Gas engine test run tracing method

Abstract

The application discloses a gas machine test running tracing method, creates a test running task, generates a unique detection tracing ID, associates and binds the to-be-tested gas machine basic information, identity information and the detection tracing ID, records a video or takes a photo during three-leak detection, obtains detection results and test running performance data after detection is completed, binds the detection results, the test running performance data, the video or the photo, a three-leak detection timestamp and the detection tracing ID to form detection data, and then uniformly associates, packs and stores the detection data as three-leak detection whole-process second-level tracing data, and ensures that the three-leak detection whole-process second-level tracing data cannot be tampered with. It can be seen that the gas machine test running tracing method realizes visualization, traceability and check of the operation of all links of the operator, and solves the technical problems of non-standard operation, missing process, difficult responsibility definition and no evidence in the three-leak test running check.

Description

Technical Field

[0001] The present invention relates to the technical field of gas engine test lines, and in particular to a method for tracing gas engine tests. Background Art

[0002] Currently, the test method for the three leaks (air leakage, water leakage, and oil leakage) of engines is mainly a semi-manual tracing mode based on manual records, independent video monitoring, and single-machine data collection. The specific implementation is as follows:

[0003] 1. On-site collection and recording method: Video collection: Ordinary industrial cameras are deployed in the test bench, engine body, pipeline, and instrument areas, only realizing local or simple network storage. The video files are named with time / equipment number, without a unified task identifier, and cannot be automatically bound to specific test tasks. Data collection: The test bench controller and sensors (pressure, temperature, flow, leakage detection, etc.) independently collect data and store it in a local industrial control computer or an independent data collection system, physically isolated from the video system, without an automatic association interface. Information entry: Core information such as engine number, operator, test time, and process nodes completely rely on manual filling of paper ledgers or entry in an independent Excel / simple system, without an automatic recognition and synchronization mechanism.

[0004] 2. Information association and tracing method: The video files, test performance data, and manual ledgers are independent of each other, without a globally unique identifier for association. When tracing, it is necessary to manually search for the corresponding segment in the video storage system according to information such as the engine number and time, match the parameter file in the data system at the same time, and then manually compare the time axis to complete the splicing. The tracing efficiency is low, error-prone, and the synchronous playback of video and data cannot be achieved.

[0005] 3. Process control, exception handling, and the inspection of the three leaks rely on manual experience judgment, without a standardized process for mandatory constraints, and there is a risk of non-standardized operations and omissions in the process. The exception alarm is only a local audible and visual prompt, and the alarm information cannot be automatically associated with the corresponding video segment and data point. It is difficult to quickly locate the problem link and the responsible entity after a failure occurs. There is no operation trace and process verification mechanism, and it is difficult to define responsibilities when quality problems occur, and there is no evidence for the process.

[0006] 4. Storage and management method: Video and data are centrally stored on local hard disks or ordinary servers, without a hierarchical storage, automatic archiving, and off-site disaster recovery mechanism. After long-term operation, the storage cost is high and the retrieval efficiency is low. The data lacks anti-tampering protection, is easily deleted or tampered with accidentally, and cannot meet the compliance requirements of quality tracing and safe production.

[0007] 5. Lack of digitalization and integration capabilities; no unified data center or platform; systems operate in isolation, making it impossible to achieve a dual traceability loop for operational behavior and actual measurement point status. It does not support high-frequency, high-precision data acquisition, failing to meet the precise control requirements of modern manufacturing for every second of testing, and is also difficult to integrate with enterprise ERP, MES, and other systems.

[0008] In summary, the current trial production line testing process relies on manual recording and experience-based judgment, resulting in technical pain points such as low data visualization, broken information traceability links, and a lack of effective technical means for process control. Summary of the Invention

[0009] To address the aforementioned shortcomings, the technical problem this invention aims to solve is to provide a gas engine commissioning traceability method, constructing a visual recording and traceability system covering the entire process of leak detection, enabling visualization, traceability, and verification of operator behavior at all stages, ensuring clear operational procedures, processes, and responsibilities during leak detection, and providing a complete chain of evidence and data.

[0010] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0011] A gas engine commissioning traceability method includes the following steps:

[0012] S10. Create a test run task, generate a unique test traceability ID for the test run task, and associate and bind the basic information of the gas generator to be tested with the test traceability ID.

[0013] S20. Identify and detect the traceability ID, obtain the corresponding early warning information, and output the early warning information through the human-computer interaction unit;

[0014] S30. Obtain the operator's identity information and bind the operator's identity information with the detection traceability ID;

[0015] S40. Re-identify the detection traceability ID, obtain the three leak detection timestamps, perform gas leak, water leak, and oil leak detection on the test gas machine, and record video or take photos during the detection process to truly record the on-site detection status.

[0016] S50, after the three leaks detection is completed, the detection results and test performance data are obtained. The detection results, test performance data, video or photos, three leaks detection timestamp and detection traceability ID are bound together to form the detection data.

[0017] S60 transmits test data and test reports to the server. The server uses the test traceability ID as the core index to uniformly associate and package basic information, operator identity information, test data and test reports into second-level traceability data for the entire process of three-leakage detection. It also uses database transactions, data hashing or blockchain notarization technology to ensure that the second-level traceability data for the entire process of three-leakage detection is tamper-proof and that all access and operation behaviors are traceable.

[0018] S70. Using the detection traceability ID, read the corresponding detection data, identify abnormal information in the three leakage detection process from the detection data, and then update the warning information based on the basic information in the detection traceability ID.

[0019] In a preferred embodiment, S10 includes:

[0020] When the gas generator enters the test preparation stage, a test task can be created automatically or manually.

[0021] Generate a unique detection traceability ID for the test run mission. The detection traceability ID can be a QR code, barcode, or RFID tag.

[0022] The basic information of the gas generator to be tested is associated and bound with the test traceability ID. The basic information includes the gas generator model, serial number, test stand number and planned test time.

[0023] The detection traceability ID is physically affixed to the gas machine.

[0024] In a preferred embodiment, S70 includes:

[0025] Use the detection traceability ID to read the corresponding detection data;

[0026] The detection data is transmitted to the AI, which identifies abnormal information during the three-leak detection process. The abnormal information includes water leakage, oil leakage, gas leakage, abnormal temperature and humidity, abnormal pressure and / or abnormal noise.

[0027] Then, based on the gas machine model in the detection traceability ID, the corresponding early warning information is updated using the abnormal information.

[0028] The preferred method is that the step of identifying abnormal information from the detection data during the three-leakage detection process in S70 specifically includes:

[0029] A deep learning-based image recognition model was constructed, which was trained on a large number of gas machine samples labeled with normal and leak states.

[0030] The video or photo in the detection data is input into the image recognition model, which automatically identifies whether there are oil stains, water stains and / or bubble features.

[0031] If oil stains, water stains, or bubbles are present, it is determined that there is abnormal information, and the location coordinates of the leak are located.

[0032] The preferred method further includes the following steps:

[0033] By scanning the detection traceability ID or by inputting at least one of the detection traceability ID, gas machine serial number, and detection date through the human-computer interaction unit, the corresponding three-leak detection process traceability data at the second level can be retrieved.

[0034] When retrieving data, a digital audit log is automatically generated, containing the retrieval user's identity, retrieval time, and retrieval purpose.

[0035] The digital audit log is associated with and synchronously stored with the retrieved data on the entire process of the three leak detection, which is traced at the second level, to achieve closed-loop traceability of the entire data flow.

[0036] The preferred method also includes a data statistics dashboard generation step, specifically including: the server periodically retrieves all second-level traceability data of the entire process of three leak detection stored within a preset time period, classifies and statistically analyzes the gas generator according to the detection results, generates a test run statistics dashboard, and visualizes the test run statistics dashboard.

[0037] The test drive statistics dashboard includes at least the following:

[0038] The total number of tests is displayed in the total number of tests within a preset time period.

[0039] The anomaly classification area is used to display the number of vehicles with three types of leaks, and to further subdivide the anomaly vehicles according to the type of leak: air, water, and oil.

[0040] The details list area is used to display the detection traceability ID, basic information, and corresponding anomaly details of the gas generators that have anomalies.

[0041] In a preferred embodiment, the human-computer interaction unit includes a display screen and / or a speaker;

[0042] S20 includes: scanning and detecting the traceability ID, retrieving the corresponding warning information from the preset warning model, displaying the warning information on the display screen and / or playing the warning information through the speaker.

[0043] In a preferred embodiment, S50 further includes:

[0044] During the process of acquiring test performance data, environmental parameter data on the test bench are collected simultaneously. The environmental parameter data includes at least ambient temperature, humidity and atmospheric pressure.

[0045] The environmental parameter data is associated and bound with the test performance data, and the environmental parameter data is normalized using a preset correction algorithm.

[0046] In a preferred embodiment, S30 further includes:

[0047] Before binding the operator's identity information with the test traceability ID, the operator is shown the historical test records and past abnormal records corresponding to the test traceability ID through the human-computer interaction unit.

[0048] Operators are required to confirm that they are aware of the historical risks before they can bind their identity and perform subsequent operations; otherwise, the test run process will be locked.

[0049] In a preferred embodiment, S40 further includes:

[0050] When recording videos or taking photos, the computer vision algorithm is used to identify the key points for detecting three leaks in the gas generator under test in real time.

[0051] When it is detected that the operator is not aligning the camera with the preset three key points for leak detection, or that the image clarity is lower than the preset threshold, the human-computer interaction unit will issue a real-time correction prompt until video or photo data that meets the quality requirements is obtained.

[0052] After adopting the above technical solution, the beneficial effects of the present invention are:

[0053] Because the gas engine commissioning traceability method of this invention generates a unique detection traceability ID based on the created commissioning task, and then associates and binds the basic information of the gas engine under test, the operator's identity information, and the detection traceability ID, the detection traceability ID runs through the entire process of commissioning early warning, operator identity binding, three leak detection, data uploading and analysis, realizing second-level accurate traceability from a single workstation to the entire life cycle; using database transactions, data hashing or blockchain evidence storage technology, it ensures that all commissioning data (including videos, results, personnel information) are solidified as soon as they are generated, completely eliminating data fraud and meeting high-level quality certification requirements; by identifying anomalies and updating early warning information in reverse, it breaks the traditional separation of recording and analysis, realizes the self-evolution of the early warning model, improves the preventive maintenance capability of subsequent commissioning, and realizes the visualization, traceability and verification of operator behavior in all aspects, solving the technical problems of non-standard operation, omissions in the process, difficulty in defining responsibility and lack of process evidence in the three leak detection of commissioning. Attached Figure Description

[0054] Figure 1 This is a flowchart illustrating the gas engine test run traceability method in this invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0056] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0057] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0058] like Figure 1 As shown, a gas engine commissioning traceability method includes the following steps:

[0059] Step S10: Create a test run task, generate a unique detection traceability ID for the test run task, and associate and bind the basic information of the gas generator to be tested with the detection traceability ID; this embodiment specifically includes the following steps:

[0060] When the gas generator enters the test preparation stage, a test task can be created automatically or manually.

[0061] Generate a unique detection traceability ID for the test run mission. The detection traceability ID can be a QR code, barcode, or RFID tag.

[0062] The basic information of the gas generator to be tested is associated and bound with the test traceability ID. The basic information includes the gas generator model, serial number, test stand number and planned test time.

[0063] The detection traceability ID should be physically affixed to a prominent location on the gas generator.

[0064] Step S20: Identify the detection traceability ID, obtain the corresponding warning information, and output the warning information through the human-machine interaction unit. The warning information indicates the potential problems in the three-leakage detection process, realizing early warning of risks and improving the safety of the detection process. In this embodiment, the human-machine interaction unit includes a display screen and / or a speaker. S20 specifically includes the following steps: scan the detection traceability ID, find the corresponding warning information from the preset warning model, and display the warning information on the display screen and / or play the warning information through the speaker.

[0065] It is important to note that the early warning model can be, but is not limited to, a dynamic decision-making system based on historical big data and static rules. This dynamic decision-making system has a pre-set static rule base for storing process limitation information for specific gas generator models. Simultaneously, the dynamic decision-making system collects and stores the test results generated from each test run as dynamic historical data in real time. When a test traceability ID is identified, the early warning model extracts the gas generator model and serial number associated with that ID. The early warning model first searches the static rule base for a general early warning for that model (e.g., high-temperature test prohibition); secondly, it searches the dynamic historical database for any past anomaly records for that serial number. If a match is found, the dynamic decision-making system generates corresponding early warning information, such as 'This equipment has previously experienced three leakage anomalies; please conduct a thorough re-inspection.' This model achieves a shift from 'post-event traceability' to 'pre-event prevention'.

[0066] Step S30: Obtain operator identity information and bind the operator identity information with the detection traceability ID; in this embodiment, operator identity information can be entered by reading work cards, logging into handheld terminals with accounts, or human-machine interaction units.

[0067] Step S40: Re-identify the detection traceability ID, obtain the three leak detection timestamps, perform gas leak, water leak, and oil leak detection on the test gas machine, and record video or take photos during the detection process to truly record the on-site detection status.

[0068] Step S50: The three leaks detection is completed, and the detection results and test performance data are obtained. The detection results, test performance data, video or photos, three leaks detection timestamp and detection traceability ID are bound together to form the detection data.

[0069] Step S60: Transmit the detection data and test report to the server. Specifically, the data can be transmitted to the server in real time or in batches via the network. The server uses the detection traceability ID as the core index to uniformly associate and package the basic information, operator identity information, detection data and test report into second-level traceability data for the entire process of three leak detection (i.e., forming a complete record of the three leak detection and test process in the digital history of the gas engine). Database transactions, data hashing or blockchain notarization technology is used to ensure that the second-level traceability data for the entire process of three leak detection is tamper-proof and that all access and operation behaviors are traceable.

[0070] It should be noted that the second-level traceability data for the entire process of three-leak detection may include, but is not limited to, test report summaries, process timelines, detailed evidence chains, and authoritative traceability documents; the test report summary includes the test conclusions, test time, and responsible person; the process timeline is fully visualized; and the detailed evidence chain includes the results and evidence of each test point.

[0071] Step S70: Using the detection traceability ID, read the corresponding detection data, identify abnormal information during the three-leakage detection process from the detection data, and then update the warning information based on the basic information in the detection traceability ID. In this embodiment, this step specifically includes:

[0072] Use the detection traceability ID to read the corresponding detection data;

[0073] The detection data is transmitted to the AI, which identifies abnormal information during the three-leak detection process. Abnormal information includes water leakage, oil leakage, gas leakage, abnormal temperature and humidity, abnormal pressure and / or abnormal noise.

[0074] Then, based on the gas machine model in the detection traceability ID, the corresponding early warning information is updated using the abnormal information.

[0075] This invention enables the early warning model to automatically update early warning information through AI, thus achieving self-evolution and continuous optimization.

[0076] The gas engine commissioning traceability method of this invention constructs a full-process, tamper-proof, and intelligent traceability foundation. Specifically, it detects traceability IDs throughout the entire process of commissioning task creation, early warning, identity binding, three-leak detection, data uploading, and AI analysis, achieving second-level accurate traceability from a single workstation to the entire lifecycle. Utilizing database transactions, data hashing, or blockchain notarization technology, it ensures that all commissioning data (including videos, results, and personnel information) is solidified once generated, completely eliminating data falsification and meeting high-level quality certification requirements. By identifying abnormal information and updating early warning information in reverse, it breaks down the traditional separation between recording and analysis, achieving self-evolution of the early warning model and improving the preventative maintenance capabilities of subsequent commissioning. In short, this invention achieves visualization, traceability, and verifiability of operator behavior at all stages, solving the technical problems of non-standard operation, incomplete processes, difficulty in defining responsibility, and lack of process evidence in three-leak detection during commissioning.

[0077] In some other embodiments, the step S70 of identifying abnormal information from the detection data during the three-leakage detection process specifically includes:

[0078] A deep learning-based image recognition model was constructed, which was trained on a large number of gas machine samples labeled with normal and leak states.

[0079] Input the video or photo from the detection data into the image recognition model, and the image recognition model will automatically identify whether there are oil stains, water stains or bubble features;

[0080] If oil stains, water stains, and / or bubbles are present, it is determined that there is abnormal information, and the location coordinates of the leak are located.

[0081] This invention utilizes a deep learning image recognition model to automatically identify oil stains, water stains, and bubble features and locate the leak coordinates, replacing the traditional subjective judgment that relies on manual visual inspection and significantly improving the accuracy of three-leak detection.

[0082] It is important to note that locating the coordinates of the leak can be achieved through, but is not limited to, a perspective transformation matrix between the pre-established image pixel coordinate system and the gas engine physical coordinate system. When the image recognition model identifies oil stains, water stains, or air bubbles, the pixel coordinates of the geometric center point of this feature in the image are extracted. The perspective transformation matrix is ​​then used to convert these pixel coordinates into three-dimensional physical coordinates on the gas engine body. Based on these three-dimensional physical coordinates, the corresponding component name can be matched against pre-stored gas engine CAD model data, thus achieving precise physical location.

[0083] In other embodiments, the gas engine commissioning traceability method of the present invention further includes the following steps:

[0084] By scanning the detection traceability ID or by inputting at least one of the detection traceability ID, gas machine serial number, and detection date through the human-computer interaction unit, the corresponding three-leak detection process traceability data at the second level can be retrieved.

[0085] When retrieving data, a digital audit log is automatically generated, containing the retrieval user's identity, retrieval time, and retrieval purpose.

[0086] The digital audit log is associated with and synchronously stored with the retrieved data on the entire process of the three leak detection, which is traced at the second level, to achieve closed-loop traceability of the entire data flow.

[0087] In other embodiments, the gas engine commissioning traceability method of the present invention further includes a data statistics dashboard generation step, specifically including: the server periodically retrieving all second-level traceability data of the entire process of three-leak detection stored within a preset time period, classifying and statistically analyzing the gas engine according to the detection results, generating a commissioning statistics dashboard, and visually displaying the commissioning statistics dashboard; wherein the commissioning statistics dashboard includes at least:

[0088] The total number of tests is displayed in the total number of tests within a preset time period.

[0089] The anomaly classification area is used to display the number of vehicles with three types of leaks, and to further subdivide the anomaly vehicles according to the type of leak: air, water, and oil.

[0090] The details list area is used to display the detection traceability ID, basic information, and corresponding anomaly details of the gas generators that have anomalies.

[0091] This invention can use a server to periodically retrieve stored data and automatically generate a visual dashboard that includes total statistics, anomaly classification (air leak / water leak / oil leak) and a detailed list. Without having to read through a large number of individual reports, it can intuitively grasp the production yield and fault distribution patterns within a specific time period, thereby achieving a leap from tracing single-point problems to macro-quality control and providing strong data support for production process improvement.

[0092] In some other embodiments, step S50 further includes:

[0093] During the process of acquiring test performance data, environmental parameter data on the test bench are collected simultaneously. The environmental parameter data includes at least ambient temperature, humidity and atmospheric pressure.

[0094] The environmental parameter data is associated with and bound to the test performance data, and a preset correction algorithm is used to normalize the environmental parameter data in order to eliminate the interference of environmental factors on the determination of test performance data.

[0095] This invention records every detail, from environmental data to operators, through environmental parameter collection and correction and audit log binding mechanisms. In the event of quality disputes or after-sales traceability, the entire dataset can be retrieved in seconds by detecting the traceability ID, and all access and retrieval activities are traceable, forming a complete legal liability loop and greatly improving the company's quality management level and customer trust.

[0096] It should be noted that the preset correction algorithm can be, but is not limited to, an atmospheric correction algorithm for internal combustion engine performance. First, real-time ambient temperature, humidity, and atmospheric pressure are collected. The atmospheric correction factor (CF) is calculated according to the formula specified in ISO 1585 or GB / T 18297 standards. Then, the measured test performance data (such as power, torque, and fuel consumption rate) is multiplied by this correction factor to convert it into theoretical performance data under standard atmospheric conditions. This normalization process eliminates test fluctuations caused by diurnal temperature variations, rainy season humidity, or low atmospheric pressure at high altitudes, ensuring the horizontal comparability of performance data from different batches of gas engines and the uniformity of judgment standards.

[0097] In some other embodiments, step S30 further includes:

[0098] Before binding the operator's identity information with the test traceability ID, the operator is shown the historical test records and past abnormal records corresponding to the test traceability ID through the human-computer interaction unit.

[0099] Operators are required to confirm that they are aware of the historical risks before they can bind their identity and perform subsequent operations; otherwise, the test run process will be locked.

[0100] Before commencing operations, operators must confirm historical test records and past anomalies through the human-machine interface unit; otherwise, the process cannot be initiated. This invention employs a mandatory risk notification mechanism, enabling operators to proactively avoid known potential faults and prevent recurring errors, thus significantly reducing safety risks during the test run.

[0101] In some other embodiments, step S40 further includes:

[0102] When recording videos or taking photos, the computer vision algorithm is used to identify the key points for detecting three leaks in the gas generator under test in real time.

[0103] When it is detected that the operator is not aligning the camera with the preset three key points for leak detection, or that the image clarity is lower than the preset threshold, the human-computer interaction unit will issue a real-time correction prompt until video or photo data that meets the quality requirements is obtained.

[0104] This invention uses computer vision algorithms to identify key points in real time, displaying the information via loudspeaker announcements or screens to identify operators, thus solving the problem of invalid evidence caused by incorrect shooting angles or blurry images from manual photography. Because this invention employs database transactions, data hashing, or blockchain storage technologies, it packages and stores basic information, personnel identity, testing data, and test reports into second-level traceability data for the entire three-leak detection process. Simultaneously, leveraging the decentralized nature of blockchain and hash algorithms, it ensures that data from each test node, once generated, cannot be maliciously tampered with. Combined with video / photo recording and visual correction, it fundamentally guarantees the authenticity and originality of testing evidence, effectively preventing favoritism in reporting or data falsification, and meeting the high reliability requirements of quality traceability in high-end manufacturing industries.

[0105] It should be clarified that the computer vision algorithm can be, but is not limited to, a real-time guided algorithm based on feature point matching. Geometric templates for key leak detection points are pre-entered for different gas engine models. During detection, the algorithm calculates in real-time the similarity between feature points captured by the camera and the pre-entered templates, as well as the variance of the Laplacian operator in the image. When the similarity is below 80% or the variance is below a set threshold, the image is deemed unqualified, and a correction prompt is immediately issued via the display screen or speaker. Only when the image simultaneously meets the conditions of positional alignment and high clarity is the recorded frame considered valid evidence.

[0106] In some embodiments of the present invention, the digital fingerprint of the detection data can be extracted and written into the distributed ledger of the blockchain network at the same time as the detection data is uploaded to the server;

[0107] When the server receives a modification request for the stored detection data, it recalculates the digital fingerprint of the current data and compares it with the original fingerprint in the blockchain ledger. If they do not match, an anti-tampering alarm is triggered and the modification operation is rejected.

[0108] In summary, the gas generator commissioning traceability method of the present invention mainly creates a globally unique detection traceability ID (digital ID card) for each gas generator to be commissioned, and forcibly binds this detection traceability ID with the entire process of commissioning preparation, three-leakage detection, operators, timestamps, and test results, automatically uploads and centrally stores it, realizing full traceability, facilitating subsequent traceability verification and AI analysis. Afterwards, only the detection traceability ID needs to be queried to completely reconstruct the entire three-leakage detection process of the gas generator, realizing digital closed-loop management of the entire commissioning process. Compared with traditional paper records or simple electronic data entry methods, it has the following significant advantages:

[0109] The data is authentic and reliable, enabling tamper-proof anti-counterfeiting and traceability; early risk warning enhances the security of the testing process; intelligent testing improves operational efficiency and accuracy; data-driven management decisions provide multi-dimensional quality insights; and end-to-end closed-loop auditing meets compliance and accountability requirements. In other words, the gas engine commissioning traceability method of this invention uses a unique ID to connect the entire process, ensuring data authenticity and tamper-proofness, and achieves intelligent early warning through AI recognition feedback, laying the foundation for accurate traceability.

[0110] In addition, the gas engine commissioning traceability method of the present invention can be applied to a gas engine commissioning traceability system. The gas engine commissioning traceability system may include an industrial control computer, a recorder, and a data acquisition station. The industrial control computer may be an upgraded version of the existing industrial control computers at each workstation. Specifically, the industrial control computer includes a Linux storage-dedicated operating system, a single control unit configured with a 64-bit multi-core processor, 16GB of memory (expandable to 128GB), and an external 240G SSD solid-state drive. It features four 2.5Gb Ethernet ports, one IPMI management port, two front USB 2.0 ports, two rear USB 3.0 ports, one front VGA port, one rear HDMI port, one RS-232 serial port, four PCI-E 3.0 slots, and 24 hot-swappable hard drive slots. It is powered by a hot-swappable 1+1 AC220V redundant power supply. The recorder's resolution is ≥320*240. The built-in camera supports recording resolutions of 2K / 25fps, 1080P / 25fps, 1280×720, and 720x576, supporting dual-stream recording. One stream records locally at 2K / 25fps while the other uploads video information to a platform or designated location at 1080P / 25fps. It has built-in non-removable storage with a capacity ≥32GB. The data acquisition station has a built-in speaker and features RJ45, USB 2.0, and USB ports. The 3.0, HDMI, and RS-232 interfaces use a web client to display data acquisition and configuration parameters.

[0111] After applying the gas engine commissioning traceability method of this invention, this gas engine commissioning traceability system constructs a visual recording and traceability system covering the entire process of checking for three leaks. This gas engine commissioning traceability system lays a solid foundation for achieving the goal of high-frequency and high-precision data collection with one meter per second during commissioning. It can effectively empower the digital upgrade and continuous optimization of the assembly and testing production line, and significantly improve the overall operational efficiency and quality management level of the production line.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications or improvements to the same gas engine test run traceability method made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for tracing the commissioning of a gas engine, characterized in that, Includes the following steps: S10. Create a test run task, generate a unique test traceability ID for the test run task, and associate and bind the basic information of the gas generator to be tested with the test traceability ID. S20. Identify and detect the traceability ID, obtain the corresponding early warning information, and output the early warning information through the human-computer interaction unit; S30. Obtain the operator's identity information and bind the operator's identity information with the detection traceability ID; S40. Re-identify the detection traceability ID, obtain the three leak detection timestamps, perform gas leak, water leak, and oil leak detection on the test gas machine, and record video or take photos during the detection process to truly record the on-site detection status. S50, after the three leaks detection is completed, the detection results and test performance data are obtained. The detection results, test performance data, video or photos, three leaks detection timestamp and detection traceability ID are bound together to form the detection data. S60 transmits test data and test reports to the server. The server uses the test traceability ID as the core index to uniformly associate and package basic information, operator identity information, test data and test reports into second-level traceability data for the entire process of three-leakage detection. It also uses database transactions, data hashing or blockchain notarization technology to ensure that the second-level traceability data for the entire process of three-leakage detection is tamper-proof and that all access and operation behaviors are traceable. S70. Using the detection traceability ID, read the corresponding detection data, identify abnormal information in the three leakage detection process from the detection data, and then update the warning information based on the basic information in the detection traceability ID.

2. The gas engine test run traceability method according to claim 1, characterized in that, S10 includes: When the gas generator enters the test preparation stage, a test task can be created automatically or manually. Generate a unique detection traceability ID for the test run mission. The detection traceability ID can be a QR code, barcode, or RFID tag. The basic information of the gas generator to be tested is associated and bound with the test traceability ID. The basic information includes the gas generator model, serial number, test stand number and planned test time. The detection traceability ID is physically affixed to the gas machine.

3. The gas engine test run traceability method according to claim 2, characterized in that, The S70 includes: Use the detection traceability ID to read the corresponding detection data; The detection data is transmitted to the AI, which identifies abnormal information during the three-leak detection process. The abnormal information includes water leakage, oil leakage, gas leakage, abnormal temperature and humidity, abnormal pressure and / or abnormal noise. Then, based on the gas machine model in the detection traceability ID, the corresponding early warning information is updated using the abnormal information.

4. The gas engine test run traceability method according to claim 1, characterized in that, The step in S70 of identifying abnormal information from the detection data during the three-leakage detection process specifically includes: A deep learning-based image recognition model was constructed, which was trained on a large number of gas machine samples labeled with normal and leak states. The video or photo in the detection data is input into the image recognition model, which automatically identifies whether there are oil stains, water stains or bubble features. If oil stains, water stains, and / or bubbles are present, it is determined that there is abnormal information, and the location coordinates of the leak are located.

5. The gas engine commissioning traceability method according to claim 1, characterized in that, It also includes the following steps: By scanning the detection traceability ID or by inputting at least one of the detection traceability ID, gas machine serial number, and detection date through the human-computer interaction unit, the corresponding three-leak detection process traceability data at the second level can be retrieved. When retrieving data, a digital audit log is automatically generated, containing the retrieval user's identity, retrieval time, and retrieval purpose. The digital audit log is associated with and synchronously stored with the retrieved data on the entire process of the three leak detection, which is traced at the second level, to achieve closed-loop traceability of the entire data flow.

6. The gas engine commissioning traceability method according to claim 5, characterized in that, It also includes the data statistics dashboard generation step, specifically including: the server periodically retrieves all the second-level traceability data of the entire process of three leak detection stored within a preset time period, classifies and statistically analyzes the gas engine according to the detection results, generates a test run statistics dashboard, and visualizes the test run statistics dashboard. The test drive statistics dashboard includes at least the following: The total number of tests is displayed in the total number of tests within a preset time period. The anomaly classification area is used to display the number of vehicles with three types of leaks, and to further subdivide the anomaly vehicles according to the type of leak: air, water, and oil. The details list area is used to display the detection traceability ID, basic information, and corresponding anomaly details of the gas generators that have anomalies.

7. The gas engine test run traceability method according to claim 1, characterized in that, The human-computer interaction unit includes a display screen and / or a speaker; S20 includes: scanning and detecting the traceability ID, retrieving the corresponding warning information from the preset warning model, displaying the warning information on the display screen and / or playing the warning information through the speaker.

8. The gas engine commissioning traceability method according to claim 1, characterized in that, The S50 further includes: During the process of acquiring test performance data, environmental parameter data on the test bench are collected simultaneously. The environmental parameter data includes at least ambient temperature, humidity and atmospheric pressure. The environmental parameter data is associated and bound with the test performance data, and the environmental parameter data is normalized using a preset correction algorithm.

9. The gas engine commissioning traceability method according to claim 1, characterized in that, The S30 further includes: Before binding the operator's identity information with the test traceability ID, the operator is shown the historical test records and past abnormal records corresponding to the test traceability ID through the human-computer interaction unit. Operators are required to confirm that they are aware of the historical risks before they can bind their identity and perform subsequent operations; otherwise, the test run process will be locked.

10. The gas engine test run traceability method according to claim 1, characterized in that, The S40 further includes: When recording videos or taking photos, the computer vision algorithm is used to identify the key points for detecting three leaks in the gas generator under test in real time. When it is detected that the operator is not aligning the camera with the preset three key points for leak detection, or that the image clarity is lower than the preset threshold, the human-computer interaction unit will issue a real-time correction prompt until video or photo data that meets the quality requirements is obtained.

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