such evidence based on fuzzy evaluation, thereby determining the confidence level of the gas transmission station equipment.

[0080] As one possible implementation, the evidence processing layer combines feature extraction results to assign confidence levels to equipment status data. When calculating the confidence level of the data, an evidence model can first be established based on the feature extraction results and prior knowledge. The confidence level of the gas transmission station equipment can then be determined based on this evidence model. The evidence model can be a rule-based expert system, a Bayesian network, or DS evidence theory, among others.

[0081] For example, in the DS evidence theory, different features are used as sources of evidence. A basic reliability assignment function is used to determine the degree to which each piece of evidence supports different propositions. Then, based on the importance and reliability of the features, corresponding weights are assigned to each piece of evidence. The determination of weights can be based on expert experience, historical data statistical analysis, or machine learning algorithms, etc.

[0082] For certain features that are more critical to assessing health, higher weights can be assigned. Finally, a suitable fusion algorithm is used to combine the confidence levels of multiple pieces of evidence to obtain a comprehensive confidence assessment result. Common fusion algorithms include Dempster's combination rule, weighted average method, and fuzzy logic operations. During the fusion process, it is necessary to consider the correlation and conflict between pieces of evidence to avoid unreasonable confidence assessments.

[0083] In some embodiments, the combination layer is used to perform correlation analysis on the confidence levels of faulty devices and associated devices based on a fuzzy clustering algorithm to determine the health of the target device group.

[0084] In some embodiments, the fuzzy clustering algorithm uses Gaussian noise as a perturbation variable.

[0085] As one possible implementation, the equipment evaluation model calculates the health of associated equipment based on equipment status data, determines the health of faulty equipment based on the health of associated equipment and the correlation between equipment in the target equipment group, and thus obtains the health of the target equipment group.

[0086] It should be understood that replacing the Dempster synthesis formula of the combination layer with a fuzzy clustering algorithm, and adding Gaussian noise as a perturbation variable to the fuzzy clustering algorithm, allows for the fusion of evidence from multiple sources, avoiding the limitations of single evidence or methods. By flexibly allocating weights based on a comprehensive consideration of multiple relevant factors, the equipment assessment model can better adapt to the characteristics and needs of different types of equipment, highlighting the impact of key indicators on equipment health. Furthermore, the addition of Gaussian noise gives the fuzzy clustering algorithm a certain tolerance for minor data changes and noise, enhancing the model's robustness. When equipment data is subject to a certain degree of noise interference, the model can still accurately assess equipment health, avoiding instability in assessment results due to data fluctuations.

[0087] In some embodiments, the device evaluation model needs to be trained before using it to obtain the device's health status, such as... Figure 2 As shown, the equipment evaluation model is trained in the following way:

[0088] S201. Obtain the training dataset.

[0089] The training dataset includes: sample equipment status data of gas transmission station equipment and target health status corresponding to the sample equipment status data of gas transmission station equipment.

[0090] In some embodiments, the DS algorithm model is used as the device evaluation model, which includes an input layer, an evidence processing layer, a combination layer, and an output layer. The sample device state data is divided into a training dataset and a test dataset, and the ratio of the training dataset to the test dataset can be 3:1 or 7:3.

[0091] As one possible approach, sample equipment status data for gas transmission station equipment can be acquired through sensors. Numerous sensors, such as temperature, pressure, flow, and vibration sensors, can be installed on various types of equipment at the gas transmission station. These sensors should be distributed in key locations to comprehensively and accurately reflect the operating status of the equipment. For example, vibration sensors can be installed on compressors to monitor their operational stability, and temperature sensors can be installed to monitor the heating of key components.

[0092] S202. Input the sample equipment status data into the initial equipment evaluation model, and perform fuzzy clustering on the sample equipment status data through the initial equipment evaluation model to obtain the predicted health of the target equipment group.

[0093] It should be understood that the introduction of a multi-level fuzzy comprehensive evaluation mechanism into the evidence processing layer combines the multi-level fuzzy comprehensive evaluation mechanism with the DS algorithm model to improve the evidence processing layer. Decision-makers can obtain a more comprehensive perspective, thereby improving the quality and effectiveness of decision-making. Furthermore, the alarm system that combines multi-level fuzzy comprehensive evaluation and DS evidence theory can automatically adjust the evaluation criteria and weights based on real-time data to adapt to different working environments and conditions.

[0094] As one possible implementation, the Dempster synthesis formula of the combined layer is replaced by the fuzzy clustering algorithm, and Gaussian noise is added as a perturbation variable to the fuzzy clustering algorithm to obtain an improved fuzzy clustering algorithm. The predicted health of the target equipment group is determined based on the improved fuzzy clustering algorithm.

[0095] In one possible implementation, the composite layer can determine the predicted health level according to the following formula:

[0096]

[0097] Where P(x) is the output of the combined layer, N is the number of training samples, and y i Let x be the fault label (fault type) corresponding to the i-th sample group. i The output signal of the evidence processing layer, N(0,ω) 2 ) indicates that it obeys 0, ω 2 The random numbers are distributed in Gaussian noise, and ε is the disturbance intensity.

[0098] S203. Train the initial equipment evaluation model based on the predicted health and target health to obtain a trained equipment evaluation model.

[0099] It should be understood that the cross-entropy loss function and the number of training iterations can be initialized before evaluating the model on the training device. The cross-entropy loss function measures the difference between the probability distribution predicted by the model and the probability distribution of the true labels. The closer the model's prediction is to the true labels, the smaller the cross-entropy loss value; conversely, the larger the loss value, the closer the model's prediction is to the true labels.

[0100] Before starting to train the model, it is necessary to determine the cross-entropy loss function to be used and set its related initial parameters to prepare an "evaluation criterion" for the model training. During the training process, the model will adjust its own parameters according to this criterion to reduce the gap between the predicted results and the actual results, thereby completing the model training.

[0101] The number of training iterations refers to the number of times the model learns using the training data. In each iteration, the model makes predictions based on the input training data (sample equipment status data of gas transmission station equipment) and calculates the loss value between the predicted result (predicted health) and the true label (target health) (calculated using the cross-entropy loss function).

[0102] The number of training iterations can range from 500 to 2000. This range can be preset based on experience and actual conditions. Too few iterations may prevent the model from fully learning the patterns in the data, resulting in poor model performance and an inability to accurately assess the health of the device. Too many iterations may lead to overfitting (i.e., the model performs well on the training data but poorly on new data), and will also increase training time and computational resource consumption.

[0103] One possible implementation is that in each iteration, the model is trained by making predictions, calculating the loss, and adjusting the model parameters. As the number of iterations increases, the model's predictive ability gradually improves, and the loss value gradually decreases. When the loss value no longer decreases significantly or decreases very little during the training process, reaching a relatively stable state, the model is considered to have converged, and this model is the trained device evaluation model.

[0104] It should be noted that after the model training is completed, the model can be validated by obtaining a test dataset to obtain the accuracy of the prediction results. The accuracy of the device evaluation model can be verified by combining this with a preset model accuracy threshold, which can be set to 0.95-0.98. If the accuracy meets the model accuracy threshold, the trained device evaluation model is output. If the accuracy does not meet the threshold, the Adam (Adaptive Moment Estimation) optimizer is used to iteratively update the hyperparameters of the initial model, and then the initial model is trained again using the training set until convergence.

[0105] Therefore, trained equipment status data is obtained by training the equipment status data. The equipment evaluation model introduces a fuzzy clustering algorithm, which can fuse equipment status data from multiple gas transmission stations, avoiding the limitations of single equipment status data, improving the accuracy of health assessment, and thus improving the accuracy of the distribution assessment results for faulty equipment.

[0106] The foregoing mainly describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the above functions, the noise reduction device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0107] This application embodiment can, based on the above method, exemplarily divide the gas transmission station equipment's distribution and evaluation device into functional modules. For example, the gas transmission station equipment's distribution and evaluation device may include various functional modules corresponding to each functional division, or two or more functions may be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the module division in this application embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0108] Figure 3 This is a schematic diagram of the structure of a gas transmission station equipment distribution evaluation device provided in an embodiment of this application. (Refer to...) Figure 3 The gas transmission station equipment distribution assessment device 300 includes: an acquisition unit 310 and a processing unit 320.

[0109] The acquisition unit 310 is used to acquire the equipment status data corresponding to the gas transmission station when the equipment at the gas transmission station is faulty. The gas transmission station includes multiple pieces of equipment at the gas transmission station, and the equipment status data includes the status data corresponding to each piece of equipment at the gas transmission station.

[0110] The processing unit 320 is used to input equipment status data into the equipment evaluation model, and to perform fuzzy clustering processing on the equipment status data through the equipment evaluation model to obtain the health status of the target equipment group. The target equipment group includes faulty equipment and associated equipment of the faulty equipment. Associated equipment refers to gas transmission station equipment that has data interaction and / or control linkage with the faulty equipment. Based on the status data corresponding to the faulty equipment and the health status of the target equipment group, the distribution evaluation result of the faulty equipment is determined. The distribution evaluation result is used to indicate the impact of the faulty equipment on the distribution process.

[0111] In some embodiments, the equipment evaluation model includes: an input layer, an evidence processing layer, and a combination layer; the input layer is used to perform dimensionality reduction processing on the input equipment status data to obtain dimensionality-reduced equipment status data; the evidence processing layer is used to extract features from the dimensionality-reduced equipment status data to obtain feature extraction results, and assign confidence scores to each gas transmission station equipment based on the feature extraction results to obtain the confidence score of each gas transmission station equipment; the combination layer is used to perform correlation analysis on the confidence scores of faulty equipment and related equipment based on a fuzzy clustering algorithm to determine the health of the target equipment group.

[0112] In some embodiments, the evidence processing layer is specifically used to perform multi-level fuzzy comprehensive evaluation processing on the feature extraction results to obtain the membership degree corresponding to the equipment status data; and to perform basic probability allocation on each gas transmission station equipment based on the membership degree to obtain the confidence degree of each gas transmission station equipment.

[0113] In some embodiments, the fuzzy clustering algorithm uses Gaussian noise as a perturbation variable.

[0114] In some embodiments, the processing unit 320 is specifically used to perform correlation analysis on the status data corresponding to the faulty device and the health of the target device group to determine the distribution evaluation result of the faulty device.

[0115] In some embodiments, the processing unit 320 is specifically used to perform moving average and wavelet transform processing on the device status data to obtain a first processing result; to perform signal decomposition on the first processing result to obtain a second processing result; to perform iterative update processing on the second processing result to obtain processed device status data; and to input the processed device status data into the device evaluation model.

[0116] In some embodiments, the processing unit 320 is further configured to acquire a training dataset, which includes: sample equipment status data of gas transmission station equipment and target health values ​​corresponding to the sample equipment status data of gas transmission station equipment; input the sample equipment status data into an initial equipment evaluation model; perform fuzzy clustering processing on the sample equipment status data through the initial equipment evaluation model to obtain the predicted health values ​​of the target equipment group; and train the initial equipment evaluation model based on the predicted health values ​​and target health values ​​to obtain a trained equipment evaluation model.

[0117] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 400 includes, but is not limited to, a processor 401 and a memory 402.

[0118] The memory 402 described above is used to store the executable instructions of the processor 401. It is understood that the processor 401 is configured to execute instructions to implement the gas transmission station equipment distribution evaluation method in the above embodiments.

[0119] It should be noted that those skilled in the art will understand that Figure 4 The device structure shown does not constitute a limitation on the device; the device may include, but is not limited to, other types of devices. Figure 4 This may indicate more or fewer components, or a combination of certain components, or a different arrangement of components.

[0120] Processor 401 is the control center of the device, connecting various parts of the device through various interfaces and lines. It performs various functions and processes data by running or executing software programs and / or modules stored in memory 402, and by calling data stored in memory 402, thereby providing overall monitoring of the device. Processor 401 may include one or more processing units. Optionally, processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, while the modem processor mainly handles wireless communication. It is understood that the modem processor may not be integrated into processor 401.

[0121] The memory 402 can be used to store software programs and various data. The memory 402 may primarily include a program storage area and a data storage area. The program storage area may store the operating system, application programs required by at least one functional module (such as a determination unit, processing unit, etc.), etc. Furthermore, the memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0122] In an exemplary embodiment, a computer-readable storage medium including instructions is also provided, such as a memory 402 including instructions, which can be executed by a processor 401 of the device 400 to implement the methods in the above embodiments.

[0123] In actual implementation, Figure 3 Both the acquisition unit 310 and the processing unit 320 can be derived from... Figure 4 The processor 401 calls the computer program stored in the memory 402 to implement the process. The specific execution process can be found in the description of the method section in the previous embodiment, and will not be repeated here.

[0124] Optionally, the computer-readable storage medium may be a non-transitory computer-readable storage medium, such as a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0125] In an exemplary embodiment, this application also provides a computer program product including one or more instructions, which can be executed by the processor 401 of the device to perform the methods described above.

[0126] It should be noted that when one or more instructions in the computer-readable storage medium or computer program product are executed by the processor of the device, they implement the various processes of the above method embodiments and achieve the same technical effect as the above method. To avoid repetition, they will not be described again here.

[0127] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0128] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0129] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the classified units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0130] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0131] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, essentially, or the part that contributes to the prior art, or a complete or partial classification of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0132] In the description of the embodiments of this application, specific features, structures, materials or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0133] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for evaluating the distribution of gas transmission station equipment, characterized in that, The method includes: In the event that the equipment at the gas transmission station is faulty, the equipment status data corresponding to the gas transmission station is obtained. The gas transmission station includes multiple pieces of equipment at the gas transmission station, and the equipment status data includes the status data corresponding to each piece of equipment at the gas transmission station. The equipment status data is input into the equipment evaluation model, and the equipment status data is subjected to fuzzy clustering processing by the equipment evaluation model to obtain the health of the target equipment group; the target equipment group includes the faulty equipment and the associated equipment of the faulty equipment; the associated equipment is the gas transmission station equipment that has data interaction and / or control linkage with the faulty equipment. Based on the status data corresponding to the faulty device and the health status of the target device group, the distribution assessment result of the faulty device is determined, and the distribution assessment result is used to indicate the impact of the faulty device on the distribution process.

2. The method according to claim 1, characterized in that, The equipment evaluation model includes: an input layer, an evidence processing layer, and a combination layer; The input layer is used to perform dimensionality reduction processing on the input device status data to obtain dimensionality-reduced device status data; The evidence processing layer is used to extract features from the dimensionality-reduced equipment status data, obtain feature extraction results, and assign confidence scores to each gas transmission station equipment based on the feature extraction results, thereby obtaining the confidence score of each gas transmission station equipment. The combined layer is used to perform correlation analysis on the confidence levels of the faulty devices and the associated devices based on a fuzzy clustering algorithm to determine the health of the target device group.

3. The method according to claim 2, characterized in that, The evidence processing layer is specifically used to perform multi-level fuzzy comprehensive evaluation processing on the feature extraction results to obtain the membership degree corresponding to the equipment status data; and to perform basic probability allocation on each gas transmission station equipment based on the membership degree to obtain the confidence degree of each gas transmission station equipment.

4. The method according to claim 2, characterized in that, The fuzzy clustering algorithm uses Gaussian noise as a perturbation variable.

5. The method according to claim 1, characterized in that, The step of determining the distribution assessment result of the faulty device based on the status data corresponding to the faulty device and the health status of the target device group includes: A correlation analysis is performed between the status data corresponding to the faulty device and the health status of the target device group to determine the distribution assessment result of the faulty device.

6. The method according to claim 1, characterized in that, The step of inputting the device status data into the device evaluation model includes: The device status data is processed by moving average and wavelet transform to obtain the first processing result; The first processing result is decomposed to obtain the second processing result; The second processing result is iteratively updated to obtain the processed device status data; The processed equipment status data is then input into the equipment evaluation model.

7. The method according to claim 1, characterized in that, The equipment evaluation model was trained in the following way: Obtain a training dataset, which includes: sample equipment status data of gas transmission station equipment and target health corresponding to the sample equipment status data of the gas transmission station equipment; The sample equipment status data is input into the initial equipment evaluation model, and the sample equipment status data is subjected to fuzzy clustering processing by the initial equipment evaluation model to obtain the predicted health of the target equipment group. The initial equipment evaluation model is trained based on the predicted health and the target health to obtain a trained equipment evaluation model.

8. An electronic device, characterized in that, include: A processor and a memory for storing processor-executable instructions; The processor is configured to execute the instructions to implement the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on a computer, cause the computer to perform the method as described in any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed on an electronic device, cause the electronic device to perform the method as described in any one of claims 1 to 7.

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