Electronic display gas collection and component analysis system

Abstract

The invention discloses a gas collection and component analysis system with an electronic display function, and relates to the technical field of gas collection, analysis and detection. And the oil-gas separation module is used for treating the gas to ensure accurate analysis. The system is also integrated with a gas flow rate detection module to monitor the gas flow rate in real time. The chemical analysis module is provided with two chemical gas washing bottles, and specific reagents are contained in the chemical gas washing bottles to react components in gas. The electronic gas analysis module is used for identifying the concentrations of different components in the gas and is provided with a display screen for real-time data display. The system is provided with a data storage module which is used for recording detection results and establishing a historical database so as to carry out trend analysis and risk identification. And the remote monitoring module displays the detection data to a cloud end, so that an operator can conveniently access the detection data in real time, and an alarm is given when the data reaches a preset threshold value. The system provides comprehensive gas monitoring and analysis functions, can effectively identify potential fault risks, and aims at effectively monitoring gas in the transformer.

Description

Technical Field

[0001] The present invention relates to the technical field of gas collection, analysis and detection, and in particular to a gas collection and component analysis system with electronic display. Background Art

[0002] With the acceleration of industrialization, gas detection technology plays an important role in many fields, especially in the power, chemical and environmental protection industries. Gas detection devices are mainly used to monitor various gas components to ensure environmental safety and normal operation of facilities. Traditional gas detection equipment focuses on the detection of a single gas component and usually lacks the ability to comprehensively analyze complex gas components. In addition, with the improvement of environmental safety and health standards, the market has put forward higher requirements for the accuracy, reliability and ease of operation of gas detection equipment.

[0003] The gas detection devices currently on the market have the following problems:

[0004] Single function: Most of the gas detection devices on the market can only perform simple gas collection and a small amount of component analysis, which is difficult to meet the needs of comprehensive detection and analysis of multiple components. This functional limitation restricts the application of detection equipment in complex gas environments.

[0005] Low detection accuracy: During the detection process, it is difficult to effectively separate impurities such as transformer oil, which leads to interference in the detection results. The lack of effective treatment of oily impurities ultimately leads to low accuracy of analysis data, affecting the safety assessment and decision-making basis of gas.

[0006] Poor separation effect: Many existing devices do not have ideal separation effects when processing gases containing oily components such as transformer oil, which directly affects the detection accuracy of subsequent analysis modules. If the oily impurities in the gas are not effectively removed, it will affect the instrument's detection of other gas components.

[0007] Complex operation: Existing gas collection and detection devices usually need to be frequently disassembled and assembled for gas collection and maintenance. Such operations are cumbersome and prone to misoperation, which increases the risk of use and affects detection efficiency.

[0008] Lack of real-time display: Most gas detection devices are not equipped with electronic display function, and detection personnel cannot view gas detection data and equipment operating status intuitively and in real time, which is not conducive to monitoring the detection process and timely adjustment of operations.

[0009] Inconvenient maintenance: Compared with new devices with open designs that are easy to replace chemical reagents and internal modules of the equipment, the closed design of existing equipment makes the replacement of chemical reagents and internal cleaning and maintenance processes cumbersome, increasing the time cost of use and maintenance.

[0010] Not suitable for continuous monitoring: Most existing equipment is designed for intermittent detection, which makes it difficult to achieve continuous monitoring and analysis of gas. In scenarios that require long-term, continuous online detection, the performance of traditional equipment is insufficient. Summary of the invention

[0011] In order to solve the above problems, the present invention proposes a gas collection and composition analysis system with electronic display, which effectively reduces the error in gas detection and improves the accuracy of analysis results through oil and gas separation.

[0012] The technical solution adopted by the present invention is:

[0013] A gas collection and component analysis system with electronic display includes an integrated housing, a gas collection module, an oil-gas separation module, a gas flow rate detection module, a chemical analysis module, and an electronic gas analysis module installed in the integrated housing;

[0014] The gas collection module collects gas in the transformer, and is provided with an air inlet exposed to the outside of the integrated shell, and is connected to the transformer through the air inlet; the oil-gas separation module performs oil-gas separation on the gas, and is connected to the pipeline of the gas collection module; the gas flow rate detection module detects the gas flow rate data, and is connected to the pipeline of the oil-gas separation module; the chemical analysis module includes two chemical washing bottles, which are connected to the pipeline of the gas flow rate detection module and contain chemical reagents that react with specific components in the gas; the electronic gas analysis module identifies the concentrations of different components in the gas sample, and is connected to the pipeline of the chemical analysis module.

[0015] Furthermore, the gas collection module is a sealed gas storage box, on which a gas observation window is provided; the integrated housing is provided with an observation hole at the position of the observation window.

[0016] Furthermore, the oil-gas separation module is provided with a pressure regulating valve and a pressure gauge exposed outside the integrated housing; the integrated housing is provided with an observation hole at the position of the pressure gauge.

[0017] Furthermore, the integrated housing is provided with acrylic door panels at the two chemical washing bottles; the chemical washing bottles are detachably mounted on the pipeline by means of snap-on washing bottle fixing clips; the chemical washing bottles can be replaced by opening the acrylic door panels.

[0018] Furthermore, the volume flow rate detection module is an electronic gas flow rate detector, which is provided with a display screen; the integrated shell is provided with an observation hole at the display screen; the electronic gas analysis module is a gas composition detector, which is provided with a display screen; the integrated shell is provided with an observation hole at the display screen.

[0019] A method for collecting and analyzing gas components, based on the above-mentioned electronically displayed gas collection and component analysis system, comprises the following steps:

[0020] Step 1, gas collection: First, the gas collected from the transformer enters the system through the gas collection module, and the collected gas enters the oil-gas separation module through the pipeline;

[0021] Step 2, oil-gas separation: In the oil-gas separation module, the oil and other components in the gas sample are effectively separated, and the separated gas sample continues to be transmitted downward and enters the gas flow rate detection module;

[0022] Step 3, flow rate detection: The gas sample passes through the gas flow rate detection module to measure its flow rate in real time, and the flow rate data is displayed. The gas flow rate data provides a reference for subsequent chemical and electronic analysis;

[0023] Step 4, chemical analysis: the body sample flows into the chemical washing bottle in the chemical analysis module, and reacts with the chemical reagent in the chemical washing bottle to remove interfering gas components and provide a preliminary analysis of the gas sample components;

[0024] Step 5, electronic analysis: The gas sample after chemical treatment enters the electronic gas analysis module for detailed component analysis; the detection results output by the electronic gas analysis module reflect the specific components and concentrations in the gas sample, thereby providing a basis for judging the working status of the transformer.

[0025] Furthermore, the electronic gas analysis module, gas flow rate detection module and oil and gas separation module are also connected to a data storage module, which records and stores the results of each detection to establish a historical database of gas detection. The data storage module marks the data with a timestamp, and the data in the historical database is used for comparative analysis, so as to observe the changing trend of gas composition and identify potential failure risks.

[0026] A method for storing and integrating data of gas composition analysis results, based on the above-mentioned electronically displayed gas collection and composition analysis system, comprises the following steps:

[0027] Step 1, data collection and marking: After each gas collection and component analysis system completes the detection, the data storage module will mark the gas composition and flow rate detection results and generate a complete data record including a timestamp;

[0028] Record the gas component concentration as C and the flow rate as V, then the data record is expressed as:

[0029] D i ={T i ,C i,V i ,P i}

[0030] Where D i represents the data record of the i-th detection; T i is the timestamp; C i is the gas component concentration vector; V i is the gas flow rate; P i is the pressure value;

[0031] Step 2, data storage: After each test, the system records the data i Stored in the historical database to form a historical data set {D1, D2, ..., D n};

[0032] Step 3, trend analysis: by analyzing the gas component concentration C in the historical data i Perform trend analysis and calculate its rate of change; if the concentration change rate is R, the calculation formula is as follows:

[0033]

[0034] In the formula, C i+1 is the gas component concentration vector detected at the i+1th time; C i is the gas component concentration vector detected for the i-th time; T i+1 is the timestamp of the i+1th detection; T i is the timestamp of the i-th detection;

[0035] If the concentration change rate R exceeds the set threshold R threshold , there is a risk of equipment abnormality.

[0036] Furthermore, the data storage module is also connected to a remote monitoring module, which uploads the detection results to the cloud via the network, and the operator accesses the data in real time through the client; when the detection data reaches a certain set threshold, the remote monitoring module automatically issues an alarm to notify the operator, indicating that there is a fault.

[0037] A remote monitoring and diagnosis method for gas composition analysis results, based on the above-mentioned electronically displayed gas collection and composition analysis system, comprises the following steps:

[0038] Step 1, data upload: After the gas collection and composition analysis system completes each test, the remote monitoring module uploads the data to i Upload to the cloud server via the Internet;

[0039] Step 2, real-time monitoring: The cloud server monitors the received data in real time and sets warning thresholds; when an abnormality is detected, an alarm is triggered;

[0040] Step 3, diagnostic model: The server integrates the diagnostic model and performs machine learning training based on the historical database to identify abnormal patterns of gas concentration changes;

[0041] Assuming that the concentration anomaly measure is E, its calculation formula is as follows:

[0042] E=f(C i ,C i-1 ,...,C i-n )

[0043] Where f is the anomaly detection function; C i is the gas component concentration vector detected for the i-th time; C i-1 is the gas component concentration vector detected at the i-1th time; C i-n The gas component concentration vector detected at the inth time;

[0044] Among them, the anomaly detection function f is trained by the machine learning model SVM; the training steps are as follows:

[0045] First, extract the gas concentration data C from the historical database i , timestamp T i and other environmental parameters; standardize the data so that different eigenvalues ​​have similar distributions. The standardization formula is as follows:

[0046]

[0047] In the formula, C i ' is the standardized concentration value; C i is the gas component concentration vector detected for the i-th time; μc is the mean of the gas concentration; σc is the standard deviation of the gas concentration;

[0048] Then, for the gas concentration data C i Extract features, including the mean, variance, and rate of change of the concentration, and extract the time series features of the past n detection data to form a feature vector: X i =[C i ,C i-1 ,...,C i-n ,R i ], R i is the concentration change rate;

[0049] The normal state and fault state data in the historical database are used to train the machine learning model SVM; the training goal is to find a hyperplane to separate the normal state from the abnormal state;

[0050] The optimization of SVM is: The constraints are: i(w·X i +b)≥1;

[0051] Among them, w is the weight vector; b is the bias; y i For label; X i is the feature vector;

[0052] For each new data sample X new =[C new ,C new-1 ,...,C new-n ,R new ], C new is the latest eigenvalue, C new-1 is the characteristic value at the previous time point, C new-n is the eigenvalue n time units ago, R new is the latest concentration change rate;

[0053] Input the trained model and output the anomaly metric E; the anomaly metric formula is:

[0054] E=f(X new )

[0055] Where f is the trained anomaly detection function;

[0056] If the output abnormality measure E is greater than the set threshold E threshold , it is judged as abnormal and triggers a fault warning.

[0057] The electronic display gas collection and composition analysis system has the following significant beneficial effects:

[0058] 1. Efficient gas monitoring; real-time data collection: The system can collect gas from the transformer in a timely manner and accurately analyze its composition to ensure real-time monitoring of the gas state. Multi-parameter analysis: Combining gas flow rate detection, oil and gas separation and chemical analysis modules, the system can fully understand the state and composition of the gas.

[0059] 2. Improved safety; Fault warning: By setting thresholds, the system can automatically issue an alarm when an abnormality is detected, reminding operators to take measures as early as possible to reduce potential risks. Data recording and analysis: The establishment of a historical database helps to analyze the changing trend of gas composition, identify potential fault risks, and provide a basis for prevention and emergency treatment.

[0060] 3. Easy to operate and maintain; integrated design: The integrated housing of the system makes each module compact and efficient, which is easy to install and maintain. Convenient replacement and maintenance: The acrylic door panel and detachable washing bottle fixing clip design make the replacement and maintenance of key components such as chemical washing bottles more convenient.

[0061] 4. Data storage and remote monitoring; historical data management: The data storage module records each test result for subsequent analysis and comparison, helping scientific and technical personnel to conduct long-term monitoring and trend analysis. Remote monitoring function: Operators can access cloud data in real time through the client, which facilitates remote monitoring and management, and speeds up information acquisition and response.

[0062] 5. User-friendly display interface; intuitive display function: The system's integrated display screen provides a visual interface for data output, allowing operators to quickly obtain information and perform on-site operations.

[0063] 6. Environmentally friendly; oil and gas separation function: Through the oil and gas separation module, the environmental pollution in the gas detection process is effectively reduced, ensuring the simultaneous implementation of safety and environmental protection.

[0064] In summary, the electronic display gas collection and composition analysis system makes gas management more intelligent and automated through functions such as efficient monitoring, safety warning, convenient maintenance and remote monitoring, and effectively guarantees the safe operation of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0066] Figure 1 It is a schematic diagram of the overall structure of the electronically displayed gas collection and component analysis system of the present invention;

[0067] Figure 2 and Figure 3 A schematic diagram of the internal connection relationship of the electronically displayed gas collection and component analysis system of the present invention;

[0068] In the figure, 1 is an integrated housing, 2 is a gas collection module, 3 is an oil-gas separation module, 4 is a gas flow rate detection module, 5 is a chemical analysis module, 6 is an electronic gas analysis module, 7 is an air inlet, 8 is a gas observation window, 9 is a pressure regulating valve, 10 is a pressure gauge, 11 is an acrylic door panel, 12 is a snap-on gas washing bottle fixing clip, 13 is a first display screen, and 14 is a second display screen. DETAILED DESCRIPTION

[0069] To make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them.

[0070] Therefore, the following detailed description of the embodiments of the present invention is not intended to limit the scope of the claimed invention, but merely represents some embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. It should be noted that the embodiments of the present invention and the features and technical solutions in the embodiments can be combined with each other without conflict. It should be noted that similar numbers and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0071] This embodiment provides a gas collection and component analysis system with electronic display; Figure 1 , Figure 2 and Figure 3 As shown, the electronically displayed gas collection and component analysis system includes an integrated housing 1, a gas collection module 2 installed in the integrated housing 1, an oil and gas separation module 3, a gas flow rate detection module 4, a chemical analysis module 5, and an electronic gas analysis module 6.

[0072] Among them, the main function of the gas collection module 2 is to collect gas from the transformer. When the transformer is running for a long time, a certain amount of gas will be generated, and these gases usually contain key information indicating the status of the transformer. The gas collected by the gas collection module 2 will be transmitted to the next module for processing to ensure that the entire system can comprehensively analyze the status of the transformer. In this embodiment, the gas collection module 2 is a sealed gas storage box, which is provided with a gas observation window 8; the integrated integrated housing 1 is provided with an observation hole at the position of the observation window; the gas collection module 2 is provided with an air inlet 7 exposed to the outside of the integrated integrated housing 1, and it is connected to the transformer through the air inlet 7.

[0073] The transformer gas sample may contain a small amount of transformer oil, which may affect the subsequent analysis results and cause errors. The oil-gas separation module 3 uses efficient separation technology to separate the oil from other components in the gas; through this separation process, the subsequent modules can receive purer gas samples, thereby improving the accuracy of the analysis. In this embodiment, the oil-gas separation module 3 is provided with a pressure regulating valve 9 exposed to the outside of the integrated housing 1, and a pressure gauge 10; the integrated housing 1 is provided with an observation hole at the position of the pressure gauge 10; the oil-gas separation module 3 is connected to the gas collection module 2 by pipeline.

[0074] Real-time detection of gas flow rate is very important for the analysis process, because changes in gas flow rate may affect the results of chemical analysis and electronic detection. The gas flow rate detection module 4 provides accurate flow data and transmits these data to subsequent modules so that subsequent analysis processes can be operated based on a stable gas flow rate. In this embodiment, the body flow rate detection module is a gas flow rate electronic detector, which is provided with a first display screen 13; the integrated integrated housing 1 is provided with an observation hole at the first display screen 13; the body flow rate detection module is connected to the oil and gas separation module 3 by pipeline.

[0075] The chemical analysis module 5 includes two chemical washing bottles, which are connected to the gas flow rate detection module 4 by pipeline, and contain chemical reagents that react with specific components in the gas; in the washing bottles, the chemical reagents react with the components in the gas sample, which helps to initially detect and remove some unnecessary gas components, and provide a purer sample for subsequent electronic detection. In this embodiment, the integrated integrated housing 1 is provided with an acrylic door panel 11 at the two chemical washing bottles; the chemical washing bottles are detachably mounted on the pipeline by a snap-on washing bottle fixing clamp 12; the chemical washing bottles can be replaced by opening the acrylic door panel 11.

[0076] The electronic gas analysis module 6 is based on advanced electronic gas analysis and can further analyze the gas sample after chemical treatment; the electronic gas analysis module 6 has a high-precision detection function, can identify the concentration of different components in the gas sample, and output the detection results in the form of data; through electronic analysis, the target components in the gas sample can be detected more accurately, thereby providing detailed information on the transformer status. In this embodiment, the electronic gas analysis module 6 is a gas component detector, which is provided with a second display screen 14; the integrated integrated housing 1 is provided with an observation hole at the second display screen 14; the electronic gas analysis module 6 is connected to the chemical analysis module 5 by pipeline.

[0077] Based on the above-mentioned electronically displayed gas collection and component analysis system, this embodiment also provides a gas collection and component analysis method, which includes the following steps:

[0078] Step 1, gas collection: First, the gas collected from the transformer enters the system through the gas collection module 2, and the collected gas enters the oil-gas separation module 3 through the pipeline;

[0079] Step 2, oil-gas separation: In the oil-gas separation module 3, the oil and other components in the gas sample are effectively separated, and the separated gas sample continues to be transmitted downward and enters the gas flow rate detection module 4;

[0080] Step 3, flow rate detection: The gas sample passes through the gas flow rate detection module 4 to measure its flow rate in real time, and the flow rate data is displayed. The gas flow rate data provides a reference for subsequent chemical and electronic analysis;

[0081] Step 4, chemical analysis: the body sample flows into the chemical washing bottle in the chemical analysis module 5, and reacts with the chemical reagent in the chemical washing bottle to remove interfering gas components and provide a preliminary analysis of the gas sample components;

[0082] Step 5, electronic analysis: the gas sample after chemical treatment enters the electronic gas analysis module 6 for detailed component analysis; the detection result output by the electronic gas analysis module 6 reflects the specific components and concentrations in the gas sample, thereby providing a basis for judging the working status of the transformer.

[0083] Example 2

[0084] This embodiment is a further improvement on the basis of Embodiment 1, and the specific improvements are as follows:

[0085] In this embodiment, a data storage module is also provided, and the data storage module is connected to the electronic gas analysis module 6, the gas flow rate detection module 4 and the oil and gas separation module 3; the data storage module records and stores the results of each detection to establish a historical database of gas detection. The data storage module marks the data with a timestamp, and the data in the historical database is used for comparative analysis, so as to observe the trend of gas composition changes and identify potential fault risks.

[0086] Based on the gas collection and component analysis system with electronic display provided with a data storage module, this embodiment also proposes a method for storing and integrating data of gas component analysis results, which method includes the following steps:

[0087] Step 1, data collection and marking: After each gas collection and component analysis system completes detection, the data storage module will mark the gas composition and flow rate detection results and generate a complete data record including a timestamp.

[0088] Record the gas component concentration as C and the flow rate as V, then the data record is expressed as:

[0089] D i ={T i ,C i ,V i ,P i}

[0090] Where D i represents the data record of the i-th detection; T i is the timestamp; C i is the gas component concentration vector; Vi is the gas flow rate; P i is the pressure value.

[0091] Step 2, data storage: After each test, the system records the data i Stored in the historical database to form a historical data set {D1, D2, ..., D n}.

[0092] Step 3, trend analysis: by analyzing the gas component concentration C in the historical data i Perform trend analysis and calculate its rate of change; if the concentration change rate is R, the calculation formula is as follows:

[0093]

[0094] In the formula, C i+1 is the gas component concentration vector detected at the i+1th time; C i is the gas component concentration vector detected for the i-th time; T i+1 is the timestamp of the i+1th detection; T i is the timestamp of the i-th detection.

[0095] If the concentration change rate R exceeds the set threshold R threshold , there is a risk of equipment abnormality.

[0096] Example 3

[0097] This embodiment is a further improvement on the basis of Embodiment 2, and the specific improvements are as follows:

[0098] In this embodiment, a remote monitoring module is also provided, and the remote monitoring module is connected to the data storage module; the remote monitoring module uploads the detection results to the cloud through the network, and the operator accesses these data in real time through the client; when the detection data reaches a certain set threshold, the remote monitoring module automatically issues an alarm to notify the operator, indicating that there is a fault.

[0099] Based on the electronic display gas collection and component analysis system provided with a data storage module and a remote monitoring module, this embodiment also proposes a remote monitoring and diagnosis method for gas component analysis results, which method includes the following steps:

[0100] Step 1, data upload: After the gas collection and composition analysis system completes each test, the remote monitoring module uploads the data to i Upload to the cloud server via the Internet.

[0101] Step 2, real-time monitoring: The cloud server monitors the received data in real time and sets warning thresholds; when an abnormality is detected, an alarm is triggered.

[0102] Step 3, diagnostic model: The server integrates the diagnostic model and performs machine learning training based on the historical database to identify abnormal patterns of gas concentration changes;

[0103] Assuming that the concentration anomaly measure is E, its calculation formula is as follows:

[0104] E=f(C i ,C i-1 ,...,C i-n )

[0105] Where f is the anomaly detection function; C i is the gas component concentration vector detected for the i-th time; C i-1 is the gas component concentration vector detected at the i-1th time; C i-n The gas component concentration vector detected at the inth time.

[0106] Among them, the anomaly detection function f is trained through a machine learning model; the training steps are as follows:

[0107] First, extract the gas concentration data C from the historical database i , timestamp T i and other environmental parameters (such as temperature, pressure, etc.);

[0108] The data is standardized so that different eigenvalues ​​have similar distributions. The standardization formula is as follows:

[0109]

[0110] In the formula, C i ' is the standardized concentration value; C i is the gas component concentration vector detected for the i-th time; μc is the mean of the gas concentration; σc is the standard deviation of the gas concentration.

[0111] Then, for the gas concentration data C i Extract features, including the mean, variance, and rate of change of concentration.

[0112] For example, if the concentration change rate is R, the calculation formula is as follows:

[0113]

[0114] In the formula, C i+1 is the gas component concentration vector detected at the i+1th time; C i is the gas component concentration vector detected for the i-th time; T i+1 is the timestamp of the i+1th detection; T i is the timestamp of the i-th detection.

[0115] Extract the time series features of the past n detection data to form a feature vector:

[0116] X i =[C i ,C i-1 ,...,C i-n ,R i ], R i is the concentration change rate.

[0117] Model training: The normal state and fault state data in the historical database are used to train the machine learning model SVM; the training goal is to find a hyperplane to separate the normal state from the abnormal state;

[0118] The optimization of SVM is: The constraints are: i (w·X i +b)≥1;

[0119] Among them, w is the weight vector; b is the bias; y i For label; X i is the feature vector.

[0120] Then for each new data sample X new =[C new ,C new-1 ,...,C new-n ,R new ], C new is the latest eigenvalue, C new-1 is the characteristic value at the previous time point, C new-n is the eigenvalue n time units ago, R new is the latest concentration change rate;

[0121] Input the trained model and output the anomaly metric E; the anomaly metric formula is:

[0122] E=f(X new )

[0123] Where f is the trained anomaly detection function.

[0124] If the output abnormality measure E is greater than the set threshold E threshold , it is judged as abnormal and triggers fault warning; the system determines whether to trigger an alarm based on the size of the abnormal measurement E. When E exceeds the set threshold E threshold When the system sounds an alarm and records abnormal information, it prompts the operator to check.

[0125] In summary, the electronic display gas collection and composition analysis system makes gas management more intelligent and automated through functions such as efficient monitoring, safety warning, convenient maintenance and remote monitoring, and effectively guarantees the safe operation of power equipment.

[0126] At the same time, it should be pointed out that the above description is only a typical embodiment of the present invention and does not limit the present invention in any form. Any changes made to the above embodiments based on the ideas and technical essence of the present invention are within the protection scope of the present invention.

Claims

1. An electronically displayed gas collection and component analysis system, characterized in that: It includes an integrated housing, a gas collection module, an oil-gas separation module, a gas flow rate detection module, a chemical analysis module, and an electronic gas analysis module installed in the integrated housing; The gas collection module collects gas in the transformer, and is provided with an air inlet exposed to the outside of the integrated shell, and is connected to the transformer through the air inlet; the oil-gas separation module performs oil-gas separation on the gas, and is connected to the pipeline of the gas collection module; the gas flow rate detection module detects the gas flow rate data, and is connected to the pipeline of the oil-gas separation module; the chemical analysis module includes two chemical washing bottles, which are connected to the pipeline of the gas flow rate detection module and contain chemical reagents that react with specific components in the gas; the electronic gas analysis module identifies the concentrations of different components in the gas sample, and is connected to the pipeline of the chemical analysis module.

2. The electronically displayed gas collection and component analysis system according to claim 1 is characterized in that: The gas collection module is a sealed gas storage box with a gas observation window; the integrated housing is provided with an observation hole at the position of the observation window.

3. The electronically displayed gas collection and component analysis system according to claim 1 is characterized in that: The oil-gas separation module is provided with a pressure regulating valve and a pressure gauge exposed outside the integrated housing; the integrated housing is provided with an observation hole at the position of the pressure gauge.

4. The electronically displayed gas collection and component analysis system according to claim 1 is characterized in that: The integrated housing is provided with acrylic door panels at two chemical washing bottles; the chemical washing bottles are detachably mounted on the pipeline by means of snap-on washing bottle fixing clips; the chemical washing bottles can be replaced by opening the acrylic door panels.

5. The electronically displayed gas collection and component analysis system according to claim 1 is characterized in that: The volume flow rate detection module is an electronic gas flow rate detector with a display screen on it; the integrated housing is provided with an observation hole at the display screen; the electronic gas analysis module is a gas composition detector with a display screen on it; the integrated housing is provided with an observation hole at the display screen.

6. A method for collecting and analyzing gas components, the method being based on the electronically displayed gas collection and component analysis system according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1, gas collection: First, the gas collected from the transformer enters the system through the gas collection module, and the collected gas enters the oil-gas separation module through the pipeline; Step 2, oil-gas separation: In the oil-gas separation module, the oil and other components in the gas sample are effectively separated, and the separated gas sample continues to be transmitted downward and enters the gas flow rate detection module; Step 3, flow rate detection: The gas sample passes through the gas flow rate detection module to measure its flow rate in real time, and the flow rate data is displayed. The gas flow rate data provides a reference for subsequent chemical and electronic analysis; Step 4, chemical analysis: the body sample flows into the chemical washing bottle in the chemical analysis module, and reacts with the chemical reagent in the chemical washing bottle to remove interfering gas components and provide a preliminary analysis of the gas sample components; Step 5, electronic analysis: The gas sample after chemical treatment enters the electronic gas analysis module for detailed component analysis; the detection results output by the electronic gas analysis module reflect the specific components and concentrations in the gas sample, thereby providing a basis for judging the working status of the transformer.

7. The electronically displayed gas collection and component analysis system according to claim 1 is characterized in that: The electronic gas analysis module, gas flow rate detection module and oil-gas separation module are also connected to a data storage module, which records and stores the results of each detection to establish a historical database of gas detection. The data storage module marks the data with a timestamp, and the data in the historical database is used for comparative analysis, so as to observe the changing trend of gas composition and identify potential failure risks.

8. A method for storing and integrating data of gas composition analysis results, the method is based on the electronically displayed gas collection and composition analysis system according to claim 7, characterized in that: The following steps are involved: Step 1, data collection and marking: After each gas collection and component analysis system completes the detection, the data storage module will mark the gas composition and flow rate detection results and generate a complete data record including a timestamp; Record the gas component concentration as C and the flow rate as V, then the data record is expressed as: D i ={T i ,C i ,V i ,P i } Where D i represents the data record of the i-th detection; T i is the timestamp; C i is the gas component concentration vector; V i is the gas flow rate; P i is the pressure value; Step 2, data storage: After each test, the system records the data i Stored in the historical database to form a historical data set {D1, D2, ..., D n }; Step 3, trend analysis: by analyzing the gas component concentration C in the historical data i Perform trend analysis and calculate its rate of change; if the concentration change rate is R, the calculation formula is as follows: In the formula, C i+1 is the gas component concentration vector detected at the i+1th time; C i is the gas component concentration vector detected for the i-th time; T i+1 is the timestamp of the i+1th detection; T i is the timestamp of the i-th detection; If the concentration change rate R exceeds the set threshold R threshold , there is a risk of equipment abnormality.

9. The electronically displayed gas collection and component analysis system according to claim 7 is characterized in that: The data storage module is also connected to a remote monitoring module, which uploads the detection results to the cloud through the network, and the operator accesses the data in real time through the client; when the detection data reaches a certain set threshold, the remote monitoring module automatically issues an alarm to notify the operator, indicating that there is a fault.

10. A remote monitoring and diagnosis method for gas composition analysis results, the method is based on the electronic display gas collection and component analysis system of claim 9, characterized in that: The following steps are involved: Step 1, data upload: After the gas collection and composition analysis system completes each test, the remote monitoring module uploads the data to i Upload to the cloud server via the Internet; Step 2, real-time monitoring: The cloud server monitors the received data in real time and sets warning thresholds; when an abnormality is detected, an alarm is triggered; Step 3, diagnostic model: The server integrates the diagnostic model and performs machine learning training based on the historical database to identify abnormal patterns of gas concentration changes; Assuming that the concentration anomaly measure is E, its calculation formula is as follows: E=f(C i ,C i-1 ,...,C i-n ) Where f is the anomaly detection function; C i is the gas component concentration vector detected for the i-th time; C i-1 is the gas component concentration vector detected at the i-1th time; C i-n The gas component concentration vector detected at the inth time; Among them, the anomaly detection function f is trained by the machine learning model SVM; the training steps are as follows: First, extract the gas concentration data C from the historical database i , timestamp T i and other environmental parameters; standardize the data so that different eigenvalues ​​have similar distributions. The standardization formula is as follows: In the formula, C i ' is the concentration value after standardization; C i is the gas component concentration vector detected for the i-th time; μc is the mean of the gas concentration; σc is the standard deviation of the gas concentration; Then, for the gas concentration data C i Extract features, including the mean, variance, and rate of change of the concentration, and extract the time series features of the past n detection data to form a feature vector: X i =[C i ,C i-1 ,...,C i-n ,R i ], R i is the concentration change rate; The normal state and fault state data in the historical database are used to train the machine learning model SVM; the training goal is to find a hyperplane to separate the normal state from the abnormal state; The optimization of SVM is: The constraints are: i (w·X i +b)≥1; Among them, w is the weight vector; b is the bias; y i For label; X i is the feature vector; For each new data sample X new =[C new ,C new-1 ,...,C new-n ,R new ], C new is the latest eigenvalue, C new-1 is the characteristic value at the previous time point, C new-n is the eigenvalue n time units ago, R new is the latest concentration change rate; Input the trained model and output the anomaly metric E; the anomaly metric formula is: E=f(X new ) Where f is the trained anomaly detection function; If the output abnormality measure E is greater than the set threshold E threshold , it is judged as abnormal and triggers a fault warning.

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