A method and system for intelligently monitoring gas supply process safety

Abstract

The present invention relates to the technical field of design of safe intelligent monitoring schemes for gas supply processes, and specifically to a method and system for safe intelligent monitoring of gas supply processes. By using a plurality of monitoring points arranged at preset intervals along a gas pipe, each of the monitoring points includes a vibration sensor, a GPS sensor, and a temperature sensor, collecting the position data of each monitoring point, the vibration frequency data of the gas pipe surface, and the temperature data, and training a gas pipe leakage automatic recognition model, and then judging whether there is a leakage in the gas pipe. If there is, the on-site staff conducts an on-site inspection of the gas pipe and uploads the inspection results. According to the inspection results, the gas pipe leakage automatic recognition model can be automatically retrained, and then the gas pipe leakage automatic recognition model can be automatically optimized, which greatly improves the safety, reliability, and intelligence of the safe intelligent monitoring of the gas supply process.

Description

Technical Field

[0001] The present invention relates to the technical field of design of safe intelligent monitoring schemes for a gas supply process, and in particular to a safe intelligent monitoring method and system for a gas supply process. Background Art

[0002] Gas supply systems tend to be more complex. However, supervisors and maintenance personnel usually detect the operating status of gas supply systems based on experience, and the judgment of problems such as pipeline leakage, under / overpressure, blockage, valve opening / closing errors, and equipment failures depends entirely on manpower. The traditional fault diagnosis model relies heavily on the technical level and experience accumulation of supervisors / maintenance personnel. On the one hand, the technical threshold is high, and on the other hand, it may work for simple gas supply systems, but for the current increasingly complex gas supply systems, the traditional model is not only inefficient, but also inaccurate.

[0003] In the prior art, multiple sensors, such as the setting of a single or multiple thresholds of an odor sensor, are often used to monitor whether a gas pipe is leaking. However, since the thresholds are all obtained through manual experience, there are large errors. That is, the setting of a single or multiple thresholds cannot accurately determine whether a gas pipe is leaking, and is easily misjudged by the system. It is difficult to comprehensively evaluate and judge the overall status data of the gas pipe over a period of time, that is, it is difficult to accurately determine whether the overall status data of the gas pipe over a period of time is in a leaking state.

[0004] Therefore, the prior art needs to be further developed. Summary of the invention

[0005] The purpose of the present invention is to overcome the above technical deficiencies and provide a method and system for intelligently monitoring the safety of a gas supply process to solve the problems existing in the prior art.

[0006] To achieve the above technical objectives, according to a first aspect of the present invention, the present invention provides a method for intelligently monitoring the safety of a gas supply process, the method comprising: S100, after the gas pipe is installed and before it is put into use, a plurality of monitoring points are arranged along the gas pipe at preset intervals, each of the monitoring points comprising a vibration sensor, a GPS sensor and a temperature sensor, the vibration sensor being used to obtain vibration frequency data of the surface of the gas pipe, the GPS sensor being used to obtain position data of the monitoring point, and the temperature sensor being used to obtain temperature data of the surface of the gas pipe; S200, recording the vibration frequency data, position data, and temperature data collected at the same monitoring point at a certain time point as the gas pipe operation state coupling data set of the monitoring point, recording the gas pipe operation state coupling data sets of multiple monitoring points collected at the same time point within a first preset time as the first gas pipe operation state coupling data set, acquiring multiple first gas pipe operation state coupling data sets collected at multiple time points according to a preset time interval, dividing the multiple first gas pipe operation state coupling data sets into training data sets and verification data sets according to a first preset ratio, and using the training data sets to train the gas pipe leakage automatic identification model; using the verification data set to optimize the trained gas pipe leakage automatic identification model, optimize the model parameters, and complete the gas pipe leakage automatic identification model training; S300. After the gas pipeline is put into use, within a first preset time, a set of coupled data of the gas pipeline operation status of multiple monitoring points collected at the same time point is recorded as a second gas pipeline operation status coupled data set, and multiple second gas pipeline operation status coupled data sets collected at multiple time points are obtained at preset time intervals, and the multiple second gas pipeline operation status coupled data sets are input into the gas pipeline leakage automatic identification model, and the identification result is output, and then it is judged whether there is a leakage in the gas pipeline, and it is determined whether to output an alarm signal about the leakage of the gas pipeline according to the judgment result. If there is, the on-site staff conducts an on-site inspection of the gas pipeline to confirm whether there is actually a leakage in the gas pipeline, and uses the cloud server to record and transmit the on-site inspection result data, obtain the on-site inspection result data, and determine whether to combine the multiple second gas pipeline operation status coupled data sets as a new training data set according to the on-site inspection result data, retrain the gas pipeline leakage automatic identification model, and then automatically optimize the gas pipeline leakage automatic identification model.

[0007] Specifically, the method includes: After the gas pipe is installed and before it is put into use, the installed gas pipe needs to be inspected to ensure that there is no leakage problem in the installed gas pipe.

[0008] Specifically, the multiple monitoring points are arranged along the gas pipeline at preset intervals, including: The number of monitoring points is calculated according to the ratio of the actual length of the gas pipe to the preset length. Specifically, the gas pipe leakage automatic identification model is trained using the training data set; the trained gas pipe leakage automatic identification model is optimized using the verification data set, the model parameters are optimized, and the gas pipe leakage automatic identification model training is completed, including: The training data set is input into the preset network layer in batches for training, and the preset network layer includes a Transformer network layer, and the Transformer network layer is used to forward propagate the first gas pipe operation state coupling data set collected at the current time point to predict the first gas pipe operation state coupling data set at the next time point, and then obtain the predicted loss value; the loss value of the preset network layer is calculated and input into the optimizer for optimization to determine the direction in which the parameter gradient of the gas pipe leakage automatic identification model decreases fastest; the gas pipe leakage automatic identification model performs back propagation according to the loss value and the parameter gradient of the model to optimize the parameters of the gas pipe leakage automatic identification model.

[0009] Specifically, the method of using the training data set to train the gas pipe leakage automatic identification model; using the verification data set to optimize the trained gas pipe leakage automatic identification model, optimizing the model parameters, and completing the gas pipe leakage automatic identification model training also includes: After each training, the verification data set is input into the preset network layer of the previous training in batches for model parameter verification, and cyclic training is performed, with the total number of training rounds set to the first preset round; the loss value of the preset network layer is recorded, and it is determined whether the loss value meets the first preset condition, and whether to end the training and output the model parameters based on the judgment result.

[0010] Specifically, the determining whether the loss value satisfies the first preset condition, and determining whether to end the training and output the model parameters according to the determination result, includes: If the loss value meets the first preset condition, the training is terminated and the parameters of the current gas pipe leakage automatic identification model are output; if the loss value does not meet the first preset condition, the training is continued; The first preset condition is that after the second preset round of training, the loss values ​​obtained in the next round of training are greater than or equal to the loss values ​​that occurred during the second preset round of training.

[0011] Specifically, the identification result is whether there is a leakage or not, the plurality of second gas pipe operation state coupling data sets are input into the gas pipe leakage automatic identification model, the identification result is output, and then it is determined whether there is a leakage in the gas pipe, and whether to output an alarm signal about the leakage in the gas pipe according to the determination result, including: If the output result is that there is a leakage, it is determined that an alarm signal about the leakage of the gas pipe is output; If the output result is that there is no leakage, a prompt signal indicating that there is no leakage in the gas pipe is output.

[0012] Specifically, the field inspection result data includes actual leakage or actual non-existence of leakage, and judging whether to combine the plurality of second gas pipe operation status coupling data sets as a newly added training data set, retrain the gas pipe leakage automatic identification model, and then automatically optimize the gas pipe leakage automatic identification model according to the field inspection result data, includes: If the field inspection result data shows that there is actually no leakage, the multiple second gas pipe operating status coupling data sets and the training result of no leakage are used as new training data sets to retrain the gas pipe leakage automatic identification model, thereby automatically optimizing the gas pipe leakage automatic identification model.

[0013] Specifically, the field inspection result data includes actual leakage or actual non-existence of leakage, and judging whether to combine the plurality of second gas pipe operation status coupling data sets as a newly added training data set, retrain the gas pipe leakage automatic identification model, and then automatically optimize the gas pipe leakage automatic identification model according to the field inspection result data, includes: If the actual inspection result data shows that there is an actual leakage, the multiple second gas pipe operating status coupled data sets and the training result that there is no leakage will not be used as new training data sets, the gas pipe leakage automatic identification model will not be retrained, and the gas pipe leakage automatic identification model will not be automatically optimized.

[0014] According to a second aspect of the present invention, there is provided a gas supply process safety intelligent monitoring system, comprising: An acquisition module, comprising a plurality of vibration sensors arranged at preset intervals along the pipeline of the gas pipe, for acquiring vibration frequency data of the gas pipe; The control module is used to record the vibration frequency data of multiple vibration sensors collected at the same time point within a first preset time as a first gas pipe operation state coupling data set, obtain multiple first gas pipe operation state coupling data sets collected at multiple time points according to a preset time interval, divide the multiple first gas pipe operation state coupling data sets into a training data set and a verification data set according to a first preset ratio, and use the training data set to train the gas pipe leakage automatic identification model; use the verification data set to optimize the trained gas pipe leakage automatic identification model, optimize the model parameters, and complete the gas pipe leakage automatic identification model training; and after the gas pipe is put into use, within the first preset time, record the gas pipe operation state coupling data set of multiple monitoring points collected at the same time point as a second gas pipe operation state coupling data set. A state coupling data set is used to obtain multiple second gas pipe operation state coupling data sets collected at multiple time points according to a preset time interval, and the multiple second gas pipe operation state coupling data sets are input into the gas pipe leakage automatic identification model, and the identification result is output, so as to judge whether there is a leakage in the gas pipe, and determine whether to output an alarm signal about the leakage of the gas pipe according to the judgment result. If there is a leakage, the on-site staff shall conduct an on-site inspection of the gas pipe to confirm whether there is actually a leakage in the gas pipe, and use the cloud server to record and transmit the on-site inspection result data, obtain the on-site inspection result data, and determine whether to combine the multiple second gas pipe operation state coupling data sets as a new training data set according to the on-site inspection result data, retrain the gas pipe leakage automatic identification model, and then automatically optimize the gas pipe leakage automatic identification model.

[0015] Beneficial effects: The present invention arranges a plurality of monitoring points at preset intervals along the gas pipe, each of which includes a vibration sensor, a GPS sensor and a temperature sensor, collects position data of each monitoring point, vibration frequency data of the gas pipe surface, and temperature data, and trains an automatic recognition model for gas pipe leakage, thereby determining whether the gas pipe has leakage. If so, on-site staff conducts an on-site inspection of the gas pipe and uploads the inspection results. The automatic recognition model for gas pipe leakage can be automatically retrained based on the inspection results, thereby automatically optimizing the automatic recognition model for gas pipe leakage, thereby achieving an overall evaluation and judgment of the overall operating status data of the gas pipe over a period of time, and more accurately judging whether the overall operating status data of the gas pipe over a period of time is in a leakage state, thereby greatly improving the safety, reliability and intelligence of safe intelligent monitoring of the gas supply process, and solving the problem that the setting of a single or multiple thresholds cannot accurately determine whether the gas pipe has leakage, thereby greatly improving the intelligence and usability of the present invention, and greatly expanding the application scenarios of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a flow chart of a method for intelligently monitoring the safety of a gas supply process provided in a specific embodiment of the present invention; Figure 2 Schematic diagram of the system composition of the gas supply process safety intelligent monitoring system provided in a specific embodiment of the present invention; Figure 3 It is a structural schematic diagram of a gas pipe leakage automatic identification model provided in a specific embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is clearly and completely described below in conjunction with the accompanying drawings of the present invention. Based on the embodiments in this application, other similar embodiments obtained by ordinary technicians in this field without making creative work should all fall within the scope of protection of this application. In addition, the directional words mentioned in the following embodiments, such as "up", "down", "left", "right", etc., are only reference to the directions of the accompanying drawings. Therefore, the directional words used are used to illustrate rather than limit the invention.

[0018] The present invention will be further described below in conjunction with the accompanying drawings and preferred embodiments.

[0019] See also Figure 1 The present invention provides a method for intelligently monitoring the safety of a gas supply process, comprising: S100. After the gas pipe is installed and before it is put into use, a plurality of monitoring points are arranged along the gas pipe at preset intervals, each of the monitoring points comprising a vibration sensor, a GPS sensor and a temperature sensor, the vibration sensor being used to obtain vibration frequency data on the surface of the gas pipe, the GPS sensor being used to obtain position data of the monitoring point, and the temperature sensor being used to obtain temperature data on the surface of the gas pipe.

[0020] It should be noted that the steps before step S100 include: A first preset time, a preset time interval, a first preset ratio, a first preset round, a second preset round, a preset interval, and a preset length are preset in the control module.

[0021] It can be understood that the first preset time, preset time interval, first preset ratio, first preset round, second preset round, preset interval and preset length can be specifically set according to the actual needs of the user of the present invention. The present invention does not limit the specific values ​​of the above parameters, and there is no regularity in setting the specific values ​​of the above parameters, as long as it is applicable to the safe intelligent monitoring method for the gas supply process proposed in the present invention.

[0022] Preferably, the present invention sets the first preset time to 30 seconds, the present invention sets the preset time interval to 2 seconds, the present invention sets the first preset ratio to 8:2, the present invention sets the first preset round to 40,000 times, the present invention sets the second preset round to 400, and the present invention preferably sets the preset length to 10 meters. The above settings can further improve the performance of the model and the efficiency of model training, and greatly improve the accuracy and reliability of the prediction of the gas pipe leakage automatic identification model of the present invention.

[0023] It can be understood that when the gas pipe is in a normal state, there are certain regularities between the vibration frequency data and temperature data of each point of the gas pipe. For example, there is a certain dynamic balance relationship between the vibration frequency data and the temperature data of each point. When there is a leak at a certain monitoring point of the gas pipe, there will be abnormal vibration at the leak. The abnormal vibration will also cause abnormal temperature changes, which will break this balance relationship, that is, the vibration data and temperature data of the monitoring point will be abnormal. Based on this, the present invention designs and trains an automatic recognition model for gas pipe leaks.

[0024] Specifically, the method includes: After the gas pipe is installed and before it is put into use, the installed gas pipe needs to be inspected to ensure that there is no leakage problem in the installed gas pipe.

[0025] It is understandable that how to ensure that the installed gas pipe does not have leakage problems can be checked by relevant staff according to industry standards, and the present invention will not go into details here.

[0026] Specifically, the multiple monitoring points are arranged along the gas pipeline at preset intervals, including: The number of monitoring points is calculated based on the ratio of the actual length of the gas pipe to the preset length.

[0027] S200, recording the vibration frequency data, position data, and temperature data collected at the same monitoring point at a certain time point as a gas pipe operation state coupling data set of the monitoring point, recording the gas pipe operation state coupling data sets of multiple monitoring points collected at the same time point within a first preset time as a first gas pipe operation state coupling data set, acquiring multiple first gas pipe operation state coupling data sets collected at multiple time points at a preset time interval, dividing the multiple first gas pipe operation state coupling data sets into a training data set and a verification data set according to a first preset ratio, and training a gas pipe leakage automatic identification model using the training data set; and optimizing the trained gas pipe leakage automatic identification model using the verification data set, optimizing the model parameters, and completing the gas pipe leakage automatic identification model training.

[0028] Specifically, the method of using the training data set to train the gas pipe leakage automatic identification model; using the verification data set to optimize the trained gas pipe leakage automatic identification model, optimizing the model parameters, and completing the gas pipe leakage automatic identification model training includes: The training data set is input into the preset network layer in batches for training, and the preset network layer includes a Transformer network layer, and the Transformer network layer is used to forward propagate the first gas pipe operation state coupling data set collected at the current time point to predict the first gas pipe operation state coupling data set at the next time point, and then obtain the predicted loss value; the loss value of the preset network layer is calculated and input into the optimizer for optimization to determine the direction in which the parameter gradient of the gas pipe leakage automatic identification model decreases fastest; the gas pipe leakage automatic identification model performs back propagation according to the loss value and the parameter gradient of the model to optimize the parameters of the gas pipe leakage automatic identification model.

[0029] It can be understood that Transformer is a deep learning model based on the self-attention mechanism, which consists of a multi-head attention mechanism and a feedforward neural network. It can process sequence data and capture long-distance dependencies. The Transformer model does not contain traditional convolutional layers, but processes each element in the input sequence through an attention layer. The typical structure of Transformer includes an encoder and a decoder, wherein the encoder is composed of multiple identical layers stacked together, and the decoder adds a self-attention layer on the basis of the encoder to process sequence generation tasks. The present invention uses Transformer to supplement global context information, enhances the model's capabilities in feature extraction and global understanding, greatly improves the performance of the model, and greatly reduces the computational cost.

[0030] Specifically, the preset network layer also includes a ResNet network layer, and the ResNet network layer is used to classify and predict the output result according to the collected first gas pipe operation status coupling data set, and the classification result includes whether there is a leakage in the gas pipe or whether there is no leakage in the gas pipe.

[0031] It should be noted here that ResNet is a deep convolutional neural network, which solves the gradient vanishing problem in deep network training by introducing residual connections. The core component of ResNet is the residual block, which usually contains two or three convolutional layers and a jump connection, which allows the gradient to bypass these convolutional layers directly. The network structure of ResNet can be very deep. These networks extract the features of the first gas pipe operating status coupled data set by stacking multiple residual blocks, and classify and predict the output results through global average pooling and fully connected layers. The classification results include whether there is a leak in the gas pipe or whether there is no leak in the gas pipe.

[0032] See also Figure 3 The present invention combines the ResNet network layer and the Transformer network layer. ResNet is good at extracting local features of vibration frequency data in the training data set, while Transformer can supplement global context information. This combination can enhance the model's capabilities in feature extraction and global understanding, greatly improve the performance of the model, and greatly reduce the computational cost.

[0033] It can be understood that the ResNet network layer is used to classify and predict the output results through the first gas pipe operation state coupling data set collected in the training data set and the verification data set. The specific steps are: (1) First, the training data set is put into the Backbones network layer of the model. Backbone refers to a series of convolutional layers that constitute the skeleton of the neural network. The main function of these layers is to extract the features of the input data. The Backbone network is usually composed of multiple convolutional layers, pooling layers, and activation functions, which can extract meaningful feature representations from the original data. Backbones is composed of multiple Backbones, and the core network layer in Backbone is the Resnet network layer. After the data set is input into the model, the first gas pipeline operation status coupling data set will be input into Backbones for training; (2) When constructing Backbone, position encoding and Resnet network layers were added. After the training data set was input, Resnet began to train layer by layer. The training extracted features from the first gas pipeline operation status coupling data set to assist Transformer in predicting whether there is a gas pipeline leak. The extracted features obtained the location information of the multiple monitoring points with the help of position encoding, which facilitated the relevant management personnel to locate abnormal monitoring points.

[0034] Specifically, the method of using the training data set to train the gas pipe leakage automatic identification model; using the verification data set to optimize the trained gas pipe leakage automatic identification model, optimizing the model parameters, and completing the gas pipe leakage automatic identification model training also includes: After each training, the verification data set is input into the preset network layer of the previous training in batches for model parameter verification, and cyclic training is performed, with the total number of training rounds set to the first preset round; the loss value of the preset network layer is recorded, and it is determined whether the loss value meets the first preset condition, and whether to end the training and output the model parameters based on the judgment result.

[0035] Specifically, after each training, the verification data set is input into the preset network layer of the previous training in batches to verify the model parameters, and the cyclic training is performed, including the following methods: When the number of training times is not enough and the model needs to be optimized, the training can only be performed from the beginning. Therefore, the present invention develops a method for continuing the following training based on the previously trained model parameter file; (1) Provide custom parameters, which can be set according to user needs to continue training based on any training parameters; (2) Load the parameters of the custom model file; (3) Put the verification data set into the custom parameter network layer for verification, obtain the loss value and record the model parameters of the model corresponding to the minimum loss value during the training process; (4) Based on this set of model parameters, forward propagation training and back propagation are performed, and the optimizer optimizes the parameters to obtain a new set of data; (5) Put the verification data into the new data network layer for inference to obtain the loss value; (6) Perform cyclic training to obtain the best model and complete model optimization.

[0036] It is understandable that the training method adopted by the present invention is batch training, which means that each time the model parameters are updated, only a part of the samples in the validation data set is used, which is called a batch. The advantages of batch training are that it can reduce memory consumption, speed up training, increase randomness, and facilitate the generalization of the model.

[0037] It should be noted that, in the present invention, batch_size=4, and the batch_size is the batch size. For example, when batch_size=4, it means that two first gas pipe operation status coupling data sets are selected from the verification data set each time and put into the model for verification.

[0038] Specifically, the determining whether the loss value satisfies the first preset condition, and determining whether to end the training and output the model parameters according to the determination result, includes: If the loss value meets the first preset condition, the training is terminated and the parameters of the current gas pipe leakage automatic identification model are output; if the loss value does not meet the first preset condition, the training is continued; The first preset condition is that after the second preset round of training, the loss values ​​obtained in the next round of training are greater than or equal to the loss values ​​that occurred during the second preset round of training.

[0039] It can be understood that the present invention preferably sets the maximum round of cyclic training to 40,000 times, which can effectively ensure the accuracy of the model prediction of the present invention and record the loss value of the model training. The present invention sets the second preset round to 400, that is, when the loss value of the training no longer decreases within 400 rounds of training, the round with the smallest loss value in the 400 rounds of training is saved as the best verification round parameter to generate the best model file. The above setting makes the training rounds no longer rely on manual judgment, but uses the deep learning model for automatic training, which improves the accuracy of the model prediction, and automatically ends the training when the accuracy meets the requirements, which greatly saves the training time, and can also effectively prevent overfitting, which greatly improves the intelligence level of the present invention and the efficiency of model training.

[0040] Furthermore, the specific process of the gas pipe leakage automatic identification model of the present invention is as follows: (1) Obtain the potential vector mean mu and logarithmic variance logvar in the forward propagation process of the gas pipe leakage automatic identification model; (2) Calculate a reasonable loss value representing the training effect: Calculate the divergence: k1=-0.5*(1+logvar-mu^2-(e^logvar)); Calculate the loss between the vibration frequency data in the validation dataset and the predicted value: loss=L1+k1*k1_weight; Among them, loss is the loss value, k1 is the divergence, k1_weight is the proportion of k1, the value is 10, L1 is the absolute difference between the predicted value and the true value, and the calculation formula of L1 is as follows: ; Among them, A is the L1 loss in pytorch, which calculates the average or sum of the absolute differences between two tensors; A has a shape of N*C*H*W, which indicates that it is a four-dimensional tensor, N represents the number of samples (Batch Size), indicating that there are N data points, C represents the number of channels (Channels), H and W represent the height and width respectively, which are usually the two-dimensional spatial dimensions of the input features; P represents the padding value, which is a Boolean type, and the shape of P is c*h*w*1. The dimensions of P are the same as those of A in terms of channel, height, and width, but there is no sample number N, which means that P is a mask or padding information for a feature map of each sample, and the Boolean value of P may be used to mark whether the elements in A are filled (True) or valid (False).

[0041] (3) The loss value when using the validation data set to validate the current model network; (4) If the validation loss of this round is less than that of the previous round, the best model parameters are replaced with the training model of this round; (5) Put the rounds and loss values ​​into the dictionary one by one; (6) Compare the loss value of the current round with the loss value of the 400 rounds before this round (parameters are provided and can be set according to actual needs, the default is 200), that is, traverse the loss values ​​from epoch-400 to epoch-1 (epoch is the training round, and comparison is performed when epoch is greater than 400). If the loss values ​​of the current round of epoch are all greater than the loss values ​​from (epoch-400) to (epoch-1), then stop training.

[0042] (7) Save the best model parameters in the cyclic verification as a ckpt model file.

[0043] S300. After the gas pipeline is put into use, within a first preset time, a set of coupled data of the gas pipeline operation status of multiple monitoring points collected at the same time point is recorded as a second gas pipeline operation status coupled data set, and multiple second gas pipeline operation status coupled data sets collected at multiple time points are obtained at preset time intervals, and the multiple second gas pipeline operation status coupled data sets are input into the gas pipeline leakage automatic identification model, and the identification result is output, and then it is judged whether there is a leakage in the gas pipeline, and it is determined whether to output an alarm signal about the leakage of the gas pipeline according to the judgment result. If there is, the on-site staff conducts an on-site inspection of the gas pipeline to confirm whether there is actually a leakage in the gas pipeline, and uses the cloud server to record and transmit the on-site inspection result data, obtain the on-site inspection result data, and determine whether to combine the multiple second gas pipeline operation status coupled data sets as a new training data set according to the on-site inspection result data, retrain the gas pipeline leakage automatic identification model, and then automatically optimize the gas pipeline leakage automatic identification model.

[0044] Specifically, the identification result is whether there is a leakage or not, the plurality of second gas pipe operation state coupling data sets are input into the gas pipe leakage automatic identification model, the identification result is output, and then it is determined whether there is a leakage in the gas pipe, and whether to output an alarm signal about the leakage in the gas pipe according to the determination result, including: If the output result is that there is a leakage, it is determined that an alarm signal about the leakage of the gas pipe is output; If the output result is that there is no leakage, a prompt signal indicating that there is no leakage in the gas pipe is output.

[0045] It is understandable that the prediction result of the gas pipe leakage automatic identification model of the present invention only includes the presence of leakage or the absence of leakage. When there is leakage, the corresponding predicted value and true value are 1, and when there is no leakage, the corresponding predicted value and true value are 0.

[0046] Specifically, the field inspection result data includes actual leakage or actual non-existence of leakage, and judging whether to combine the plurality of second gas pipe operation status coupling data sets as a newly added training data set, retrain the gas pipe leakage automatic identification model, and then automatically optimize the gas pipe leakage automatic identification model according to the field inspection result data, includes: If the field inspection result data shows that there is actually no leakage, the multiple second gas pipe operating status coupling data sets and the training result of no leakage are used as new training data sets to retrain the gas pipe leakage automatic identification model, thereby automatically optimizing the gas pipe leakage automatic identification model.

[0047] Specifically, the field inspection result data includes actual leakage or actual non-existence of leakage, and judging whether to combine the plurality of second gas pipe operation status coupling data sets as a newly added training data set, retrain the gas pipe leakage automatic identification model, and then automatically optimize the gas pipe leakage automatic identification model according to the field inspection result data, includes: If the actual inspection result data shows that there is an actual leakage, the multiple second gas pipe operating status coupled data sets and the training result that there is no leakage will not be used as new training data sets, the gas pipe leakage automatic identification model will not be retrained, and the gas pipe leakage automatic identification model will not be automatically optimized.

[0048] It can be understood that the present invention arranges multiple monitoring points at preset intervals along the gas pipe, each of which includes a vibration sensor, a GPS sensor and a temperature sensor, collects position data of each monitoring point, vibration frequency data of the gas pipe surface, and temperature data, and trains a gas pipe leakage automatic recognition model to determine whether the gas pipe is leaking. If so, the on-site staff conducts an on-site inspection of the gas pipe and uploads the inspection results. The gas pipe leakage automatic recognition model can be automatically retrained based on the inspection results, and then the gas pipe leakage automatic recognition model is automatically optimized, thereby realizing an overall evaluation and judgment of the overall operating status data of the gas pipe over a period of time, and more accurately judging whether the overall operating status data of the gas pipe over a period of time is in a leakage state, thereby greatly improving the safety, reliability and intelligence of the safe intelligent monitoring of the gas supply process, and solving the problem that the setting of a single or multiple thresholds cannot accurately determine whether the gas pipe is leaking, thereby greatly improving the intelligence and usability of the present invention and greatly expanding the application scenarios of the present invention.

[0049] See also Figure 2 The present invention provides another embodiment, which provides a gas supply process safety intelligent monitoring system, the gas supply process safety intelligent monitoring system comprising: The acquisition module 100 includes a plurality of vibration sensors arranged at preset intervals along the gas pipe, and is used to acquire vibration frequency data of the gas pipe; The control module 200 is used to record the vibration frequency data of multiple vibration sensors collected at the same time point within a first preset time as a first gas pipe operation state coupling data set, obtain multiple first gas pipe operation state coupling data sets collected at multiple time points according to a preset time interval, divide the multiple first gas pipe operation state coupling data sets into a training data set and a verification data set according to a first preset ratio, and use the training data set to train the gas pipe leakage automatic recognition model; use the verification data set to optimize the trained gas pipe leakage automatic recognition model, optimize the model parameters, and complete the gas pipe leakage automatic recognition model training; and after the gas pipe is put into use, within the first preset time, record the gas pipe operation state coupling data set of multiple monitoring points collected at the same time point as a second gas pipe leakage automatic recognition model. A plurality of second gas pipe operation state coupling data sets collected at multiple time points are obtained according to a preset time interval, and the plurality of second gas pipe operation state coupling data sets are input into the gas pipe leakage automatic identification model, and the identification result is output, so as to judge whether there is leakage in the gas pipe, and determine whether to output an alarm signal about the leakage of the gas pipe according to the judgment result; if so, the on-site staff shall conduct an on-site inspection of the gas pipe to confirm whether there is actually leakage in the gas pipe, and use the cloud server to record and transmit the on-site inspection result data, obtain the on-site inspection result data, and determine whether to combine the plurality of second gas pipe operation state coupling data sets as a new training data set according to the on-site inspection result data, and retrain the gas pipe leakage automatic identification model, so as to automatically optimize the gas pipe leakage automatic identification model.

[0050] It should be noted here that the present invention utilizes a plurality of monitoring points arranged at preset intervals along the gas pipe, each of the monitoring points comprising a vibration sensor, a GPS sensor and a temperature sensor, to collect position data of each monitoring point, vibration frequency data of the gas pipe surface, and temperature data, and trains a gas pipe leakage automatic recognition model to determine whether there is a leakage in the gas pipe. If so, on-site staff conducts an on-site inspection of the gas pipe and uploads the inspection results. The gas pipe leakage automatic recognition model can be automatically retrained based on the inspection results, and then the gas pipe leakage automatic recognition model is automatically optimized, thereby realizing an overall evaluation and judgment of the overall operating status data of the gas pipe over a period of time, and more accurately judging whether the overall operating status data of the gas pipe over a period of time is in a leakage state, thereby greatly improving the safety, reliability and intelligence of the safe intelligent monitoring of the gas supply process, and solving the problem that the setting of a single or multiple thresholds cannot accurately determine whether the gas pipe is leaking, thereby greatly improving the intelligence and usability of the present invention and greatly expanding the application scenarios of the present invention.

[0051] In a preferred embodiment, the present application further provides an electronic device, the electronic device comprising: A memory; and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the method for intelligent monitoring of gas supply process safety is implemented. The computer device can be broadly a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected via a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory may provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices via a network. When the computer program is executed by the processor, the steps of the method of the present invention are performed.

[0052] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0053] It will be appreciated by a person skilled in the art that the method steps of the present invention may be performed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program may be stored in a non-temporary computer-readable storage medium, which causes the steps of the present invention to be performed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, database, or other media herein may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0054] It can be understood that the present invention arranges multiple monitoring points at preset intervals along the gas pipe, each of which includes a vibration sensor, a GPS sensor and a temperature sensor, collects position data of each monitoring point, vibration frequency data of the gas pipe surface, and temperature data, and trains a gas pipe leakage automatic recognition model to determine whether the gas pipe is leaking. If so, the on-site staff conducts an on-site inspection of the gas pipe and uploads the inspection results. The gas pipe leakage automatic recognition model can be automatically retrained based on the inspection results, and then the gas pipe leakage automatic recognition model is automatically optimized, thereby realizing an overall evaluation and judgment of the overall operating status data of the gas pipe over a period of time, and more accurately judging whether the overall operating status data of the gas pipe over a period of time is in a leakage state, thereby greatly improving the safety, reliability and intelligence of the safe intelligent monitoring of the gas supply process, and solving the problem that the setting of a single or multiple thresholds cannot accurately determine whether the gas pipe is leaking, thereby greatly improving the intelligence and usability of the present invention and greatly expanding the application scenarios of the present invention.

[0055] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0056] The specific implementation of the present invention described above does not constitute a limitation on the protection scope of the present invention. Any other corresponding changes and modifications made based on the technical concept of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. A method for intelligent safety monitoring of a gas supply process, characterized in that: The method comprises: S100, after the gas pipe is installed and before it is put into use, a plurality of monitoring points are arranged along the gas pipe at preset intervals, each of the monitoring points comprising a vibration sensor, a GPS sensor and a temperature sensor, the vibration sensor being used to obtain vibration frequency data of the surface of the gas pipe, the GPS sensor being used to obtain position data of the monitoring point, and the temperature sensor being used to obtain temperature data of the surface of the gas pipe; S200, recording the vibration frequency data, position data, and temperature data collected at the same monitoring point at a certain time point as the gas pipe operation state coupling data set of the monitoring point, recording the gas pipe operation state coupling data sets of multiple monitoring points collected at the same time point within a first preset time as the first gas pipe operation state coupling data set, acquiring multiple first gas pipe operation state coupling data sets collected at multiple time points according to a preset time interval, dividing the multiple first gas pipe operation state coupling data sets into training data sets and verification data sets according to a first preset ratio, and using the training data sets to train the gas pipe leakage automatic identification model; using the verification data set to optimize the trained gas pipe leakage automatic identification model, optimize the model parameters, and complete the gas pipe leakage automatic identification model training; S300. After the gas pipeline is put into use, within a first preset time, a set of coupled data of the gas pipeline operation status of multiple monitoring points collected at the same time point is recorded as a second gas pipeline operation status coupled data set, and multiple second gas pipeline operation status coupled data sets collected at multiple time points are obtained at preset time intervals, and the multiple second gas pipeline operation status coupled data sets are input into the gas pipeline leakage automatic identification model, and the identification result is output, and then it is judged whether there is a leakage in the gas pipeline, and it is determined whether to output an alarm signal about the leakage of the gas pipeline according to the judgment result. If there is, the on-site staff conducts an on-site inspection of the gas pipeline to confirm whether there is actually a leakage in the gas pipeline, and uses the cloud server to record and transmit the on-site inspection result data, obtain the on-site inspection result data, and determine whether to combine the multiple second gas pipeline operation status coupled data sets as a new training data set according to the on-site inspection result data, retrain the gas pipeline leakage automatic identification model, and then automatically optimize the gas pipeline leakage automatic identification model.

2. The method for intelligent safety monitoring of the gas supply process according to claim 1, characterized in that: The method comprises: After the gas pipe is installed and before it is put into use, the installed gas pipe needs to be inspected to ensure that there is no leakage problem in the installed gas pipe.

3. The method for intelligent safety monitoring of the gas supply process according to claim 1, characterized in that: The arrangement of multiple monitoring points along the gas pipeline at preset intervals includes: The number of monitoring points is calculated based on the ratio of the actual length of the gas pipe to the preset length.

4. The method for intelligent safety monitoring of the gas supply process according to claim 1, characterized in that: The method of using the training data set to train the gas pipe leakage automatic identification model; using the verification data set to optimize the trained gas pipe leakage automatic identification model, optimizing the model parameters, and completing the gas pipe leakage automatic identification model training includes: The training data set is input into the preset network layer in batches for training, and the preset network layer includes a Transformer network layer, and the Transformer network layer is used to forward propagate the first gas pipe operation state coupling data set collected at the current time point to predict the first gas pipe operation state coupling data set at the next time point, and then obtain the predicted loss value; the loss value of the preset network layer is calculated and input into the optimizer for optimization to determine the direction in which the parameter gradient of the gas pipe leakage automatic identification model decreases fastest; the gas pipe leakage automatic identification model performs back propagation according to the loss value and the parameter gradient of the model to optimize the parameters of the gas pipe leakage automatic identification model.

5. The method for intelligent safety monitoring of gas supply process according to claim 4, characterized in that: The method of using the training data set to train the gas pipe leakage automatic identification model; using the verification data set to optimize the trained gas pipe leakage automatic identification model, optimizing the model parameters, and completing the gas pipe leakage automatic identification model training also includes: After each training, the verification data set is input into the preset network layer of the previous training in batches for model parameter verification, and cyclic training is performed, with the total number of training rounds set to the first preset round; the loss value of the preset network layer is recorded, and it is determined whether the loss value meets the first preset condition, and whether to end the training and output the model parameters based on the judgment result.

6. The method for intelligent safety monitoring of gas supply process according to claim 5, characterized in that: The determining whether the loss value satisfies the first preset condition, and determining whether to end the training and output the model parameters according to the determination result, includes: If the loss value meets the first preset condition, the training is terminated and the parameters of the current gas pipe leakage automatic identification model are output; if the loss value does not meet the first preset condition, the training is continued; The first preset condition is that after the second preset round of training, the loss values ​​obtained in the next round of training are greater than or equal to the loss values ​​that occurred during the second preset round of training.

7. The method for intelligent safety monitoring of gas supply process according to claim 1, characterized in that: The identification result is whether there is a leakage or not, the plurality of second gas pipe operation state coupling data sets are input into the gas pipe leakage automatic identification model, the identification result is output, and then it is determined whether there is a leakage in the gas pipe, and whether to output an alarm signal about the leakage in the gas pipe according to the determination result, including: If the output result is that there is a leakage, it is determined that an alarm signal about the leakage of the gas pipe is output; If the output result is that there is no leakage, a prompt signal indicating that there is no leakage in the gas pipe is output.

8. The method for intelligent safety monitoring of gas supply process according to claim 1, characterized in that: The field inspection result data includes an actual leakage situation or an actual non-existence of leakage situation, and the determining, based on the field inspection result data, whether to combine the plurality of second gas pipe operation status coupling data sets as a newly added training data set, retrain the gas pipe leakage automatic identification model, and then automatically optimize the gas pipe leakage automatic identification model includes: If the field inspection result data shows that there is actually no leakage, the multiple second gas pipe operating status coupled data sets and the training result of no leakage are used as new training data sets to retrain the gas pipe leakage automatic identification model, thereby automatically optimizing the gas pipe leakage automatic identification model.

9. The method for intelligent safety monitoring of gas supply process according to claim 1, characterized in that: The field inspection result data includes an actual leakage situation or an actual non-existence of leakage situation, and the determining, based on the field inspection result data, whether to combine the plurality of second gas pipe operation status coupling data sets as a newly added training data set, retrain the gas pipe leakage automatic identification model, and then automatically optimize the gas pipe leakage automatic identification model includes: If the actual inspection result data shows that there is an actual leakage, the multiple second gas pipe operating status coupled data sets and the training result that there is no leakage will not be used as a new training data set, the gas pipe leakage automatic identification model will not be retrained, and the gas pipe leakage automatic identification model will not be automatically optimized.

10. A gas supply process safety intelligent monitoring system, characterized in that: include: An acquisition module, comprising a plurality of vibration sensors arranged at preset intervals along the pipeline of the gas pipe, for acquiring vibration frequency data of the gas pipe; The control module is used to record the vibration frequency data of multiple vibration sensors collected at the same time point within a first preset time as a first gas pipe operation state coupling data set, obtain multiple first gas pipe operation state coupling data sets collected at multiple time points according to a preset time interval, divide the multiple first gas pipe operation state coupling data sets into a training data set and a verification data set according to a first preset ratio, and use the training data set to train the gas pipe leakage automatic identification model; use the verification data set to optimize the trained gas pipe leakage automatic identification model, optimize the model parameters, and complete the gas pipe leakage automatic identification model training; and after the gas pipe is put into use, within the first preset time, record the gas pipe operation state coupling data set of multiple monitoring points collected at the same time point as a second gas pipe operation state coupling data set. A state coupling data set is used to obtain multiple second gas pipe operation state coupling data sets collected at multiple time points according to a preset time interval, and the multiple second gas pipe operation state coupling data sets are input into the gas pipe leakage automatic identification model, and the identification result is output, so as to judge whether there is a leakage in the gas pipe, and determine whether to output an alarm signal about the leakage of the gas pipe according to the judgment result. If there is a leakage, the on-site staff shall conduct an on-site inspection of the gas pipe to confirm whether there is actually a leakage in the gas pipe, and use the cloud server to record and transmit the on-site inspection result data, obtain the on-site inspection result data, and determine whether to combine the multiple second gas pipe operation state coupling data sets as a new training data set according to the on-site inspection result data, retrain the gas pipe leakage automatic identification model, and then automatically optimize the gas pipe leakage automatic identification model.

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