An oil depot equipment early warning method and system based on visual recognition and voiceprint analysis

Abstract

The application relates to the technical field of oil depot equipment early warning, and discloses an oil depot equipment early warning method and system based on visual identification and voiceprint analysis, which comprises the following steps: establishing the spatial binding relationship among the camera, the valve and the microphone in the target area of the oil depot, obtaining visual image data and valve control instruction data; identifying the valve closing state and screening the target valve; collecting the target sound signal and performing internal leakage detection data shielding processing; constructing a dynamic reference sound library and performing adaptive noise cancellation on the target sound signal; extracting a voiceprint feature vector and performing time synchronization and fusion diagnosis, and outputting graded internal leakage early warning information. The application combines visual closing state determination with voiceprint analysis, realizes targeted identification and graded early warning of the internal leakage of the oil depot valve, improves early warning accuracy and reduces false alarm interference.

Description

Technical Field

[0001] This invention relates to the field of early warning technology for oil depot equipment, and more specifically, to an early warning method and system for oil depot equipment based on visual recognition and voiceprint analysis. Background Technology

[0002] During oil depot operations, including receiving and dispatching oil, transferring oil between tanks, disconnecting pipelines, isolating operations, and emergency shut-off, numerous valves are typically required to control the connection and isolation of different storage tanks, pipelines, and operational units. This is particularly crucial in scenarios such as pipeline isolation after oil transfer operations, isolation before switching between different oil products, and static conditions after the inlet and outlet passages of storage tanks are closed. Valves must maintain a continuous seal when closed to prevent cross-flow, backflow, or cross-line flow of the medium. If internal leakage occurs in a closed valve, the medium may slowly enter downstream or adjacent pipelines through the valve core seal, potentially leading to oil mixing, abnormal tank levels, metering errors, and safety risks.

[0003] Currently, valve operation status monitoring in oil depots relies heavily on manual inspections, valve opening and closing command records, and comprehensive judgment based on changes in process parameters such as pressure, flow rate, and liquid level in the SCADA system. These methods can usually detect external leaks, obvious drips, or faults that have caused significant fluctuations in pipeline pressure and flow. However, when a valve has received a closing command and there are no obvious external abnormalities, internal leaks, especially early, minute leaks, are often highly concealed, small in volume, and long-lasting. They do not leave obvious external traces over a considerable period and are unlikely to cause significant pressure, flow, or level changes in a short time, making them difficult to detect in a timely manner.

[0004] Especially in scenarios where oil depot valves are densely distributed, pipelines are laid in parallel, and pumps and auxiliary equipment operate continuously, there is significant mechanical vibration interference, pipeline noise interference, and mutual influence caused by the close proximity of multiple valves. In such situations, even if minor anomalies in local process parameters are detected, it is often difficult to quickly determine whether the anomaly is caused by internal leakage from a closed valve, and it is also difficult to accurately locate the specific valve. Consequently, existing technologies for early internal leakage monitoring of closed valves in oil depots suffer from several problems: difficulty in identifying early, minute internal leaks, delayed anomaly detection, a high false positive rate, and difficulty in locating leaking valves. Summary of the Invention

[0005] In view of this, the present invention proposes an early warning method and system for oil depot equipment based on visual recognition and voiceprint analysis, aiming to solve the problems of existing technologies in the early warning process of valve internal leakage in the closed state of oil depots, such as difficulty in detecting minute internal leaks, delayed warnings, high false alarm and false alarm rates, and difficulty in accurately locating abnormal valves.

[0006] In one aspect, the present invention proposes an early warning method for oil depot equipment based on visual recognition and voiceprint analysis, comprising: Establish spatial binding relationships between cameras, valves, and microphones within the target area of ​​the oil depot to acquire visual image data and valve control command data corresponding to the valves. Based on the visual image data, the actuator switching state of the valve is identified, and at least one structural state information is extracted from the valve stem displacement, handwheel rotation angle, and flange clearance. The physical closing state of the valve is determined based on the actuator switching state and structural state information. Based on the valve control command data, target valves that simultaneously satisfy the physical closing state and the command closing state are selected to obtain the prior constraint information of the closing state of the target valve. Based on the prior constraint information of the closed state and the spatial binding relationship, the microphone bound to the target valve space is activated to collect the target sound signal, and the sound signal collected by the microphone corresponding to the non-target valve is subjected to internal leakage detection data shielding processing. Acquire reference acoustic signals in the same area as the target valve, construct a dynamic reference acoustic library containing the acoustic signals of the valve in its non-closed state and the ambient background acoustic signals, update the dynamic reference acoustic library and adjust the noise cancellation parameters according to the real-time changes in the valve state, and perform adaptive noise cancellation processing on the target acoustic signal to obtain an enhanced acoustic signal; Extract time-domain features, frequency-domain features, and time-frequency-domain features from the enhanced acoustic signal to construct the acoustic signature vector corresponding to the valve internal leakage. The prior constraint information of the closed state is synchronized with the voiceprint feature vector in time and fused for diagnosis to obtain the internal leakage probability and corresponding leakage level of the target valve. When the internal leakage probability is greater than or equal to the preset warning threshold, the graded internal leakage warning information corresponding to the target valve is output according to the leakage level.

[0007] Furthermore, when establishing the spatial binding relationship between cameras, valves, and microphones within the target area of ​​the oil depot, the following steps are included: The system acquires the installation location, shooting direction, and shooting range of each camera, and the installation location and acquisition range of each microphone. It then calibrates the position of each valve within the target area of ​​the oil depot, obtaining the spatial location identifier of each valve. Based on the shooting range of each camera, it determines the valve set corresponding to each camera, and based on the acquisition range of each microphone, it determines the valve set corresponding to each microphone. The system matches the valve sets corresponding to each camera with the valve sets corresponding to each microphone, establishing a one-to-one or one-to-many correspondence between valves and cameras / microphones. Finally, it stores these correspondences to obtain the spatial binding relationship between cameras, valves, and microphones.

[0008] Furthermore, when determining the physical closed state of the valve based on the actuator switching state and structural state information, the following steps are included: The actuator switch state is determined to be either closed or open; the structural state information is matched with the corresponding structural position state under the valve closing condition to determine at least one of the following: valve stem displacement corresponding to the valve stem closing position, handwheel rotation angle corresponding to the handwheel closing position, and flange clearance corresponding to the flange closing state; when the actuator switch state is closed and the structural state information corresponds to the structural position state under the valve closing condition, the valve is determined to be in a physically closed state.

[0009] Further, based on the valve control command data, when selecting target valves that simultaneously satisfy both the physical closed state and the command closed state, and obtaining the prior constraint information of the closed state of the target valves, the following is included: Read the valve control command data corresponding to each valve, and parse it to obtain the valve identifier and command type; identify the valve with the command type of "closed command" as the candidate valve in the command closed state; match the valve in the physical closed state with the candidate valve in the command closed state according to the valve identifier, and filter out the target valve that simultaneously satisfies the physical closed state and the command closed state; associate the valve identifier, physical closed state and command closed state of the target valve to obtain the prior constraint information of the closed state of the target valve.

[0010] Further, based on the prior constraint information of the closed state and the spatial binding relationship, when activating the microphone spatially bound to the target valve to collect the target acoustic signal, and performing internal leakage detection data masking processing on the acoustic signals collected by the microphones corresponding to non-target valves, the process includes: Read the target valve identifier from the prior constraint information of the closed state; retrieve the microphone identifier corresponding to the target valve identifier in the spatial binding relationship; send a collection start command to the microphone corresponding to the microphone identifier, and obtain the sound signal collected by the microphone as the target sound signal; perform internal leakage detection data masking processing on the other microphone collection channels that do not correspond to the target valve identifier, and retain the sound signals collected by the other microphones.

[0011] Furthermore, when acquiring reference acoustic signals located in the same area as the target valve and constructing a dynamic reference acoustic library containing the acoustic signals of the valve in its non-closed state and the ambient background acoustic signals, the process includes: The area range corresponding to the target valve is determined based on the spatial binding relationship; the current status information of each valve is read within the area, and valves in the non-closed state are filtered out; the microphone spatially bound to the non-closed valve is used to collect the corresponding valve sound signal as the non-closed valve sound signal; ambient sound signals other than the non-closed valve sound signal are collected within the area as ambient background sound signals; the non-closed valve sound signal and the ambient background sound signal are classified and stored according to the area identifier, signal source and collection time to construct the dynamic reference sound library.

[0012] Furthermore, when updating the dynamic reference acoustic library and adjusting the noise cancellation parameters based on real-time changes in the valve state, and performing adaptive noise cancellation processing on the target acoustic signal to obtain an enhanced acoustic signal, the process includes: The system acquires real-time status information of each valve within the area where the target valve is located. When the status information indicates that a valve has entered a non-closed state, the valve sound signal collected by the microphone bound to the space of the valve that has entered the non-closed state is added to the dynamic reference sound library. When the status information indicates that a valve has exited the non-closed state, the valve sound signal corresponding to that valve is removed from the dynamic reference sound library. The system retrieves the reference sound signal corresponding to the current area status from the updated dynamic reference sound library. The system adjusts the noise cancellation parameters based on the signal changes of the reference sound signal and the target sound signal. The system performs noise cancellation processing on the target sound signal based on the adjusted noise cancellation parameters to obtain the enhanced sound signal.

[0013] Further, when extracting time-domain features, frequency-domain features, and time-frequency-domain features from the enhanced acoustic signal to construct the acoustic signature feature vector corresponding to the valve internal leakage, the process includes: performing frame-by-frame processing on the enhanced acoustic signal; extracting at least one time-domain feature from the root mean square, peak factor, and kurtosis for each frame of the enhanced acoustic signal; performing spectral analysis on each frame of the enhanced acoustic signal to extract at least one frequency-domain feature from the center frequency, bandwidth, and harmonic components; performing time-frequency analysis on each frame of the enhanced acoustic signal to extract the time-frequency-domain features from the wavelet packet energy entropy; and combining the extracted time-domain features, frequency-domain features, and time-frequency-domain features to obtain the acoustic signature feature vector corresponding to the enhanced acoustic signal.

[0014] Furthermore, when performing time synchronization and fusion diagnosis on the prior constraint information of the closed state and the voiceprint feature vector to determine the internal leakage probability and leakage level of the target valve, the process includes: Extract the time information corresponding to the prior constraint information of the closed state and the acquisition time information corresponding to the acoustic feature vector; align the time information and acquisition time information according to a preset time window to obtain the prior constraint information of the closed state and the acoustic feature vector within the same time slice; perform correlation analysis on the time-synchronized prior constraint information of the closed state and the acoustic feature vector to obtain the internal leakage probability of the target valve; determine the leakage level of the target valve based on the internal leakage probability and the matching result of the acoustic feature vector with the features corresponding to the preset leakage level; the leakage level includes micro-leakage, slight leakage, moderate leakage and severe leakage.

[0015] Compared with existing technologies, the beneficial effects of this invention are as follows: By establishing a spatial binding relationship between cameras, valves, and microphones within the target area of ​​the oil depot, and based on the visual image data and valve control command data corresponding to the valves, the target valves can be accurately associated and their states filtered first, thus limiting subsequent early warning targets to valves that simultaneously meet both physical and command-based closing states, thereby improving the targeting of early warning targets; by activating only the microphone spatially bound to the target valve to collect the target sound signal after the target valve is determined, and performing internal leakage detection data shielding processing on the sound signals collected by microphones corresponding to non-target valves, the interference of sound signals from non-target valves and other channels on the internal leakage detection of the target valve can be reduced; by acquiring reference sound signals in the same area as the target valve, a system containing... A dynamic reference acoustic library is constructed for the acoustic signals of valves in the non-closed state and the ambient background acoustic signals. The dynamic reference acoustic library is updated and the noise cancellation parameters are adjusted according to the real-time changes in the valve state. Adaptive noise cancellation processing is performed on the target acoustic signal, which can improve the identifiability of acoustic components related to valve internal leakage in the target acoustic signal. By extracting time-domain features, frequency-domain features, and time-frequency-domain features from the enhanced acoustic signal, an acoustic signature feature vector corresponding to valve internal leakage is constructed. The prior constraint information of the closed state is synchronized with the acoustic signature feature vector in time and fused for diagnosis, which can improve the accuracy of determining the probability and level of internal leakage of the target valve. When the probability of internal leakage reaches the preset warning threshold, the corresponding graded internal leakage warning information is output according to the leakage level, which helps to improve the timeliness of the warning results of oil depot equipment and the pertinence of graded treatment.

[0016] On the other hand, this application also provides an oil depot equipment early warning system based on visual recognition and voiceprint analysis, used to implement the above-mentioned oil depot equipment early warning method based on visual recognition and voiceprint analysis, including: The spatial binding module is configured to establish spatial binding relationships between cameras, valves, and microphones within the target area of ​​the oil depot, and to acquire visual image data and valve control command data corresponding to the valves. The closed state recognition module is configured to recognize the actuator switching state of the valve based on the visual image data, and extract at least one structural state information from the valve stem displacement, handwheel rotation angle, and flange clearance. Based on the actuator switching state and structural state information, the module determines the physical closed state of the valve, and based on the valve control command data, filters out target valves that simultaneously satisfy the physical closed state and the command closed state, and obtains the prior constraint information of the closed state of the target valve. The target acoustic signal acquisition module is configured to, based on the prior constraint information of the closed state and the spatial binding relationship, activate the microphone spatially bound to the target valve to acquire the target acoustic signal, and perform internal leakage detection data masking processing on the acoustic signals acquired by the microphones corresponding to non-target valves; The signal enhancement module is configured to acquire a reference acoustic signal in the same area as the target valve, construct a dynamic reference acoustic library containing the acoustic signal of the valve in a non-closed state and the ambient background acoustic signal, update the dynamic reference acoustic library and adjust the noise cancellation parameters according to the real-time changes in the valve state, and perform adaptive noise cancellation processing on the target acoustic signal to obtain an enhanced acoustic signal. The voiceprint feature extraction module is configured to extract time-domain features, frequency-domain features, and time-frequency-domain features from the enhanced acoustic signal to construct a voiceprint feature vector corresponding to the valve internal leakage. The fusion warning module is configured to synchronize and fuse the prior constraint information of the closed state with the voiceprint feature vector in time to obtain the internal leakage probability and corresponding leakage level of the target valve. When the internal leakage probability is greater than or equal to a preset warning threshold, the module outputs graded internal leakage warning information corresponding to the target valve according to the leakage level.

[0017] It is understandable that the aforementioned oil depot equipment early warning system and method based on visual recognition and voiceprint analysis have the same beneficial effects, and will not be elaborated further here. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating an oil depot equipment early warning method based on visual recognition and voiceprint analysis, provided as an embodiment of the present invention; Figure 2 This is a functional block diagram of an oil depot equipment early warning system based on visual recognition and voiceprint analysis, provided as an embodiment of the present invention. Detailed Implementation

[0019] Embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While the accompanying drawings show exemplary embodiments of the present disclosure, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] See Figure 1 As shown, this application proposes an early warning method for oil depot equipment based on visual recognition and voiceprint analysis, including: S1: Establish spatial binding relationships between cameras, valves, and microphones within the target area of ​​the oil depot, and acquire visual image data and valve control command data corresponding to the valves; S2: Identify the actuator switching state of the valve based on visual image data, and extract at least one structural state information from valve stem displacement, handwheel rotation angle, and flange clearance. Determine the physical closing state of the valve based on the actuator switching state and structural state information, and filter out target valves that simultaneously satisfy the physical closing state and the command closing state based on valve control command data, and obtain the prior constraint information of the closing state of the target valve. S3: Based on the prior constraint information of the closed state and the spatial binding relationship, start the microphone bound to the target valve space to collect the target sound signal, and perform internal leakage detection data shielding processing on the sound signal collected by the microphone corresponding to the non-target valve. S4: Acquire reference acoustic signals in the same area as the target valve, construct a dynamic reference acoustic library containing the acoustic signals of the valve in its non-closed state and the ambient background acoustic signals, update the dynamic reference acoustic library and adjust the noise cancellation parameters according to the real-time changes in the valve state, and perform adaptive noise cancellation processing on the target acoustic signal to obtain the enhanced acoustic signal; S5: Extract time-domain features, frequency-domain features, and time-frequency-domain features from the enhanced acoustic signal to construct the acoustic signature vector corresponding to the valve internal leakage; S6: Synchronize and fuse the prior constraint information of the closed state with the acoustic feature vector in time to obtain the internal leakage probability and corresponding leakage level of the target valve. When the internal leakage probability is greater than or equal to the preset warning threshold, output the graded internal leakage warning information corresponding to the target valve according to the leakage level.

[0021] Specifically, in one feasible implementation, the target area of ​​the oil depot can be the tank farm inlet / outlet main pipeline area, the loading / unloading operation pipeline area, the pump and valve centralized arrangement area, or the storage tank switching pipeline area. Based on the site layout diagram, camera installation locations, microphone installation locations, and valve number information, a spatial binding relationship between cameras, valves, and microphones is established. This spatial binding relationship can be specifically formed into a mapping table, which records the camera number, microphone number, area number, and relative position relationship for each valve. This allows for direct access to the corresponding acquisition channel after a specific valve is identified on the visual side. The visual image data can be from inspection cameras. The camera continuously captures valve image frames at a preset frame rate, such as 5 to 25 frames per second. The camera's frame rate can be adjusted based on valve movement speed, image clarity, and structural status recognition stability. Specifically, valve image samples under different frame rate conditions can be captured on-site, and the accuracy of actuator on / off status recognition, stability of valve stem displacement extraction, handwheel angle recognition deviation, and flange clearance recognition clarity can be compared. The frame rate with the lowest data processing burden while meeting recognition accuracy requirements is selected as the target frame rate. Valve control command data can be valve opening commands, valve closing commands, etc., output from SCADA systems, DCS systems, or field controllers. Valve commands and their timestamps are used to identify the valve actuator switching status in the visual image data. The actuator switching status refers to the current state of the actuator: open, closed, or switching from open to closed, or from closed to open. Simultaneously, at least one structural state information is extracted from the image: valve stem displacement, handwheel rotation angle, and flange clearance. Valve stem displacement represents the axial displacement of the valve stem relative to the installation reference position; handwheel rotation angle represents the angular displacement of the handwheel relative to the preset initial position; and flange clearance represents the visible gap width or degree of closure of the flange connection in the image. To ensure that this structural state information can be used for judgment... Before equipment commissioning, sample images of different valves in fully open, fully closed, and intermediate positions can be collected to form corresponding structural position templates for the closed position. For example, for a certain type of gate valve, the exposed length range of the valve stem in the fully closed state can be recorded; for a certain type of ball valve, the direction of the handwheel or actuator indicator corresponding to the closed position can be recorded; and for flange clearance, the image feature range under normal closed installation conditions can be recorded. Subsequently, the extracted structural state information in real time will be matched with the structural position template for the closed position. When the actuator switch status is displayed as closed and the structural state information is consistent with the closed position, it is determined to be a physical closed state.In conjunction with reading valve control command data, valves whose most recent valid control command was a closing command and which have not received a reverse opening command within a preset confirmation time window are identified as being in a command-closed state. The preset confirmation time window can be set according to the oil depot control system refresh cycle and valve action duration. This preset confirmation time window can be obtained through on-site commissioning. Specifically, it can be determined by statistically analyzing the time distribution from receiving a closing command to reaching a stable closed state for different types of valves, and selecting a time range that covers the stable closing process of most valves as the confirmation time window. For example, it could be 3 seconds, 5 seconds, or 10 seconds. Specifically, it can be determined by statistically analyzing the time distribution from receiving a command to reaching a stable closed state for common valve actuators during on-site commissioning. The stable time distribution is obtained; then, the physical closed state and the command closed state are cross-filtered, and only valves that simultaneously satisfy both are identified as target valves, and prior constraint information for the closed state is generated. The prior constraint information for the closed state can include at least the target valve identifier, the region it belongs to, the closed confirmation time, the corresponding microphone identifier, and the effective duration of the closed state. The meaning of this prior constraint is that subsequent internal leakage diagnosis is only carried out on valves that have been confirmed to be in the closed state, eliminating the normal flow sound generated by valves in the open state or switching state from the source; according to the prior constraint information for the closed state and the spatial binding relationship, the acquisition is sent to the microphone bound to the target valve. The system initiates a process to acquire the acoustic signal output from the microphone as the target acoustic signal. It then performs internal leakage detection data masking on the acoustic signals acquired by microphones corresponding to non-target valves. This data masking means preventing acoustic signals from non-target channels from entering the internal leakage determination link of the target valve, but not stopping their acquisition, thus allowing these acoustic signals to still serve as reference sound sources for subsequent dynamic reference sound library construction. To weaken the non-internal leakage components in the target acoustic signal, it is necessary to acquire reference acoustic signals located in the same area as the target valve and construct a dynamic reference sound library. The reference acoustic signals include two types: one is the valve acoustic signal acquired by the microphone corresponding to a non-closed valve in the same area. The first type of signal reflects the flow noise characteristics generated during normal transport, switching, or fluid passing through other valves. The second type is the ambient background sound signal, which reflects environmental interference unrelated to the target internal leakage, such as pump operation, pipeline vibration, wind noise, and motor noise. To ensure the representativeness of the dynamic reference sound library, the reference sound signals can be classified and stored according to area identification, valve type, operating condition, acquisition time, and noise type. The valve status in the area is monitored in real time. When a valve enters a non-closed state, its corresponding sound signal is added to the dynamic reference sound library. When a valve exits a non-closed state, the real-time update of the sound signal corresponding to that valve is stopped.During adaptive noise cancellation processing, the reference acoustic signal closest to the current region state is retrieved from the updated dynamic reference acoustic library. Noise cancellation parameters are adjusted based on the differences between the target acoustic signal and the reference acoustic signal in terms of energy variation, dominant frequency distribution, bandwidth variation, and temporal continuity. For example, filter coefficients, gain compensation, or cancellation weights can be updated on a short-frame basis to weaken the interference components shared by the target acoustic signal in the reference acoustic signal, resulting in an enhanced acoustic signal. The initial values ​​of the noise cancellation parameters can be obtained through multiple rounds of calibration under leak-free conditions. Target and reference acoustic signals can be acquired separately under leak-free conditions, and multiple sets of different filter coefficients, gain coefficients, or cancellation weights can be used. Weighted comparison tests are conducted to calculate the residual noise energy, signal-to-noise ratio improvement, and retention of anomalous sound features before and after noise cancellation. A parameter set balancing noise reduction and anomalous feature fidelity is selected as the initial values ​​for the noise cancellation parameters. During system operation, the noise cancellation parameters are then corrected online based on the real-time difference between the reference and target sound signals. Specifically, samples are collected under several typical background conditions such as pump operation, static conditions, and valve switching. A parameter set that reduces residual noise energy while maintaining high fidelity of the target anomalous sound components is selected as the initial values, and subsequently dynamically corrected based on the real-time signal. After obtaining the enhanced sound signal, it is processed by frame segmentation, for example, from 20 milliseconds to 100 milliseconds. The frame is segmented in milliseconds with a frame length of 10 to 50 milliseconds, and then time-domain, frequency-domain, and time-frequency-domain features are extracted. Time-domain features include root mean square (RMS), peak factor, and kurtosis, reflecting signal amplitude fluctuations, impulsiveness, and spike intensity. Frequency-domain features include center frequency, bandwidth, and harmonic components, reflecting spectral distribution changes caused by internal fluid leakage or gap discharge. Time-frequency-domain features include wavelet packet energy entropy, reflecting the degree of energy dispersion across different times and frequency bands. These features are then combined in a preset order to form the acoustic signature feature vector corresponding to the valve's internal leakage. During the time synchronization phase, the time information corresponding to the prior constraint information of the closed state and the acoustic signature feature vector are extracted. The corresponding acquisition time information is aligned according to a preset time window. The time window can be set comprehensively based on the camera frame rate, control command refresh cycle, and microphone sampling cycle. For example, it can be set to 100 milliseconds to 1 second to ensure that the status information and voiceprint features involved in the fusion diagnosis correspond to the same valve operating condition within the same time period. During the fusion diagnosis stage, the closed state confirmation result can be used as a necessary constraint condition and correlated with the voiceprint feature vector to determine whether the current enhanced sound signal has internal leakage characteristics under the premise that the valve is closed, and output the internal leakage probability of the target valve. The internal leakage probability can be understood as the credibility of the current target valve having an internal leakage event, and its value range can be normalized to 0 to 1.To trigger an early warning, a preset early warning threshold needs to be set. This threshold can be determined jointly using historical labeled samples, trial operation data, and false alarm / missed alarm statistics. The preset threshold can be adjusted by comparing normally closed samples with internal leakage samples. Specifically, the internal leakage probability distributions for the two types of samples can be calculated separately. A boundary point is selected as the early warning threshold based on the false alarm and missed alarm rate requirements. For example, during the commissioning phase, samples of known normally closed valves and known internal leakage valves are collected, and the internal leakage probability distributions are calculated separately. A boundary point that balances false alarm and missed alarm rates is selected as the early warning threshold. In one embodiment, this threshold could be 0.6, 0.7, or 0.8. The specific value can be adjusted based on the on-site tolerance for false alarms and safety requirements. The leakage level is determined based on the internal leakage probability and the matching results between the acoustic signature vector and the features corresponding to the preset leakage level. The leakage level can include micro-leakage, light leakage, moderate leakage, and severe leakage. Leakage levels can be categorized through bench tests or field calibration. For example, under known leakage conditions, acoustic fingerprint samples corresponding to different leakage amounts can be collected, and the distribution ranges of root mean square, center frequency, bandwidth, and wavelet packet energy entropy under different levels can be statistically analyzed to form characteristic patterns corresponding to each leakage level. In an optional embodiment, micro-leakage can correspond to a leakage amount less than or equal to 0.5 cubic meters per hour, light leakage can correspond to a leakage amount greater than 0.5 cubic meters per hour but less than or equal to 2 cubic meters per hour, moderate leakage can correspond to a leakage amount greater than 2 cubic meters per hour but less than or equal to 5 cubic meters per hour, and severe leakage can correspond to a leakage amount greater than 5 cubic meters per hour. When the internal leakage probability is greater than or equal to a preset warning threshold, graded internal leakage warning information is output according to the determined leakage level. The graded internal leakage warning information can include at least the valve number, the area it belongs to, the warning time, the internal leakage probability, and the leakage level.

[0022] When a camera malfunctions, the visual status update of the corresponding valve is paused, and the most recent valid closed state result is maintained for a preset short validity period. When a microphone malfunctions, the acoustic analysis of the corresponding valve is stopped and an abnormal acquisition indicator is output. When a communication link malfunctions, the data cached before the malfunction is called for short-term analysis. When the target valve receives an opening command during the detection process, or the visual recognition result shows that the target valve has left the closed state, the internal leakage detection process of the current target valve is interrupted, and the corresponding prior constraint information of the closed state is cleared. For warning results that are subsequently verified by manual verification as false alarms, the corresponding samples can be marked as false alarm samples and used to update the warning threshold, dynamic reference acoustic library, or leakage level corresponding features.

[0023] In some embodiments of this application, establishing the spatial binding relationship between cameras, valves, and microphones within the target area of ​​the oil depot includes: The system obtains the installation location, shooting direction, and shooting range of each camera, as well as the installation location and acquisition range of each microphone. It then calibrates the position of each valve within the target area of ​​the oil depot, obtaining the spatial location identifier of each valve. Based on the shooting range of each camera, it determines the valve set corresponding to each camera; based on the acquisition range of each microphone, it determines the valve set corresponding to each microphone. The system matches the valve sets corresponding to each camera with the valve sets corresponding to each microphone, establishing a one-to-one or one-to-many correspondence between valves and cameras / microphones. Finally, it stores the correspondence to obtain the spatial binding relationship between cameras, valves, and microphones.

[0024] Specifically, in one feasible implementation, obtaining the installation position, shooting direction, and shooting range of each camera can be achieved by reading the camera installation log information and determining the installation coordinates, height, pitch angle, and orientation angle of each camera within the target area of ​​the oil depot based on on-site measurement results. The installation position refers to the fixed spatial position of the camera relative to a preset area reference point; the shooting direction refers to the main viewing direction pointed to by the optical axis of the camera lens; and the shooting range refers to the area that the camera can cover under the current focal length, field of view, and installation angle conditions. Obtaining the installation position and acquisition range of each microphone can be achieved by obtaining the installation coordinates, installation height, and pickup direction of each microphone relative to a reference point, and then determining the location based on on-site measurement results. The test results determine the effective acquisition range of each microphone for sound sources at different distances and directions. The acquisition range refers to the area within which the microphone can stably acquire sound signals that can be used for subsequent analysis. For example, standard sound sources can be placed at different distances from the microphone to measure the signal amplitude attenuation and signal-to-noise ratio (SNR) changes. The area where the SNR meets the preset requirements is determined as the acquisition range of the corresponding microphone. In one embodiment, the preset requirements can be an SNR of not less than 6 dB, 10 dB, or 15 dB. The specific value can be determined according to the background noise level at the oil depot and the accuracy requirements of subsequent acoustic signature analysis. The positions of each valve within the target area of ​​the oil depot are calibrated to obtain the spatial position markings of each valve. This can be based on the layout of the oil depot. Based on the layout map, equipment installation diagram, and on-site inspection results, each valve is assigned a unique valve number, and at least one of the following is determined: the valve's installation location, its region, adjacent pipeline information, and orientation information. This forms a spatial location identifier that uniquely represents the valve's spatial position. This spatial location identifier can be in the form of a region number plus a valve number, or it can be in the form of coordinate information associated with an equipment number. Furthermore, based on the shooting range of each camera, the valve set corresponding to each camera is determined, and based on the acquisition range of each microphone, the valve set corresponding to each microphone is determined. Specifically, this can be achieved by spatially superimposing the spatial location identifier of each valve with the camera shooting range and the microphone acquisition range. If a certain valve position... If a valve is within the effective field of view of a camera, it is assigned to the valve set corresponding to that camera. If a valve is within the effective pickup range of a microphone, it is assigned to the valve set corresponding to that microphone. Then, the valve sets corresponding to each camera and the valve sets corresponding to each microphone are matched to establish a one-to-one or one-to-many correspondence between valves and cameras / microphones. A one-to-one correspondence means that a valve corresponds to a unique main camera and a unique main microphone. A one-to-many correspondence means that a valve corresponds to multiple candidate cameras and / or multiple candidate microphones. This situation usually occurs when the valve is located in the overlapping area of ​​the fields of view of multiple cameras or the overlapping area of ​​the pickup range of multiple microphones.For one-to-many correspondences, the primary binding device can be selected based on at least one of the following rules: distance priority, least occlusion priority, complete viewpoint priority, and signal-to-noise ratio priority. The remaining devices are designated as auxiliary binding devices. Distance priority prioritizes cameras or microphones closer to the valve space. Least occlusion priority prioritizes cameras with unobstructed or minimally obstructed views. Signal-to-noise ratio priority prioritizes microphones with minimal background interference when acquiring sound signals from the valve area. The correspondence is then stored to obtain the spatial binding relationship between the camera, valve, and microphone. Storage can involve writing the valve number, area number, corresponding camera number, corresponding microphone number, binding type, and priority information into a preset mapping table or database to form spatial binding relationship data that can be directly accessed later. When a specific target valve is identified, the corresponding visual and acoustic acquisition sources can be quickly located based on this spatial binding relationship.

[0025] In some embodiments of this application, determining the physical closed state of the valve based on actuator switching state and structural state information includes: The actuator switch state is determined to be either closed or open; the structural state information is matched with the corresponding structural position state under the valve closing condition to determine at least one of the following: valve stem displacement corresponding to the valve stem closing position, handwheel rotation angle corresponding to the handwheel closing position, and flange clearance corresponding to the flange closing state; when the actuator switch state is closed and the structural state information corresponds to the structural position state under the valve closing condition, the valve is determined to be in a physically closed state.

[0026] Specifically, in one feasible implementation, the actuator switching state refers to the current open / closed indication state of the valve actuator as identified from the visual image. This can be manifested as the actuator indicator pointing to the closed position, the actuator stroke indicator window displaying a closed symbol, or the actuator's external linkages and limiters being in the closed position. Based on this, the actuator switching state can be distinguished as closed or open. The open state can include open, partially open, and transitional states during the opening / closing process. Structural state information, on the other hand, is image feature information reflecting the actual mechanical position of the valve. It works in conjunction with the actuator switching state to avoid misjudgments based solely on control instructions or single visual markings. Specifically, the valve stem displacement can be... This can be understood as the axial extension or retraction of the valve stem relative to a preset installation reference position. The handwheel rotation angle can be understood as the rotation angle of the handwheel relative to the closing reference direction. The flange clearance can be understood as the gap width, fit, or relative position of adjacent components in the image of the flange connection. To enable the structural status information to determine the physical closure status, the structural position of the valve under standard closure conditions can be pre-collected to form corresponding closure condition reference information. This reference information can be obtained through pre-operation calibration, post-maintenance reset confirmation, or on-site manual confirmation of the fully closed state followed by image acquisition. For example, when confirming that a valve is fully closed, the exposed length of its valve stem, the pointing position of the handwheel, and the image features of the flange connection are recorded. This information will then be acquired in real-time. The acquired structural state information is matched with the reference information. This matching refers to determining whether the current structural state information is consistent with or within the allowable deviation range of the structural position under valve closure conditions. For example, for valve stem displacement, the current exposed length of the valve stem can be compared to whether it is basically consistent with the exposed length of the valve stem under fully closed conditions; for handwheel rotation angle, the current handwheel direction can be compared to whether it is consistent with the corresponding direction under fully closed conditions; for flange clearance, the current flange fit can be compared to whether it is consistent with the connection state under closure conditions. The allowable deviation range can be determined based on different valve types, different installation conditions, and image acquisition resolution. For example, based on multiple fully closed sampling results of the same type of valve, the fluctuation range of its displacement, angle, or clearance characteristics can be statistically analyzed, and this fluctuation range can be used as... The allowable deviation range during the determination is defined. Based on this, the actuator switch state is determined to be either closed or not closed. Then, the structural state information is matched with the corresponding structural position state under the closed condition to determine whether the valve stem is in the closed position, whether the handwheel is in the closed position, and whether the flange is in the closed state. When the actuator switch state is closed and at least one of the structural state information is consistent with the structural position state under the closed condition, the valve is determined to be in a physically closed state. In other words, the physically closed state in this embodiment does not only indicate that the system has issued a closing command, but also indicates that the valve actuator shows that it is closed and the valve's key mechanical structure has actually reached the closed position, thereby improving the authenticity and reliability of the closed state determination.

[0027] In some embodiments of this application, when selecting target valves that simultaneously satisfy both physical and commanded closed states based on valve control command data, and obtaining prior constraint information on the closed state of the target valves, the process includes: Read the valve control command data corresponding to each valve, and parse it to obtain the valve identifier and command type; identify the valve with the command type of "close" as the candidate valve in the command closed state; match the valve in the physical closed state with the candidate valve in the command closed state according to the valve identifier, and filter out the target valve that simultaneously satisfies the physical closed state and the command closed state; associate the valve identifier, physical closed state and command closed state of the target valve to obtain the prior constraint information of the target valve's closed state.

[0028] Specifically, in one feasible implementation, valve control command data can originate from the oil depot's existing SCADA system, DCS system, PLC control unit, or other valve control terminals. It typically includes at least a valve identifier, command type, command issuance time, and optional execution feedback information. The valve identifier uniquely identifies the controlled valve, and the command type indicates whether the current control action is a closing, opening, stopping, or holding command. After reading the valve control command data corresponding to each valve, the control message, control record, or event log is parsed to extract the valve identifier and command type corresponding to each valve. The most recent valid control command closest to the current visual recognition time is then prioritized. The instruction serves as the current basis for judgment. A valid control instruction here refers to a control instruction that is complete in format, has a reliable source, and is not overridden by subsequent opposing instructions. For example, if a valve receives a closing instruction at time T1 and an opening instruction at time T2, then the opening instruction should be considered the current valid instruction after T2. Valves with a closing instruction type are identified as candidate valves in the instruction-closed state. The instruction-closed state indicates that the system has issued a closing request to the valve from a control logic perspective, or that the valve's most recent valid control action was a closing action. However, this state itself is not necessarily equivalent to the valve being mechanically closed; therefore, it needs to be jointly judged with the physical closing state obtained from the aforementioned visual recognition. Valves in the physically closed state are compared with those in the instruction-closed state. Candidate valves in the closed state are matched according to their valve identifiers. Only when the same valve is visually determined to be physically closed and controlled by a command to close is it selected as the target valve. This avoids misjudging valves that are abnormally executed, stuck, or malfunctioning as closed valves based solely on a single control command. During this process, a valid command time window can be set to constrain the time consistency between the control command and the visual recognition result. Specifically, the time when the visual recognition confirms the physical closure state and the time when the corresponding closing command is issued must be within a preset time range to avoid using premature historical closing commands in the current selection. The time window can be determined based on the valve actuator's action duration, the control system refresh cycle, and on-site conditions. The communication delay is determined comprehensively, for example, it can be 3 to 30 seconds. A shorter time window can be used for electric ball valves with faster action, and a longer time window can be used for large-diameter gate valves with slower action. Specifically, it can be obtained by statistically analyzing the time distribution of different valves from receiving the closing command to reaching a stable closed state during on-site debugging. After screening out the target valves that simultaneously meet the physical closed state and the command closed state, the valve identifier, physical closed state and command closed state of the target valve are associated to obtain the prior constraint information of the target valve's closed state. The meaning of the prior constraint information of the closed state is that the valve should be in the closed working condition before the subsequent acoustic signal analysis, and this is used as a prerequisite for subsequent internal leakage detection. Only valves that meet this prior constraint will enter the internal leakage detection link.In one embodiment, the prior constraint information for the closed state, in addition to including the valve identifier, physical closed state, and command closed state, may further include information such as the closed confirmation time, the area number, the corresponding microphone identifier, and the duration of the valid state. This is to facilitate the rapid retrieval of the corresponding acoustic signal acquisition channel in subsequent steps and ensure the timeliness and traceability of the target valve screening results.

[0029] In some embodiments of this application, when activating a microphone spatially bound to the target valve to collect target acoustic signals based on prior constraint information of the closed state and spatial binding relationship, and performing internal leakage detection data masking processing on acoustic signals collected by microphones corresponding to non-target valves, the process includes: Read the target valve identifier from the prior constraint information of the closed state; retrieve the microphone identifier corresponding to the target valve identifier in the spatial binding relationship; send a collection start command to the microphone corresponding to the microphone identifier, and obtain the sound signal collected by the microphone as the target sound signal; perform internal leakage detection data masking processing on the other microphone collection channels that do not correspond to the target valve identifier, and retain the sound signals collected by the other microphones.

[0030] Specifically, in one feasible implementation, the prior constraint information for the closed state includes at least the target valve identifier and the corresponding closure confirmation result. If necessary, it may further include the area number where the target valve is located, the closure confirmation time, and the validity period of the state. Therefore, after reading the target valve identifier from the prior constraint information for the closed state, the target valve identifier can be directly used as a search key to find the corresponding microphone identifier in the pre-established spatial binding relationship, thereby determining the target acquisition channel for acquiring the acoustic signal of the target valve. Here, the spatial binding relationship serves to establish a fixed correspondence between the same physical valve and the acoustic signal acquisition device that best matches its location. To avoid mistaking acoustic signals from adjacent valves, parallel pipelines, or other equipment for the target valve's acoustic signal, a acquisition start command is sent to the microphone after the corresponding microphone identifier is retrieved. This acquisition start command can be a control command to initiate real-time acquisition, increase the sampling frequency, open a specified time window data buffer, or switch to internal leakage detection mode. The specific form used depends on the control protocol of the on-site acquisition terminal. For example, in normal inspection mode, only low-frequency listening is performed, but after the target valve is confirmed to be closed, the corresponding microphone is switched to high-sensitivity continuous acquisition mode to ensure that weak internal leakage sounds can be captured. The acoustic signal acquired by the microphone serves as the target acoustic signal. This means that the acoustic signal will be the primary analysis target for internal leakage identification of the target valve in the subsequent processing chain. Its acquisition duration can be set according to the duration of the closed state and the warning timeliness requirements. For example, it can be continuously acquired for 10 seconds, 30 seconds, 60 seconds, or continuously acquired throughout the entire closed state. The specific duration can be determined by statistically analyzing the time required for the stable appearance of minute internal leakage sounds during on-site debugging. Internal leakage detection data masking is performed on the remaining microphone acquisition channels that do not correspond to the target valve identifier. This masking does not mean stopping the operation of the remaining microphones or deleting their acquisition results, but rather excluding their acquired acoustic signals from the internal leakage determination chain of the target valve. It serves as direct input data for calculating the probability of internal leakage and determining the leakage level of the target valve, preventing sound signals from other valves, adjacent pipelines, pumps, or ambient noise sources from mixing into the detection results of the target valve. In other words, the data shielding process only restricts the use of the target valve for internal leakage detection, without affecting the other microphones from continuing to collect on-site sound signals. This is also the basis for the subsequent construction of a dynamic reference sound library. Therefore, retaining the sound signals collected by the other microphones is to ensure that when there are other non-closed valves, equipment vibrations, or ambient noise in the same area, these reference sound signals that are not directly related to the internal leakage of the target valve but will interfere with the target sound signal can still be extracted and used for subsequent noise cancellation processing.In one embodiment, to avoid timing disruption caused by simultaneous switching of the target valve and non-target valves, a channel locking time window, such as 1 to 5 seconds, can be set after the target valve is confirmed. Within this time window, the continuous acquisition of the target channel is prioritized, and the data from the non-target channel is marked for reference purposes. Alternatively, different data labels can be assigned to the target channel and non-target channel, with the target channel data labeled as the main detection channel data and the non-target channel data labeled as the reference channel data. This ensures that during subsequent signal enhancement and fusion diagnostics, the system can clearly distinguish which acoustic signals are used for internal leak identification of the target valve and which are only used for background interference characterization and reference sound library updates.

[0031] In some embodiments of this application, when acquiring reference acoustic signals located in the same area as the target valve and constructing a dynamic reference acoustic library containing the acoustic signals of the valve in its non-closed state and the ambient background acoustic signals, the following steps are included: The area range corresponding to the target valve is determined based on the spatial binding relationship; the current status information of each valve within the area is read, and valves in the non-closed state are filtered out; the microphones spatially bound to the non-closed valves are used to collect the corresponding valve sound signals as non-closed valve sound signals; ambient sound signals other than non-closed valve sound signals within the area are collected as ambient background sound signals; the non-closed valve sound signals and ambient background sound signals are classified and stored according to area identification, signal source and collection time to construct a dynamic reference sound library.

[0032] Specifically, in one feasible implementation, the reference acoustic signal located in the same area as the target valve refers to an acoustic signal that is spatially adjacent to the target valve and can significantly affect the target valve's acquisition channel along the acoustic propagation path. Its purpose is to characterize the acoustic components within the local area where the target valve is located, excluding the leakage sound within the target valve, thereby providing a reference for subsequent noise cancellation. The area range can be determined based on the aforementioned spatial binding relationship, for example, using the pipeline unit, valve group unit, pump-valve linkage unit, or a preset radius range where the target valve is located as the area boundary. In one embodiment, devices within the effective pickup range of the main microphone bound to the target valve can be considered as devices in the same area, or all valves and auxiliary equipment corresponding to the area number where the target valve is located can be considered as objects in the same area. The specific value can be determined based on the density of on-site equipment, the microphone pickup radius, and the sound propagation attenuation. For example, a smaller area range can be used in pump areas with small valve spacing, while a larger range can be appropriately used in areas with long pipe corridors and sparse equipment distribution. After determining the area range, the current status information of each valve within the area is read. The current status information can be derived from the valve position status, control command feedback status, and visual recognition results in the SCADA system. Based on the result of the judgment, or a combination of both, a non-closed state refers to a valve that is not currently in a stable closed operating condition. Specifically, this can include an open state, a partially open state, and a state in the process of switching from open to closed or from closed to open. Valves in the non-closed state are then identified. Next, the corresponding valve sound signal is collected by the microphone spatially bound to the non-closed valve. This signal mainly reflects the flow sound, throttling sound, flushing sound, and mechanical motion sound generated when fluid passes through the non-closed valve. In oil depots, this is often the most significant sound signal in the target sound signal. To interfere with the source, it is also necessary to collect ambient sound signals within the area other than those of valves that are not in the closed state, as ambient background sound signals. Here, ambient background sound signals refer to sound signals that do not directly correspond to a valve that is not in the closed state, but are environmental acoustic components formed by factors such as pump operation, pipeline vibration, wind noise, motor noise, support resonance, and personnel and vehicle activities. In specific acquisition, signals can be collected by other microphones in the area that are not bound to valves that are not in the closed state, or the remaining ambient sound components can be extracted after the sound signals of valves that are in the closed state have been filtered out by time period, frequency band separation, or source differentiation.To ensure more accurate subsequent retrieval, the acoustic signals from valves in their non-closed state and ambient background acoustic signals need to be categorized and stored according to region identifier, signal source, and acquisition time. The region identifier indicates the physical region to which the reference acoustic signal belongs; the signal source distinguishes whether the signal originates from a specific valve channel or the ambient background channel; and the acquisition time indicates the corresponding operating time. If necessary, additional information such as valve type, valve status, sampling frequency, duration, background operating conditions, and signal quality level can be recorded to form a searchable and updatable dynamic reference acoustic library. In one embodiment, this dynamic reference acoustic library can be stored as an acoustic signal dataset divided by region, with each reference acoustic record storing the region number, sound source category, valve number, acquisition start and end times, and feature summary information. This allows for the priority retrieval of reference acoustic signals that match the target valve's region, current time, and operating status during subsequent noise cancellation, improving the reference acoustic library's coverage of real-world interference.

[0033] In some embodiments of this application, when updating the dynamic reference acoustic library and adjusting the noise cancellation parameters according to the real-time changes in the valve state, and performing adaptive noise cancellation processing on the target acoustic signal to obtain an enhanced acoustic signal, the process includes: The system acquires real-time status information of all valves within the target valve's area. When the status information indicates that a valve has entered a non-closed state, the valve's acoustic signal, collected by the microphone bound to the space of the valve in the non-closed state, is added to the dynamic reference sound library. When the status information indicates that a valve has exited the non-closed state, the update of the valve's acoustic signal corresponding to that valve is stopped from the dynamic reference sound library. The system retrieves the reference acoustic signal corresponding to the current area's status from the updated dynamic reference sound library. The noise cancellation parameters are adjusted based on the signal changes of the reference acoustic signal and the target acoustic signal. Based on the adjusted noise cancellation parameters, the target acoustic signal undergoes noise cancellation processing to obtain an enhanced acoustic signal.

[0034] Specifically, in one feasible implementation, updating the dynamic reference acoustic library based on real-time changes in valve status refers to continuously tracking the current operating status of other valves in the target valve's area during internal leakage detection, and synchronously adjusting the composition of the reference acoustic signal accordingly, so that the reference acoustic library always reflects the actual interference composition of the area at the current moment. Valve status information can come from at least one of the following: valve position feedback from the SCADA system, valve control command execution results, actuator switching status obtained from visual recognition, and structural status information. To improve the timeliness and reliability of status judgment, the status information can be polled or updated according to a preset refresh cycle or triggered by events. The refresh cycle can be determined based on the control system's refresh frequency and the valve's operating speed in the field. For example, it can be 100 milliseconds, 500 milliseconds, or 1 second. A shorter refresh cycle can be used for switching areas with frequent operations, while a longer refresh cycle can be used for steady-state operation areas. When the status information shows that a valve has entered a non-closed state, it indicates that the valve has begun to introduce new flow noise or mechanical noise into the area where the target valve is located. At this time, the valve sound signal collected by the microphone bound to the valve space is added to the dynamic reference sound library, so that the dynamic reference sound library can promptly incorporate the new interference source. Correspondingly, when the status information shows that a valve has exited a non-closed state, it indicates that the valve no longer continuously generates open or semi-open noise. Or, interference sounds related to switching actions. In this case, the valve sound signal corresponding to that valve is stopped from being updated in the dynamic reference sound library. Stopping the update means that subsequent sound signals collected from that valve will no longer be considered as valid components of the current reference sound library in real time, but it does not require physically deleting previously stored historical sound signals. After completing the dynamic reference sound library update, the reference sound signal corresponding to the current area state is retrieved from the updated dynamic reference sound library. Corresponding to the current area state means that the selected reference sound signal is as consistent as possible with the environment of the current target valve in terms of area number, valve operating state combination, proximity of acquisition time, and sound source type. For example, when the area where the target valve is located currently has... When two non-closed valves and a continuously vibrating pump are in operation, the valve sound signal collected from the same area and similar valves under similar opening conditions, as well as the ambient background sound signal collected within the same time period, are preferentially selected as reference sound signals. The noise cancellation parameters are adjusted according to the signal changes of the reference sound signal and the target sound signal. The signal changes can include at least one of the following: signal energy change, main frequency change, bandwidth change, spectral peak distribution change, short-term amplitude fluctuation, and time continuity change. The noise cancellation parameters can be understood as parameters used to control the degree of cancellation of the target sound signal by the reference sound signal in the noise cancellation process, such as filter coefficients, gain coefficients, weighting coefficients, or step size parameters.To ensure the noise cancellation parameters have reasonable initial values, target and corresponding reference acoustic signals under leak-free conditions can be collected during system deployment or on-site commissioning. By comparing the residual noise energy, signal-to-noise ratio improvement, and abnormal sound retention before and after noise cancellation, a set of parameters that can effectively suppress background interference without excessively weakening abnormal features can be selected as initial parameters. For example, a set of reference filter coefficients or reference weight ranges can be determined through comparative tests of multiple sets of different parameters. During real-time operation, the noise cancellation parameters are adaptively adjusted based on the degree of difference between the current reference and target acoustic signals. For example, when the reference and target acoustic signals have high consistency in dominant frequency distribution and energy change, the noise cancellation weight is appropriately increased; when the difference between the two increases, the cancellation weight is appropriately decreased to avoid over-interpreting features that may belong to the target valve's internal leakage characteristics. Abnormal components are cancelled out. Based on the adjusted noise cancellation parameters, the target acoustic signal is subjected to noise cancellation processing to obtain an enhanced acoustic signal. This enhanced acoustic signal, compared to the original target acoustic signal, weakens regional interference sounds, background ambient sounds, and other non-target valve operating sounds unrelated to the target valve's internal leakage, while retaining or relatively highlighting acoustic components related to the target valve's internal leakage. This provides a more stable and reliable input signal for subsequent extraction of time-domain, frequency-domain, and time-frequency-domain features. In an optional embodiment, the enhancement effect can also be evaluated online, for example, by comparing the energy stability, dominant frequency concentration, or short-time signal-to-noise ratio of the signal before and after noise cancellation. When the enhancement effect is lower than the preset requirements, the reference acoustic signal selection result is updated again, and the noise cancellation parameters are readjusted to further improve the enhanced acoustic signal's ability to characterize the real internal leakage sound.

[0035] In some embodiments of this application, when extracting time-domain features, frequency-domain features, and time-frequency-domain features from the enhanced acoustic signal to construct the acoustic signature feature vector corresponding to valve internal leakage, the following steps are included: The enhanced acoustic signal is processed by framing; at least one time-domain feature from root mean square, peak factor and kurtosis is extracted from each frame of the enhanced acoustic signal; spectral analysis is performed on each frame of the enhanced acoustic signal to extract at least one frequency-domain feature from center frequency, bandwidth and harmonic components; time-frequency analysis is performed on each frame of the enhanced acoustic signal to extract time-frequency domain features from wavelet packet energy entropy; the extracted time-domain features, frequency-domain features and time-frequency domain features are combined to obtain the acoustic signature feature vector corresponding to the enhanced acoustic signal.

[0036] Specifically, in one feasible implementation, to ensure that the subsequently extracted acoustic features reflect both the instantaneous changes in the leakage sound within the valve over a short period and maintain the continuity between adjacent time slices, the enhanced acoustic signal is subjected to frame segmentation. This involves dividing the continuous acoustic signal into multiple short-time analysis segments according to a preset frame length and frame shift. The frame length can be determined based on the persistence and spectral stability of the target leakage sound, for example, it can be 20 to 100 milliseconds, preferably 30 to 60 milliseconds. The frame shift can be 10 to 50 milliseconds to ensure some overlap between adjacent frames, thereby avoiding excessively coarse segmentation of the acoustic signal that could lead to abrupt feature changes. The values ​​of the frame length and frame shift can be determined by... The field sample calibration is used to obtain acoustic signals under known normal closing and known internal leakage conditions. The stability and discriminative power of the features under different frame lengths and frame shift combinations are compared, and the parameter set that can better distinguish between internal and non-internal leakage states with small fluctuations is selected as the final setting. After framing, time-domain, frequency-domain, and time-frequency-domain features are extracted from the enhanced acoustic signal of each frame. The root mean square (RMS) is used to reflect the overall energy level of the acoustic signal in that frame. When the valve experiences internal leakage, the leakage sound usually increases the local acoustic energy; therefore, the RMS can be used to characterize the intensity of the internal leakage sound. The peak factor reflects the prominence of the signal peak relative to the overall energy, and can reflect the intensity of the signal peak. Does it exhibit localized sudden enhancement or pulse-like fluctuations? Kurtosis reflects the sharpness of the signal amplitude distribution and can be used to distinguish between relatively stable background noise and abnormal sounds with localized spikes. Spectral analysis is performed on each frame of enhanced sound signal to extract at least one frequency domain feature from the center frequency, bandwidth, and harmonic components. The center frequency reflects the frequency position where the main energy is concentrated in that frame, the bandwidth reflects the range of energy expansion in the frequency domain, and the harmonic components reflect whether there are multiple sets of peak components distributed at certain intervals in the spectrum. Valve internal leakage often exhibits different dominant frequency positions, bandwidths, and harmonic junctions under different leakage amounts, different medium flow velocities, and different gap morphologies. Therefore, the above frequency domain features can better reflect the differences between internal leakage sound, equipment vibration sound, and ambient background sound. Time-frequency analysis is performed on each frame of enhanced sound signal to extract the time-frequency domain features in wavelet packet energy entropy. Wavelet packet energy entropy is used to characterize the dispersion of signal energy distribution in different frequency bands and time periods. When the signal contains complex disturbance components caused by internal leakage, its energy distribution in each sub-frequency band usually changes. Therefore, wavelet packet energy entropy can characterize the complexity and uncertainty of the enhanced sound signal. In practical implementation, wavelet packet decomposition can be performed on each frame of enhanced sound signal first, and then the energy proportion of each decomposed frequency band can be counted to obtain the corresponding wavelet packet energy entropy.After completing the above feature extraction, the extracted time-domain features, frequency-domain features, and time-frequency-domain features are combined to obtain the acoustic signature feature vector corresponding to the enhanced acoustic signal. This combination can be understood as arranging various features in a predetermined order to form a unified feature data sequence. Before combination, features of different dimensions can be normalized or standardized to avoid imbalances in subsequent analysis caused by excessively large numerical ranges of certain features. The parameters for normalization or standardization can be obtained statistically from normal and internal leakage samples collected on-site. For example, the maximum and minimum ranges, average values, and fluctuation ranges of each feature in historical samples can be used as the processing basis. The resulting acoustic signature feature vector can simultaneously contain multi-dimensional information reflecting signal energy levels, amplitude fluctuation characteristics, spectral distribution characteristics, and time-frequency complexity characteristics, thus more comprehensively describing the acoustic state of the target valve at the current moment and providing a reliable feature basis for subsequent internal leakage probability determination and leakage level assessment.

[0037] In some embodiments of this application, when performing time synchronization and fusion diagnosis on prior constraint information of the closed state and acoustic signature feature vector to determine the internal leakage probability and leakage level of the target valve, the following steps are included: Extract the time information corresponding to the prior constraint information of the closed state and the acquisition time information corresponding to the acoustic feature vector; align the time information and acquisition time information according to the preset time window to obtain the prior constraint information of the closed state and the acoustic feature vector within the same time slice; perform correlation analysis on the prior constraint information of the closed state and the acoustic feature vector after time synchronization to obtain the internal leakage probability of the target valve; determine the leakage level of the target valve based on the internal leakage probability and the matching result of the acoustic feature vector with the features corresponding to the preset leakage level; the leakage level includes micro-leakage, slight leakage, moderate leakage and severe leakage.

[0038] Specifically, in one feasible implementation, the time information corresponding to the prior constraint information of the closed state can be the closing confirmation time, the closing duration start time, or the closing state update time when the target valve is determined to simultaneously satisfy the physical closed state and the command closed state. The acquisition time information corresponding to the acoustic feature vector can be the acquisition start time, the frame time slice number, or the feature extraction completion time corresponding to the enhanced acoustic signal. Since visual recognition, control command reading, and acoustic signal acquisition usually have different data refresh cycles, the above time information needs to be aligned according to a preset time window to ensure that the data participating in the fusion diagnosis comes from the actual operating state of the same valve in the same time period. The preset time window can be based on the camera... The frame rate, control system refresh cycle, microphone sampling and processing cycle, and valve state change speed are comprehensively considered to determine the optimal time window. For example, in a scenario where the camera captures data at 10 frames per second, the control command refresh cycle is 1 second, and the sound signal forms a set of features in 50-millisecond or 100-millisecond intervals, the preset time window can be set to 100-millisecond to 1 second, preferably 200-millisecond to 500-millisecond. The specific time window can be determined by comparing the state mismatch rate, false alarm rate, and missed alarm rate under different time windows during on-site commissioning. When the time window is too small, effective data loss is likely due to asynchronous data from multiple sources; when the time window is too large, state information and voiceprint features from different time periods may be incorrectly merged. After time alignment, the prior knowledge of the closed state within the same time slice is obtained. The constraint information and acoustic signature vectors, with the same time slice referring to the data set that corresponds to the same target valve, the same closing condition, and the same local acoustic scene within a preset time window; correlation analysis is performed on the prior constraint information of the closing state and the acoustic signature vector after time synchronization. This correlation analysis refers to comparing and comprehensively judging the abnormal acoustic features reflected in the acoustic signature vector item by item, using the prior constraint information of the closing state as a premise, to determine whether the current enhanced sound signal has characteristic manifestations consistent with the internal leakage of the valve. For example, if the acoustic signature vector in the corresponding time slice simultaneously shows abnormal increase in sound energy, center frequency shift, bandwidth expansion, and time-frequency energy distribution complexity, assuming the target valve has been confirmed to be closed. If phenomena such as increased leakage occur, it can be considered that there is a high probability of internal leakage in that time slice. The probability of internal leakage can be understood as the credibility of the current target valve experiencing an internal leakage event. Its value can be represented by a normalized result between 0 and 1. The larger the value, the higher the probability of internal leakage corresponding to that time slice. The probability of internal leakage can be determined based on the comparative statistics of historical normal samples and historical internal leakage samples. Specifically, before system deployment or during trial operation, acoustic fingerprint samples in the normal closed state and acoustic fingerprint samples in the known internal leakage state can be collected respectively. The frequency and distribution differences of different feature combinations in the normal samples and internal leakage samples can be statistically analyzed. Based on this, the correspondence between the current acoustic fingerprint feature vector and the internal leakage state can be established, and then the correspondence can be converted into the internal leakage probability.After obtaining the internal leakage probability, it is necessary to determine the leakage level of the target valve based on the matching results between the acoustic signature feature vector and the features corresponding to the preset leakage level. The features corresponding to the preset leakage level refer to the typical acoustic signature feature range or feature pattern under different leakage levels that are established in advance. These features can be obtained through bench calibration, on-site simulated leakage tests, or compilation of historical real leakage samples. For example, under known leakage conditions, enhanced acoustic signals corresponding to micro-leakage, light leakage, moderate leakage, and heavy leakage conditions are collected respectively, and the distribution intervals of features such as root mean square, peak factor, center frequency, bandwidth, harmonic components, and wavelet packet energy entropy under each level are statistically analyzed to form feature templates corresponding to each leakage level. In actual judgment, the current acoustic signature feature vector can be matched with the features corresponding to each leakage level one by one, and the level with the highest matching degree can be selected as the leakage level of the current target valve. The leakage levels include micro-leakage, light leakage, moderate leakage, and heavy leakage. In an optional embodiment, a micro-leakage corresponds to a leakage rate less than or equal to 0.5 cubic meters per hour; a minor leak corresponds to a leakage rate greater than 0.5 cubic meters per hour but less than or equal to 2 cubic meters per hour; a moderate leak corresponds to a leakage rate greater than 2 cubic meters per hour but less than or equal to 5 cubic meters per hour; and a severe leak corresponds to a leakage rate greater than 5 cubic meters per hour. These level boundaries can be determined by the changing trends of acoustic signature characteristics under different leakage conditions in field tests, or they can be modified according to the actual operation and maintenance requirements of the oil depot. For example, for operational scenarios more sensitive to the risk of cross-contamination, the identification sensitivity corresponding to micro-leakage can be appropriately increased, so that even a small leakage rate can be classified into the warning level. Through the above time synchronization and fusion diagnostic process, it can be ensured that a higher internal leakage probability and corresponding leakage level are output only when the valve is in a confirmed closed state and the acoustic signature characteristics match the internal leakage characteristics, thereby improving the consistency and reliability between the warning results and the actual internal leakage state of the valve.

[0039] In another preferred embodiment based on the above embodiments, see [reference] Figure 2 As shown, this embodiment provides an oil depot equipment early warning system based on visual recognition and voiceprint analysis, including: The spatial binding module is configured to establish spatial binding relationships between cameras, valves, and microphones within the target area of ​​the oil depot, and to acquire visual image data and valve control command data corresponding to the valves. The closed state recognition module is configured to recognize the actuator switching state of the valve based on visual image data, and extract at least one structural state information from valve stem displacement, handwheel rotation angle, and flange clearance. Based on the actuator switching state and structural state information, the physical closed state of the valve is determined, and based on the valve control command data, the target valve that simultaneously satisfies the physical closed state and the command closed state is selected to obtain the prior constraint information of the closed state of the target valve. The target acoustic signal acquisition module is configured to activate the microphone spatially bound to the target valve to acquire the target acoustic signal based on the prior constraint information of the closed state and the spatial binding relationship, and to perform internal leakage detection data shielding processing on the acoustic signals acquired by the microphones corresponding to non-target valves. The signal enhancement module is configured to acquire a reference acoustic signal in the same area as the target valve, construct a dynamic reference acoustic library containing the acoustic signal of the valve in the non-closed state and the ambient background acoustic signal, update the dynamic reference acoustic library and adjust the noise cancellation parameters according to the real-time changes in the valve state, and perform adaptive noise cancellation processing on the target acoustic signal to obtain the enhanced acoustic signal. The voiceprint feature extraction module is configured to extract time-domain features, frequency-domain features, and time-frequency-domain features from the enhanced acoustic signal to construct the voiceprint feature vector corresponding to the valve internal leakage. The fusion early warning module is configured to synchronize and fuse the prior constraint information of the closed state with the acoustic feature vector in time to obtain the internal leakage probability and corresponding leakage level of the target valve. When the internal leakage probability is greater than or equal to the preset early warning threshold, the module outputs the graded internal leakage early warning information corresponding to the target valve according to the leakage level.

[0040] Understandably, by setting up a spatial binding module, a closed state recognition module, a target acoustic signal acquisition module, a signal enhancement module, an acoustic feature extraction module, and a fusion early warning module, this implementation method can organically link the originally relatively independent visual information, control command information, and acoustic signal information in the oil depot. The system first filters target objects based on the valve's closed state, then acquires and processes the corresponding acoustic signals for the target valve, thereby reducing interference from non-target valves and environmental noise on the internal leakage judgment results. By constructing a dynamic reference acoustic library and performing adaptive noise cancellation processing on the target acoustic signals, the system helps improve the identifiability of acoustic components related to internal leakage, making the subsequently extracted acoustic features more stable. Based on this, time synchronization and fusion diagnosis are performed according to the prior constraint information of the closed state and the acoustic feature vector, ensuring that the early warning results are well consistent with the actual operating state of the target valve. This improves the targeting and accuracy of early warnings for oil depot equipment and facilitates the hierarchical identification and timely early warning of valve internal leakage anomalies.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope defined by the present invention.

Claims

1. A method for early warning of oil depot equipment based on visual recognition and voiceprint analysis, characterized in that, include: Establish spatial binding relationships between cameras, valves, and microphones within the target area of ​​the oil depot to acquire visual image data and valve control command data corresponding to the valves. Based on the visual image data, the actuator switching state of the valve is identified, and at least one structural state information is extracted from the valve stem displacement, handwheel rotation angle, and flange clearance. The physical closing state of the valve is determined based on the actuator switching state and structural state information. Based on the valve control command data, target valves that simultaneously satisfy the physical closing state and the command closing state are selected to obtain the prior constraint information of the closing state of the target valve. Based on the prior constraint information of the closed state and the spatial binding relationship, the microphone bound to the target valve space is activated to collect the target sound signal, and the sound signal collected by the microphone corresponding to the non-target valve is subjected to internal leakage detection data shielding processing. Acquire reference acoustic signals in the same area as the target valve, construct a dynamic reference acoustic library containing the acoustic signals of the valve in its non-closed state and the ambient background acoustic signals, update the dynamic reference acoustic library and adjust the noise cancellation parameters according to the real-time changes in the valve state, and perform adaptive noise cancellation processing on the target acoustic signal to obtain an enhanced acoustic signal; Extract time-domain features, frequency-domain features, and time-frequency-domain features from the enhanced acoustic signal to construct the acoustic signature vector corresponding to the valve internal leakage. The prior constraint information of the closed state is synchronized with the voiceprint feature vector in time and fused for diagnosis to obtain the internal leakage probability and corresponding leakage level of the target valve. When the internal leakage probability is greater than or equal to the preset warning threshold, the graded internal leakage warning information corresponding to the target valve is output according to the leakage level.

2. The oil depot equipment early warning method based on visual recognition and voiceprint analysis according to claim 1, characterized in that, When establishing spatial binding relationships between cameras, valves, and microphones within the target area of ​​the oil depot, the following should be included: The system acquires the installation location, shooting direction, and shooting range of each camera, and the installation location and acquisition range of each microphone. It then calibrates the position of each valve within the target area of ​​the oil depot, obtaining the spatial location identifier of each valve. Based on the shooting range of each camera, it determines the valve set corresponding to each camera, and based on the acquisition range of each microphone, it determines the valve set corresponding to each microphone. The system matches the valve sets corresponding to each camera with the valve sets corresponding to each microphone, establishing a one-to-one or one-to-many correspondence between valves and cameras / microphones. Finally, it stores these correspondences to obtain the spatial binding relationship between cameras, valves, and microphones.

3. The oil depot equipment early warning method based on visual recognition and voiceprint analysis according to claim 2, characterized in that, When determining the physical closed state of the valve based on the actuator switching state and structural state information, the following are included: The actuator switch state is determined to be either closed or open; the structural state information is matched with the corresponding structural position state under the valve closing condition to determine at least one of the following: valve stem displacement corresponding to the valve stem closing position, handwheel rotation angle corresponding to the handwheel closing position, and flange clearance corresponding to the flange closing state; when the actuator switch state is closed and the structural state information corresponds to the structural position state under the valve closing condition, the valve is determined to be in a physically closed state.

4. The oil depot equipment early warning method based on visual recognition and voiceprint analysis according to claim 3, characterized in that, Based on the valve control command data, target valves that simultaneously satisfy both physical and command-driven closed states are selected. When obtaining the prior constraint information for the closed state of the target valves, the following is included: Read the valve control command data corresponding to each valve, and parse it to obtain the valve identifier and command type; identify the valve with the command type of "closed command" as the candidate valve in the command closed state; match the valve in the physical closed state with the candidate valve in the command closed state according to the valve identifier, and filter out the target valve that simultaneously satisfies the physical closed state and the command closed state; associate the valve identifier, physical closed state and command closed state of the target valve to obtain the prior constraint information of the closed state of the target valve.

5. The oil depot equipment early warning method based on visual recognition and voiceprint analysis according to claim 4, characterized in that, Based on the prior constraint information of the closed state and the spatial binding relationship, when activating the microphone spatially bound to the target valve to collect the target acoustic signal, and performing internal leakage detection data masking processing on the acoustic signals collected by the microphones corresponding to non-target valves, the process includes: Read the target valve identifier from the prior constraint information of the closed state; retrieve the microphone identifier corresponding to the target valve identifier in the spatial binding relationship; send a collection start command to the microphone corresponding to the microphone identifier, and obtain the sound signal collected by the microphone as the target sound signal; perform internal leakage detection data masking processing on the other microphone collection channels that do not correspond to the target valve identifier, and retain the sound signals collected by the other microphones.

6. The oil depot equipment early warning method based on visual recognition and voiceprint analysis according to claim 5, characterized in that, When acquiring reference acoustic signals located in the same area as the target valve and constructing a dynamic reference acoustic library that includes the acoustic signals of the valve in its non-closed state and the ambient background acoustic signals, the following steps are included: The area range corresponding to the target valve is determined based on the spatial binding relationship; the current status information of each valve is read within the area, and valves in the non-closed state are filtered out; the microphone spatially bound to the non-closed valve is used to collect the corresponding valve sound signal as the non-closed valve sound signal; ambient sound signals other than the non-closed valve sound signal are collected within the area as ambient background sound signals; the non-closed valve sound signal and the ambient background sound signal are classified and stored according to the area identifier, signal source and collection time to construct the dynamic reference sound library.

7. The oil depot equipment early warning method based on visual recognition and voiceprint analysis according to claim 6, characterized in that, The dynamic reference acoustic library is updated and noise cancellation parameters are adjusted according to real-time changes in valve status. Adaptive noise cancellation processing is then performed on the target acoustic signal to obtain an enhanced acoustic signal, including: The system acquires real-time status information of each valve within the area where the target valve is located. When the status information indicates that a valve has entered a non-closed state, the valve sound signal collected by the microphone bound to the space of the valve that has entered the non-closed state is added to the dynamic reference sound library. When the status information indicates that a valve has exited the non-closed state, the valve sound signal corresponding to that valve is removed from the dynamic reference sound library. The system retrieves the reference sound signal corresponding to the current area status from the updated dynamic reference sound library. The system adjusts the noise cancellation parameters based on the signal changes of the reference sound signal and the target sound signal. The system performs noise cancellation processing on the target sound signal based on the adjusted noise cancellation parameters to obtain the enhanced sound signal.

8. The oil depot equipment early warning method based on visual recognition and voiceprint analysis according to claim 7, characterized in that, When extracting time-domain features, frequency-domain features, and time-frequency-domain features from the enhanced acoustic signal to construct the acoustic signature feature vector corresponding to the valve's internal leakage, the following steps are included: The enhanced acoustic signal is processed by frame segmentation; at least one time-domain feature among root mean square, peak factor, and kurtosis is extracted from each frame of the enhanced acoustic signal; spectrum analysis is performed on each frame of the enhanced acoustic signal to extract at least one frequency-domain feature among center frequency, bandwidth, and harmonic components; time-frequency analysis is performed on each frame of the enhanced acoustic signal to extract time-frequency domain features from wavelet packet energy entropy; the extracted time-domain features, frequency-domain features, and time-frequency domain features are combined to obtain the acoustic signature feature vector corresponding to the enhanced acoustic signal.

9. The oil depot equipment early warning method based on visual recognition and voiceprint analysis according to claim 8, characterized in that, When performing time synchronization and fusion diagnosis on the prior constraint information of the closed state and the acoustic signature feature vector to determine the internal leakage probability and leakage level of the target valve, the process includes: Extract the time information corresponding to the prior constraint information of the closed state and the acquisition time information corresponding to the acoustic feature vector; align the time information and acquisition time information according to a preset time window to obtain the prior constraint information of the closed state and the acoustic feature vector within the same time slice; perform correlation analysis on the time-synchronized prior constraint information of the closed state and the acoustic feature vector to obtain the internal leakage probability of the target valve; determine the leakage level of the target valve based on the internal leakage probability and the matching result of the acoustic feature vector with the features corresponding to the preset leakage level; the leakage level includes micro-leakage, slight leakage, moderate leakage and severe leakage.

10. An oil depot equipment early warning system based on visual recognition and voiceprint analysis, used to implement the oil depot equipment early warning method based on visual recognition and voiceprint analysis as described in any one of claims 1-9, characterized in that, include: The spatial binding module is configured to establish spatial binding relationships between cameras, valves, and microphones within the target area of ​​the oil depot, and to acquire visual image data and valve control command data corresponding to the valves. The closed state recognition module is configured to recognize the actuator switching state of the valve based on the visual image data, and extract at least one structural state information from the valve stem displacement, handwheel rotation angle, and flange clearance. Based on the actuator switching state and structural state information, the module determines the physical closed state of the valve, and based on the valve control command data, filters out target valves that simultaneously satisfy the physical closed state and the command closed state, and obtains the prior constraint information of the closed state of the target valve. The target acoustic signal acquisition module is configured to, based on the prior constraint information of the closed state and the spatial binding relationship, activate the microphone spatially bound to the target valve to acquire the target acoustic signal, and perform internal leakage detection data masking processing on the acoustic signals acquired by the microphones corresponding to non-target valves; The signal enhancement module is configured to acquire a reference acoustic signal in the same area as the target valve, construct a dynamic reference acoustic library containing the acoustic signal of the valve in a non-closed state and the ambient background acoustic signal, update the dynamic reference acoustic library and adjust the noise cancellation parameters according to the real-time changes in the valve state, and perform adaptive noise cancellation processing on the target acoustic signal to obtain an enhanced acoustic signal. The voiceprint feature extraction module is configured to extract time-domain features, frequency-domain features, and time-frequency-domain features from the enhanced acoustic signal to construct a voiceprint feature vector corresponding to the valve internal leakage. The fusion warning module is configured to synchronize and fuse the prior constraint information of the closed state with the voiceprint feature vector in time to obtain the internal leakage probability and corresponding leakage level of the target valve. When the internal leakage probability is greater than or equal to a preset warning threshold, the module outputs graded internal leakage warning information corresponding to the target valve according to the leakage level.

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