In-situ segmented diagnosis method and device for resistance distribution of boiler flue by sound wave-differential pressure

Abstract

The application relates to the technical field of thermal power generation, in particular to a sound wave-differential pressure in-situ segmented diagnosis method and device for boiler flue resistance distribution, which comprises the following steps: arranging differential pressure-acoustic combined sensing nodes at certain intervals in the axial direction of a boiler flue, and constructing a segmented diagnosis network according to diagnosis sections formed between the nodes, so as to determine the reference pressure drop and reference sound wave propagation characteristic parameters of each diagnosis section; collecting the real-time pressure drop and sound wave propagation characteristic parameters of each diagnosis section online, and respectively calculating the pressure drop change ratio and acoustic deviation degree; determining the diagnosis section with the pressure drop change ratio greater than or equal to a first threshold value and the acoustic deviation degree greater than or equal to a second threshold value as a target resistance section, and determining the position information, so as to perform sound wave-differential pressure in-situ segmented diagnosis on the boiler flue resistance distribution. Therefore, the problems that in the prior art, it is difficult to accurately identify the specific ash deposition position of the flue, the treatment measures are blind and inefficient, and the economic efficiency, safety and stability of the boiler operation are reduced are solved.

Description

Technical Field

[0001] This application relates to the field of thermal power generation technology, and in particular to an acoustic-differential pressure in-situ segmented diagnostic method and apparatus for boiler flue resistance distribution. Background Technology

[0002] During operation, fly ash carried by flue gas in thermal power plant boilers gradually accumulates on the inner walls of the flue, especially in areas with lower flue gas velocities or abrupt changes in cross-sectional area. This ash accumulation directly leads to a reduction in the flow cross-sectional area and an increase in the local resistance coefficient, significantly increasing the total resistance of the flue and raising the power consumption of the induced draft fan. In severe cases, it can induce secondary combustion in the flue, stalling of the induced draft fan, or even forced load reduction in the boiler, causing safety problems.

[0003] Existing technologies for assessing flue dust accumulation and resistance anomalies primarily rely on methods such as indirectly inferring blockage status from the overall differential pressure of the flue, offline flow field simulation using CFD (Computational Fluid Dynamics), artificial intelligence-based time-series trend prediction, and cyclic sampling via multi-channel pressure tapping pipelines. However, these conventional technologies can only characterize the overall resistance level of the flue and cannot pinpoint the spatial distribution of local resistance within the flue.

[0004] Therefore, the relevant technologies can only characterize the overall resistance level of the flue, making it difficult to accurately identify the specific location of ash accumulation and blockage in the flue. This leads to blind and inefficient treatment measures, as well as high operation and maintenance costs, reducing the economic efficiency and safety stability of boiler operation, which urgently needs to be addressed. Summary of the Invention

[0005] This application provides an acoustic-differential pressure in-situ segmented diagnostic method and device for boiler flue resistance distribution, in order to solve the problems in related technologies such as difficulty in accurately identifying the specific location of ash accumulation and blockage in the flue, leading to blind and inefficient treatment measures, and reducing the economic efficiency and safety stability of boiler operation.

[0006] The first aspect of this application provides an in-situ segmented acoustic-differential pressure diagnostic method for boiler flue resistance distribution, comprising the following steps: setting differential pressure-acoustic joint sensing nodes at preset intervals along the boiler flue axis, and constructing a segmented diagnostic network based on diagnostic segments formed between adjacent differential pressure-acoustic joint sensing nodes; determining the reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment based on the segmented diagnostic network; collecting the real-time pressure drop and real-time acoustic wave propagation characteristic parameters of each diagnostic segment online, determining the pressure drop change ratio based on the real-time pressure drop and the corresponding reference pressure drop, and determining the acoustic deviation based on the real-time acoustic wave propagation characteristic parameters and the corresponding reference acoustic wave propagation characteristic parameters; identifying diagnostic segments where the pressure drop change ratio is greater than or equal to a first preset threshold and the acoustic deviation is greater than or equal to a second preset threshold as target resistance segments, and determining the location information of the target resistance segments to perform in-situ acoustic-differential pressure segmented diagnostics on the boiler flue resistance distribution.

[0007] Optionally, in one embodiment of this application, determining the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment based on the segmented diagnostic network includes: when the boiler is in the target operating state and the degree of ash accumulation in the boiler flue is less than a preset ash accumulation standard, synchronously collecting static pressure data of each differential pressure-acoustic joint sensing node and acoustic wave propagation characteristic parameters between adjacent differential pressure-acoustic joint sensing nodes using the segmented diagnostic network; and performing statistical averaging processing on the collected multiple sets of static pressure data and acoustic wave propagation characteristic parameters to determine the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment.

[0008] Optionally, in one embodiment of this application, after determining the reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment, the method further includes: re-collecting static pressure data and acoustic wave propagation characteristic parameters when the boiler is in the target operating state and the degree of ash accumulation in the boiler flue is less than the preset ash accumulation standard, according to a preset operating cycle; and updating the reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment based on the re-collected static pressure data and acoustic wave propagation characteristic parameters to obtain updated reference pressure drop and reference acoustic wave propagation characteristic parameters.

[0009] Optionally, in one embodiment of this application, the online acquisition of the real-time pressure drop and real-time acoustic wave propagation characteristic parameters of each diagnostic segment includes: acquiring the static pressure distribution of each differential pressure-acoustic joint sensing node relative to a target reference point based on a preset diagnostic cycle to calculate the real-time pressure drop of each diagnostic segment; transmitting a set of precoded acoustic wave pulses using the upstream node of adjacent differential pressure-acoustic joint sensing nodes, and demodulating the transmitted precoded acoustic wave pulses using the downstream node of the adjacent differential pressure-acoustic joint sensing nodes to determine the real-time acoustic wave propagation characteristic parameters; wherein the real-time acoustic wave propagation characteristic parameters include at least one of the actual propagation time of the acoustic wave pulses between adjacent differential pressure-acoustic joint sensing nodes and the attenuation of the received signal amplitude.

[0010] Optionally, in one embodiment of this application, after performing acoustic-differential pressure in-situ segmented diagnosis of the boiler flue resistance distribution, the method further includes: obtaining the flue name, node number interval, and station range from the location information of the target resistance section; generating diagnostic alarm information and corresponding diagnostic processing suggestions based on the flue name, the node number interval, and the station range; and sending the diagnostic alarm information and corresponding diagnostic processing suggestions to a preset terminal for displaying the diagnostic alarm information and corresponding diagnostic processing suggestions on the preset terminal.

[0011] A second aspect of this application provides an acoustic-differential pressure in-situ segmented diagnostic device for boiler flue resistance distribution, comprising: a construction module, configured to set differential pressure-acoustic joint sensing nodes at preset intervals along the axial direction of the boiler flue, and construct a segmented diagnostic network based on diagnostic segments formed between adjacent differential pressure-acoustic joint sensing nodes; a first determination module, configured to determine a reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment based on the segmented diagnostic network; a second determination module, configured to collect real-time pressure drop and real-time acoustic wave propagation characteristic parameters for each diagnostic segment online, and determine a pressure drop change ratio based on the real-time pressure drop and the corresponding reference pressure drop, and determine an acoustic deviation based on the real-time acoustic wave propagation characteristic parameters and the corresponding reference acoustic wave propagation characteristic parameters; and a diagnostic module, configured to identify diagnostic segments where the pressure drop change ratio is greater than or equal to a first preset threshold and the acoustic deviation is greater than or equal to a second preset threshold as target resistance segments, and determine the location information of the target resistance segments, so as to perform acoustic-differential pressure in-situ segmented diagnostics on the boiler flue resistance distribution.

[0012] Optionally, in one embodiment of this application, the first determining module includes: a first acquisition unit, used to synchronously acquire static pressure data of each differential pressure-acoustic joint sensing node and acoustic wave propagation characteristic parameters between adjacent differential pressure-acoustic joint sensing nodes using the segmented diagnostic network when the boiler is in the target operating state and the degree of ash accumulation in the boiler flue is less than a preset ash accumulation standard; and a first determining unit, used to perform statistical averaging processing on the acquired multiple sets of static pressure data and acoustic wave propagation characteristic parameters to determine the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment.

[0013] Optionally, in one embodiment of this application, the apparatus further includes: a data acquisition module, configured to, after determining the reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment, re-acquire static pressure data and acoustic wave propagation characteristic parameters when the boiler is in the target operating state and the degree of ash accumulation in the boiler flue is less than a preset ash accumulation standard, according to a preset operating cycle; and an update module, configured to, after determining the reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment, update the reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment based on the re-acquired static pressure data and acoustic wave propagation characteristic parameters, to obtain updated reference pressure drop and reference acoustic wave propagation characteristic parameters.

[0014] Optionally, in one embodiment of this application, the second determining module includes: a second acquisition unit, configured to acquire the static pressure distribution of each differential pressure-acoustic joint sensing node relative to a target reference point based on a preset diagnostic cycle, so as to calculate the real-time pressure drop of each diagnostic segment; and a second determining unit, configured to transmit a set of precoded acoustic pulse trains using the upstream nodes of adjacent differential pressure-acoustic joint sensing nodes, and demodulate the transmitted precoded acoustic pulse trains using the downstream nodes of the adjacent differential pressure-acoustic joint sensing nodes, so as to determine the real-time acoustic wave propagation characteristic parameters; wherein the real-time acoustic wave propagation characteristic parameters include at least one of the actual propagation time of the acoustic pulses between adjacent differential pressure-acoustic joint sensing nodes and the attenuation of the received signal amplitude.

[0015] Optionally, in one embodiment of this application, the apparatus further includes: an acquisition module, configured to acquire the flue name, node number range, and station range from the location information of the target resistance section; and a generation module, configured to generate diagnostic alarm information and corresponding diagnostic processing suggestions based on the flue name, the node number range, and the station range, and send the diagnostic alarm information and corresponding diagnostic processing suggestions to a preset terminal for displaying the diagnostic alarm information and corresponding diagnostic processing suggestions on the preset terminal.

[0016] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the acoustic-differential pressure in-situ segmented diagnosis method for boiler flue resistance distribution as described in the above embodiments.

[0017] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described acoustic-differential pressure in-situ segmented diagnostic method for boiler flue resistance distribution.

[0018] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the above-described acoustic-differential pressure in-situ segmented diagnostic method for boiler flue resistance distribution.

[0019] This application embodiment can set differential pressure-acoustic joint sensing nodes at certain intervals along the axial direction of the boiler flue, and construct a segmented diagnostic network based on the diagnostic segments formed between the nodes. Then, the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment are determined. Combined with the real-time pressure drop and real-time acoustic wave propagation characteristic parameters of each diagnostic segment collected online, the pressure drop change ratio and acoustic deviation are calculated respectively. Diagnostic segments with a pressure drop change ratio greater than or equal to a first threshold and an acoustic deviation greater than or equal to a second threshold are identified as target resistance segments, and their location information is determined. This allows for in-situ segmented diagnosis of the boiler flue resistance distribution using acoustic waves and differential pressure, effectively improving the boiler's operational economy and safety stability. This solves the problems in related technologies where it is difficult to accurately identify the specific location of ash accumulation and blockage in the flue, leading to blind and inefficient treatment measures, and reducing the boiler's operational economy and safety stability.

[0020] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0021] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of an acoustic-differential pressure in-situ segmented diagnostic method for boiler flue resistance distribution according to an embodiment of this application; Figure 2 This is a schematic diagram of the boiler tail flue system and sensor node arrangement according to a specific embodiment of this application; Figure 3 This is a schematic diagram of the structure of an acoustic-differential pressure in-situ segmented diagnostic device for boiler flue resistance distribution according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application. Detailed Implementation

[0022] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0023] The following describes, with reference to the accompanying drawings, an acoustic-differential pressure in-situ segmented diagnostic method and apparatus for boiler flue resistance distribution according to embodiments of this application. Addressing the problems mentioned in the background art, such as the difficulty in accurately identifying the specific location of ash accumulation and blockage in the flue, leading to blind and inefficient treatment measures and reduced boiler operating economy and safety stability, this application provides an acoustic-differential pressure in-situ segmented diagnostic method for boiler flue resistance distribution. In this method, differential pressure-acoustic joint sensing nodes are set at certain intervals along the boiler flue axis, and a segmented diagnostic network is constructed based on the diagnostic segments formed between the nodes. Then, the reference pressure drop and reference acoustic propagation characteristic parameters of each diagnostic segment are determined. Combining the real-time pressure drop and real-time acoustic propagation characteristic parameters of each diagnostic segment collected online, the pressure drop change ratio and acoustic deviation are calculated respectively. Diagnostic segments with a pressure drop change ratio greater than or equal to a first threshold and an acoustic deviation greater than or equal to a second threshold are identified as target resistance segments, and their location information is determined. This allows for acoustic-differential pressure in-situ segmented diagnostics of boiler flue resistance distribution, effectively improving boiler operating economy and safety stability. This solves the problem in related technologies where it is difficult to accurately identify the specific location of ash accumulation and blockage in the flue, leading to blind and inefficient treatment measures and reducing the economic efficiency and safety stability of boiler operation.

[0024] This application embodiment establishes an acoustic-differential pressure in-situ segmented diagnostic system for boiler flue resistance distribution. The system includes a differential pressure-acoustic joint sensor node array, a data acquisition and communication module, a central processing unit, a human-machine interface, and an alarm module.

[0025] Specifically, the differential pressure-acoustic joint sensing node array consists of N sensing nodes (N≥2) arranged along the flue axial direction. Each node includes a micro differential pressure sensor (accuracy better than ±1%FS) and a set of high-temperature resistant piezoelectric acoustic transducers with an operating frequency in the range of 20kHz to 200kHz, adapted to the flue gas temperature. The acoustic propagation time between each pair of adjacent nodes is accurately measured by the time difference method. The transducer housing is sealed to the pressure tapping short pipe on the flue wall, and its radiation surface is flush with or slightly concave to the inner wall of the flue to prevent ash accumulation and airflow erosion.

[0026] The data acquisition and communication module, deployed in the field or connected to each node via distributed I / O, performs signal conditioning, high-speed synchronous A / D conversion, and uploads the acquired static pressure and acoustic waveform data to the central processing unit via fieldbus or industrial Ethernet. The module employs a synchronous triggering mechanism, ensuring that the static pressure and acoustic transceiver signals from all sensing nodes are acquired simultaneously, thus eliminating diagnostic errors caused by fluctuations in the flue gas flow field at different times.

[0027] The central processing unit (CPU), typically an industrial control computer with diagnostic software or directly integrated into the computing station of a DCS (Distributed Control System), performs the following functions: real-time segmented voltage drop and acoustic parameter calculation, reference model storage and management, two-parameter comparison and logical judgment, high-resistance section location calculation, and data interface communication with the DCS. Specifically, the CPU receives collected data, calculates the real-time voltage drop and real-time acoustic wave propagation characteristic parameters for each diagnostic segment, compares them with pre-stored reference parameters, and determines high-resistance sections based on preset two-parameter criteria. The CPU integrates a reference parameter management module, which can update the reference parameters by collecting current operating condition data at set intervals or triggered by manual commands.

[0028] The human-machine interface and alarm module are used to provide the display of flue resistance distribution curves, real-time pressure drop bar charts for each diagnostic section, prominent indications of high resistance sections (color changes, flashing), historical trend queries, and alarm information management. The human-machine interface and alarm module are connected to the power plant's distributed control system, displaying the flue structure and high resistance section indications in the form of flowcharts, and providing guidance on handling measures.

[0029] Therefore, the embodiments of this application can accurately locate the concentrated parts of flue resistance online through the joint diagnosis of differential pressure and acoustic dual physical fields. The following will be described in detail as an example of the acoustic-differential pressure in-situ segmented diagnosis system for the resistance distribution of this boiler flue.

[0030] Specifically, Figure 1 This is a schematic flowchart of an acoustic-differential pressure in-situ segmented diagnostic method for boiler flue resistance distribution provided in an embodiment of this application.

[0031] like Figure 1 As shown, the acoustic-differential pressure in-situ segmented diagnostic method for the resistance distribution of the boiler flue includes the following steps: In step S101, differential pressure-acoustic joint sensing nodes are set at preset intervals along the axial direction of the boiler flue, and a segmented diagnostic network is constructed based on the diagnostic segments formed between adjacent differential pressure-acoustic joint sensing nodes.

[0032] It is understood that, in this embodiment of the application, differential pressure-acoustic joint sensing nodes can be set along the boiler flue axial direction at preset intervals. For example, they can be set at equal intervals, such as between 5 and 15 meters. Alternatively, based on the physical segmentation structure of the flue, such as the denitrification section, air preheater section, dust collector front section, induced draft fan front section, etc., pressure taps can be opened on the wall along the flue axial direction, and differential pressure-acoustic joint sensing nodes can be installed at each pressure tap. Each node includes at least one micro differential pressure sensor and a set of acoustic wave transmitting / receiving transducers. The emitting surface of the acoustic wave transmitting / receiving transducers is flush with or slightly recessed from the inner wall of the flue, without intruding into the internal flow field of the flue. Then, the flue area between adjacent nodes forms a diagnostic segment, and all diagnostic segments are connected in series to form a complete flue-side diagnostic network, i.e., a segmented diagnostic network. This deployment and networking method realizes refined segment division and in-situ synchronous monitoring along the flue, improving the completeness of boiler flue resistance spatial distribution identification and segment positioning accuracy.

[0033] In step S102, based on the segmented diagnostic network, the reference voltage drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment are determined.

[0034] It is understood that, in the embodiments of this application, when the boiler is operating well and the degree of flue ash accumulation is relatively minor (which can be confirmed by the operator based on the operating period after maintenance or after sufficient soot blowing), the static pressure data of each differential pressure-acoustic joint sensing node and the acoustic wave propagation characteristic parameters between adjacent nodes are synchronously collected through the diagnostic network. The acoustic wave propagation characteristic parameters include at least one of the propagation time of the acoustic wave pulse or the attenuation of the received signal amplitude between adjacent nodes. After continuously collecting data sets under multiple normal operating conditions and performing statistical averaging, the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment are determined. By establishing a standardized reference system adapted to operating conditions, the accuracy and adaptability of subsequent real-time resistance comparison diagnosis are improved.

[0035] Optionally, in one embodiment of this application, the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment are determined based on a segmented diagnostic network, including: when the boiler is in the target operating state and the degree of ash accumulation in the boiler flue is less than the preset ash accumulation standard, the static pressure data of each differential pressure-acoustic joint sensing node and the acoustic wave propagation characteristic parameters between adjacent differential pressure-acoustic joint sensing nodes are synchronously collected using the segmented diagnostic network; the collected static pressure data and acoustic wave propagation characteristic parameters are statistically averaged to determine the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment.

[0036] In this embodiment of the application, the target operating state is the boiler operating condition is good; the preset ash accumulation standard is the state of slight ash accumulation in the flue, which can be confirmed by the operator according to the operating period after maintenance or after sufficient soot blowing, and is not specifically limited here.

[0037] As one possible implementation, this embodiment of the application can, under conditions of good boiler operation and minimal flue ash accumulation (which can be confirmed by operators based on the operating period after maintenance or thorough soot blowing), synchronously collect the static pressure values ​​of each node and the acoustic wave propagation characteristic parameters between adjacent nodes through the diagnostic network, including the propagation time t_i0 of the acoustic pulse and the intensity attenuation A_i0 of the received signal (or directly use the relative attenuation value). Multiple data sets under normal operating conditions are continuously collected, and after statistical averaging, a reference pressure drop value ΔP_i0 and a reference acoustic parameter library for each diagnostic segment are established and stored in the central processing unit; this improves the representativeness and stability of the reference parameters, as well as the accuracy of subsequent flue resistance anomaly identification and segment diagnosis.

[0038] Optionally, in one embodiment of this application, after determining the reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment, the method further includes: re-collecting static pressure data and acoustic wave propagation characteristic parameters when the boiler is in the target operating state and the degree of ash accumulation in the boiler flue is less than the preset ash accumulation standard, according to a preset operating cycle; and updating the reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment based on the re-collected static pressure data and acoustic wave propagation characteristic parameters to obtain the updated reference pressure drop and reference acoustic wave propagation characteristic parameters.

[0039] In actual implementation, this embodiment can select a target operating state with stable boiler load and constant operating conditions according to a certain fixed operating cycle of the system or in response to manual active update instructions. Under clean operating conditions that ensure the degree of ash accumulation in the boiler flue is always less than a certain ash accumulation standard, the static pressure data of each differential pressure-acoustic joint sensing node and the acoustic wave propagation characteristic parameters between adjacent nodes are re-collected synchronously based on the segmented diagnostic network. The re-collected multiple sets of valid sample data are screened, denoised, and statistically averaged. Based on the new data after processing, the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment are iteratively corrected and dynamically updated, replacing the original reference data. The updated reference pressure drop and reference acoustic wave propagation characteristic parameters adapted to the current unit operating characteristics are obtained, which effectively improves the timeliness and adaptability of the reference system and effectively avoids diagnostic misjudgments and deviations caused by long-term use of fixed references.

[0040] It should be noted that the preset operating cycle is set by relevant technical personnel and is not specifically limited here.

[0041] In step S103, the real-time voltage drop and real-time acoustic wave propagation characteristic parameters of each diagnostic segment are collected online, and the voltage drop change ratio is determined based on the real-time voltage drop and the corresponding reference voltage drop. The acoustic deviation is determined based on the real-time acoustic wave propagation characteristic parameters and the corresponding reference acoustic wave propagation characteristic parameters.

[0042] It is understood that the embodiments of this application can conduct uninterrupted online monitoring of each diagnostic section of the boiler flue according to a certain diagnostic cycle, synchronously collecting real-time pressure drop data of each diagnostic section and real-time acoustic wave propagation characteristic parameters between adjacent differential pressure-acoustic joint sensing nodes; comparing the real-time pressure drop collected by each diagnostic section with the corresponding pre-calibrated reference pressure drop, quantifying the pressure drop change ratio of each diagnostic section, and simultaneously performing difference and ratio analysis between the real-time acoustic wave propagation characteristic parameters and the matched reference acoustic wave propagation characteristic parameters to accurately solve for the acoustic deviation degree corresponding to each diagnostic section, where the acoustic deviation degree is the ratio of real-time propagation time to reference propagation time, or the ratio of real-time attenuation to reference attenuation. This provides a quantifiable evaluation basis in two dimensions for subsequent resistance anomaly judgment, effectively improving the comprehensiveness of flue resistance state characterization and the reliability of anomaly judgment.

[0043] Optionally, in one embodiment of this application, the real-time pressure drop and real-time acoustic wave propagation characteristic parameters of each diagnostic segment are collected online, including: based on a preset diagnostic cycle, collecting the static pressure distribution of each differential pressure-acoustic joint sensing node relative to a target reference point to calculate the real-time pressure drop of each diagnostic segment; using the upstream node of the adjacent differential pressure-acoustic joint sensing node to transmit a set of precoded acoustic wave pulses, and using the downstream node of the adjacent differential pressure-acoustic joint sensing node to demodulate the transmitted precoded acoustic wave pulses to determine the real-time acoustic wave propagation characteristic parameters; wherein, the real-time acoustic wave propagation characteristic parameters include at least one of the actual propagation time of the acoustic wave pulses between adjacent differential pressure-acoustic joint sensing nodes and the attenuation of the received signal amplitude.

[0044] In some embodiments, during normal boiler operation, synchronous data acquisition is performed on each segment node at a certain diagnostic cycle, such as every 1 hour or every 8 hours. Specifically, the static pressure distribution of each node relative to the reference point is obtained by the differential pressure sensor, and the time-segmented pressure drop between adjacent nodes is calculated: ΔP_i=P_i-P_{i+1}, where P_i is the measured static pressure of the i-th differential pressure-acoustic joint sensing node, which is the flue gas static pressure value of the node relative to the system set reference point; P_{i+1} is the measured static pressure of the (i+1)-th sensing node adjacent downstream along the flue gas flow.

[0045] Secondly, the upstream node of the adjacent differential pressure-acoustic joint sensing node transmits a set of precoded acoustic pulse trains to the downstream node. The downstream node receives and demodulates the pulses, and records the actual propagation time t_i and the received signal amplitude A_i of the acoustic waves in the flue gas of the diagnostic section.

[0046] Next, the measured segmented voltage drop ΔP_i is compared with the reference voltage drop ΔP_i0 to obtain the voltage drop change ratio: R_i = ΔP_i / ΔP_i0, The acoustic deviation is obtained by comparing the measured sound wave attenuation with the reference attenuation: L_i=(A_i0-A_i) / A_i0, Where A_i is the amplitude of the received signal, and A_i0 is the amplitude of the real-time received signal.

[0047] Alternatively, the amplitude ratio of the real-time received signal amplitude to the reference received signal amplitude can be used for determination, which will not be elaborated here.

[0048] Therefore, the embodiments of this application can improve the synchronization, anti-interference ability and completeness of real-time parameter acquisition of flue gas by periodically and synchronously acquiring static pressure data and pre-coded acoustic signals and extracting multi-dimensional acoustic features.

[0049] In step S104, the diagnostic segment with a pressure drop change ratio greater than or equal to the first preset threshold and an acoustic deviation greater than or equal to the second preset threshold is determined as the target resistance segment, and the location information of the target resistance segment is determined so as to perform in-situ segmented diagnosis of the boiler flue resistance distribution by acoustic wave-differential pressure.

[0050] In this embodiment of the application, the target resistance section is a high resistance section.

[0051] It is understood that the embodiments of this application can establish a two-dimensional criterion, namely, the pressure drop dimension: if the pressure drop change ratio R_i of a certain diagnostic segment i exceeds the first threshold η1 (preferably η1∈[1.2,1.5]), then the resistance of that segment is marked as abnormal; the acoustic dimension: if the acoustic deviation of the amplitude attenuation of the acoustic wave received signal of a certain diagnostic segment i relative to the reference exceeds the second threshold η2, wherein, preferably η2 corresponds to an amplitude ratio reduced to below 0.6, or an attenuation factor greater than 1.5, or a significant increase in sound wave propagation time exceeding the normal fluctuation range, then it is confirmed that the fly ash concentration in the flue gas medium of that segment is abnormally increased or that ash accumulation is blocking the sound propagation path; when the judgment conditions of the two dimensions are met simultaneously, the diagnostic segment is comprehensively judged as the target resistance segment, i.e., the high resistance segment. Thus, the precise location information such as the flue area, node number interval, and flue pile number corresponding to the target resistance segment is extracted and recorded simultaneously, and the acoustic wave-differential pressure in-situ segmented diagnosis of the resistance distribution along the boiler flue is completed by relying on the dual physical field fusion method of differential pressure and acoustic wave. This improves the accuracy and positioning precision of high-resistance section identification, and effectively reduces the probability of misjudgment and missed judgment caused by single parameter discrimination.

[0052] Optionally, in one embodiment of this application, after performing in-situ segmented diagnosis of the boiler flue resistance distribution using acoustic wave-differential pressure, the method further includes: obtaining the flue name, node number interval, and station range from the location information of the target resistance section; generating diagnostic alarm information and corresponding diagnostic processing suggestions based on the flue name, node number interval, and station range; and sending the diagnostic alarm information and corresponding diagnostic processing suggestions to a preset terminal to display the diagnostic alarm information and corresponding diagnostic processing suggestions on the preset terminal.

[0053] In some embodiments, the present application can output the location information of the confirmed target resistance section, i.e., the high resistance section, such as the specific flue name, node number range, and station range, to the human-machine interface in a list and graphical manner. This information is highlighted in the boiler flue system flowchart and triggers an alarm message to the power plant's distributed control system (DCS) or operation optimization platform. Simultaneously, suggested handling measures are provided, such as "It is recommended to perform on-site soot blowing on the section from the denitrification outlet to the air preheater inlet" or "Please check the dust collector inlet flue guide plate for ash accumulation." By accurately analyzing the multi-dimensional location information of the resistance section and automatically generating alarms and operation and maintenance suggestions, and pushing them to the relevant departments, the efficiency of fault information transmission and the pertinence and timeliness of operation and maintenance handling are improved.

[0054] It should be noted that the default terminal can be the computer of the operation and maintenance personnel, etc., and the specific settings can be configured by those skilled in the art, without any specific limitations here.

[0055] Therefore, compared with the prior art, this application can divide the flue into multiple diagnostic sections for independent detection, achieving meter-level precise positioning of abnormal resistance locations, and linking corresponding soot blowers or maintenance ports. This transitions from overall fuzzy alarms to precise local positioning, improving soot blowing efficiency and reducing media consumption. Simultaneously, it introduces the dimension of sound wave propagation characteristics, utilizing the dual physical effects of increased pressure drop due to ash accumulation and attenuation of sound wave signals for mutual verification. Even if a single sensor experiences temporary failure or signal disturbance, the data from the other dimension can still maintain monitoring, significantly reducing the probability of false alarms and missed alarms. The sensors are installed in-situ, non-intrusively at the pressure taps on the flue wall, without affecting the flue gas flow field. They are not easily worn or blocked by fly ash, and existing reserved interfaces can be used, resulting in short modification time, low cost, and support for long-term online operation. The diagnostic process can be carried out automatically during boiler operation, with synchronous data acquisition without lag errors. It can promptly capture local resistance changes caused by load, coal quality, or soot blowing operations, providing real-time data support for operation optimization. The system interface is standardized and can be directly connected to existing DCS or SIS (Safety Instrumented System). Safety Instrumented System (SIBS) reduces the need for additional instrumentation and is applicable not only to coal-fired boilers but also to resistance distribution monitoring in flue gas ducts of oil-fired, gas-fired, and biomass boilers, as well as industrial ventilation and dust removal ducts.

[0056] The working principle of the embodiments of this application will be described in detail below with a specific example.

[0057] This embodiment uses a 600MW supercritical coal-fired boiler as the application example. The boiler employs a balanced ventilation system. The tail flue system, arranged sequentially along the flue gas flow direction, includes a selective catalytic reduction (SCR) denitrification unit, a rotary three-compartment air preheater, a dual-chamber four-field electrostatic precipitator, and two adjustable-blade axial-flow induced draft fans. The fan outlets converge into the chimney for emission into the atmosphere. From the denitrification unit outlet, the flue gas flows through various equipment and is finally discharged through the chimney. Along the way, due to fly ash deposition and equipment resistance, different degrees of resistance distribution exist in each section of the flue.

[0058] Prior to implementing this application, a site survey of the flue system was conducted, clarifying the direction, cross-sectional dimensions, elbow locations, expansion joint locations, and existing pressure tap distribution of each flue section. Based on statistical analysis of ash-prone areas from historical boiler operating data, a scheme was determined to arrange five differential pressure-acoustic combined sensing nodes along the flue axis. For example... Figure 2 As shown, these five differential pressure-acoustic joint sensing nodes are denoted as node N1, node N2, node N3, node N4 and node N5, respectively.

[0059] Node N1 is installed at the top of the vertical flue at the outlet of the denitrification unit. This flue section has a rectangular cross-section, extending from the top of the denitrification reactor and then turning upwards. Installing a node at this location effectively monitors the resistance of the flue between the denitrification unit outlet and the air preheater inlet. Since the flue gas temperature at the denitrification outlet is typically between 320℃ and 380℃, the sensor and transducer used here are selected to have a temperature resistance rating of at least 400℃.

[0060] Node N2 is installed in the horizontal section of the outlet flue gas duct of the air preheater. During air preheater operation, some fly ash carried by the flue gas will deposit on the surface of the heat transfer elements and at the bottom of the outlet flue gas duct, especially under low load conditions when the flue gas velocity decreases, the ash accumulation is more significant. This node is used to capture the resistance changes of the air preheater and its outlet section.

[0061] Node N3 is installed on the straight section of the flue before the inlet bell of the electrostatic precipitator. This area is a transition zone where the flue cross-section gradually increases, resulting in a significant decrease in flue gas velocity and making it a typical zone prone to fly ash settling. Historically, this boiler has repeatedly experienced insufficient induced draft fan output due to severe ash accumulation in the flue inlet of the dust collector. Therefore, the importance of resistance monitoring in this section was particularly emphasized in the design.

[0062] Node N4 is installed on the straight section of the electrostatic precipitator outlet flue. This node is used to include the flue of the precipitator body as a diagnostic section in the monitoring, and at the same time provides data support for the overall resistance between the precipitator inlet and outlet.

[0063] Node N5 is installed on the flue before the inlet header of the induced draft fan. This node is adjacent to the inlet of the induced draft fan and is used to ultimately output the cumulative resistance information of the entire flue before the induced draft fan.

[0064] Four diagnostic sections are formed between adjacent nodes. Diagnostic section one is between nodes N1 and N2, covering the flue area from the outlet of the denitrification unit to the flue gas side outlet of the air preheater; diagnostic section two is between nodes N2 and N3, covering the flue area from the outlet of the air preheater to the inlet of the electrostatic precipitator; diagnostic section three is between nodes N3 and N4, covering the electrostatic precipitator body and its inlet and outlet connecting flue; diagnostic section four is between nodes N4 and N5, covering the flue area from the outlet of the dust collector to the inlet of the induced draft fan.

[0065] The installation structure of each sensing node is as follows: Figure 2 As shown. Taking node N2 as an example, the specific installation method is as follows: A pressure tapping hole with a nominal diameter of 25 mm is pre-drilled on the flue wall. A DN25 carbon steel pressure tapping short pipe is used. The root of the short pipe is firmly connected to the flue wall plate through a full-penetration fillet weld to ensure reliable sealing and withstand the slight negative or positive pressure environment during flue operation. The front opening of the pressure tapping short pipe is flush with the inner wall of the flue and must not extend into the internal flow field of the flue to avoid local turbulence or ash accumulation. The differential pressure sensor is connected to the outer end interface of the pressure tapping short pipe through a stainless steel pressure guide pipe. The pressure guide pipe has an appropriate slope to avoid water and ash accumulation affecting the measurement accuracy, and a drain valve is installed at the lowest point of the pipe. The selected differential pressure sensor has a range of 0 to 2 kPa, an accuracy class better than 0.075% of the full scale, an output of 4 to 20 mA standard industrial signal, and features temperature compensation and overload resistance. The acoustic transceiver uses an integrated piezoelectric ultrasonic transceiver with a nominal operating frequency of 40 kHz. The housing is made of stainless steel, and the radiating surface is protected with a titanium alloy film to enhance its anti-corrosion performance. The transceiver connects to the pre-reserved interface at the outer end of the pressure tapping pipe via an external threaded interface. After installation, ensure that the transceiver's acoustic radiating surface is flush with or slightly recessed inwards from the inner wall of the flue, with a recess not exceeding two millimeters. This installation method effectively prevents ash buildup on the transceiver surface and direct erosion by flue gas. When installing two transceivers at adjacent nodes, ensure that their axes are aligned so that the acoustic waves can propagate along the flue axial direction through the flue gas column using the shortest path.

[0066] The data acquisition and communication module is installed in a local junction box near each sensor node. The enclosure has a protection rating of at least IP65, making it suitable for dusty and humid environments in power plants. This module integrates a high-precision analog-to-digital converter and a synchronous trigger circuit, communicating with the central processing unit via a fieldbus. The synchronous trigger circuit ensures that all nodes acquire static pressure data and receive acoustic transceiver signals simultaneously, thus eliminating data mismatch issues caused by natural fluctuations in the flue gas flow field at different times.

[0067] The central processing unit uses an industrial-grade industrial control computer, which is deployed in the power plant's electronic equipment room. This industrial control computer is pre-installed with the diagnostic software of this application. The software's functional modules include signal preprocessing, segmented voltage drop calculation, extraction of sound wave propagation time and attenuation, reference parameter library management, execution of dual-parameter ratio criteria, and a visualization output interface.

[0068] After completing all hardware installation, wiring, and power-on debugging, the system first enters the baseline modeling phase. Baseline modeling is performed after the boiler has completed full soot blowing and has been running stably for eight hours. At this time, the degree of ash accumulation on the flue inner wall is at a relatively low and controlled state, serving as a reference baseline for normal operating conditions. The system continuously collects data for two hours at a cycle of ten minutes, obtaining a total of twelve sets of valid sample data. For each diagnostic segment, the arithmetic mean of the twelve sets of baseline pressure drop data is taken as the baseline pressure drop value for that segment; the arithmetic mean of the twelve sets of acoustic wave propagation time difference data is taken as the baseline propagation time for that segment; and the arithmetic mean of the twelve sets of received signal amplitude data is normalized with the transmitted amplitude from the upstream node to calculate the baseline attenuation. The baseline dataset is stored in the database of the central processing unit and marked with timestamps and operating condition labels.

[0069] Taking diagnostic segment two as an example, after processing, the twelve sets of data collected during the baseline modeling stage resulted in a baseline voltage drop of 280 Pascals, a normalized baseline received signal peak value of 720 millivolts, and a baseline sound wave propagation time of 5.8 milliseconds. These values ​​serve as benchmarks for comparison in subsequent online diagnostic stages.

[0070] After the benchmark dataset is initially established, the system is set to automatically prompt for a benchmark update and verification every 720 hours of cumulative operation; alternatively, operators can manually trigger a benchmark re-acquisition command based on the condition after planned maintenance or manual deep cleaning. After each benchmark update, historical benchmark data is archived to track the long-term evolution trend of drag characteristics.

[0071] The system then switches to online diagnostic mode. The online diagnostic cycle needs to take into account the dust accumulation rate, sensor lifespan, and operator intervention response time. In this embodiment, a complete segmented diagnostic scan along the entire process is performed every four hours.

[0072] Taking an actual diagnostic process on a day after commissioning as an example, the diagnostic program automatically started at 4:00 AM. A synchronous trigger signal was simultaneously sent from the central processing unit to the data acquisition modules of the five sensor nodes. All nodes completed the acquisition of static pressure waveforms and acoustic wave transceiver waveforms within the same time window. After the static pressure values ​​output by the differential pressure sensors at each node were uploaded in real time, the software calculated the real-time pressure drop of the four segments according to the order of adjacent nodes. The real-time pressure drop of diagnostic segment one was 155 Pa, the real-time pressure drop of diagnostic segment two was 410 Pa, the real-time pressure drop of diagnostic segment three was 420 Pa, and the real-time pressure drop of diagnostic segment four was 125 Pa.

[0073] Simultaneously, the acoustic transceiver completes the transmission, propagation, and reception of acoustic pulses within each diagnostic section. The transducer at node N2, acting as the transmitter, transmits a coded acoustic waveform consisting of five pulses to the receiver at node N3. The receiver accurately identifies the pulse arrival time using a cross-correlation algorithm. After deducting inherent circuit delays and transducer response delays, the actual propagation time of the acoustic wave in the flue gas within diagnostic section two is calculated to be 6.1 ms, an increase of approximately 5.2% compared to the baseline value of 5.8 ms. The peak value of the received signal drops to 340 mV, and compared to the baseline value of 720 mV, the amplitude attenuates to 47.2%, an increase of approximately 2.12 times.

[0074] The central processing unit compares the voltage drop and acoustic data of each diagnostic segment with the benchmark dataset. The voltage drop change ratio for diagnostic segment one is 1.03, and the acoustic amplitude attenuation shows no significant change, indicating this segment is normal. The voltage drop change ratio for diagnostic segment two is 410Pa / 280Pa, resulting in 1.46, exceeding the preset first threshold of 1.3. The acoustic deviation, converted to a received amplitude drop to 47.2% of the benchmark, corresponds to an attenuation increase factor of 2.12, exceeding the preset second threshold of 1.5 times. Since both conditions are met, diagnostic segment two is classified as a high-resistance segment.

[0075] Although the pressure drop change ratio of diagnostic segment three was 420Pa / 400Pa, which equals 1.05 and is within the first threshold range, it requires special explanation: Diagnostic segment three corresponds to the electrostatic precipitator body section, and its resistance is mainly affected by the electric field operating conditions, electrode plate ash accumulation, and rapping program. Its normal fluctuation range is relatively large and differs from the free ash accumulation in the inlet and outlet flues, thus requiring the setting of an independent diagnostic threshold. After comprehensive consideration, diagnostic segment three was not judged as abnormal in this scan. All parameters of diagnostic segment four were within the normal fluctuation range, and no abnormalities were found.

[0076] After the diagnostic results are generated, the central processing unit immediately sends the high-resistance section identification information to the power plant's distributed control system operator station. In the operator station's flue gas system screen, the pipeline area corresponding to diagnostic section two changes from a normal green display to a flashing yellow warning color. Simultaneously, an alarm message pops up: "High-resistance section warning: The pressure drop in the flue gas section from the air preheater outlet to the dust collector inlet (diagnostic section two) has abnormally increased to 1.46 times the baseline value. It is recommended to check the ash accumulation in this section and activate the corresponding soot blower." The alarm message also triggers the audible and visual alarm in the central control room via hard-wired connection, ensuring that operating personnel can perceive the fault warning immediately.

[0077] After receiving the alarm, the operations team, under the command of the shift leader, reviewed the historical pressure drop trend curve of diagnostic section two. They found that the pressure drop in this section had been gradually increasing in the last three diagnostic data points, reaching approximately 320 Pa, 360 Pa, and the current 410 Pa, confirming that the ash accumulation was worsening. Based on the diagnostic location, the operations team activated three telescopic steam soot blowers located in the flue section from the air preheater outlet to the dust collector inlet, performing a concentrated sequential soot blowing operation on this section. The soot blowing lasted approximately thirty minutes before ending. About an hour later, the system executed a manual diagnostic command. The retest results showed that the real-time pressure drop in diagnostic section two had dropped to 290 Pa, the peak value of the acoustic wave received signal had rebounded to 680 mV, essentially returning to near the baseline level, and the pressure drop change ratio had dropped to 1.04. The system automatically deactivated the alarm, and the corresponding pipeline in the flow chart returned to its normal green display.

[0078] The entire diagnostic system interacts with the power plant's existing distributed control system using the standard OPC (OLE for Process Control) communication protocol, eliminating the need for hardware modifications to the original control system. Historical diagnostic data is automatically stored in the data server, generating daily, weekly, and monthly flue gas friction loss reports to support equipment status trend analysis and maintenance planning.

[0079] Additionally, when the boiler tail flue includes a wet desulfurization unit, sensor nodes can be added at the inlet and outlet of the desulfurization tower to integrate the desulfurization section into the diagnostic network. In this case, it is important to note that the flue gas after desulfurization has a low temperature, high humidity, and contains corrosive droplets. Therefore, the sensor nodes should be made of corrosion-resistant materials, and the pressure-conducting pipelines should be equipped with heat tracing and drainage measures. The operating frequency of the acoustic transducer can be adjusted within the range of 20kHz to 200kHz based on the actual attenuation, balancing penetration capability and anti-interference performance.

[0080] Secondly, in situations where space is limited or it is inconvenient to install new pressure taps, existing measuring holes or spare interfaces in the flue can be used to install sensing nodes, reducing on-site construction work. For flues with large cross-sectional dimensions, multiple differential pressure taps can be symmetrically arranged at the same cross-sectional location and connected by an equalizing ring to eliminate measurement deviations caused by uneven static pressure distribution on the cross-section.

[0081] Secondly, the software can be configured with delayed repetitive diagnostic logic. When a segment is identified as a high-resistance section in a diagnostic test, the system does not immediately issue an alarm. Instead, it automatically performs a retest on the suspected segment after a preset short delay (e.g., ten minutes). If the retest result still meets the two-parameter anomaly criteria, an alarm is formally triggered. If the retest result returns to the normal range, the previous anomaly is considered a false alarm caused by a momentary disturbance in the flue gas condition and is therefore excluded. This mechanism effectively reduces the probability of false alarms caused by random disturbances.

[0082] According to the acoustic-differential pressure in-situ segmented diagnosis method for boiler flue resistance distribution proposed in this application, differential pressure-acoustic joint sensing nodes can be set at certain intervals along the boiler flue axis. A segmented diagnosis network is constructed based on the diagnostic segments formed between the nodes. Then, the reference pressure drop and reference acoustic propagation characteristic parameters of each diagnostic segment are determined. Combining the real-time pressure drop and real-time acoustic propagation characteristic parameters of each diagnostic segment acquired online, the pressure drop change ratio and acoustic deviation are calculated respectively. Diagnostic segments with a pressure drop change ratio greater than or equal to a first threshold and an acoustic deviation greater than or equal to a second threshold are identified as target resistance segments, and their location information is determined. This allows for in-situ segmented diagnosis of boiler flue resistance distribution using acoustic-differential pressure, effectively improving the boiler's operational economy and safety stability. This solves the problems in related technologies where it is difficult to accurately identify the specific location of ash accumulation and blockage in the flue, leading to blind and inefficient treatment measures, and reducing the boiler's operational economy and safety stability.

[0083] Next, referring to the accompanying drawings, an acoustic-differential pressure in-situ segmented diagnostic device for boiler flue resistance distribution according to an embodiment of this application is described.

[0084] Figure 3 This is a block diagram of an acoustic-differential pressure in-situ segmented diagnostic device for boiler flue resistance distribution according to an embodiment of this application.

[0085] like Figure 3 As shown, the acoustic-differential pressure in-situ segmented diagnostic device 10 for boiler flue resistance distribution includes: a construction module 100, a first determination module 200, a second determination module 300, and a diagnostic module 400.

[0086] Specifically, module 100 is used to set differential pressure-acoustic joint sensing nodes along the axial direction of the boiler flue at preset intervals, and to construct a segmented diagnostic network based on the diagnostic segments formed between adjacent differential pressure-acoustic joint sensing nodes.

[0087] The first determining module 200 is used to determine the reference voltage drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment based on the segmented diagnostic network.

[0088] The second determining module 300 is used to collect the real-time voltage drop and real-time acoustic wave propagation characteristic parameters of each diagnostic segment online, and determine the voltage drop change ratio based on the real-time voltage drop and the corresponding reference voltage drop, and determine the acoustic deviation based on the real-time acoustic wave propagation characteristic parameters and the corresponding reference acoustic wave propagation characteristic parameters.

[0089] The diagnostic module 400 is used to identify the diagnostic segments with a pressure drop change ratio greater than or equal to a first preset threshold and an acoustic deviation greater than or equal to a second preset threshold as target resistance segments, and to determine the location information of the target resistance segments in order to perform in-situ segmented diagnosis of the boiler flue resistance distribution using acoustic wave-differential pressure.

[0090] Optionally, in one embodiment of this application, the first determining module 200 includes: a first acquisition unit and a first determining unit.

[0091] The first acquisition unit is used to synchronously acquire static pressure data of each differential pressure-acoustic joint sensing node and acoustic wave propagation characteristic parameters between adjacent differential pressure-acoustic joint sensing nodes when the boiler is in the target operating state and the degree of ash accumulation in the boiler flue is less than the preset ash accumulation standard, using a segmented diagnostic network. The first determining unit is used to perform statistical averaging on multiple sets of collected static pressure data and sound wave propagation characteristic parameters to determine the reference pressure drop and reference sound wave propagation characteristic parameters for each diagnostic segment.

[0092] Optionally, in one embodiment of this application, the apparatus 10 of this application embodiment further includes: a data acquisition module and an update module.

[0093] The acquisition module is used to re-acquire static pressure data and acoustic wave propagation characteristic parameters when the boiler is in the target operating state and the degree of ash accumulation in the boiler flue is less than the preset ash accumulation standard, after determining the reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment, according to the preset operating cycle.

[0094] The update module is used to update the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment based on the reacquired static pressure data and acoustic wave propagation characteristic parameters after determining the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment, so as to obtain the updated reference pressure drop and reference acoustic wave propagation characteristic parameters.

[0095] Optionally, in one embodiment of this application, the second determining module 300 includes: a second acquisition unit and a second determining unit.

[0096] The second acquisition unit is used to acquire the static pressure distribution of each differential pressure-acoustic joint sensing node relative to the target reference point based on a preset diagnostic cycle, so as to calculate the real-time pressure drop of each diagnostic segment.

[0097] The second determining unit is used to transmit a set of precoded acoustic pulse trains using the upstream node of the adjacent differential pressure-acoustic joint sensing node, and to demodulate the transmitted precoded acoustic pulse trains using the downstream node of the adjacent differential pressure-acoustic joint sensing node, so as to determine the real-time acoustic wave propagation characteristic parameters; wherein, the real-time acoustic wave propagation characteristic parameters include at least one of the actual propagation time of the acoustic pulses between adjacent differential pressure-acoustic joint sensing nodes and the attenuation of the received signal amplitude.

[0098] Optionally, in one embodiment of this application, the apparatus 10 of this application embodiment further includes: an acquisition module and a generation module.

[0099] The acquisition module is used to acquire the flue name, node number range, and station range from the location information of the target resistance section.

[0100] The generation module is used to generate diagnostic alarm information and corresponding diagnostic handling suggestions based on the flue name, node number range and station number range, and send the diagnostic alarm information and corresponding diagnostic handling suggestions to the preset terminal for display.

[0101] It should be noted that the explanation of the aforementioned embodiment of the acoustic-differential pressure in-situ segmented diagnostic method for boiler flue resistance distribution also applies to the acoustic-differential pressure in-situ segmented diagnostic device for boiler flue resistance distribution in this embodiment, and will not be repeated here.

[0102] The acoustic-differential pressure in-situ segmented diagnostic device for boiler flue resistance distribution proposed in this application can set differential pressure-acoustic joint sensing nodes at certain intervals along the boiler flue axis, and construct a segmented diagnostic network based on the diagnostic segments formed between the nodes. Then, the reference pressure drop and reference acoustic propagation characteristic parameters of each diagnostic segment are determined. Combining the real-time pressure drop and real-time acoustic propagation characteristic parameters of each diagnostic segment acquired online, the pressure drop change ratio and acoustic deviation are calculated respectively. Diagnostic segments with a pressure drop change ratio greater than or equal to a first threshold and an acoustic deviation greater than or equal to a second threshold are identified as target resistance segments, and their location information is determined. This allows for in-situ segmented diagnostics of boiler flue resistance distribution using acoustic-differential pressure, effectively improving the boiler's operational economy and safety stability. This solves the problems in related technologies where it is difficult to accurately identify the specific location of ash accumulation and blockage in the flue, leading to blind and inefficient treatment measures, and reducing the boiler's operational economy and safety stability.

[0103] Figure 4A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0104] When the processor 402 executes the program, it implements the acoustic-differential pressure in-situ segmented diagnosis method for boiler flue resistance distribution provided in the above embodiments.

[0105] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0106] The memory 401 is used to store computer programs that can run on the processor 402.

[0107] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0108] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0109] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0110] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0111] This embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described acoustic-differential pressure in-situ segmented diagnosis method for boiler flue resistance distribution.

[0112] This embodiment also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned acoustic-differential pressure in-situ segmented diagnosis method for boiler flue resistance distribution.

[0113] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0114] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0115] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0116] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0117] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0118] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0119] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0120] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for in-situ segmented diagnosis of boiler flue resistance distribution using acoustic wave-differential pressure, characterized in that, Includes the following steps: Differential pressure-acoustic joint sensing nodes are set along the axis of the boiler flue at a preset interval, and a segmented diagnostic network is constructed based on the diagnostic segments formed between adjacent differential pressure-acoustic joint sensing nodes. Based on the segmented diagnostic network, the reference voltage drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment are determined; The real-time voltage drop and real-time acoustic wave propagation characteristic parameters of each diagnostic segment are collected online, and the voltage drop change ratio is determined based on the real-time voltage drop and the corresponding reference voltage drop. The acoustic deviation is determined based on the real-time acoustic wave propagation characteristic parameters and the corresponding reference acoustic wave propagation characteristic parameters. The diagnostic segments in which the pressure drop change ratio is greater than or equal to a first preset threshold and the acoustic deviation is greater than or equal to a second preset threshold are identified as target resistance segments, and the location information of the target resistance segments is determined, so as to perform in-situ segmented diagnosis of the boiler flue resistance distribution using acoustic wave-differential pressure.

2. The method according to claim 1, characterized in that, The determination of the reference voltage drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment based on the segmented diagnostic network includes: When the boiler is in the target operating state and the ash accumulation in the boiler flue is less than the preset ash accumulation standard, the segmented diagnostic network is used to synchronously collect the static pressure data of each differential pressure-acoustic joint sensing node and the acoustic wave propagation characteristic parameters between adjacent differential pressure-acoustic joint sensing nodes. Statistical averaging was performed on the collected static pressure data and acoustic wave propagation characteristic parameters to determine the reference pressure drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment.

3. The method according to claim 2, characterized in that, After determining the reference voltage drop and reference acoustic wave propagation characteristic parameters for each diagnostic segment, the following is also included: According to the preset operating cycle, static pressure data and sound wave propagation characteristic parameters are collected again when the boiler is in the target operating state and the degree of ash accumulation in the boiler flue is less than the preset ash accumulation standard. Based on the reacquired static pressure data and acoustic wave propagation characteristic parameters, the reference pressure drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment are updated to obtain the updated reference pressure drop and reference acoustic wave propagation characteristic parameters.

4. The method according to claim 1, characterized in that, The online acquisition of real-time voltage drop and real-time acoustic wave propagation characteristic parameters for each diagnostic segment includes: Based on a preset diagnostic cycle, the static pressure distribution of each differential pressure-acoustic joint sensing node relative to the target reference point is collected to calculate the real-time pressure drop of each diagnostic segment. A set of precoded acoustic pulse trains is transmitted by the upstream node of the adjacent differential pressure-acoustic joint sensing node, and the received precoded acoustic pulse trains are demodulated by the downstream node of the adjacent differential pressure-acoustic joint sensing node to determine the real-time acoustic wave propagation characteristic parameters. The real-time acoustic wave propagation characteristic parameters include at least one of the actual propagation time of the acoustic pulse between adjacent differential pressure-acoustic joint sensing nodes and the attenuation of the received signal amplitude.

5. The method according to claim 1, characterized in that, After performing acoustic-differential pressure in-situ segmented diagnosis of the boiler flue resistance distribution, the method further includes: Obtain the flue name, node number range, and station range from the location information of the target resistance section; Based on the flue name, the node number range, and the station number range, diagnostic alarm information and corresponding diagnostic handling suggestions are generated, and the diagnostic alarm information and corresponding diagnostic handling suggestions are sent to a preset terminal for display on the preset terminal.

6. An acoustic-differential pressure in-situ segmented diagnostic device for boiler flue resistance distribution, characterized in that, include: The module is used to set differential pressure-acoustic joint sensing nodes along the axial direction of the boiler flue at a preset interval, and to construct a segmented diagnostic network based on the diagnostic segments formed between adjacent differential pressure-acoustic joint sensing nodes. The first determining module is used to determine the reference voltage drop and reference acoustic wave propagation characteristic parameters of each diagnostic segment based on the segmented diagnostic network. The second determining module is used to collect the real-time voltage drop and real-time acoustic wave propagation characteristic parameters of each diagnostic segment online, and determine the voltage drop change ratio based on the real-time voltage drop and the corresponding reference voltage drop, and determine the acoustic deviation based on the real-time acoustic wave propagation characteristic parameters and the corresponding reference acoustic wave propagation characteristic parameters. The diagnostic module is used to identify the diagnostic segments where the pressure drop change ratio is greater than or equal to a first preset threshold and the acoustic deviation is greater than or equal to a second preset threshold as target resistance segments, and to determine the location information of the target resistance segments, so as to perform acoustic wave-differential pressure in-situ segmented diagnosis of the boiler flue resistance distribution.

7. The apparatus according to claim 6, characterized in that, The first determining module includes: The first acquisition unit is used to synchronously acquire static pressure data of each differential pressure-acoustic joint sensing node and acoustic wave propagation characteristic parameters between adjacent differential pressure-acoustic joint sensing nodes using the segmented diagnostic network when the boiler is in the target operating state and the degree of ash accumulation in the boiler flue is less than the preset ash accumulation standard. The first determining unit is used to perform statistical averaging on multiple sets of collected static pressure data and sound wave propagation characteristic parameters to determine the reference pressure drop and reference sound wave propagation characteristic parameters for each diagnostic segment.

8. An electronic device, characterized in that, include: The system includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the acoustic-differential pressure in-situ segmented diagnostic method for boiler flue resistance distribution as described in any one of claims 1-5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the acoustic-differential pressure in-situ segmented diagnostic method for boiler flue resistance distribution as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed by a processor to implement the acoustic-differential pressure in-situ segmented diagnostic method for boiler flue resistance distribution as described in any one of claims 1-5.

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