Intelligent unattended LNG point supply station monitoring system

Abstract

The invention discloses an intelligent unattended LNG (Liquefied Natural Gas) point supply station monitoring system, relates to the technical field of LNG, and solves the problems that when a plurality of field monitoring devices transmit real-time data to a remote monitoring end remotely, the missing rate of the real-time data is high due to the influence of a network state and a transmission distance, and the real-time data cannot be lost. Therefore, the problems of high fault error reporting rate of the LNG point supply station and low fault detection efficiency of the LNG point supply station are solved. The system comprises an LNG point supply station monitoring system, a monitoring center and a plurality of on-site monitoring devices. A special mark identifier is used as a unique identifier of an LNG point supply station in a monitoring center, so that the fault error reporting rate of the LNG point supply station is reduced, and missing data are transmitted again; performing LNG leakage prediction on the lossless data table through a multi-mode fault prediction model to generate an LNG leakage prediction result, and further controlling corresponding accident emergency equipment to perform emergency control on the LNG point supply station; therefore, the fault detection efficiency of the LNG point supply station is improved.

Description

Technical Field

[0001] The invention relates to the technical field of liquefied natural gas, and in particular to an intelligent unattended LNG point supply station monitoring system. Background Art

[0002] In recent years, natural gas has gradually become the core of my country's primary energy structure. my country's natural gas extraction and use levels are increasing year by year, both in terms of quantity and quality. As the source of the natural gas industry, the importance of natural gas extraction is self-evident. However, most of my country's natural gas point supply stations still use manual inspections, but there are problems such as human omissions, which seriously affect the safe operation of LNG point supply stations.

[0003] LNG (liquefied natural gas) is a clean, efficient and environmentally friendly energy source that has been widely used. LNG point supply stations are important production and distribution bases for LNG. With their advantages of dispersed locations, rich varieties and rapid response, they have become an important supplement to meet market demand. In LNG point supply stations, real-time monitoring is required to ensure safe operation.

[0004] Patent No. CN2017110203190 discloses a remote monitoring system for the LNG industry chain, including a server, a hardware part and a software part. The hardware part includes a memory, and the software part includes an application stored in the memory. The above application includes a functional module that can realize the following functions: user management, geographic information, operation and maintenance quality control, real-time monitoring, statistical reports and dispatch and distribution. The communication protocol between the server and the hardware device is a request-response mode protocol between the communication requester and the communication responder. The communication protocol data packet consists of a packet header, a prefix, a data segment, a checksum and a packet tail. The prefix includes an instruction number, a data packet number and a unique device number. The technical solution of the above application combines intelligent hardware with software applications to realize intelligent, real-time monitoring and deployment of the county town of the LNG industry chain, achieve the optimal solution, and save manpower and time.

[0005] Patent No. CN2015105040527 discloses a remote monitoring system for LNG ships, which consists of ship-side equipment and a shore-side data center. The ship-side equipment mainly includes a display processor, a video monitor and a 3G / 4G / VPN router, and the shore-side data center mainly includes multiple functional servers, multiple switches and VPN routers. The above system uses the Internet of Things network to link with the client to form a set of interconnected LNG ship remote monitoring system. The ship-side equipment and the shore-side data center together form an information monitoring system to realize all the monitoring functions of this system. Its implementation method includes: 1) data collection: 2) analysis and transmission: 3) classification preprocessing: 4) targeted processing: 5) real-time adjustment. The collected data is sent back to the system platform through the network, so that the platform can dynamically understand the operating status and parameters of the ship equipment, and can timely analyze and discover the fault of the ship equipment, so that the ship equipment can be repaired and maintained in a timely and effective manner, and realize the information management of the operating ship.

[0006] Although the above patents all utilize remote monitoring systems to remotely monitor the LNG industry chain (including point supply stations), when a number of on-site monitoring devices remotely transmit real-time data to the remote monitoring end, the real-time data loss rate is high due to the influence of network status and transmission distance, resulting in a high fault error rate of the LNG point supply station and low fault detection efficiency of the LNG point supply station. Summary of the invention

[0007] The purpose of the present invention is to provide an intelligent unattended LNG point supply station monitoring system, which can reduce the fault error rate of the LNG point supply station by using a special mark as the unique identifier of the LNG point supply station in the monitoring center; through a multi-modal fault prediction model, LNG leakage prediction is performed on a lossless data table to generate an LNG leakage prediction result, and then the corresponding accident emergency equipment is controlled to perform emergency control on the LNG point supply station; so as to improve the fault detection efficiency of the LNG point supply station.

[0008] The present invention utilizes the following technical solutions:

[0009] An intelligent unattended LNG point supply station monitoring system includes a monitoring center and several on-site monitoring devices, including:

[0010] The on-site monitoring equipment includes data acquisition equipment, a control and analysis center, accident emergency equipment and a data transmission module; the on-site monitoring equipment collects and processes environmental video data, natural gas flow data and natural gas concentration data of the LNG point supply station through the data acquisition equipment to form a lossless data table; the control and analysis center uses a multi-modal fault prediction model to predict LNG leakage on the lossless data table to generate an LNG leakage prediction result, and then controls the corresponding accident emergency equipment to perform emergency control on the LNG point supply station; the LNG leakage prediction result, environmental video data, natural gas flow data and natural gas concentration data are transmitted to the monitoring center through the data transmission module;

[0011] The monitoring center includes a storage management unit and a remote monitoring unit. The monitoring center stores and manages environmental video data, natural gas flow data, and natural gas concentration data transmitted by a number of on-site monitoring devices through the storage management unit, and monitors the working status of the on-site monitoring devices in real time through the remote monitoring unit.

[0012] Preferably, the storage management unit is used to store the environmental video data, natural gas flow data and natural gas concentration data transmitted by each on-site monitoring device, and manage the location information and alarm events of each LNG supply station;

[0013] The remote monitoring unit is used to monitor the working status of the on-site monitoring equipment in real time and display it through the remote terminal device.

[0014] Preferably, the data acquisition device is used to collect and process the environmental video data, natural gas flow data and natural gas concentration data of each LNG supply station to form a lossless data table;

[0015] The control and analysis center is used to predict LNG leakage on the non-destructive data table to generate LNG leakage prediction results and emergency control signals, and then control the corresponding accident emergency equipment to perform emergency control on the LNG point supply station;

[0016] Accident emergency equipment, used to carry out emergency control of LNG point supply stations according to the emergency control signal of the control and analysis center;

[0017] Data transmission module, used to transmit LNG leakage prediction results and emergency control signals to accident emergency equipment, and to transmit environmental video data, natural gas flow data and natural gas concentration data to the monitoring center;

[0018] Data acquisition equipment includes pressure transmitters, temperature sensors, combustible gas detectors, liquid level sensors, natural gas flow meters and network cameras; accident emergency equipment includes valve opening converters; data transmission protocols include Modbus protocol and FTP protocol.

[0019] Preferably, the steps of data acquisition and data processing of the environmental video data, natural gas flow data and natural gas concentration data by the data acquisition device are as follows:

[0020] S1: Collect environmental video data of the LNG point supply station by calling the network camera, and obtain the natural gas flow data of the LNG point supply station in real time through the natural gas flow meter; and detect the natural gas concentration data in the atmospheric environment of the LNG point supply station through the combustible gas detector;

[0021] S2: The data acquisition device uses a data block algorithm to divide the acquired environmental video data, natural gas flow data, and natural gas concentration data into a number of data blocks according to a preset time interval;

[0022] S3: The data acquisition device uses a data classification algorithm to cluster the information data in each data block according to the mark identifier generated by the basic information of each LNG supply station to obtain several small data blocks;

[0023] S4: The data collection device uses a data association algorithm to associate and normalize the contents of the small data blocks according to the markings of each LNG supply station, so as to construct a complete data table for each LNG supply station;

[0024] S5: The data acquisition device uses an encryption compression algorithm to encrypt and losslessly compress the complete data table to form a lossless data table.

[0025] Preferably, the control and analysis center uses a multi-modal fault prediction model to perform LNG leakage prediction on the lossless data table to generate the LNG leakage prediction result in the following steps:

[0026] A: The multimodal fault prediction model uses a data separation layer to divide the lossless data table into image and video data and natural gas data, and extracts and enhances the image and video data frame by frame to obtain an enhanced image frame set;

[0027] B: using the first extraction branch of the feature extraction layer to perform feature extraction on the enhanced image frame set to obtain image frame feature values; using the second extraction branch of the feature extraction layer to perform feature extraction on the natural gas data to obtain natural gas feature values;

[0028] C: Using the target detection branch of the iterative learning layer, the extracted image frame feature values ​​are iteratively trained several times to obtain the image weight matrix; using the fault prediction branch of the iterative learning layer, the extracted natural gas feature values ​​are iteratively trained several times to obtain the flow-concentration weight matrix;

[0029] D: The prediction and recognition layer inputs the real-time acquired image video data and natural gas data into the image weight matrix and flow-concentration weight matrix respectively to generate LNG leakage prediction results.

[0030] Preferably, step A includes the following steps:

[0031] A1: Decrypt and decompress the lossless data table using the decryption algorithm and decompression algorithm;

[0032] A2: Classify the contents of the decompressed lossless data table using a clustering algorithm according to the data type; the data type includes video format and numerical format;

[0033] A3: Sort the contents in the video format in the lossless data table according to the time series and the marking identifier to obtain the image video data; and use the data cleaning algorithm to remove the noise and outliers from the contents in the numerical format in the lossless data table to obtain the natural gas data;

[0034] A4: The video data is converted into frame-by-frame images using a video frame extraction algorithm, and the image enhancement algorithm is used to perform contrast adjustment, brightness adjustment, edge enhancement, noise removal, histogram equalization, and smoothing and sharpening on the frame-by-frame images to obtain an enhanced image frame set.

[0035] Preferably, the steps for obtaining the mark identification of the LNG point supply station are:

[0036] a: Obtain the latitude and longitude address, network address and site serial number of each LNG supply station;

[0037] The station serial number is obtained by calculating the business license number, production safety license number and special equipment certificate number of each LNG supply station;

[0038] b: Convert the latitude and longitude addresses into binary coordinate codes, and convert the network address into ternary network codes;

[0039] c: extracting the binary longitude code from the binary coordinate code and splicing it with the ternary network code to generate a first splicing code; at the same time, extracting the binary latitude code from the binary coordinate code and splicing it with the site serial number to generate a second splicing code;

[0040] d: performing a Hadamard operation on the first concatenated code and the site serial number to generate a first identification code; and performing a Kronecker operation on the second concatenated code and the ternary network code to generate a second identification code;

[0041] e: Perform bilinear interpolation operation on the first identification code and the second identification code to obtain the marking identification of each LNG supply station.

[0042] The present invention reduces the fault error rate of the LNG point supply station by using a special marking identifier as the unique identifier of the LNG point supply station in the monitoring center; performs LNG leakage prediction on the lossless data table through a multi-modal fault prediction model to generate an LNG leakage prediction result, and then controls the corresponding accident emergency equipment to perform emergency control on the LNG point supply station; so as to improve the fault detection efficiency of the LNG point supply station. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0044] Figure 1 This is the principle block diagram of the LNG point supply station monitoring system;

[0045] Figure 2 This is the principle block diagram of the multi-modal fault prediction model;

[0046] Figure 3 A flowchart for obtaining a tag identifier. DETAILED DESCRIPTION

[0047] The present invention is described in detail below with reference to the accompanying drawings and embodiments:

[0048] like Figure 1-Figure 3 As shown, the intelligent unattended LNG point supply station monitoring system, monitoring center and several on-site monitoring devices described in the present invention include:

[0049] The on-site monitoring equipment includes data acquisition equipment, a control and analysis center, accident emergency equipment and a data transmission module; the on-site monitoring equipment collects and processes environmental video data, natural gas flow data and natural gas concentration data of the LNG point supply station through the data acquisition equipment to form a lossless data table; the control and analysis center uses a multi-modal fault prediction model to predict LNG leakage on the lossless data table to generate an LNG leakage prediction result, and then controls the corresponding accident emergency equipment to perform emergency control on the LNG point supply station; the LNG leakage prediction result, environmental video data, natural gas flow data and natural gas concentration data are transmitted to the monitoring center through the data transmission module;

[0050] The monitoring center includes a storage management unit and a remote monitoring unit. The monitoring center stores and manages environmental video data, natural gas flow data, and natural gas concentration data transmitted by a number of on-site monitoring devices through the storage management unit, and monitors the working status of the on-site monitoring devices in real time through the remote monitoring unit.

[0051] In the present invention, the storage management unit is used to store the environmental video data, natural gas flow data and natural gas concentration data transmitted by each on-site monitoring device, and manage the location information and alarm events of each LNG supply station;

[0052] Remote monitoring unit, used to monitor the working status of on-site monitoring equipment in real time and display it through remote terminal equipment;

[0053] In this embodiment, the monitoring center also includes a communication server, a database server, an FTP server and a Web server; the monitoring center has the functions of real-time data monitoring, map information management, alarm event management, data query and report management, user information management and system operation log, etc., which are conventional technologies in the field and will not be described in detail here;

[0054] In the present invention, the on-site monitoring equipment includes data acquisition equipment, a control and analysis center, accident emergency equipment and a data transmission module;

[0055] Data acquisition equipment, used to collect and process environmental video data, natural gas flow data and natural gas concentration data of each LNG supply station to form a lossless data table;

[0056] The control and analysis center is used to predict LNG leakage on the non-destructive data table to generate LNG leakage prediction results and emergency control signals, and then control the corresponding accident emergency equipment to perform emergency control on the LNG point supply station;

[0057] Accident emergency equipment, used to carry out emergency control of LNG point supply stations according to the emergency control signal of the control and analysis center;

[0058] Data transmission module, used to transmit LNG leakage prediction results and emergency control signals to accident emergency equipment, and to transmit environmental video data, natural gas flow data and natural gas concentration data to the monitoring center;

[0059] Data acquisition equipment includes pressure transmitters, temperature sensors, combustible gas detectors, liquid level sensors, natural gas flow meters and network cameras; accident emergency equipment includes valve opening converters; data transmission protocols include Modbus protocol and FTP protocol;

[0060] In this embodiment, the field monitoring equipment is connected via RS-485 bus; the data transmission module is a hybrid structure of C / S and B / S; the natural gas flow meter is used to measure instantaneous flow, daily flow, cumulative flow and other data, and the model of the natural gas flow meter is LWJ-RTU / V1 model flow meter, and the natural gas flow meter supports Modbus communication protocol; the model of the network camera is CS-C2C-31WFR;

[0061] In the present invention, the steps of data acquisition and data processing of the environmental video data, natural gas flow data and natural gas concentration data by the data acquisition device are as follows:

[0062] S1: The data acquisition device collects environmental video data of the LNG point supply station by calling the network camera, and obtains the natural gas flow data of the LNG point supply station in real time through the natural gas flow meter; it also detects the natural gas concentration data in the atmospheric environment of the LNG point supply station through the combustible gas detector;

[0063] S2: The data acquisition device uses a data block algorithm to divide the acquired environmental video data, natural gas flow data, and natural gas concentration data into a number of data blocks according to a preset time interval;

[0064] S3: The data acquisition device uses a data classification algorithm to cluster the information data in each data block according to the mark identifier generated by the basic information of each LNG supply station to obtain several small data blocks;

[0065] S4: The data collection device uses a data association algorithm to associate and normalize the contents of the small data blocks according to the markings of each LNG supply station, so as to construct a complete data table for each LNG supply station;

[0066] S5: The data acquisition device uses an encryption compression algorithm to encrypt and losslessly compress the complete data table to form a lossless data table;

[0067] In the present invention, the control and analysis center uses a multi-modal fault prediction model to perform LNG leakage prediction on the lossless data table to generate the LNG leakage prediction result in the following steps:

[0068] A: The multimodal fault prediction model uses a data separation layer to divide the lossless data table into image and video data and natural gas data, and extracts and enhances the image and video data frame by frame to obtain an enhanced image frame set;

[0069] In this embodiment, step A includes the following steps:

[0070] A1: The control and analysis center uses the decryption algorithm and decompression algorithm to decrypt and decompress the lossless data table;

[0071] A2: The control and analysis center classifies the contents of the decompressed lossless data table using a clustering algorithm according to the data type; the data type includes video format and numerical format;

[0072] A3: The control and analysis center sorts the video format content in the lossless data table according to the time series and mark identifier to obtain image video data; and uses the data cleaning algorithm to remove noise and abnormal values ​​from the numerical format content in the lossless data table to obtain natural gas data;

[0073] A4: The control and analysis center uses the video frame extraction algorithm to convert the image video data into frame-by-frame images, and uses the image enhancement algorithm to perform contrast adjustment, brightness adjustment, edge enhancement, noise removal, histogram equalization, and smoothing and sharpening on the frame-by-frame images to obtain an enhanced image frame set;

[0074] B: The multimodal fault prediction model uses the first extraction branch of the feature extraction layer to extract features from the enhanced image frame set to obtain image frame feature values; and uses the second extraction branch of the feature extraction layer to extract features from the natural gas data to obtain natural gas feature values;

[0075] In this embodiment, the first extraction branch uses two 3×3 convolutional layers, three 5×5 convolutional layers, two convolutional blocks consisting of a 3×1 convolutional layer and a 1×3 convolutional layer, and three 3×3 average pooling layers to perform feature extraction on the enhanced image frame set to obtain image frame feature values, and further obtain real-time on-site environmental features and on-site equipment features of each LNG supply station;

[0076] The second extraction branch uses three 3×3 convolutional layers, four 5×5 convolutional layers, and one cascaded 5×5 maximum pooling layer to extract features from the natural gas data to obtain natural gas feature values, and then analyze the relationship between the natural gas flow rate in the LNG point supply station and the natural gas concentration in the atmospheric environment;

[0077] C: The multimodal fault prediction model uses the target detection branch of the iterative learning layer to iteratively train the extracted image frame feature values ​​for several times to obtain the image weight matrix; and uses the fault prediction branch of the iterative learning layer to iteratively train the extracted natural gas feature values ​​for several times to obtain the flow-concentration weight matrix;

[0078] In this embodiment, the target detection branch uses 2 parallel LETTERbox modules, 3 series CBS modules, 2 C3K2 modules, 3 parallel SPPF modules, 2 series C2PSA modules and 2 BCE activation functions to perform several iterative training on the extracted image frame feature values ​​to obtain the image weight matrix, so as to track suspicious persons in the on-site environment in real time when a failure occurs in the LNG point supply station;

[0079] The fault prediction branch uses two serial 3×3 convolutional layers, two 5×5 adaptive pooling layers, two InceptionV2 blocks, two InceptionV4 blocks, four 3×3 maximum pooling layers, and one ReLU activation function to iteratively train the extracted natural gas feature values ​​for several times to obtain the flow-concentration weight matrix, thereby determining whether a natural gas leak occurs at the LNG point supply station based on the relationship between the natural gas flow in the LNG point supply station and the natural gas concentration in the atmospheric environment;

[0080] D: The prediction and recognition layer inputs the real-time acquired image video data and natural gas data into the image weight matrix and flow-concentration weight matrix respectively to generate LNG leakage prediction results;

[0081] In this embodiment, the prediction and recognition layer also uses three shuffle blocks, four 1×1 convolutional layers, three fully connected layers, two 7×7 adaptive pooling layers and a feedback optimization function to correct the LNG leakage prediction results of the image weight matrix and the flow-concentration weight matrix;

[0082]

[0083] Among them, Loss represents the feedback optimization function, Model 1 Represents the image weight matrix, Model 2 represents the flow-concentration weight matrix, σ represents the number of iterative training layers, G represents the quantization function, μ1 represents the calculation accuracy coefficient, and B σ represents the iterative training loss function of each layer, μ2 represents the preset optimization weight coefficient, E σ Indicates the optimization efficiency of each layer’s iterative training;

[0084] In the present invention, the steps for obtaining the mark identification of the LNG point supply station are:

[0085] a: Obtain the latitude and longitude address, network address and site serial number of each LNG supply station;

[0086] The station serial number is obtained by calculating the business license number, production safety license number and special equipment certificate number of each LNG supply station;

[0087] b: Convert the latitude and longitude addresses into binary coordinate codes, and convert the network address into ternary network codes;

[0088] c: extracting the binary longitude code from the binary coordinate code and splicing it with the ternary network code to generate a first splicing code; at the same time, extracting the binary latitude code from the binary coordinate code and splicing it with the site serial number to generate a second splicing code;

[0089] d: performing a Hadamard operation on the first concatenated code and the site serial number to generate a first identification code; and performing a Kronecker operation on the second concatenated code and the ternary network code to generate a second identification code;

[0090] e: Perform bilinear interpolation operation on the first identification code and the second identification code to obtain the marking identification of each LNG supply station.

[0091] Example:

[0092] The workflow of the LNG point supply station monitoring system is as follows: the on-site monitoring equipment collects and processes the environmental video data, natural gas flow data and natural gas concentration data of the LNG point supply station through data acquisition equipment (pressure transmitter, temperature sensor, combustible gas detector, liquid level sensor, natural gas flow meter and network camera) to form a lossless data table: the environmental video data of the LNG point supply station is collected by calling the network camera, and the natural gas flow data of the LNG point supply station is obtained in real time through the natural gas flow meter; the combustible gas detector is used to detect the natural gas concentration data in the atmospheric environment of the LNG point supply station; the data acquisition equipment uses The data block algorithm divides the acquired environmental video data, natural gas flow data and natural gas concentration data into several data blocks according to the preset time interval; the data acquisition equipment uses the data classification algorithm to cluster the information data in each data block according to the mark identification generated by the basic information of each LNG point supply station to obtain several small data blocks; the data acquisition equipment uses the data association algorithm to associate and normalize the content in the small data block according to the mark identification of each LNG point supply station to construct a complete data table for each LNG point supply station; the data acquisition equipment uses the encryption compression algorithm to encrypt and losslessly compress the complete data table to form a lossless data table;

[0093] The control and analysis center uses a multimodal fault prediction model to predict LNG leakage on the lossless data table to generate LNG leakage prediction results, and then control the corresponding accident emergency equipment (valve opening converter) to perform emergency control on the LNG point supply station: the multimodal fault prediction model uses a data separation layer to divide the lossless data table into image video data and natural gas data, and extracts and enhances the image video data frame by frame to obtain an enhanced image frame set: the decryption algorithm and the decompression algorithm are used to decrypt and decompress the lossless data table; the clustering algorithm is used to classify the content of the decompressed lossless data table according to the data type; the data type includes video format and numerical format; the video format content in the lossless data table is sorted according to the time series and mark identifier to obtain the image video data; and the numerical format content in the lossless data table is removed from the noise and outliers by using the data cleaning algorithm to obtain the natural gas data; the video frame extraction algorithm is used to convert the image video data into frame-by-frame images, and the image enhancement algorithm is used to adjust the contrast, brightness, edge enhancement, noise removal, histogram equalization and smoothing and sharpening of the frame-by-frame images to obtain the enhanced image frame set;

[0094] Obtain the longitude and latitude address, network address and site serial number of each LNG supply station; the site serial number is obtained by calculating the business license number, production safety license number and special equipment certificate number of each LNG supply station; convert the longitude and latitude address into a binary coordinate code, and convert the network address into a ternary network code; extract the binary longitude code in the binary coordinate code and splice it with the ternary network code to generate a first splicing code; at the same time, extract the binary latitude code in the binary coordinate code and splice it with the site serial number to generate a second splicing code; perform a Hadamard operation on the first splicing code and the site serial number to generate a first identification code; at the same time, perform a Kronecker operation on the second splicing code and the ternary network code to generate a second identification code; perform a bilinear interpolation operation on the first identification code and the second identification code to obtain the marking identification of each LNG supply station;

[0095] The first extraction branch of the feature extraction layer is used to extract features from the enhanced image frame set to obtain image frame feature values; the second extraction branch of the feature extraction layer is used to extract features from the natural gas data to obtain natural gas feature values; the target detection branch of the iterative learning layer is used to iteratively train the extracted image frame feature values ​​for several times to obtain an image weight matrix; the fault prediction branch of the iterative learning layer is used to iteratively train the extracted natural gas feature values ​​for several times to obtain a flow-concentration weight matrix; the prediction and recognition layer inputs the image video data and natural gas data obtained in real time into the image weight matrix and the flow-concentration weight matrix respectively to generate an LNG leakage prediction result;

[0096] The data transmission module uses the data transmission protocol (Modbus protocol and FTP protocol) to transmit the LNG leakage prediction results, environmental video data, natural gas flow data and natural gas concentration data to the monitoring center; at the same time, the storage management unit of the monitoring center stores the environmental video data, natural gas flow data and natural gas concentration data transmitted by each on-site monitoring equipment, and manages the location information and alarm events of each LNG supply station; the remote monitoring unit of the monitoring center monitors the working status of the on-site monitoring equipment in real time and displays it through the remote terminal device.

Claims

1. An intelligent unattended LNG point supply station monitoring system, characterized by: It includes a monitoring center and several on-site monitoring equipment, including: The on-site monitoring equipment includes data acquisition equipment, a control and analysis center, accident emergency equipment and a data transmission module; the on-site monitoring equipment collects and processes environmental video data, natural gas flow data and natural gas concentration data of the LNG point supply station through the data acquisition equipment to form a lossless data table; the control and analysis center uses a multi-modal fault prediction model to predict LNG leakage on the lossless data table to generate an LNG leakage prediction result, and then controls the corresponding accident emergency equipment to perform emergency control on the LNG point supply station; the LNG leakage prediction result, environmental video data, natural gas flow data and natural gas concentration data are transmitted to the monitoring center through the data transmission module; The monitoring center includes a storage management unit and a remote monitoring unit. The monitoring center stores and manages the environmental video data, natural gas flow data and natural gas concentration data transmitted by a number of on-site monitoring devices through the storage management unit, and monitors the working status of the on-site monitoring devices in real time through the remote monitoring unit.

2. The intelligent unattended LNG supply station monitoring system according to claim 1 is characterized by: The data acquisition equipment is used to collect and process environmental video data, natural gas flow data and natural gas concentration data of each LNG supply station to form a lossless data table; The control and analysis center is used to predict LNG leakage on the non-destructive data table to generate LNG leakage prediction results and emergency control signals, and then control the corresponding accident emergency equipment to perform emergency control on the LNG point supply station; Accident emergency equipment, used to carry out emergency control of LNG point supply stations according to the emergency control signal of the control and analysis center; Data transmission module, used to transmit LNG leakage prediction results and emergency control signals to accident emergency equipment, and to transmit environmental video data, natural gas flow data and natural gas concentration data to the monitoring center; Data acquisition equipment includes pressure transmitters, temperature sensors, combustible gas detectors, liquid level sensors, natural gas flow meters and network cameras; accident emergency equipment includes valve opening converters; data transmission protocols include Modbus protocol and FTP protocol.

3. The intelligent unattended LNG supply station monitoring system according to claim 1 is characterized by: The storage management unit is used to store the environmental video data, natural gas flow data and natural gas concentration data transmitted by each on-site monitoring device, and manage the location information and alarm events of each LNG supply station; The remote monitoring unit is used to monitor the working status of the on-site monitoring equipment in real time and display it through the remote terminal device.

4. The intelligent unattended LNG supply station monitoring system according to claim 1 is characterized by: The steps of data acquisition and data processing of the environmental video data, natural gas flow data and natural gas concentration data by the data acquisition device are as follows: S1: The data acquisition device collects environmental video data of the LNG point supply station by calling the network camera, and obtains the natural gas flow data of the LNG point supply station in real time through the natural gas flow meter; it also detects the natural gas concentration data in the atmospheric environment of the LNG point supply station through the combustible gas detector; S2: The data acquisition device uses a data block algorithm to divide the acquired environmental video data, natural gas flow data, and natural gas concentration data into a number of data blocks according to a preset time interval; S3: The data acquisition device uses a data classification algorithm to cluster the information data in each data block according to the mark identifier generated by the basic information of each LNG supply station to obtain several small data blocks; S4: The data collection device uses a data association algorithm to associate and normalize the contents of the small data blocks according to the markings of each LNG supply station, so as to construct a complete data table for each LNG supply station; S5: The data acquisition device uses an encryption compression algorithm to encrypt and losslessly compress the complete data table to form a lossless data table.

5. The intelligent unattended LNG supply station monitoring system according to claim 1 is characterized by: The control and analysis center uses a multi-modal fault prediction model to perform LNG leakage prediction on the lossless data table to generate the LNG leakage prediction result in the following steps: A: The multimodal fault prediction model uses a data separation layer to divide the lossless data table into image video data and natural gas data, and extracts and enhances the image video data frame by frame to obtain an enhanced image frame set; B: using the first extraction branch of the feature extraction layer to extract features from the enhanced image frame set to obtain image frame feature values; Using the second extraction branch of the feature extraction layer to extract features from the natural gas data to obtain natural gas feature values; C: Using the target detection branch of the iterative learning layer, the extracted image frame feature values ​​are iteratively trained several times to obtain the image weight matrix; The fault prediction branch of the iterative learning layer is used to iteratively train the extracted natural gas characteristic values ​​for several times to obtain a flow-concentration weight matrix; D: The prediction and recognition layer inputs the real-time acquired image video data and natural gas data into the image weight matrix and the flow-concentration weight matrix respectively to generate LNG leakage prediction results.

6. The intelligent unattended LNG supply station monitoring system according to claim 5 is characterized by: The step A comprises the following steps: A1: The control and analysis center uses the decryption algorithm and decompression algorithm to decrypt and decompress the lossless data table; A2: The control and analysis center classifies the contents of the decompressed lossless data table using a clustering algorithm according to the data type; the data type includes video format and numerical format; A3: The control and analysis center sorts the video format content in the lossless data table according to the time series and tag identifier to obtain image video data; and uses the data cleaning algorithm to remove noise and abnormal values ​​from the numerical format content in the lossless data table to obtain natural gas data; A4: The control and analysis center uses a video frame extraction algorithm to convert image video data into frame-by-frame images, and uses an image enhancement algorithm to perform contrast adjustment, brightness adjustment, edge enhancement, noise removal, histogram equalization, and smoothing and sharpening on the frame-by-frame images to obtain an enhanced image frame set.

7. The intelligent unattended LNG supply station monitoring system according to claim 4 is characterized by: The steps for obtaining the mark identification of the LNG point supply station are as follows: a: Obtain the latitude and longitude address, network address and site serial number of each LNG supply station; The station serial number is obtained by calculating the business license number, production safety license number and special equipment certificate number of each LNG supply station; b: Convert the latitude and longitude addresses into binary coordinate codes, and convert the network address into ternary network codes; c: extracting the binary longitude code from the binary coordinate code and splicing it with the ternary network code to generate a first splicing code; at the same time, extracting the binary latitude code from the binary coordinate code and splicing it with the site serial number to generate a second splicing code; d: performing a Hadamard operation on the first concatenated code and the site serial number to generate a first identification code; and performing a Kronecker operation on the second concatenated code and the ternary network code to generate a second identification code; e: Perform bilinear interpolation operation on the first identification code and the second identification code to obtain the marking identification of each LNG supply station.

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