An intelligent monitoring system

Abstract

The present application relates to the field of intelligent monitoring, especially to a kind of for water vapor heat power equipment adds micro strong base intelligent monitoring system, the present application is by being set to the cooperative work of acquisition module, clustering construction module, screening module, intelligent monitoring module and intelligent early warning module, based on acquisition module obtains the detection signal of heat pipe section, clustering construction module combines pipeline layout information and carries out clustering analysis to signal, screening module verifies signal quality and calls corresponding cluster set, intelligent monitoring module carries out synchronous verification analysis to determine anomaly, and intelligent early warning module sends early warning signal, the system provides strong guarantee for the safe operation of water vapor heat power equipment through intelligent monitoring and early warning, improves the accuracy and reliability of monitoring.

Description

Technical Field

[0001] This invention relates to an intelligent monitoring system for monitoring water vapor thermal equipment under operating conditions with a slightly strong alkali. Background Technology

[0002] With the widespread application of water-steam thermal equipment in industrial production and energy supply, the importance of its safe, stable, and efficient operation is becoming increasingly prominent. Water-steam thermal equipment is not only closely related to energy conversion and transmission, but also to the continuity of the production process and the stability of product quality. Monitoring water-steam thermal equipment under the special operating condition of adding a slightly strong alkali is of significant practical importance for improving the operation and management level of water-steam thermal equipment and ensuring the safe and stable operation of industrial production.

[0003] Chinese Patent Publication No. CN119024463A discloses a water vapor monitoring method, system, device, and medium, relating to the field of meteorological technology. It includes: acquiring historical ERA5 data and BeiDou data of the area to be measured; obtaining an initial weighted average temperature based on the historical ERA5 data; constructing a weighted average temperature function model; inputting the historical ERA5 data into the weighted average temperature function model to obtain a training dataset; training a random forest model using the training dataset; inputting the current ERA5 data into the weighted average temperature function model to obtain a prediction dataset; inputting the prediction dataset into the trained random forest model to obtain the final weighted average temperature; and retrieving atmospheric precipitable water from the final weighted average temperature to achieve water vapor monitoring of the area to be measured. This invention overcomes the limitation of the random forest model in predicting weighted average temperature by measured meteorological parameters, improving the prediction accuracy of the final weighted average temperature.

[0004] However, the following problems still exist in the existing technology. In practice, the piping systems of water-steam-thermal equipment are complex in structure and operate in variable environments. Their monitoring signals are easily interfered with by various environmental factors, resulting in a large amount of noise in the signals. Traditional monitoring methods often rely on a single signal amplitude or simple threshold to make judgments, which makes it difficult to accurately distinguish between actual equipment anomalies and reduces the reliability and accuracy of the monitoring system. Summary of the Invention

[0005] Therefore, the present invention provides an intelligent monitoring system for adding a slightly strong alkali to water vapor and heat equipment, which overcomes the problem that the existing technology often relies on a single signal amplitude or a simple threshold to judge, making it difficult to accurately distinguish between real equipment abnormalities and thus reducing the reliability and accuracy of the monitoring system.

[0006] To achieve the above objectives, the present invention provides an intelligent monitoring system for adding a slightly strong alkali to a water vapor thermal power equipment, comprising, The acquisition module includes several signal detection units deployed along the thermal pipeline section to acquire target detection signals; A clustering construction module, which is connected to the acquisition module, is used to acquire input pipeline layout information, acquire target detection signals of each thermal pipeline segment in the same time domain segment for synchronous variation analysis, and cluster each thermal pipeline segment based on the synchronous variation analysis results and pipeline layout information to construct a synchronous observation cluster set. The screening module, which is connected to the clustering construction module, is used to acquire the vibration detection signal of the thermal pipeline section acquired by the acquisition module, verify the signal quality of the vibration detection signal, and determine whether to call the synchronous observation cluster set corresponding to the vibration detection signal based on the verification result. The intelligent monitoring module, connected to the clustering construction module and the screening module, is used to call the synchronous observation cluster set based on the thermal pipeline segment, and to call the vibration detection signal of the remaining thermal pipeline segment as an auxiliary signal based on the synchronous observation cluster set for synchronous verification analysis. This includes synchronously constructing the time-domain curves of the vibration detection signal and each of the auxiliary signals, capturing the anomaly characteristics of the vibration detection signal, and verifying the synchronicity between the anomaly characteristics in the auxiliary signals and the anomaly characteristics of the vibration detection signal, so as to determine whether there is an anomaly in the thermal pipeline segment corresponding to the vibration detection signal. An intelligent early warning module, which is connected to the intelligent monitoring module, is used to issue a corresponding early warning signal in response to the judgment result of the intelligent monitoring module; The synchronous variation analysis includes capturing the variation characteristics of target detection signals within the same time domain segment and determining the synchronous variation probability.

[0007] Furthermore, the clustering construction module is used to perform synchronous variation analysis on target detection signals within the same time domain segment, including, It is used to capture the variation characteristics of target detection signals of each thermal pipeline section within the same time domain, determine the time period of variation characteristics, record the time domain difference between the time periods of variation characteristics, and analyze the number of synchronous variations of each thermal pipeline section. Used to determine the probability of synchronous mutation based on the number of synchronous mutations and the number of times the mutation characteristic time period occurs; If the temporal difference between the time periods of the mutation characteristics is less than the predetermined temporal difference threshold, then synchronous mutation is determined to exist.

[0008] Furthermore, the clustering construction module is also used to determine the time period of the mutation characteristics, including If there is an anomaly feature, and the proportion of the time period corresponding to the anomaly feature within the corresponding same time domain segment is greater than a predetermined proportion threshold, then the time period is determined to be the anomaly feature time period. If the vibration amplitude at any given time is greater than a predetermined vibration threshold, then the vibration amplitude at that time is determined to be an abnormal characteristic.

[0009] Furthermore, the clustering construction module is used to cluster each thermal pipeline segment based on the synchronous anomaly analysis results and pipeline layout information, in order to construct a synchronous observation cluster set, including, Used to determine the distance between each section of the heating pipeline based on pipeline layout information; If the distance is less than a preset distance threshold and the synchronous mutation probability is greater than or equal to the preset synchronous mutation threshold, then each of the thermal pipeline segments is clustered to construct a synchronous observation cluster set.

[0010] Furthermore, the screening module is used to verify the signal quality of the vibration detection signal, including: Used to determine the vibration amplitude of each section of the thermal pipeline at different times; The variance of the vibration amplitude is used to calculate the variance, and the variance is used as a verification index of the signal quality to verify the signal quality of the vibration detection signal.

[0011] Furthermore, the screening module is used to determine whether to invoke the synchronous observation cluster set corresponding to the vibration detection signal based on the verification results, including: If the variance of the vibration amplitude is greater than or equal to a preset variance threshold, then the synchronous observation cluster set corresponding to the vibration detection signal is invoked.

[0012] Furthermore, the intelligent monitoring module is used to verify the synchronization between the anomaly characteristics in the auxiliary signal and the anomaly characteristics in the vibration detection signal, including: This is used to determine the absolute time difference between the time period corresponding to the anomaly characteristics of each auxiliary signal and the time period corresponding to the anomaly characteristics of the vibration detection signal; If the average absolute time difference is less than the preset synchronization time threshold, it is determined that the variation characteristics in the auxiliary signal are synchronous with the variation characteristics of the vibration detection signal.

[0013] Furthermore, the intelligent monitoring module is used to determine whether there is an anomaly in the thermal pipeline section corresponding to the vibration detection signal, including: If the anomaly characteristics in the auxiliary signal are synchronous with the anomaly characteristics of the vibration detection signal, then it is determined that there is no abnormality in the thermal pipeline section corresponding to the vibration detection signal.

[0014] Furthermore, the intelligent monitoring module is also used to perform anomaly analysis on each thermal pipe segment that has not invoked the synchronous observation cluster set corresponding to the vibration detection signal, including, This is used to set constraints based on the vibration amplitude of each thermal pipeline section in order to determine whether there is an anomaly in the thermal pipeline section corresponding to the vibration detection signal.

[0015] Furthermore, the constraints include, If the vibration amplitude is within the standard threshold range, it is determined that there is no abnormality in the thermal pipeline section corresponding to the vibration detection signal.

[0016] Compared with existing technologies, this invention, through the coordinated operation of a data acquisition module, a clustering construction module, a screening module, an intelligent monitoring module, and an intelligent early warning module, acquires detection signals from thermal pipeline sections using the data acquisition module. The clustering construction module performs cluster analysis on the signals in conjunction with pipeline layout information. The screening module verifies the signal quality and calls the corresponding cluster set. The intelligent monitoring module performs synchronous verification analysis to determine anomalies. The intelligent early warning module issues early warning signals. This system provides strong protection for the safe operation of water and steam thermal equipment through intelligent monitoring and early warning, and improves the accuracy and reliability of monitoring.

[0017] In particular, this invention clusters various thermal pipeline segments based on synchronous anomaly analysis results combined with pipeline layout information to construct a synchronous observation cluster set. In practice, due to the complex structure of the pipeline system in water-steam thermal equipment and the variable operating environment, monitoring signals are easily interfered with by various environmental factors, resulting in a large amount of noise in the signals. For example, external factors such as passing vehicles or personnel activities can cause pipeline vibrations. These vibration signals are mixed with the actual abnormal signals of the equipment, making it difficult to accurately distinguish the true abnormal situation of the equipment by relying solely on the signal of a single pipeline segment, easily leading to misjudgments. However, by constructing a synchronous observation cluster set, signals from multiple pipeline segments can be analyzed comprehensively. On the one hand, this can better reduce noise interference and improve monitoring accuracy; on the other hand, cluster analysis based on pipeline layout information can better identify which pipeline segments exhibit similar behavioral patterns under specific conditions. For example, if pipeline segments in a certain cluster set frequently exhibit anomalies, this pattern can be identified through cluster analysis, allowing for better optimization of monitoring resource allocation, thereby providing strong protection for the safe operation of water-steam thermal equipment and improving the accuracy and reliability of monitoring.

[0018] In particular, this invention verifies the signal quality of the vibration detection signal and determines whether to invoke the corresponding synchronous observation cluster set based on the verification results. In reality, the operating environment of water and steam thermal equipment is complex and variable, and monitoring signals are easily interfered with by various external factors, such as passing vehicles and personnel activities. These factors can cause pipeline vibration, potentially resulting in vibration detection signals containing both real fault characteristics and strong noise interference. When the variance of the vibration amplitude is large, it indicates that the signal fluctuation is relatively severe, and this severe fluctuation is more likely caused by abnormal equipment operation or strong interference. In this case, invoking the corresponding synchronous observation cluster set allows for the comprehensive analysis of signals from multiple related pipeline sections, thereby more accurately distinguishing between real anomalies and noise interference. Simultaneously, for pipeline sections where the synchronous observation cluster set is not invoked—that is, those with small signal variance and stable fluctuations—the system does not ignore the possibility of anomalies but independently determines them by setting vibration amplitude constraints. For example, if the vibration amplitude continuously exceeds the standard threshold range, the pipeline section is determined to have an anomaly. In this way, erroneous decisions caused by misinterpretation of a single signal can be better avoided, and the operating status of water vapor and heat equipment can be monitored more comprehensively, thus providing a strong guarantee for the safe operation of water vapor and heat equipment and improving the accuracy and reliability of monitoring.

[0019] In particular, this invention determines whether there is an anomaly in the thermal pipeline section corresponding to the vibration detection signal by verifying the synchronicity between the anomaly characteristics in the auxiliary signal and the anomaly characteristics in the vibration detection signal. In complex water-steam thermal equipment pipeline systems, vibration signals are easily affected by various external environmental factors, such as equipment start-up and shutdown, fluid pressure fluctuations, and external mechanical vibrations. These interferences usually have systematic characteristics, meaning they will produce synchronous vibration responses in adjacent pipeline sections or those with similar operating conditions. If the anomaly characteristics in the auxiliary signal are synchronous with the anomaly characteristics of the target signal in the time domain, it indicates that the anomaly is likely caused by systematic external factors or fluctuations in normal operating conditions, rather than a local fault. Conversely, if the anomaly characteristics of the target signal do not show a synchronous response in the auxiliary signal, it indicates that the anomaly is localized and is very likely caused by a real anomaly such as pipeline leakage, structural damage, or local blockage. In this case, the system needs to trigger an early warning. This discrimination mechanism based on signal synchronicity improves the accuracy of fault identification, reduces false alarms and missed alarms, thereby providing strong protection for the safe operation of water-steam thermal equipment and improving the accuracy and reliability of monitoring. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the intelligent monitoring system for adding a slightly strong alkali to a water vapor thermal equipment, according to an embodiment of the invention. Figure 2 A logical decision graph for determining whether to construct a synchronous observation cluster set in an embodiment of the invention; Figure 3 A logic decision diagram for determining whether to invoke the synchronous observation cluster set corresponding to the vibration detection signal in an embodiment of the invention; Figure 4 This is a logic block diagram of the synchronous observation cluster set analysis corresponding to the vibration detection signal in an embodiment of the invention. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] Please see Figure 1 The diagram shown is a structural schematic of an intelligent monitoring system for adding a slightly strong alkali to a steam-heating equipment according to an embodiment of the invention. The intelligent monitoring system for adding a slightly strong alkali to a steam-heating equipment according to an embodiment of the invention includes: The acquisition module includes several signal detection units deployed along the thermal pipeline section to acquire target detection signals; A clustering construction module, which is connected to the acquisition module, is used to acquire input pipeline layout information, acquire target detection signals of each thermal pipeline segment in the same time domain segment for synchronous variation analysis, and cluster each thermal pipeline segment based on the synchronous variation analysis results and pipeline layout information to construct a synchronous observation cluster set. The screening module, which is connected to the clustering construction module, is used to acquire the vibration detection signal of the thermal pipeline section acquired by the acquisition module, verify the signal quality of the vibration detection signal, and determine whether to call the synchronous observation cluster set corresponding to the vibration detection signal based on the verification result. The intelligent monitoring module, connected to the clustering construction module and the screening module, is used to call the synchronous observation cluster set based on the thermal pipeline segment, and to call the vibration detection signal of the remaining thermal pipeline segment as an auxiliary signal based on the synchronous observation cluster set for synchronous verification analysis. This includes synchronously constructing the time-domain curves of the vibration detection signal and each of the auxiliary signals, capturing the anomaly characteristics of the vibration detection signal, and verifying the synchronicity between the anomaly characteristics in the auxiliary signals and the anomaly characteristics of the vibration detection signal, so as to determine whether there is an anomaly in the thermal pipeline segment corresponding to the vibration detection signal. An intelligent early warning module, which is connected to the intelligent monitoring module, is used to issue a corresponding early warning signal in response to the judgment result of the intelligent monitoring module; The synchronous variation analysis includes capturing the variation characteristics of target detection signals within the same time domain segment and determining the synchronous variation probability.

[0025] Specifically, there are no restrictions on the structure of the clustering construction module, screening module, intelligent monitoring module, and intelligent early warning module. They can be composed of logical components or combinations of logical components, including field-programmable processors, computers, or microprocessors in computers.

[0026] Specifically, there are no restrictions on the structure of the signal detection unit. For example, it can be a piezoelectric vibration sensor, as long as it can acquire the vibration detection signal of the thermal pipeline section with high accuracy in real time. Other forms are also possible, which will not be elaborated here.

[0027] Specifically, the pipeline layout information includes the pipeline connection network topology and the distance information between each pipeline, which can be determined in advance by those skilled in the art or based on the drawings when the pipeline is laid, and will not be elaborated further.

[0028] Specifically, the clustering construction module is used to perform synchronous variation analysis on target detection signals within the same time domain segment, including, It is used to capture the variation characteristics of target detection signals of each thermal pipeline section within the same time domain, determine the time period of variation characteristics, record the time domain difference between the time periods of variation characteristics, and analyze the number of synchronous variations of each thermal pipeline section. Used to determine the probability of synchronous mutation based on the number of synchronous mutations and the number of times the mutation characteristic time period occurs; If the temporal difference between the time periods of the mutation characteristics is less than the predetermined temporal difference threshold, then synchronous mutation is determined to exist.

[0029] In practice, preferably, the absolute time difference between time periods is determined by calculating the timestamps of the start and end points of the anomaly characteristic time periods of each thermal pipeline segment, and is used as the temporal domain difference degree between the anomaly characteristic time periods.

[0030] In practice, the ratio of the number of synchronous mutations to the number of times the mutation characteristic time period occurs is determined as the synchronous mutation probability.

[0031] In implementation, the purpose of the predetermined time-domain difference threshold is to characterize the difference blocks between time periods of anomaly characteristics. The predetermined time-domain difference threshold is predetermined. Those skilled in the art can determine the mean of the time-domain difference between time periods of anomaly characteristics by statistically analyzing signals under normal operating conditions, thus representing the signal changes under normal conditions. To represent fluctuations, the predetermined time-domain difference threshold is set as the product of the mean and the overlap error coefficient. Typically, the overlap error coefficient is selected within the range of [0.85, 1.35], and is preferably 1.25 in implementation.

[0032] Specifically, the clustering construction module is also used to determine the time periods of anomaly characteristics, including: If there is an anomaly feature, and the proportion of the time period corresponding to the anomaly feature within the corresponding same time domain segment is greater than a predetermined proportion threshold, then the time period is determined to be the anomaly feature time period. If the vibration amplitude at any time is greater than a predetermined vibration threshold, then the vibration amplitude at that time is determined to be an abnormal characteristic.

[0033] In implementation, the purpose of the predetermined percentage threshold is to characterize the significance of the time period of anomaly within a predetermined time period, that is, to determine whether the anomaly within that time period is significant enough to be identified as a time period of anomaly. The predetermined percentage threshold is predetermined, and is usually selected within the range [0.2, 0.5], preferably 0.3 in implementation.

[0034] In practice, the purpose of the predetermined vibration threshold is to characterize the degree of abnormality in vibration amplitude, i.e., to determine whether the vibration amplitude exceeds the normal range, thereby identifying possible abnormal situations. The predetermined vibration threshold is predetermined; those skilled in the art can determine the maximum vibration amplitude by analyzing historical vibration data of the equipment under normal operating conditions to represent the maximum vibration situation. To represent vibration fluctuations, the predetermined vibration threshold is set as the product of the mean and the vibration error coefficient. Typically, the vibration error coefficient is selected within the range of [1.05, 1.35], and is preferably 1.25 in practice.

[0035] Please see Figure 2 As shown, this is a logic decision diagram for determining whether to construct a synchronous observation cluster set according to an embodiment of the invention. Specifically, the cluster construction module is used to cluster each thermal pipeline segment based on the synchronous anomaly analysis results and pipeline layout information to construct a synchronous observation cluster set, including... Used to determine the distance between each section of the heating pipeline based on pipeline layout information; If the distance is less than a preset distance threshold and the synchronous mutation probability is greater than or equal to the preset synchronous mutation threshold, then each of the thermal pipeline segments is clustered to construct a synchronous observation cluster set.

[0036] In implementation, the purpose of the distance threshold is to characterize the range of mutual influence between thermal pipeline segments. The distance threshold is predetermined; those skilled in the art can statistically analyze the distances between each thermal pipeline segment and adjacent pipelines using pipeline distribution information to determine the average distance between adjacent pipeline segments, thus representing the distance situation under normal conditions. To indicate potential mutual influence between thermal pipeline segments, the distance threshold is set as the product of the average distance and a distance accuracy coefficient. Typically, the distance accuracy coefficient is selected within the range of [1.15, 1.45], and is preferably 1.35 in implementation.

[0037] This invention clusters various thermal pipeline segments based on synchronous anomaly analysis results combined with pipeline layout information to construct a synchronous observation cluster set. In practice, due to the complex structure of the pipeline system in water and steam thermal equipment and the variable operating environment, monitoring signals are easily interfered with by various environmental factors, resulting in a large amount of noise in the signals. For example, external factors such as passing vehicles or personnel activities can cause pipeline vibrations. These vibration signals are mixed with the actual abnormal signals of the equipment, making it difficult to accurately distinguish the true abnormal situation of the equipment by relying solely on the signal of a single pipeline segment, easily leading to misjudgments. However, by constructing a synchronous observation cluster set, signals from multiple pipeline segments can be analyzed comprehensively. On the one hand, this can better reduce noise interference and improve monitoring accuracy; on the other hand, cluster analysis based on pipeline layout information can better identify which pipeline segments exhibit similar behavioral patterns under specific conditions. For example, if pipeline segments in a certain cluster set frequently exhibit anomalies, this pattern can be identified through cluster analysis, allowing for better optimization of monitoring resource allocation. This provides strong protection for the safe operation of water and steam thermal equipment and improves the accuracy and reliability of monitoring.

[0038] Specifically, the screening module is used to verify the signal quality of the vibration detection signal, including: Used to determine the vibration amplitude of each section of the thermal pipeline at different times; The variance of the vibration amplitude is used to calculate the variance, and the variance is used as a verification index of the signal quality to verify the signal quality of the vibration detection signal.

[0039] Please see Figure 3The diagram shown illustrates the logic for determining whether to invoke the synchronous observation cluster set corresponding to the vibration detection signal according to an embodiment of the invention. Specifically, the screening module determines whether to invoke the synchronous observation cluster set corresponding to the vibration detection signal based on the verification results, including: If the variance of the vibration amplitude is greater than or equal to a preset variance threshold, then the synchronous observation cluster set corresponding to the vibration detection signal is invoked.

[0040] In implementation, the preset variance threshold aims to characterize the stability and reliability of the vibration detection signal. The variance threshold is predetermined; those skilled in the art can determine the mean variance of the vibration amplitude by analyzing data from the vibration detection signal under normal operating conditions, thus representing the signal fluctuation under normal circumstances. To indicate potential abnormal fluctuations in the vibration detection signal, the variance threshold is set as the product of the mean and the variance accuracy coefficient. Typically, the variance accuracy coefficient is selected within the range of [0.85, 1.25], and is preferably 0.95 in implementation.

[0041] This invention verifies the signal quality of the vibration detection signal and determines whether to invoke the corresponding synchronous observation cluster set based on the verification results. In reality, the operating environment of water and steam thermal equipment is complex and variable, and monitoring signals are easily interfered with by various external factors, such as passing vehicles and personnel activities. These factors can cause pipeline vibration, potentially resulting in vibration detection signals containing both real fault characteristics and strong noise interference. When the variance of the vibration amplitude is large, it indicates that the signal fluctuation is relatively severe, and this severe fluctuation is more likely caused by abnormal equipment operation or strong interference. In this case, invoking the corresponding synchronous observation cluster set allows for the comprehensive analysis of signals from multiple related pipeline sections, thereby more accurately distinguishing between real anomalies and noise interference. Simultaneously, for pipeline sections that do not invoke the synchronous observation cluster set—that is, those with small signal variance and stable fluctuations—the system does not ignore the possibility of anomalies but independently determines them by setting vibration amplitude constraints. For example, if the vibration amplitude continuously exceeds the standard threshold range, the pipeline section is determined to have an anomaly. In this way, erroneous decisions caused by misinterpretation of a single signal can be better avoided, and the operating status of water vapor and heat equipment can be monitored more comprehensively, thus providing a strong guarantee for the safe operation of water vapor and heat equipment and improving the accuracy and reliability of monitoring.

[0042] Specifically, the intelligent monitoring module is used to verify the synchronization between the anomaly characteristics in the auxiliary signal and the anomaly characteristics in the vibration detection signal, including, This is used to determine the absolute time difference between the time period corresponding to the anomaly characteristics of each auxiliary signal and the time period corresponding to the anomaly characteristics of the vibration detection signal; If the average absolute time difference is less than the preset synchronization time threshold, it is determined that the variation characteristics in the auxiliary signal are synchronous with the variation characteristics of the vibration detection signal.

[0043] In implementation, the preset synchronization time threshold aims to characterize the time synchronization accuracy between the vibration detection signal and the auxiliary signal. Those skilled in the art can statistically analyze the signal data under normal operating conditions to determine the mean of the signal time deviation, thus representing the normal time synchronization status. To indicate potential abnormal fluctuations, the synchronization time threshold is set as the product of the mean and the time error coefficient. Typically, the time error coefficient is selected within the range of [0.75, 1.35], and is preferably 1.25 in practice.

[0044] Please see Figure 4 The diagram shown is a logical block diagram of the synchronous observation clustering set analysis corresponding to the vibration detection signal according to an embodiment of the invention. Specifically, the intelligent monitoring module is used to determine whether there is an anomaly in the thermal pipeline section corresponding to the vibration detection signal, including... If the anomaly characteristics in the auxiliary signal are synchronous with the anomaly characteristics of the vibration detection signal, then it is determined that there is no abnormality in the thermal pipeline section corresponding to the vibration detection signal.

[0045] This invention determines whether an anomaly exists in the corresponding thermal pipeline section by verifying the synchronicity between the anomaly characteristics in the auxiliary signal and the anomaly characteristics in the vibration detection signal. In complex water-steam thermal equipment pipeline systems, vibration signals are easily affected by various external environmental factors, such as equipment start-up and shutdown, fluid pressure fluctuations, and external mechanical vibrations. These interferences typically have systematic characteristics, meaning they generate synchronous vibration responses in adjacent pipeline sections or those with similar operating conditions. If the anomaly characteristics in the auxiliary signal are synchronous with those in the target signal in the time domain, it indicates that the anomaly is likely caused by systematic external factors or fluctuations in normal operating conditions, rather than a local fault. Conversely, if the anomaly characteristics of the target signal do not show a synchronous response in the auxiliary signal, it indicates that the anomaly is localized and is highly likely caused by a real anomaly such as pipeline leakage, structural damage, or local blockage. In this case, the system needs to trigger an early warning. This discrimination mechanism based on signal synchronicity improves the accuracy of fault identification, reduces false alarms and missed alarms, thus providing strong protection for the safe operation of water-steam thermal equipment and improving the accuracy and reliability of monitoring.

[0046] Specifically, the intelligent monitoring module is also used to perform anomaly analysis on each thermal pipe section that has not invoked the synchronous observation cluster set corresponding to the vibration detection signal, including, This is used to set constraints based on the vibration amplitude of each thermal pipeline section in order to determine whether there is an anomaly in the thermal pipeline section corresponding to the vibration detection signal.

[0047] Specifically, the constraints include, If the vibration amplitude is within the standard threshold range, it is determined that there is no abnormality in the thermal pipeline section corresponding to the vibration detection signal.

[0048] In implementation, the purpose of the standard threshold range is to characterize the normal fluctuation range of the vibration detection signal, that is, to determine whether the vibration amplitude is within the expected fluctuation range under normal operating conditions. The standard threshold is predetermined; those skilled in the art can determine the mean vibration amplitude by analyzing historical data to represent the vibration under normal conditions. To represent abnormal fluctuations, 1.35 times the mean is used as the upper limit of the standard threshold range, and 0.75 times the mean is used as the lower limit, thus constructing a closed interval for the standard threshold range.

[0049] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An intelligent monitoring system for monitoring the operation of a water-steam-thermal equipment under conditions of adding a slightly strong alkali, characterized in that, include: The acquisition module includes several signal detection units deployed along the thermal pipeline section to acquire target detection signals; A clustering construction module, which is connected to the acquisition module, is used to acquire input pipeline layout information, acquire target detection signals of each thermal pipeline segment in the same time domain segment for synchronous variation analysis, and cluster each thermal pipeline segment based on the synchronous variation analysis results and pipeline layout information to construct a synchronous observation cluster set. The screening module, which is connected to the clustering construction module, is used to acquire the vibration detection signal of the thermal pipeline section acquired by the acquisition module, verify the signal quality of the vibration detection signal, and determine whether to call the synchronous observation cluster set corresponding to the vibration detection signal based on the verification result. The intelligent monitoring module, connected to the clustering construction module and the screening module, is used to call the synchronous observation cluster set based on the thermal pipeline section, and to call the vibration detection signals of the remaining thermal pipeline sections as auxiliary signals based on the synchronous observation cluster set for synchronous verification analysis. This includes simultaneously constructing time-domain curves of the vibration detection signal and each of the auxiliary signals, capturing the anomaly characteristics of the vibration detection signal, and verifying the synchronicity between the anomaly characteristics in the auxiliary signals and the anomaly characteristics of the vibration detection signal, so as to determine whether there is an anomaly in the thermal pipeline section corresponding to the vibration detection signal. An intelligent early warning module, which is connected to the intelligent monitoring module, is used to issue a corresponding early warning signal in response to the judgment result of the intelligent monitoring module; The synchronous variation analysis includes capturing the variation characteristics of target detection signals within the same time domain segment and determining the synchronous variation probability.

2. The intelligent monitoring system according to claim 1, characterized in that, The clustering construction module is used to perform synchronous variation analysis on target detection signals within the same time domain segment, including, It is used to capture the variation characteristics of target detection signals of each thermal pipeline section within the same time domain, determine the time period of variation characteristics, record the time domain difference between the time periods of variation characteristics, and analyze the number of synchronous variations of each thermal pipeline section. Used to determine the probability of synchronous mutation based on the number of synchronous mutations and the number of times the mutation characteristic time period occurs; If the temporal difference between the time periods of the mutation characteristics is less than the predetermined temporal difference threshold, then synchronous mutation is determined to exist.

3. The intelligent monitoring system according to claim 2, characterized in that, The clustering construction module is also used to determine the time periods of the mutation characteristics. If there is an anomaly feature, and the proportion of the time period corresponding to the anomaly feature within the corresponding same time domain segment is greater than a predetermined proportion threshold, then the time period is determined to be the anomaly feature time period. If the vibration amplitude at any time is greater than a predetermined vibration threshold, then the vibration amplitude at that time is determined to be an abnormal characteristic.

4. The intelligent monitoring system according to claim 1, characterized in that, The clustering construction module is used to cluster each thermal pipeline segment based on the synchronous anomaly analysis results and pipeline layout information, in order to construct a synchronous observation cluster set, including, Used to determine the distance between each section of the heating pipeline based on pipeline layout information; If the distance is less than a preset distance threshold and the synchronous mutation probability is greater than or equal to the preset synchronous mutation threshold, then each of the thermal pipeline segments is clustered to construct a synchronous observation cluster set.

5. The intelligent monitoring system according to claim 1, characterized in that, The screening module is used to verify the signal quality of the vibration detection signal, including: Used to determine the vibration amplitude of each section of the thermal pipeline at different times; The variance of the vibration amplitude is used to calculate the variance, and the variance is used as a verification index of the signal quality to verify the signal quality of the vibration detection signal.

6. The intelligent monitoring system according to claim 5, characterized in that, The screening module is used to determine, based on the verification results, whether to invoke the synchronous observation cluster set corresponding to the vibration detection signal, including... If the variance of the vibration amplitude is greater than or equal to a preset variance threshold, then the synchronous observation cluster set corresponding to the vibration detection signal is invoked.

7. The intelligent monitoring system according to claim 1, characterized in that, The intelligent monitoring module is used to verify the synchronization between the anomaly characteristics in the auxiliary signal and the anomaly characteristics in the vibration detection signal, including: This is used to determine the absolute time difference between the time period corresponding to the anomaly characteristics of each auxiliary signal and the time period corresponding to the anomaly characteristics of the vibration detection signal; If the average absolute time difference is less than the preset synchronization time threshold, it is determined that the variation characteristics in the auxiliary signal are synchronous with the variation characteristics of the vibration detection signal.

8. The intelligent monitoring system according to claim 7, characterized in that, The intelligent monitoring module is used to determine whether there is an anomaly in the thermal pipeline section corresponding to the vibration detection signal, including: If the anomaly characteristics in the auxiliary signal are synchronous with the anomaly characteristics of the vibration detection signal, then it is determined that there is no abnormality in the thermal pipeline section corresponding to the vibration detection signal.

9. The intelligent monitoring system according to claim 1, characterized in that, The intelligent monitoring module is also used to perform anomaly analysis on each thermal pipe section that has not invoked the synchronous observation cluster set corresponding to the vibration detection signal, including, This is used to set constraints based on the vibration amplitude of each thermal pipeline section in order to determine whether there is an anomaly in the thermal pipeline section corresponding to the vibration detection signal.

10. The intelligent monitoring system according to claim 9, characterized in that, The constraints include, If the vibration amplitude is within the standard threshold range, it is determined that there is no abnormality in the thermal pipeline section corresponding to the vibration detection signal.

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