Valve inner leakage monitoring method and device, electronic equipment and storage medium

Abstract

The invention discloses a valve inner leakage monitoring method and device, electronic equipment and a storage medium, through the application, temperature and acoustic multi-source monitoring data of a valve area can be integrated, comprehensive extraction and analysis of inner leakage feature information are enhanced through a multi-source data fusion technology, a targeted leakage quantification model is constructed, and the accuracy of valve inner leakage monitoring is improved. The inner leakage degree of the valve is accurately evaluated, the limitation of lack of multi-mode cooperation in the prior art is broken through, meanwhile, the evaluation result is transmitted to the control system in real time to trigger adjustment operation, and leakage expansion is restrained in time; the technical effects of improving the accuracy of valve inner leakage identification and the accuracy of leakage quantity quantification, triggering valve adjusting operation in time, reducing linkage safety risks, guaranteeing stable operation of a thermal power plant and assisting in improving the energy efficiency of the power plant are achieved.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method and apparatus for monitoring internal leakage in valves, electronic equipment, and storage medium. Background Technology

[0002] Intelligent monitoring technology for thermal power plants serves as a core support for the digital transformation of the power industry and is widely used in the field of full-condition operation management of key units.

[0003] In existing technical solutions, discrete sensors and independent monitoring platforms are directly used without establishing a multimodal collaborative mechanism of wireless sensing, distributed optical fiber and AI video. This may lead to problems such as insufficient accuracy in identifying internal leaks in valves and excessive errors in quantifying leakage. These technical defects not only restrict energy efficiency improvement, but may also trigger a chain of safety risks such as pipeline corrosion and rupture, seriously threatening the stable operation of power plants. Summary of the Invention

[0004] This disclosure provides a method, apparatus, electronic device, and storage medium for monitoring internal leakage in valves.

[0005] According to a first aspect of this disclosure, a method for monitoring internal leakage in a valve is provided, comprising: Collect monitoring data from the valve area, including temperature and acoustic data; Based on the monitoring data, internal leakage characteristic information is extracted; A leakage quantification model is constructed using multi-source data fusion technology, and the degree of internal leakage of the valve is evaluated based on the internal leakage characteristic information. The evaluation results are transmitted to the control system to trigger an adjustment operation on the valve.

[0006] Optionally, the monitoring data collected from the valve area includes: Temperature distribution information in the valve area is acquired synchronously using multiple sensing devices; The acquisition time sequence from different sensors is aligned to generate spatiotemporally consistent fused temperature field data.

[0007] Optionally, the extraction and internal leakage feature information based on the monitoring data includes: The acoustic signature of the valve is extracted from the acoustic data; in particular, filtering technology is used to suppress environmental noise in real time, thereby improving the accuracy of feature extraction.

[0008] Optionally, the construction of the leakage quantification model using multi-source data fusion technology includes: A leakage calculation model was established based on the correlation between temperature change trends and acoustic characteristics. The monitoring data is processed in real time through edge computing nodes to meet the preset response time requirements.

[0009] Optionally, transmitting the evaluation results to the control system to trigger a regulating operation on the valve includes: A standard communication interface is used to interact with the power plant control system to ensure that data transmission delay is within a set threshold. Data compression technology can be used to optimize transmission efficiency and improve the response speed of control commands.

[0010] According to a second aspect of this disclosure, a valve internal leakage monitoring device is provided, comprising: The acquisition unit is used to acquire monitoring data of the valve area, including temperature data and acoustic data. Extraction unit, used to extract internal leakage feature information based on the monitoring data; An evaluation unit is used to construct a leakage quantification model using multi-source data fusion technology and to evaluate the degree of internal leakage of the valve based on the internal leakage characteristic information. A transmission unit is used to transmit the evaluation results to the control system to trigger a regulating operation on the valve.

[0011] Optionally, the acquisition unit is further configured to: Temperature distribution information in the valve area is acquired synchronously using multiple sensing devices; The acquisition time sequence from different sensors is aligned to generate spatiotemporally consistent fused temperature field data.

[0012] Optionally, the extraction unit is further configured to: The acoustic signature of the valve is extracted from the acoustic data; in particular, filtering technology is used to suppress environmental noise in real time, thereby improving the accuracy of feature extraction.

[0013] Optionally, the evaluation unit is also used for: A leakage calculation model was established based on the correlation between temperature change trends and acoustic characteristics. The monitoring data is processed in real time through edge computing nodes to meet the preset response time requirements.

[0014] Optionally, the transmission unit is further configured to: A standard communication interface is used to interact with the power plant control system to ensure that data transmission delay is within a set threshold. Data compression technology can be used to optimize transmission efficiency and improve the response speed of control commands.

[0015] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0016] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0017] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0018] The valve internal leakage monitoring method, device, electronic equipment, and storage medium disclosed herein, through the integration of multi-source monitoring data of temperature and acoustics in the valve area, enhance the comprehensive extraction and analysis of internal leakage characteristic information through multi-source data fusion technology, construct a targeted leakage quantification model, and accurately assess the degree of valve internal leakage. This overcomes the limitations of existing technologies that lack multimodal collaboration. Simultaneously, the assessment results are transmitted to the control system in real time to trigger adjustment operations and promptly curb the expansion of leakage. Therefore, it can solve the technical problems of existing technologies that, due to the use of discrete sensors and independent monitoring platforms and the lack of a multimodal collaboration mechanism, result in insufficient accuracy in valve internal leakage identification and excessive leakage quantification errors, thereby restricting energy efficiency improvement, causing safety risks such as pipeline corrosion and rupture, and threatening the stable operation of power plants. It achieves the technical effects of improving the accuracy of valve internal leakage identification and leakage quantification, timely triggering of valve adjustment operations, reducing cascading safety risks, ensuring the stable operation of thermal power plants, and contributing to the improvement of power plant energy efficiency.

[0019] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0020] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 This is a schematic flowchart illustrating a method for monitoring internal leakage of a valve provided in an embodiment of this disclosure. Figure 2 A schematic diagram of a valve internal leakage monitoring device provided in an embodiment of this disclosure; Figure 3 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0021] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0022] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for monitoring valve internal leakage according to embodiments of the present disclosure.

[0023] Figure 1 This is a schematic flowchart illustrating a method for monitoring internal leakage of a valve provided in an embodiment of this disclosure.

[0024] like Figure 1 As shown, the method includes the following steps: Step 101: Collect monitoring data of the valve area, including temperature data and acoustic data; Monitoring data includes temperature and acoustic data. The valve area typically refers to the cluster of high-temperature, high-pressure valves near the turbine or boiler piping in thermal power plants, such as main steam valves and drain valves. These valves are prone to internal leakage or damage during long-term operation. Temperature data is acquired through wireless temperature sensors deployed on the valve surface to monitor localized temperature changes caused by internal leakage, such as abnormal temperature increases or uneven temperature distribution near the leak point. Acoustic data is collected by acoustic sensors to capture sound signals from valve operation, including vibration noise and specific frequency sound waves generated by fluid leakage, thereby identifying internal leaks or mechanical faults. The data acquisition process must ensure real-time and continuous data transmission to support subsequent analysis and diagnosis.

[0025] This method improves monitoring reliability through multi-source data fusion, providing a foundation for valve condition assessment and avoiding the subjectivity and missed detection problems of traditional manual inspections. The entire data acquisition process is integrated into the power plant's existing monitoring system, achieving automated data acquisition without additional manual intervention, thus ensuring the safe and stable operation of the unit.

[0026] Step 102: Extract and internal leakage feature information based on the monitoring data; The extraction process begins with preprocessing the raw temperature and acoustic data, including signal filtering, noise reduction, and standardization, to eliminate interference from the industrial environment. Subsequently, temperature distribution characteristics are analyzed from the temperature data to identify abnormal temperature gradients and specific temperature rise patterns, which are related to localized heat accumulation caused by steam or high-temperature water leaks. Simultaneously, sound pressure level changes, specific frequency band energy distribution, and acoustic signature features are extracted from the acoustic data. These acoustic features reflect the high-frequency hissing or turbulent noise generated when there is internal leakage in the valve.

[0027] By comprehensively analyzing multi-source feature information, a feature vector is constructed to identify the internal leakage status of valves, providing key input parameters for subsequent diagnosis. This feature extraction step effectively transforms raw monitoring data into quantifiable and analyzable internal leakage characterization indicators, overcoming the subjective limitations of traditional manual leak identification by sound.

[0028] Step 103: Construct a leakage quantification model using multi-source data fusion technology, and evaluate the degree of internal leakage of the valve based on the internal leakage characteristic information; Multi-source data fusion technology integrates internal leakage characteristic information from temperature and acoustic data. By combining feature-level fusion with decision-level fusion, it eliminates the uncertainty of a single data source and improves model robustness. When constructing the leakage quantification model, machine learning algorithms such as support vector machines or neural networks are used to train historical internal leakage samples, establishing a nonlinear mapping relationship between feature vectors and leakage amounts. The model output is a continuous or discrete internal leakage degree index, such as leakage level or estimated leakage flow. During the evaluation process, the real-time extracted internal leakage features are input into the trained leakage quantification model to calculate the current valve's internal leakage status, achieving the conversion from features to quantification results. This step, through the complementarity of multi-source information, overcomes the misjudgment problem caused by the reliance on a single parameter in traditional methods, improving the accuracy and reliability of internal leakage diagnosis. It provides a scientific basis for power plant valve maintenance, supports early warning and precise intervention, thereby reducing energy consumption and operation and maintenance costs. The entire evaluation process is integrated into the power plant's intelligent monitoring platform, achieving automated analysis without manual intervention, ensuring the safe and efficient operation of the unit.

[0029] Step 104: The evaluation results are transmitted to the control system to trigger the adjustment operation for the valve.

[0030] The assessment results, including indicators such as internal leakage level or quantified leakage amount, are transmitted in real time to the power plant's distributed control system via industrial communication protocols such as Modbus or OPCUA, ensuring seamless data integration. Upon receiving the assessment results, the control system automatically generates adjustment commands, driving actuators to adjust valve openings, partially close valves, or completely isolate valves, thereby curbing media leakage and preventing further deterioration of internal leakage. Redundant communication channels are used during transmission to ensure reliability, avoiding data loss or delays and achieving closed-loop linkage between monitoring and control.

[0031] This step, through an automated response mechanism, reduces delays caused by manual intervention, improves the timeliness and accuracy of valve operation and maintenance, effectively reduces energy loss and equipment risks caused by internal leakage, and supports the safe and stable operation of the power plant. The entire process is embedded in the existing intelligent monitoring platform to ensure efficient coordination between assessment results and control actions, and strengthens the unit's adaptive management capabilities under all operating conditions.

[0032] In some embodiments, the monitoring data collected in the valve area includes: Temperature distribution information in the valve area is acquired synchronously using multiple sensing devices; The acquisition time sequence from different sensors is aligned to generate spatiotemporally consistent fused temperature field data.

[0033] When collecting temperature data in the valve area, multiple sensing devices are used to simultaneously acquire temperature distribution information on the valve surface. These multiple sensing devices include wireless temperature sensors and distributed fiber optic temperature sensors deployed at key parts of the valve. These devices simultaneously measure the temperature values ​​at different locations on the valve at a high sampling frequency, forming a dense temperature array covering the entire valve area, thereby capturing local thermal anomalies or temperature gradient changes caused by internal leakage.

[0034] Synchronous acquisition ensures that all sensor data are collected under the same time reference, avoiding measurement errors caused by time offset. Subsequently, the acquisition timing from different sensing devices is aligned. This process is achieved through a timestamp synchronization algorithm, which adjusts the data streams of each sensor to a unified time axis, eliminating the impact of communication delays or differences in sampling intervals between devices, and ensuring data consistency in time and space.

[0035] The aligned temperature data is further processed using data fusion technology to generate spatiotemporally consistent fused temperature field data. This data fusion process employs weighted averaging or Kalman filtering methods to integrate information from multiple sensor sources, constructing a continuous and accurate two-dimensional or three-dimensional temperature distribution map that accurately reflects the overall thermal state of the valve. This fused temperature field data enhances the reliability and resolution of temperature monitoring, providing high-quality input for subsequent internal leakage feature extraction, effectively improving the accuracy of valve condition assessment, and reducing the risk of false alarms due to single sensor failure or environmental interference. The entire acquisition and processing process is integrated into the power plant's intelligent monitoring system, achieving automated data synchronization and fusion without manual calibration, ensuring the continuity and stability of unit valve monitoring.

[0036] In some embodiments, the extraction and internal leakage feature information based on the monitoring data includes: The acoustic signature of the valve is extracted from the acoustic data; in particular, filtering technology is used to suppress environmental noise in real time, thereby improving the accuracy of feature extraction.

[0037] Acoustic data is collected through acoustic sensors deployed in the valve area, including various sound signals generated during valve operation. Operational acoustic signatures refer to the unique sound patterns of the valve under normal operation and internal leakage conditions, such as resonant components in specific frequency bands or sound pressure level variations. These features can effectively distinguish between internal valve leakage and normal phenomena such as mechanical vibration. The separation process employs filtering technology to suppress environmental noise in real time. Filtering techniques include digital bandpass filtering and adaptive filtering algorithms, filtering out background interference such as steam jets, equipment vibration, or electromagnetic noise in the high-temperature, high-pressure environment of the power plant, while retaining the core acoustic components directly related to valve operation.

[0038] Real-time suppression ensures dynamic adjustment of filter parameters during data acquisition, rapidly responding to environmental changes and preventing noise accumulation from affecting feature extraction, thereby improving the accuracy of feature extraction. By accurately separating operational acoustic signature features, subsequent analysis can more reliably identify early signs of valve internal leakage, enhancing the sensitivity and robustness of the monitoring system and providing clean and high-value input data for valve condition diagnosis. The entire processing flow is integrated into the intelligent monitoring platform, achieving automated acoustic signal processing without manual intervention, ensuring the continuity and effectiveness of valve monitoring across all operating conditions.

[0039] In some embodiments, constructing a leakage quantification model using multi-source data fusion technology includes: A leakage calculation model was established based on the correlation between temperature change trends and acoustic characteristics. The monitoring data is processed in real time through edge computing nodes to meet the preset response time requirements.

[0040] In constructing a leakage quantification model using multi-source data fusion technology, a key component is establishing a leakage calculation model based on the correlation between temperature change trends and acoustic characteristics. The temperature change trend refers to the temperature curve formed over time at monitoring points on the valve surface, including the rate of temperature rise and the spatial temperature gradient distribution. Acoustic characteristics encompass specific frequency band energy and sound pressure level fluctuations extracted from operational acoustic signatures. The correlation is established through statistical analysis methods or machine learning algorithms, revealing the intrinsic link between abnormal temperature patterns and changes in acoustic characteristics, forming a mathematical model capable of calculating leakage based on multi-source input parameters. This model, trained with historical leakage data, maps both temperature trend parameters and acoustic characteristic parameters into a quantitative leakage index, achieving precise quantification of internal leakage levels.

[0041] Real-time processing of monitoring data is achieved through edge computing nodes deployed in smart gateway devices near the valve area. These nodes possess local computing and storage capabilities, enabling them to directly process temperature and acoustic data, and perform feature extraction and model calculation tasks. Real-time processing encompasses the entire process of data preprocessing, feature analysis, and model inference, ensuring immediate internal leakage assessment after data acquisition and meeting preset response time requirements. This requirement, set at the second level according to power plant control needs, avoids delays caused by data transmission to the cloud. This approach effectively reduces system communication load, improves the real-time performance and reliability of internal leakage monitoring, and provides timely and accurate input for subsequent control operations. The entire model building and processing workflow is deeply integrated into the power plant's existing infrastructure, achieving distributed collaboration between monitoring and computation, and ensuring efficient operation of valve condition assessment.

[0042] In some embodiments, transmitting the evaluation results to the control system to trigger a regulating operation on the valve includes: A standard communication interface is used to interact with the power plant control system to ensure that data transmission delay is within a set threshold. Data compression technology can be used to optimize transmission efficiency and improve the response speed of control commands.

[0043] This invention relates to a method for full-condition monitoring of key units in thermal power plants. During the transmission of evaluation results to the control system to trigger valve regulation operations, a standard communication interface is used for data interaction with the power plant control system. Data transmission delay is strictly controlled within a set threshold, which is predefined based on the real-time control requirements of the power plant and is typically within the range of seconds, to avoid delays in regulation operations due to communication latency, thus affecting the internal leakage control effect. Simultaneously, data compression technology optimizes transmission efficiency. This technology employs lossless compression algorithms such as LZ77 or Huffman coding to compress the evaluation result data in real time, reducing data transmission volume, thereby improving network bandwidth utilization, reducing communication load, and significantly improving the response speed of control commands. This ensures that valve regulation operations, such as opening adjustment or isolation commands, can be executed quickly, promptly curbing the deterioration of internal leakage. The entire transmission process is integrated into the power plant's intelligent monitoring platform, achieving seamless linkage between monitoring data and control actions, enhancing system real-time performance and reliability, supporting the safe and stable operation of the unit, and reducing operation and maintenance costs.

[0044] Corresponding to the above-described method for monitoring internal leakage of valves, this invention also proposes a device for monitoring internal leakage of valves. Since the device embodiments of this invention correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to in the method embodiments described above, and will not be repeated here.

[0045] Figure 2 This is a schematic diagram of the structure of a valve internal leakage monitoring device provided in an embodiment of this disclosure, as shown below. Figure 2 As shown, it includes: Acquisition unit 21 is used to acquire monitoring data of the valve area, including temperature data and acoustic data; Extraction unit 22 is used to extract internal leakage feature information based on the monitoring data; Evaluation unit 23 is used to construct a leakage quantification model using multi-source data fusion technology and evaluate the degree of internal leakage of the valve based on the internal leakage characteristic information; The transmission unit 24 is used to transmit the evaluation results to the control system to trigger the adjustment operation for the valve.

[0046] Furthermore, in one possible implementation of this disclosure, the acquisition unit 21 is further configured to: Temperature distribution information in the valve area is acquired synchronously using multiple sensing devices; The acquisition time sequence from different sensors is aligned to generate spatiotemporally consistent fused temperature field data.

[0047] Furthermore, in one possible implementation of this disclosure, the extraction unit 22 is further configured to: The acoustic signature of the valve is extracted from the acoustic data; in particular, filtering technology is used to suppress environmental noise in real time, thereby improving the accuracy of feature extraction.

[0048] Furthermore, in one possible implementation of this disclosure embodiment, the evaluation unit 23 is further configured to: A leakage calculation model was established based on the correlation between temperature change trends and acoustic characteristics. The monitoring data is processed in real time through edge computing nodes to meet the preset response time requirements.

[0049] Furthermore, in one possible implementation of this disclosure, the transmission unit 24 is further configured to: A standard communication interface is used to interact with the power plant control system to ensure that data transmission delay is within a set threshold. Data compression technology can be used to optimize transmission efficiency and improve the response speed of control commands.

[0050] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of the embodiments of this disclosure, and the principle is the same. Therefore, the embodiments of this disclosure are not limited thereto.

[0051] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0052] Figure 3A schematic block diagram of an example electronic device 400 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0053] like Figure 3 As shown, device 400 includes a computing unit 401, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 402 or a computer program loaded from storage unit 408 into RAM (Random Access Memory) 403. RAM 403 may also store various programs and data required for the operation of device 400. The computing unit 401, ROM 402, and RAM 403 are interconnected via bus 404. I / O (Input / Output) interface 405 is also connected to bus 404.

[0054] Multiple components in device 400 are connected to I / O interface 405, including: input unit 406, such as keyboard, mouse, etc.; output unit 407, such as various types of monitors, speakers, etc.; storage unit 408, such as disk, optical disk, etc.; and communication unit 409, such as network card, modem, wireless transceiver, etc. Communication unit 409 allows device 400 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0055] The computing unit 401 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 401 performs the various methods and processes described above, such as the method for monitoring internal leakage in a valve. For example, in some embodiments, the method for monitoring internal leakage in a valve can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed on device 400 via ROM 402 and / or communication unit 409. When the computer program is loaded into RAM 403 and executed by the computing unit 401, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 401 may be configured to perform the aforementioned valve internal leakage monitoring method by any other suitable means (e.g., by means of firmware).

[0056] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0057] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0058] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0059] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0060] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0061] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0062] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0063] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0064] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for monitoring internal leakage in a valve, characterized in that, include: Collect monitoring data from the valve area, including temperature and acoustic data; Based on the monitoring data, internal leakage characteristic information is extracted; A leakage quantification model is constructed using multi-source data fusion technology, and the degree of internal leakage of the valve is evaluated based on the internal leakage characteristic information. The evaluation results are transmitted to the control system to trigger an adjustment operation on the valve.

2. The method according to claim 1, characterized in that, The monitoring data collected from the valve area includes: Temperature distribution information in the valve area is acquired synchronously using multiple sensing devices; The acquisition time sequence from different sensors is aligned to generate spatiotemporally consistent fused temperature field data.

3. The method according to claim 1, characterized in that, The extraction and internal leakage feature information based on the monitoring data includes: The acoustic signature of the valve is extracted from the acoustic data; in particular, filtering technology is used to suppress environmental noise in real time, thereby improving the accuracy of feature extraction.

4. The method according to claim 1, characterized in that, The method of constructing a leakage quantification model using multi-source data fusion technology includes: A leakage calculation model was established based on the correlation between temperature change trends and acoustic characteristics. The monitoring data is processed in real time through edge computing nodes to meet the preset response time requirements.

5. The method according to claim 1, characterized in that, The step of transmitting the evaluation results to the control system to trigger a regulating operation on the valve includes: A standard communication interface is used to interact with the power plant control system to ensure that data transmission delay is within a set threshold. Data compression technology can be used to optimize transmission efficiency and improve the response speed of control commands.

6. A device for monitoring internal leakage of a valve, characterized in that, include: The acquisition unit is used to acquire monitoring data of the valve area, including temperature data and acoustic data. Extraction unit, used to extract internal leakage feature information based on the monitoring data; An evaluation unit is used to construct a leakage quantification model using multi-source data fusion technology and to evaluate the degree of internal leakage of the valve based on the internal leakage characteristic information. A transmission unit is used to transmit the evaluation results to the control system to trigger a regulating operation on the valve.

7. The apparatus according to claim 6, characterized in that, The acquisition unit is also used for: Temperature distribution information in the valve area is acquired synchronously using multiple sensing devices; The acquisition time sequence from different sensors is aligned to generate spatiotemporally consistent fused temperature field data.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

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