Method and system for monitoring displacement of cathode wires inside an electrostatic precipitator

Abstract

The present application relates to the technical field of fault diagnosis and early warning of electric dust collectors, and particularly relates to a method and system for monitoring displacement of cathode wires inside an electric dust collector, the method comprising the following steps: performing time synchronization processing on the obtained electromagnetic signals and acoustic signals to obtain time-synchronized electromagnetic signals and acoustic signals; based on a preset space-time correlation condition, determining whether the time-synchronized electromagnetic signals and the time-synchronized acoustic signals constitute a collaborative abnormal event, obtaining a collaborative abnormal event determination result and a joint feature of the collaborative abnormal event; through a combination of collaborative analysis of the electromagnetic signals and the acoustic signals, abnormal event screening based on space-time correlation, feature compensation based on operating condition parameters, and matching with a preset fault mode, weak signals caused by initial local small displacement of the cathode wires can be effectively distinguished from complex background noise and operating condition fluctuations, achieving the effects of high sensitivity, high reliability, online early warning, and precise positioning.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fault diagnosis and early warning of electric dust precipitators, and particularly relates to a method and system for monitoring displacement of internal cathode wires of an electric dust precipitator. BACKGROUND

[0002] An electric dust precipitator is a core device of a flue gas purification system of a thermal power plant. A cathode wire system in the electric dust precipitator generates corona discharge through high-voltage direct-current excitation, so that dust particles in flue gas are charged and collected by an anode plate. Spatial position accuracy of the cathode wire is crucial to uniformity and strength of an electric field, and directly affects dust removal efficiency. These metal cathode wires are densely arranged between the anode plates. The electric dust precipitator operates in a high-temperature flue gas environment, and the flue gas contains corrosive components such as sulfur oxides and is accompanied by high-concentration dust. Under such harsh conditions, the cathode wire material will degrade in performance due to creep and oxidation corrosion, and at the same time, withstands mechanical vibration caused by flue gas scouring and a rapping cleaning device. These factors together cause the cathode wire to possibly appear relaxation, droop or local bending deformation after long-term operation, and deviate from the initial design position. This spatial position deviation, i.e., displacement, changes an effective distance (inter-electrode distance) between the cathode wire and the anode plate. Changes in the inter-electrode distance cause changes in local electric field strength, and when the inter-electrode distance is reduced to a certain extent due to cathode wire displacement, the local electric field strength is significantly increased, which can exceed the breakdown field strength of the gas medium, and cause spark or arc discharge. This not only forces the operation to be reduced in voltage and affects dust removal, but also can cause damage to the equipment or safety hazards.

[0003] Current cathode wire displacement monitoring faces severe challenges, mainly in the following aspects: extreme harshness of signal acquisition: the high-temperature, high-dust and corrosive environment inside the electric dust precipitator seriously threatens the service life of the sensor and signal acquisition. Submersion and weakness of the target signal: the weak signal (discharge change or mechanical activity) caused by the small displacement is submerged by strong background noise (corona radiation, rapping vibration, pneumatic noise). Confusion of working condition fluctuations and local fault characteristics: overall signal changes caused by conventional working condition fluctuations (temperature, humidity, dust concentration, load change) are easily confused with local displacement fault characteristics, leading to misjudgment.

[0004] Existing monitoring methods have limitations. Manual shutdown inspection cannot be real-time and is not economical. Online systems based on optics or lasers are plagued by problems such as limited installation and field of view due to dust attenuation and scattering of light signals, high-temperature influence on element tolerance, and structural limitations, and are difficult to achieve comprehensive and reliable coverage monitoring of a large number of densely arranged cathode wires, especially for early small displacements.

[0005] The existing technology needs to be improved in view of the above problems. SUMMARY

[0006] The application aims to solve the problems in the prior art and provides a method and system for monitoring displacement of a cathode wire in an electric dust collector.

[0007] In a first aspect, the application provides a method for monitoring displacement of a cathode wire in an electric dust collector, which is used for early warning of initial local slight displacement of the cathode wire, and the method comprises the following steps:

[0008] acquiring electromagnetic signals and acoustic signals in the electric dust collector;

[0009] performing time synchronization processing on the acquired electromagnetic signals and acoustic signals to obtain time-synchronized electromagnetic signals and acoustic signals;

[0010] judging whether the time-synchronized electromagnetic signals and time-synchronized acoustic signals constitute a cooperative abnormal event based on a preset space-time correlation condition to obtain a cooperative abnormal event judgment result and a joint feature of the cooperative abnormal event;

[0011] acquiring operating condition parameters of the electric dust collector;

[0012] compensating the joint feature of the cooperative abnormal event according to the acquired operating condition parameters to obtain a compensated joint feature;

[0013] matching the compensated joint feature with a preset fault response mode representing initial local slight displacement of the cathode wire to obtain a matching result;

[0014] when the matching result is matching success, determining that initial local slight displacement of the cathode wire occurs, performing fault source positioning based on the time-synchronized electromagnetic signals and the time-synchronized acoustic signals, and outputting a warning indication containing positioning information.

[0015] The core innovation of the application is that the cooperative analysis of electromagnetic signals and acoustic signals, abnormal event screening based on space-time correlation, feature compensation based on operating condition parameters, and matching with a preset fault mode are combined, so that weak signals caused by initial local slight displacement of the cathode wire are effectively distinguished from complex background noise and operating condition fluctuations, achieving the effects of high sensitivity, high reliability, online early warning, and accurate positioning.

[0016] In a second aspect, a system for monitoring displacement of a cathode wire in an electric dust collector is provided, which is used for early warning of initial local slight displacement of the cathode wire, and the system comprises:

[0017] a signal acquisition module for acquiring electromagnetic signals and acoustic signals in the electric dust collector;

[0018] The signal synchronization processing module is configured to perform time synchronization processing on the obtained electromagnetic signal and the obtained acoustic signal to obtain a time-synchronized electromagnetic signal and a time-synchronized acoustic signal.

[0019] The cooperative abnormal event judgment module is configured to judge whether the time-synchronized electromagnetic signal and the time-synchronized acoustic signal constitute a cooperative abnormal event based on a preset space-time correlation condition, obtain a cooperative abnormal event judgment result and a joint feature of the cooperative abnormal event, and use the space-time correlation condition to represent that the electromagnetic signal and the acoustic signal are derived from the same physical source.

[0020] The working condition parameter acquisition module is configured to acquire a running working condition parameter of the electric dust collector.

[0021] The joint feature compensation module is configured to compensate the joint feature of the cooperative abnormal event based on the acquired running working condition parameter to obtain a compensated joint feature.

[0022] The fault mode matching module is configured to match the compensated joint feature with a preset fault response mode representing the initial local slight displacement of the cathode wire to obtain a matching result.

[0023] The early warning output module is configured to determine that the initial local slight displacement of the cathode wire occurs when the matching result is a matching success, perform fault source positioning based on the time-synchronized electromagnetic signal and the time-synchronized acoustic signal, and output an early warning instruction containing positioning information.

[0024] Compared with the prior art, the present application has the following beneficial effects:

[0025] By acquiring and synchronizing electromagnetic and acoustic signals, judging a cooperative abnormal event, combining a running working condition to compensate a feature, and matching the compensated feature with a self-adaptively corrected fault mode, fault source positioning is finally performed, which effectively solves the problem that the initial local slight displacement of the cathode wire is difficult to be early detected, accurately detected and localized in a harsh environment, has the advantages that early, accurate and localized early warning of the initial local slight displacement of the cathode wire in the electric dust collector can be realized, and challenges brought by a harsh environment, weak signals, complex noise and working condition fluctuations can be effectively overcome. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 The present application is a method flowchart.

[0027] Figure 2 The present application is a system structure schematic diagram.

[0028] In the figure: 201, signal acquisition module; 202, signal synchronization processing module; 203, cooperative abnormal event judgment module; 204, working condition parameter acquisition module; 205, joint feature compensation module; 206, fault mode matching module; 207, early warning output module. DETAILED DESCRIPTION

[0029] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar notations represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by reference to the drawings are exemplary only, and are used only for the purpose of explaining the present application, and cannot be understood as limiting the present application. The terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0030] As Figure 1 shown in a kind of internal cathode wire displacement monitoring method of electric dust collector, for the early warning of initial local small displacement of cathode wire, method includes the following steps:

[0031] S101, the electromagnetic signal and acoustic signal in the electric dust collector are acquired;

[0032] S102, the electromagnetic signal and acoustic signal obtained are time-synchronized, to obtain time-synchronized electromagnetic signal and acoustic signal;

[0033] S103, based on the preset space-time correlation condition, whether the time-synchronized electromagnetic signal and time-synchronized acoustic signal constitute a cooperative abnormal event is judged, to obtain the cooperative abnormal event judgment result and the joint feature of cooperative abnormal event, and the space-time correlation condition is used to characterize that the electromagnetic signal and acoustic signal originate from the same physical source;

[0034] S104, the running condition parameter of electric dust collector is acquired;

[0035] S105, the joint feature of cooperative abnormal event is compensated according to the running condition parameter obtained, to obtain the compensated joint feature;

[0036] S106, the compensated joint feature is matched with the preset fault response mode for characterizing initial local small displacement of cathode wire, to obtain the matching result;

[0037] S107, when the matching result is a matching success, determining that a cathode wire initial local slight displacement occurs, and performing fault source positioning based on the time-synchronized electromagnetic signal and the time-synchronized acoustic signal, and outputting a pre-warning instruction containing positioning information.

[0038] The method first acquires electromagnetic signals and acoustic signals in the electric dust collector. The electromagnetic signals refer to electromagnetic radiation generated by ionization processes such as corona discharge and micro-spark discharge, which can be acquired by using a broadband electromagnetic sensor or an antenna array, and are mainly used to capture abnormal information related to discharge characteristics. The acoustic signals refer to sound waves generated by mechanical vibration, material stress release, or discharge, which can be acquired by using a microphone array or a piezoelectric sensor, and are mainly used to capture abnormal information related to structural changes or mechanical activities. Acquiring these signals is the basis for subsequent analysis.

[0039] Then, the acquired electromagnetic signals and acoustic signals are subjected to time synchronization processing to obtain time-synchronized electromagnetic signals and acoustic signals. Time synchronization processing refers to ensuring that signals collected by different sensors have a unified time reference through a high-precision clock or a synchronous triggering mechanism, which is mainly used to ensure that the correlation between electromagnetic signals and acoustic signals in subsequent analysis is accurate, and to avoid misjudgment due to different time synchronization.

[0040] Based on a preset spatio-temporal correlation condition, it is determined whether the time-synchronized electromagnetic signals and the time-synchronized acoustic signals constitute a collaborative abnormal event, to obtain a collaborative abnormal event judgment result and a joint feature of the collaborative abnormal event. The preset spatio-temporal correlation condition is used to represent that the electromagnetic signals and the acoustic signals originate from the same physical source, which refers to judging whether the electromagnetic signals and the acoustic signals appear within a reasonable time difference and spatial range based on the propagation speed of the signals inside the electric dust collector and the deployment position of the sensors, which can be achieved by calculating the time difference of the signals and comparing it with a threshold value determined based on a propagation model, and is mainly used to exclude the interference of background noise or single signal abnormality, and to identify electromagnetic and acoustic joint abnormalities that may be caused by the same physical event (such as cathode wire partial discharge or vibration). The collaborative abnormal event refers to an abnormal combination of electromagnetic signals and acoustic signals that have a correlation in time and space, which is mainly used to focus on specific events related to potential faults. The joint feature of the collaborative abnormal event refers to a set of parameters that comprehensively describe the electromagnetic and acoustic characteristics of the collaborative abnormal event, which can include the spectral feature, amplitude feature, and pulse shape feature of the electromagnetic signal, the frequency feature, energy feature, and duration feature of the acoustic signal, and the time delay and correlation between them, etc., which is mainly used to provide a basis for subsequent fault diagnosis.

[0041] Meanwhile, the operating condition parameters of the electric dust collector are acquired. The operating condition parameters refer to various physical quantities that affect the internal environment and operating state of the electric dust collector, which can include flue gas temperature, pressure, humidity, dust concentration, gas composition, power supply voltage, operating current, etc., and are mainly used to reflect the current operating state of the electric dust collector, because these parameters will affect the signal propagation characteristics and corona discharge characteristics.

[0042] The joint features of the collaborative abnormal event are compensated according to the acquired operating condition parameters to obtain compensated joint features. Compensation refers to modifying the joint features of the collaborative abnormal event according to the operating condition parameters to eliminate the influence of operating condition fluctuations on the features and highlight the feature changes caused by faults, which can be implemented in a manner based on a regression model, a machine learning model, or a lookup table, etc., and is mainly used to improve the accuracy of fault diagnosis and avoid misjudgment caused by operating condition changes. The compensated joint features refer to features that have been modified by operating condition influences and can better reflect the true fault state.

[0043] The compensated joint features are matched with a preset fault response mode representing initial local micro-displacement of the cathode wire to obtain a matching result. The preset fault response mode representing initial local micro-displacement of the cathode wire refers to a typical performance mode of the joint features of the collaborative abnormal event when initial micro-displacement of the cathode wire occurs, which is established based on historical data, experiments, or simulations, and can be represented by a feature vector, a mode template, or a classifier model, and is mainly used to provide a judgment standard for identifying whether a specific type of fault has occurred. Matching refers to comparing the current compensated joint features with the preset fault response mode to judge the similarity or degree of conformity between the two, which can be implemented by distance calculation, correlation analysis, or classification algorithm, and is mainly used to determine whether the current abnormal event conforms to the feature mode of initial local micro-displacement of the cathode wire.

[0044] When the matching result is a match, it is determined that initial local micro-displacement of the cathode wire has occurred, and fault source positioning is performed based on the time-synchronized electromagnetic signal and the time-synchronized acoustic signal, and a pre-warning indication containing positioning information is output. Fault source positioning refers to calculating the spatial position of the signal source using the characteristics (such as time difference of arrival, signal strength) of the time-synchronized electromagnetic signal and acoustic signal and the sensor position information, which can be implemented by using a time difference of arrival positioning algorithm, an angle of arrival positioning algorithm, or a signal strength positioning algorithm, and is mainly used to determine the specific cathode wire or section where the fault occurs. The pre-warning indication refers to outputting alarm information containing the fault type and positioning information, which can be presented in the form of text, graphics, or sound, and is mainly used to timely notify the operator to take processing measures.

[0045] The scheme of the present application lays a foundation for subsequent joint analysis by acquiring electromagnetic signals and acoustic signals inside the electric dust collector and performing strict time synchronization processing. Based on preset spatio-temporal correlation conditions, the system can screen out electromagnetic-acoustic cooperative abnormal events with correlation in time and space from a large number of background signals, which greatly reduces the false alarm rate, because the real fault events often produce electromagnetic and acoustic responses at the same time, and these responses are related in time and space. The operating condition parameters of the electric dust collector are acquired, and these parameters are used to compensate the joint features of the cooperative abnormal events, effectively eliminating the influence of working condition fluctuations such as flue gas environment and power supply state on the signal features, so that the compensated features can more accurately reflect the abnormal state of the cathode wire itself. The compensated joint features are matched with the preset fault response mode representing the initial local small displacement of the cathode wire, and through the method of pattern recognition, it is accurately judged whether the current abnormal event conforms to the characteristic mode of the initial small displacement. Once the matching is successful, the system not only determines that a fault has occurred, but also locates the fault source using the time-synchronized electromagnetic signals and acoustic signals to determine the specific location of the displacement and output a warning indication containing the positioning information, thereby realizing early and accurate warning and positioning of the initial local small displacement of the cathode wire. The whole process forms a closed loop from signal acquisition to final warning, and each step closely cooperates to solve the problem of identifying weak fault signals in harsh environments.

[0046] As an embodiment of the present application, based on the preset spatio-temporal correlation conditions, whether the time-synchronized electromagnetic signals and the time-synchronized acoustic signals constitute a cooperative abnormal event is judged, and the cooperative abnormal event judgment result and the joint features of the cooperative abnormal event are obtained, and the steps include:

[0047] Acquiring operating environment parameters affecting signal propagation inside the electric dust collector;

[0048] Determining the propagation speed of the signals inside the electric dust collector according to the acquired operating environment parameters;

[0049] Based on the determined propagation speed and sensor deployment information, adjusting the time correlation parameter or the space correlation parameter in the preset spatio-temporal correlation conditions to obtain the adjusted spatio-temporal correlation conditions;

[0050] Based on the adjusted spatio-temporal correlation conditions, whether the time-synchronized electromagnetic signals and the time-synchronized acoustic signals constitute a cooperative abnormal event is judged, and the cooperative abnormal event judgment result and the joint features of the cooperative abnormal event are obtained, and the adjusted spatio-temporal correlation conditions are used to represent that the electromagnetic signals and the acoustic signals are from the same physical source.

[0051] The operation environment parameter affecting signal propagation inside the electric dust collector refers to a physical or chemical parameter in the internal environment of the electric dust collector that can change the propagation characteristics (such as speed and attenuation) of electromagnetic signals and acoustic signals, which can be gas temperature, pressure, humidity, flue gas composition, dust concentration, etc., and the purpose is to obtain actual environmental information affecting signal propagation; the propagation speed of the signal inside the electric dust collector refers to the actual propagation rate of the electromagnetic signal or acoustic signal under the current internal environment of the electric dust collector, which can be determined according to the obtained operation environment parameter through a pre-established physical model, empirical formula or reference to experimental data table, and the purpose is to quantify the propagation characteristics of the signal under the current environment; the sensor deployment information refers to the actual installation position and spatial layout information of the sensors used to collect electromagnetic signals and acoustic signals inside the electric dust collector, which can be the three-dimensional coordinates of the sensors or the relative position with respect to the internal structure of the electric dust collector, and the purpose is to provide the basis for calculating the signal propagation distance and judging the spatial correlation; the time correlation parameter or the spatial correlation parameter in the preset space-time correlation condition refers to the initial time difference threshold or the spatial distance threshold used to determine whether the electromagnetic signal and the acoustic signal originate from the same physical source when the real-time operation environment is not considered, which can be a fixed value set based on theoretical calculation or experimental data under typical working conditions, and the purpose is to provide a preliminary correlation judgment standard; adjustment refers to modifying or optimizing the preset time correlation parameter or spatial correlation parameter according to the determined signal propagation speed and sensor deployment information, so that it is more consistent with the signal propagation characteristics under the current actual operation environment, which can be achieved by scaling, table correction or model calculation, etc., and the purpose is to improve the accuracy of the space-time correlation judgment; the adjusted space-time correlation condition refers to the time correlation threshold or the spatial correlation threshold obtained after adjustment, which can better reflect the actual signal propagation characteristics inside the current electric dust collector, and is used to replace the preset space-time correlation condition for judgment of collaborative abnormal events, and the purpose is to make the judgment standard dynamically adapt to environmental changes.

[0052] The scheme of the present application obtains the operating environment parameters affecting the signal propagation inside the electric dust collector, and determines the actual propagation speed of the signal inside the electric dust collector according to these parameters. Based on the determined propagation speed and the known sensor deployment information, the spatio-temporal correlation condition for judging whether the electromagnetic signal and the acoustic signal originate from the same physical source is dynamically adjusted. This adjustment can be modifying the preset time correlation parameter, such as allowing a larger time difference, or modifying the spatial correlation parameter, such as considering the bending or reflection of the signal propagation path. Finally, the spatio-temporal correlation condition after this dynamic adjustment is used to judge whether the time-synchronized electromagnetic signal and acoustic signal constitute a coordinated abnormal event. This dynamically adjusted spatio-temporal correlation condition can more accurately reflect whether the signals indeed come from the same physical source, thereby improving the accuracy of the coordinated abnormal event judgment and reducing false positives or false negatives caused by environmental changes. By introducing the dynamic adaptability of environmental parameters in the key step of coordinated abnormal event judgment, the judgment of the coordinated abnormal event itself becomes more accurate and reliable, thereby providing more accurate input for subsequent operating condition parameter compensation and fault mode matching, and improving the accuracy and reliability of the entire early warning method.

[0053] As an embodiment of the present application, the step of compensating the joint feature of the coordinated abnormal event according to the obtained operating condition parameters comprises:

[0054] From the obtained multiple operating condition parameters, a group of target operating condition parameters having a coupling effect on the joint feature of the coordinated abnormal event are identified;

[0055] For each target operating condition parameter in the identified group of target operating condition parameters, the independent influence amount of the target operating condition parameter on the joint feature of the coordinated abnormal event is determined under the condition that other target operating condition parameters in the group of target operating condition parameters are in a preset reference state;

[0056] The total influence amount of the identified group of target operating condition parameters acting on the joint feature of the coordinated abnormal event is determined;

[0057] Based on the difference between the determined total influence amount and the arithmetic sum of the independent influence amounts of the target operating condition parameters in the group of target operating condition parameters, the coupling influence amount of the group of target operating condition parameters on the joint feature of the coordinated abnormal event is determined;

[0058] The joint feature of the coordinated abnormal event is compensated based on the determined independent influence amounts of the target operating condition parameters and the determined coupling influence amount of the group of target operating condition parameters, and the compensated joint feature is obtained.

[0059] The group of target operating condition parameters having a coupling effect on the joint feature of the collaborative abnormal event is identified from the acquired multiple operating condition parameters by analyzing the influence of different combinations of operating condition parameters on the joint feature of the collaborative abnormal event, and finding a set of parameters whose joint influence is not simply additive, but has a mutual enhancement or weakening effect. The coupling relationship between the parameters and the influence degree of the joint feature can be identified by statistical analysis, correlation analysis or machine learning methods, and the purpose is to focus on key operating condition parameters that have a significant influence on the joint feature and have complex interactions, thereby improving the pertinence and efficiency of compensation. The preset reference state is a reference operating condition point or range set for evaluating the independent influence of a single target operating condition parameter, and in this state, other target operating condition parameters are fixed or maintained at typical and stable values to isolate the influence of a single parameter. The parameter values of the preset reference state can be determined according to the design parameters, rated operating conditions or historical stable operating data of the electric dust collector. The independent influence amount is the degree of change in the joint feature of the collaborative abnormal event caused by the change of a single target operating condition parameter when other target operating condition parameters remain unchanged in the preset reference state. The total influence amount is the total change caused by the joint action of the identified group of target operating condition parameters on the joint feature of the collaborative abnormal event. The coupling influence amount is the difference between the total influence of the joint action of the group of target operating condition parameters and the sum of the independent influences of each parameter, which quantifies the influence of the interaction between parameters on the joint feature.

[0060] The scheme of the present application avoids complex analysis of all operating condition parameters by first identifying a set of key operating condition parameters having a coupling effect on the joint feature of the collaborative abnormal event, thereby improving processing efficiency. Then, by determining the independent influence of each target operating condition parameter when other parameters are in the preset reference state, the mutual interference between parameters can be removed, and the effect of a single parameter can be accurately quantified. At the same time, by determining the total influence of the joint action of the group of parameters, the overall effect of the comprehensive action of multiple parameters in actual operation is captured. The coupling influence amount is calculated based on the difference between the total influence amount and the sum of the independent influence amounts, which directly quantifies the contribution of the coupling effect between parameters to the joint feature. Finally, the independent influence amount and the coupling influence amount are combined to compensate for the joint feature of the collaborative abnormal event, so that the compensation process not only considers the direct influence of a single parameter, but more importantly, considers the complex interaction between parameters, thereby more accurately correcting the influence of the joint feature caused by operating condition fluctuations. Compared with the compensation method considering only the independent influence of a single operating condition parameter, the present application can more accurately reflect the characteristics of the collaborative abnormal event, reduce the compensation error caused by operating condition coupling, and improve the accuracy of subsequent fault mode matching, thereby improving the early warning accuracy of the initial local micro displacement of the cathode wire.

[0061] As an embodiment of the present application, the step of identifying a set of target operating condition parameters having a coupling effect on the joint feature of the collaborative abnormal event from the acquired plurality of operating condition parameters comprises:

[0062] Under a preset update condition, a plurality of operating condition parameters in a historical time period and a joint feature of a collaborative abnormal event corresponding to the plurality of operating condition parameters in the historical time period are acquired;

[0063] Based on the acquired plurality of operating condition parameters in the historical time period and the joint feature of the collaborative abnormal event corresponding to the plurality of operating condition parameters in the historical time period, a difference between a comprehensive influence of different parameter combinations in the plurality of operating condition parameters in the historical time period on the joint feature of the corresponding collaborative abnormal event and a sum of independent influences of each parameter is analyzed to obtain a difference;

[0064] According to the obtained difference, a discriminant condition for identifying a set of target operating condition parameters having a coupling effect on the joint feature of the collaborative abnormal event from the currently acquired plurality of operating condition parameters is adjusted to obtain an adjusted discriminant condition;

[0065] Based on the obtained adjusted discriminant condition and the currently acquired plurality of operating condition parameters, a set of target operating condition parameters having a coupling effect on the joint feature of the collaborative abnormal event is identified from the currently acquired plurality of operating condition parameters.

[0066] The preset update condition refers to a specific state or event for triggering historical data acquisition, analysis, and discriminant condition adjustment, which can be achieved in a manner based on a time period, based on a performance index change, or based on a specific event occurrence, and the purpose is to ensure that the logic of identifying coupled parameters can be dynamically updated according to the changes in the actual operation of the electric dust collector. The historical time period refers to a past time range for collecting operating condition parameters and joint feature data of collaborative abnormal events, which can be determined in a manner of fixed time length, sliding window, or based on a data volume threshold, and the purpose is to provide sufficient data samples for analyzing the mutual influence between parameters. The difference refers to a value or index quantitatively obtained by comparing and analyzing the comprehensive influence of different parameter combinations and the result of simply superimposing the independent influences of each parameter, which can be represented in a manner of statistical index, model residual, or specific algorithm output, and the purpose is to represent the degree of interaction (coupling) between parameters. The discriminant condition refers to a rule or standard for judging whether one or a set of operating condition parameters has a significant coupling effect on the joint feature of the collaborative abnormal event, which can be defined in a manner of threshold, model parameter, or classifier output, and the purpose is to select target parameters having coupling characteristics from a plurality of operating condition parameters.

[0067] The scheme of the present application periodically or event-drivenly acquires historical operation data under preset updating conditions, including various operation condition parameters and corresponding joint features of abnormal events. Due to the accumulation of these historical data, the scheme can provide a basis for subsequent in-depth analysis of the complex relationship between parameters. Based on these historical data, the scheme further analyzes the comprehensive influence of different operation condition parameter combinations on the joint features, and compares it with the simple superposition result of the influence of each parameter alone, so as to quantify the interaction or coupling degree between parameters and obtain the difference. Through this comparative analysis, the complex coupling effect hidden under the simple superposition of parameters can be revealed. According to the obtained difference, the scheme can dynamically adjust the discrimination condition for identifying target operation condition parameters, so that the discrimination condition can more accurately reflect the real coupling characteristics of parameters under the current operation state. Due to the adaptive adjustment of the discrimination condition according to historical data, the subsequent identification of target parameters is more targeted and accurate. Finally, based on the adjusted discrimination condition and the current operation condition parameters, the scheme can accurately identify a set of target operation condition parameters that have a coupling effect on the joint features of abnormal events from the currently acquired parameters. Through this series of steps, the scheme can overcome the challenges brought by the complex coupling relationship between operation condition parameters, provide a more accurate and more relevant target parameter set for subsequent compensation of joint features, and thus improve the effectiveness of compensation.

[0068] As an embodiment of the present application, the step of matching the compensated joint feature with the preset fault response mode representing the initial local micro-displacement of the cathode line includes:

[0069] matching the compensated joint feature with the preset fault response mode representing the initial local micro-displacement of the cathode line to obtain a current matching result;

[0070] When the current matching result does not meet the preset success criterion, and the operation state of the electric precipitator meets the preset mode correction trigger condition, then:

[0071] acquiring a plurality of compensated joint feature records within a period of time after the mode correction trigger condition is met, which do not meet the preset success criterion in matching with the preset fault response mode;

[0072] based on the acquired plurality of compensated joint feature records, analyzing the common characteristics represented by the plurality of compensated joint feature records, and comparing the common characteristics with the preset fault response mode to determine the difference parameters between them;

[0073] according to the determined difference parameters, adjusting the internal parameters or the structure definition of the preset fault response mode to obtain a corrected fault response mode;

[0074] The original preset fault response mode is replaced by the modified fault response mode, so that when the step of matching the compensated joint features with the fault response mode is performed subsequently, the modified fault response mode is used to obtain a subsequent matching result.

[0075] The pattern modification trigger condition is a specific combination of conditions for starting the adaptive adjustment process of the fault response mode, which can be implemented by the number of matching failures or the failure rate exceeding a threshold, a specific operating condition parameter deviating from a normal range, or a combination of both, and the purpose is to start the pattern modification when the matching effect is poor and the operating environment may change.

[0076] The common characteristics are the common rules or statistical characteristics exhibited in the plurality of compensated joint feature records, which can be implemented by calculating the mean, median, distribution range of the feature vector, or identifying frequently occurring feature combinations, and the purpose is to extract the direction or nature of the pattern shift from the matching failed data.

[0077] The difference parameter is a quantitative difference between the common characteristics and the preset fault response mode, which can be implemented by the feature value difference, weight difference, threshold offset, or model structure difference description, and the purpose is to accurately guide the adjustment of the fault response mode.

[0078] The adjustment of the internal parameters or the structure definition of the preset fault response mode means modifying the mode according to the determined difference parameter, which can be implemented by updating the threshold, weight, rule set, or model structure in the mode, and the purpose is to make the mode more suitable for the current actual situation.

[0079] The scheme of the present application preliminarily judges whether there is a cathode wire initial local small displacement by matching the compensated joint feature with a preset fault response mode. When the matching result fails to reach a preset success criterion and the system monitors that the operating state of the electric dust collector meets a preset mode correction triggering condition, the system does not immediately determine a fault, but starts a mode self-adaptive correction process. This is because the matching failure may not be due to an actual fault, but due to a deviation between the preset mode and the current operating state. By starting the correction only when a specific triggering condition is met, unnecessary frequent adjustment can be avoided. In the correction process, the system collects a plurality of sets of records of the compensated joint features that fail to match for a period of time. These records represent feature samples that the mode fails to correctly identify under the current operating conditions. Based on these samples, the system analyzes the characteristics commonly exhibited by them, finds systematic differences between these failed samples and the current preset mode, and quantifies these differences as difference parameters. According to these difference parameters, the system adjusts the internal parameters (e.g., the weights and thresholds of the features) or the structural definition of the preset fault response mode, generating a corrected fault response mode. Subsequently, the system replaces the original mode with this corrected mode. In this way, the subsequently obtained compensated joint features will be matched with this corrected fault response mode that is more suitable for the current actual operating state. This online self-adaptive correction mechanism enables the fault response mode to be dynamically updated as the operating state of the electric dust collector changes, thereby improving the accuracy of the matching, reducing false negatives and false positives. The mode correction mechanism combined with the step of obtaining the compensated joint feature enables the system to use the feature information that is compensated for the working conditions and more real to drive the adaptive adjustment of the mode, ensuring the effectiveness and pertinence of the mode correction, and thereby improving the robustness of the entire early warning method and enhancing its reliability.

[0080] As an embodiment of the present application, the steps of locating the fault source based on the time-synchronized electromagnetic signal and the time-synchronized acoustic signal include:

[0081] extracting electromagnetic positioning feature parameters from the time-synchronized electromagnetic signal;

[0082] extracting acoustic positioning feature parameters from the time-synchronized acoustic signal;

[0083] determining the position of the fault source according to the extracted electromagnetic positioning feature parameters and the extracted acoustic positioning feature parameters, and combining the differences in the propagation characteristics of the electromagnetic signal and the acoustic signal inside the electric dust collector.

[0084] The electromagnetic positioning characteristic parameter refers to a signal attribute capable of reflecting the position information of an electromagnetic signal source, which can be a signal arrival time, signal strength, signal waveform feature or spectrum feature extracted from a time-synchronized electromagnetic signal, and the purpose is to provide electromagnetic information basis for fault source positioning. The acoustic positioning characteristic parameter refers to a signal attribute capable of reflecting the position information of an acoustic signal source, which can be a signal arrival time, signal strength, signal waveform feature or spectrum feature extracted from a time-synchronized acoustic signal, and the purpose is to provide acoustic information basis for fault source positioning. The difference in the propagation characteristics of electromagnetic signals and acoustic signals inside the electric dust collector refers to the differences in the propagation speed, attenuation degree, reflection, refraction and other characteristics of electromagnetic signals and acoustic signals when they propagate in the complex environment inside the electric dust collector (such as high temperature, high dust, corrosive gas, equipment structure, etc.), and the purpose is to use these differences to correct the positioning result and improve the positioning accuracy.

[0085] The scheme of the present application extracts electromagnetic positioning characteristic parameters from time-synchronized electromagnetic signals and acoustic positioning characteristic parameters from time-synchronized acoustic signals, obtaining two different modal information about the position of the fault source. Since the response mechanisms of electromagnetic signals and acoustic signals to the fault source are different, and there are inherent differences in their propagation characteristics inside the electric dust collector, for example, the propagation speed of electromagnetic signals is close to the speed of light, and it is relatively less affected by the medium, but it is easily affected by metal structure reflection and scattering; the propagation speed of acoustic signals is much slower, and it is significantly affected by gas temperature, pressure, composition, dust concentration, etc., and it also decays faster. Due to these differences, relying solely on a single signal or not distinguishing the signal characteristics for positioning can easily produce errors. The present scheme further extracts electromagnetic positioning characteristic parameters and acoustic positioning characteristic parameters, and combines the differences in the propagation characteristics of electromagnetic signals and acoustic signals inside the electric dust collector, comprehensively utilizes the complementary information of the two signals, and considers their propagation behavior in the actual environment, to correct and optimize the positioning result. This way of combining different modal signal characteristics and considering the propagation differences can effectively overcome the limitations of single signal positioning and the influence of complex environment on signal propagation, so as to more accurately determine the position of the fault source. In addition, the present scheme is executed after determining that the initial local small displacement of the cathode wire occurs, and the determination is based on the matching result of the working condition compensated cooperative abnormal event joint feature and the fault response mode, which ensures that the signals used for positioning are screened and optimized, and are derived from the real cooperative abnormal event related to the initial local small displacement of the cathode wire, avoiding positioning of signals caused by background noise or working condition fluctuations, and further improving the reliability and accuracy of positioning.

[0086] As an embodiment of the present application, the step of combining the differences in the propagation characteristics of electromagnetic signals and acoustic signals inside the electric dust collector includes:

[0087] establish a propagation model of electromagnetic signals and acoustic signals in the interior of the electric dust collector;

[0088] acquire an operating environment parameter in the interior of the electric dust collector;

[0089] correct the propagation model according to the acquired operating environment parameter to obtain a corrected propagation model;

[0090] determine the location of the fault source by using the propagation characteristic difference represented by the corrected propagation model and combining the propagation characteristic difference of the electromagnetic signals and the acoustic signals in the interior of the electric dust collector.

[0091] The propagation model refers to a mathematical or physical model describing the propagation law of electromagnetic signals and acoustic signals in a specific medium and environment, for example, can represent the propagation speed, attenuation coefficient, scattering characteristics, etc. of the signals, which can be realized by a theoretical model based on physical laws or a statistical model based on historical data and experience, aiming to lay a foundation for subsequent analysis of signal propagation characteristics. The operating environment parameter refers to a physical quantity affecting the propagation of electromagnetic signals and acoustic signals in the interior of the electric dust collector, for example, can include flue gas temperature, pressure, humidity, dust concentration, gas composition, etc., used to reflect the actual environmental state in the interior of the electric dust collector. The correction refers to adjusting the parameters or structure in the propagation model according to the actually acquired operating environment parameter, so that it more accurately reflects the signal propagation characteristics under the current environment, aiming to correct the deviation of the initial model. The propagation characteristic difference represented by the corrected propagation model refers to the different performances of electromagnetic signals and acoustic signals in terms of propagation speed, attenuation degree, multipath effect, etc. represented by the corrected propagation model, used to more accurately depict the propagation behavior of the two kinds of signals under a specific environment.

[0092] The scheme of the present application provides a preliminary description of the signal propagation law by establishing a propagation model of electromagnetic signals and acoustic signals inside the electric dust collector. Subsequently, real-time operating environment parameters inside the electric dust collector are obtained, which directly affect the propagation behavior of the signals. According to the obtained real-time operating environment parameters, the initial propagation model is corrected, and this correction process enables the model to adapt to the complex and dynamically changing environment inside the electric dust collector, thereby obtaining a corrected propagation model that more accurately reflects the signal propagation characteristics under the current environment. The corrected propagation model can more accurately represent the propagation speed, attenuation, and other characteristics of electromagnetic signals and acoustic signals under the current environment, as well as the differences between them. Using the more accurate propagation characteristic information provided by the corrected propagation model, combined with the positioning feature parameters (such as signal arrival time difference, signal intensity, etc.) extracted from the time-synchronized electromagnetic signals and acoustic signals, the calculation of the fault source position is carried out. This method no longer simply relies on the original signal features, but fully considers the actual behavior of the signals in the complex propagation path, especially the differences in the propagation characteristics of electromagnetic and acoustic signals under different environments, thereby enabling more accurate inference of the location of the fault source. In this way, the present application further improves the positioning accuracy and robustness on the basis of extracting electromagnetic and acoustic positioning feature parameters, and solves the problem of positioning error caused by uncertain signal propagation characteristics in complex environments.

[0093] As an embodiment of the present application, the step of determining the overall influence amount generated when the identified set of target working condition parameters jointly act on the joint feature of the cooperative abnormal event includes:

[0094] Obtaining the joint feature of the cooperative abnormal event;

[0095] Obtaining the current values of the identified set of target working condition parameters;

[0096] Obtaining the joint feature of the cooperative abnormal event under the preset reference state;

[0097] Calculating the difference between the obtained joint feature of the cooperative abnormal event and the obtained joint feature of the cooperative abnormal event under the preset reference state to obtain the overall influence amount.

[0098] Wherein, the preset reference state refers to the reference working condition state of the electric dust collector when it is stably running and has no obvious abnormalities, which can be determined by historical operation data analysis, expert experience setting or through specific experiment calibration. Wherein, the joint feature of the cooperative abnormal event under the preset reference state refers to the reference value or reference range of the joint feature exhibited when the cooperative abnormal event occurs under the above-mentioned preset reference state, which can be obtained by statistical analysis or modeling of the cooperative abnormal event data collected under the preset reference state.

[0099] The scheme of the present application obtains the joint feature of the current cooperative abnormal event, which reflects the abnormal performance under the current operation state of the electric dust collector. At the same time, the current values of a group of target working condition parameters recognized at the current time are obtained, which have a coupling effect on the joint feature, and the current values reflect the current operation condition. In order to quantify the overall influence of these working condition parameters on the joint feature, the scheme further obtains the joint feature of the cooperative abnormal event under a preset reference state, which represents the reference state of the normal operation of the electric dust collector. By calculating the difference between the joint feature of the current cooperative abnormal event and the joint feature of the cooperative abnormal event under the preset reference state, the overall influence quantity can be obtained. This method can reflect the overall influence of the joint feature of the cooperative abnormal event caused by the joint action of the target working condition parameters by comparing the difference between the joint features under the current state and the reference state. This overall influence quantity calculation method based on the difference avoids the errors caused by direct measurement or estimation, and provides input for subsequent calculation of the coupling influence quantity based on the overall influence quantity and the independent influence quantity, and execution of joint feature compensation. The overall influence quantity acquisition is the basis for realizing the calculation of the coupling influence quantity and the compensation of the joint feature, thereby improving the precision of the compensation of the joint feature of the cooperative abnormal event, and further improving the precision of the early warning of the initial local micro displacement of the cathode wire.

[0100] As an embodiment of the present application, the step of establishing a propagation model of the electromagnetic signal and the acoustic signal inside the electric dust collector includes:

[0101] Obtain the running environment parameters inside the electric dust collector, including gas pressure P, gas temperature T, gas humidity H, dust concentration C_dust in flue gas, and flue gas key component composition G_comp; obtain the center frequency f_em of the electromagnetic signal and the center frequency f_ac of the acoustic signal; obtain the device structure parameter S_geo inside the electric dust collector; obtain the electrical operation parameters of the electric dust collector, including operating voltage U and operating current I;

[0102] Determine the reference propagation speed v_ac of the acoustic signal based on the gas temperature T and the flue gas key component composition G_comp; determine the reference attenuation coefficient a_ac of the acoustic signal based on the gas pressure P, the gas temperature T, the gas humidity H, the dust concentration C_dust in the flue gas, and the center frequency f_ac of the acoustic signal;

[0103] Determine the reference propagation speed v_em of the electromagnetic signal based on the gas temperature T, the gas pressure P, and the dust concentration C_dust in the flue gas; determine the reference attenuation coefficient a_em of the electromagnetic signal based on the gas temperature T, the gas pressure P, the dust concentration C_dust in the flue gas, the center frequency f_em of the electromagnetic signal, and the device structure parameter S_geo;

[0104] obtain running environment parameters, electrical operation parameters and corresponding measured propagation characteristics in a historical time period to form a calibration data set D_hist;

[0105] determine a propagation speed correction amount Δv_ac and an attenuation coefficient correction amount Δα_ac of the acoustic signal based on the calibration data set D_hist, the running environment parameters and the electrical operation parameters; determine a propagation speed correction amount Δv_em and an attenuation coefficient correction amount Δα_em of the electromagnetic signal;

[0106] obtain a corrected propagation speed v_ac_corr of the acoustic signal in combination with the reference propagation speed v_ac and the propagation speed correction amount Δv_ac of the acoustic signal; obtain a corrected attenuation coefficient α_ac_corr of the acoustic signal in combination with the reference attenuation coefficient α_ac and the attenuation coefficient correction amount Δα_ac of the acoustic signal;

[0107] obtain a corrected propagation speed v_em_corr of the electromagnetic signal in combination with the reference propagation speed v_em and the propagation speed correction amount Δv_em of the electromagnetic signal; obtain a corrected attenuation coefficient α_em_corr of the electromagnetic signal in combination with the reference attenuation coefficient α_em and the attenuation coefficient correction amount Δα_em of the electromagnetic signal;

[0108] use the corrected propagation speed v_ac_corr, the corrected attenuation coefficient α_ac_corr, the corrected propagation speed v_em_corr and the corrected attenuation coefficient α_em_corr as propagation characteristics represented by a propagation model of the electromagnetic signal and the acoustic signal inside the electric dust collector.

[0109] Among them, the equipment structural parameter S_geo refers to the physical structure information inside the electrostatic precipitator, which can be characterized by information such as electrode spacing, cathode wire type, electric field segmentation, and the location of internal obstacles. Its purpose is to reflect the influence of the internal space of the electrostatic precipitator on signal propagation. The key component composition of the flue gas G_comp refers to the trace or minor components in the flue gas that have a significant impact on the acoustic signal propagation speed. It can be characterized by the concentration and proportion of components such as SO2, SO3, and NOx. Its purpose is to more accurately calculate the acoustic signal propagation speed. The calibration dataset D_hist refers to the electrostatic precipitator operating environment parameters, electrical operating parameters, and corresponding measured propagation characteristics recorded over a historical period. It can be stored in a database or file format, and its purpose is to provide a data foundation for model calibration. The measured propagation characteristics refer to the signal obtained through actual measurement. The propagation speed and attenuation rate of acoustic signals from one point to another within an electrostatic precipitator can be obtained by receiving signals and calculating them using a sensor array. This provides accurate propagation data for model calibration. The propagation speed correction Δv_ac and attenuation coefficient correction Δα_ac refer to adjustments made to the baseline propagation speed v_ac and baseline attenuation coefficient α_ac of the acoustic signal based on historical data and current operating parameters. These adjustments can be calculated using machine learning models or regression analysis methods to compensate for deficiencies in the baseline model. Similarly, the propagation speed correction Δv_em and attenuation coefficient correction Δα_em refer to adjustments made to the baseline propagation speed v_em and baseline attenuation coefficient α_em of the electromagnetic signal based on historical data and current operating parameters. These adjustments can also be calculated using machine learning models or regression analysis methods to compensate for deficiencies in the baseline model.

[0110] like Figure 2 The system shown is an internal cathode wire displacement monitoring system for electrostatic precipitators, used for early warning of initial minor local displacement of the cathode wire. The system includes:

[0111] Signal acquisition module 201 is used to acquire electromagnetic and acoustic signals inside the electrostatic precipitator;

[0112] The signal synchronization processing module 202 is used to perform time synchronization processing on the acquired electromagnetic signals and acoustic signals to obtain time-synchronized electromagnetic signals and acoustic signals.

[0113] The collaborative anomaly judgment module 203 is used to determine whether time-synchronized electromagnetic signals and time-synchronized acoustic signals constitute a collaborative anomaly event based on preset spatiotemporal correlation conditions, and to obtain the collaborative anomaly event judgment result and the joint characteristics of the collaborative anomaly event. The spatiotemporal correlation conditions are used to characterize that the electromagnetic signal and the acoustic signal originate from the same physical source.

[0114] The working condition parameter acquisition module 204 is configured to acquire a working condition parameter of the electric dust collector.

[0115] The joint feature compensation module 205 is configured to compensate the joint feature of the cooperative abnormal event according to the acquired working condition parameter, to obtain a compensated joint feature.

[0116] The fault mode matching module 206 is configured to match the compensated joint feature with a preset fault response mode representing the initial local slight displacement of the cathode wire, to obtain a matching result.

[0117] The early warning output module 207 is configured to, when the matching result is a matching success, determine that the initial local slight displacement of the cathode wire occurs, and perform fault source positioning based on the time-synchronized electromagnetic signal and the time-synchronized acoustic signal, and output an early warning instruction containing positioning information.

[0118] The basic principle, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application.

Claims

1. A method for monitoring the displacement of the internal cathode wire of an electric dust precipitator, for early warning of initial local small displacement of the cathode wire, characterized in that, The method comprises the following steps: Obtaining electromagnetic signals and acoustic signals in an electric dust collector; Time synchronization processing is performed on the obtained electromagnetic signals and acoustic signals to obtain time-synchronized electromagnetic signals and acoustic signals; Based on a preset space-time correlation condition, it is determined whether the time-synchronized electromagnetic signals and the time-synchronized acoustic signals constitute a collaborative abnormal event, and a collaborative abnormal event judgment result and a collaborative abnormal event joint feature are obtained; Obtaining the operation condition parameters of the electric dust collector; According to the obtained operation condition parameters, the collaborative abnormal event joint feature is compensated to obtain a compensated joint feature; The compensated joint feature is matched with a preset fault response mode representing a cathode wire initial local small displacement to obtain a matching result; When the matching result is a match, it is determined that a cathode wire initial local small displacement occurs, and fault source positioning is performed based on the time-synchronized electromagnetic signals and the time-synchronized acoustic signals, and a warning indication containing positioning information is output.

2. The method of claim 1, wherein the method further comprises: The space-time correlation condition is used to represent that the electromagnetic signals and the acoustic signals are derived from the same physical source.

3. The method of claim 2, wherein the method further comprises: The step of determining whether the time-synchronized electromagnetic signals and the time-synchronized acoustic signals constitute a collaborative abnormal event based on the preset space-time correlation condition to obtain a collaborative abnormal event judgment result and a collaborative abnormal event joint feature comprises: Obtaining operation environment parameters affecting signal propagation inside the electric dust collector; According to the obtained operation environment parameters, the propagation speed of signals inside the electric dust collector is determined; Based on the determined propagation speed and sensor deployment information, the time correlation parameter or the space correlation parameter in the preset space-time correlation condition is adjusted to obtain an adjusted space-time correlation condition; Based on the adjusted space-time correlation condition, it is determined whether the time-synchronized electromagnetic signals and the time-synchronized acoustic signals constitute a collaborative abnormal event, and a collaborative abnormal event judgment result and a collaborative abnormal event joint feature are obtained, and the adjusted space-time correlation condition is used to represent that the electromagnetic signals and the acoustic signals are derived from the same physical source.

4. The method of claim 1, wherein the method further comprises: The step of compensating the collaborative abnormal event joint feature based on the obtained operation condition parameters to obtain a compensated joint feature comprises: From the obtained multiple operation condition parameters, a group of target condition parameters having a coupling effect on the collaborative abnormal event joint feature are identified; For each target condition parameter in the identified group of target condition parameters, under the condition that other target condition parameters in the group of target condition parameters are in a preset reference state, the independent influence amount of the target condition parameter on the collaborative abnormal event joint feature is determined; The total influence amount of the identified group of target condition parameters acting on the collaborative abnormal event joint feature is determined; Based on the difference between the total influence amount and the arithmetic sum of the independent influence amounts of each target condition parameter in the group of target condition parameters, the coupling influence amount of the group of target condition parameters on the collaborative abnormal event joint feature is determined; Compensation is performed on the joint feature of the collaborative abnormal event according to the independent influence amount of each determined target working condition parameter and the coupling influence amount of the determined group of target working condition parameters, to obtain a compensated joint feature.

5. A method of monitoring the displacement of internal cathode wires in an electro-precipitator according to claim 4, wherein, The step of identifying a group of target working condition parameters having a coupling effect on the joint feature of the collaborative abnormal event from the obtained multiple running working condition parameters comprises: Under a preset updating condition, multiple running working condition parameters in a historical time period and joint features of collaborative abnormal events corresponding to the multiple running working condition parameters in the historical time period are obtained; Based on the obtained multiple running working condition parameters in the historical time period and the joint features of collaborative abnormal events corresponding to the multiple running working condition parameters in the historical time period, a difference between a comprehensive influence of different parameter combinations in the multiple running working condition parameters in the historical time period on the corresponding joint features of collaborative abnormal events and a sum of independent influences of each parameter is obtained; According to the obtained difference, a discrimination condition for identifying a group of target working condition parameters having a coupling effect on the joint feature of the current collaborative abnormal event from the currently obtained multiple running working condition parameters is adjusted, to obtain an adjusted discrimination condition; Based on the obtained adjusted discrimination condition and the currently obtained multiple running working condition parameters, a group of target working condition parameters having a coupling effect on the joint feature of the collaborative abnormal event is identified from the currently obtained multiple running working condition parameters.

6. The method of claim 1, wherein the method further comprises: The step of matching the compensated joint feature with a preset fault response mode representing an initial local micro-displacement of the cathode wire to obtain a matching result comprises: The compensated joint feature is matched with a preset fault response mode representing an initial local micro-displacement of the cathode wire to obtain a current matching result. When the current matching result does not reach a preset success criterion, and the running state of the electric dust collector meets a preset mode correction triggering condition, then: A plurality of compensated joint feature records are obtained, which are all unable to reach the preset success criterion in matching with the preset fault response mode within a period of time after the mode correction triggering condition is met; Based on the obtained multiple compensated joint feature records, common characteristics represented by the multiple compensated joint feature records are analyzed, and the common characteristics are compared with the preset fault response mode to determine a difference parameter therebetween; According to the determined difference parameter, internal parameters or structure definitions of the preset fault response mode are adjusted to obtain a corrected fault response mode; The original preset fault response mode is replaced by the corrected fault response mode, so that when the step of matching the compensated joint feature with the fault response mode is subsequently performed, the corrected fault response mode is used to obtain a subsequent matching result.

7. The method of claim 1, wherein the method further comprises: The step of performing fault source positioning based on the time-synchronized electromagnetic signal and the time-synchronized acoustic signal comprises: An electromagnetic positioning feature parameter is extracted from the time-synchronized electromagnetic signal; An acoustic positioning feature parameter is extracted from the time-synchronized acoustic signal; The location of the fault source is determined according to the extracted electromagnetic positioning characteristic parameters and the extracted acoustic positioning characteristic parameters, and in combination with the difference in the propagation characteristics of the electromagnetic signal and the acoustic signal inside the electric dust collector.

8. A method of monitoring the displacement of internal cathode wires in an electrostatic precipitator according to claim 7, wherein, The step of combining the difference in the propagation characteristics of the electromagnetic signal and the acoustic signal inside the electric dust collector comprises: a propagation model of the electromagnetic signal and the acoustic signal inside the electric dust collector is established; operation environment parameters inside the electric dust collector are acquired; the propagation model is corrected according to the acquired operation environment parameters to obtain a corrected propagation model; the location of the fault source is determined by using the difference in the propagation characteristics represented by the corrected propagation model, in combination with the difference in the propagation characteristics of the electromagnetic signal and the acoustic signal inside the electric dust collector.

9. The method of claim 4, wherein the method further comprises: The step of determining the overall impact amount generated when the identified set of target working condition parameters jointly act on the joint feature of the collaborative abnormal event comprises: the joint feature of the collaborative abnormal event is acquired; the current values of the identified set of target working condition parameters are acquired; the joint feature of the collaborative abnormal event under the preset reference state is acquired; the difference between the acquired joint feature of the collaborative abnormal event and the acquired joint feature of the collaborative abnormal event under the preset reference state is calculated to obtain the overall impact amount.

10. An electrical dust precipitator internal cathode wire displacement monitoring system for early warning of initial local small displacement of cathode wire, characterized in that, The system comprises: a signal acquisition module configured to acquire an electromagnetic signal and an acoustic signal inside an electric dust collector; a signal synchronization processing module configured to perform time synchronization processing on the acquired electromagnetic signal and acoustic signal to obtain time-synchronized electromagnetic and acoustic signals; a collaborative abnormal event judgment module configured to determine, based on a preset spatiotemporal correlation condition, whether the time-synchronized electromagnetic signal and the time-synchronized acoustic signal constitute a collaborative abnormal event, to obtain a collaborative abnormal event judgment result and a joint feature of the collaborative abnormal event, wherein the spatiotemporal correlation condition is used to represent that the electromagnetic signal and the acoustic signal originate from the same physical source; a working condition parameter acquisition module configured to acquire an operating working condition parameter of the electric dust collector; a joint feature compensation module configured to compensate the joint feature of the collaborative abnormal event according to the acquired operating working condition parameter to obtain a compensated joint feature; a fault mode matching module configured to match the compensated joint feature with a preset fault response mode representing an initial local slight displacement of a cathode wire to obtain a matching result; a warning output module configured to determine that an initial local slight displacement of a cathode wire occurs when the matching result is a matching success, and to perform fault source positioning based on the time-synchronized electromagnetic signal and the time-synchronized acoustic signal, and to output a warning indication containing positioning information.

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