Intelligent monitoring and management system for dust removal air duct unblocking robot

Abstract

The invention discloses an intelligent monitoring and management system for a dust removal air duct unblocking robot, and relates to the technical field of intelligent control. According to the technical scheme, through analog-to-digital conversion and multiple signal filtering technologies of a signal processing module, data transmission delay in a high-temperature or strong electromagnetic interference environment is effectively reduced; the real-time performance and precision of operation feedback of the unblocking robot are improved; the data monitoring module utilizes a plurality of sensors to combine with a filtering algorithm, so that the monitoring precision in a complex electric power environment is improved, and the accuracy and reliability of the unblocking effect are ensured; the fault prediction analysis module calculates a fault prediction value Fyz in real time and evaluates the fault prediction value Fyz, and if the fault prediction value Fyz exceeds a fault tolerance threshold value C, an early warning instruction is sent out immediately to guarantee the continuity of a key task; the intelligent management module sets a management threshold value Qj, dynamically evaluates a blockage clearing effectiveness index Cjxzb, and if the management threshold value Qj is lower than the threshold value, intervention means such as monitoring precision improvement, blockage clearing strategy adjustment and an intermittent blockage clearing mode are started, so that the efficient and stable blockage clearing process is ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent monitoring and management system for a dust removal air duct clearing robot. Background Art

[0002] The dust removal duct unblocking robot is an intelligent device used to clean dust and foreign matter inside the duct. Its technology originated from the needs of traditional industrial dust removal. The early unblocking methods mainly relied on manual use of long poles or brushes for unblocking, which was time-consuming, labor-intensive and posed a safety hazard. With the development of industrialization, especially in heavy industry and construction, duct unblocking has become an important part of ensuring ventilation efficiency and air quality. The development of unblocking robots has benefited from the progress of sensing technology, image recognition, automatic control and robotic arm technology, and can efficiently clear obstacles in complex duct structures. Modern unblocking robots are usually equipped with remote control systems, real-time monitoring and feedback functions, which can reduce human intervention while maintaining efficient unblocking, and improve work safety and accuracy. This type of equipment not only greatly improves the cleaning efficiency, but also optimizes the maintenance and management of ducts through data collection, providing technical support for the intelligent development of industrial dust removal.

[0003] The current dust removal and air duct clearing robots used in the power industry have the following technical disadvantages:

[0004] 1. Real-time monitoring delay: In high temperature or strong electromagnetic interference environment, there is a delay in monitoring data transmission, which may lead to untimely feedback from the operation of the clearing robot, thus affecting the clearing efficiency and operation accuracy.

[0005] 2. Insufficient sensor accuracy: The monitoring system of the blockage clearing robot relies on a variety of sensors (such as temperature, humidity, dust concentration, etc.), but some sensors are not accurate enough in complex power environments, which may lead to false alarms or missed alarms, affecting the reliable evaluation of the blockage clearing effect.

[0006] 3. Lack of fault prediction capabilities: Existing systems are mostly passive monitoring systems, which make it difficult to timely predict or diagnose faults in the clearing robot itself. It may suddenly fail during critical tasks, affecting the continuity of the overall dust removal process. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides an intelligent monitoring and management system for a dust removal and air duct clearing robot, which solves the technical problems of real-time monitoring delay, insufficient sensor accuracy and lack of fault prediction capability in the background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent monitoring and management system for a dust removal duct clearing robot, including a region division module, a data monitoring module, a signal processing module, a fault prediction and analysis module and an intelligent management module;

[0009] The area division module is used to pre-select a specific air duct area in the power plant, divide the specific air duct area into a plurality of monitoring sub-areas, and set monitoring points in the monitoring sub-areas;

[0010] The data monitoring module is used to monitor and record the high temperature environment related data, electromagnetic interference intensity related data and dust density related data in each monitoring sub-area; at the same time, the state related data of the clearing robot in each monitoring sub-area is monitored and collected and recorded in real time;

[0011] The signal processing module is used to convert all the data recorded in the data monitoring module into digital signals, process the digital signals through a signal filtering algorithm, perform preprocessing and storage through digital computing technology, and construct and store them into a sub-area clearing data set;

[0012] The fault prediction analysis module is used to respectively construct a fault prediction ratio coefficient Pfb, a temperature threshold Tyz, an electromagnetic interference intensity Edy, and a dust accumulation density Gdm; then the fault prediction value Fyz is obtained by calculation and evaluated, and a fault warning instruction is issued externally; secondly, according to the dust accumulation density Gdm in the monitoring sub-area, the clearing efficiency coefficient Qxcs is calculated, and the fault prediction value Fyz in the monitoring sub-area is associated with the clearing efficiency coefficient Qxcs, and the clearing effectiveness index Cjxzb in the corresponding monitoring sub-area is obtained by fitting;

[0013] The intelligent management module is used to pre-set the management threshold Qj, and compare and evaluate the clearing effectiveness index Cjxzb in the monitoring sub-area with the management threshold Qj. According to the evaluation results, management intervention measures are taken for the current monitoring sub-area to adjust the monitoring and management accuracy of the clearing robot in high temperature and electromagnetic interference environments.

[0014] Preferably, the area division module divides the specific air duct area into several groups of monitoring sub-areas according to the structural characteristics of the air duct, dust accumulation and clearing requirements; determines the distribution position and boundary range of each monitoring sub-area by preliminarily analyzing the dust accumulation distribution and blockage risk of the air duct, and divides the air duct into easy-to-block, relatively easy-to-block and regular areas according to the blockage possibility, temperature fluctuation and wind pressure difference information; then, deploys monitoring points in each monitoring sub-area.

[0015] Preferably, the sensors deployed by the data monitoring module at the monitoring points in each monitoring sub-area include thermal sensors, electromagnetic sensors and laser particle sensors, which collect real-time data related to the high temperature environment, data related to the electromagnetic interference intensity and data related to the dust density in the pipe; finally, the state-related data of the blockage clearing robot in each monitoring sub-area, including the robot motor operation data, are collected through the built-in state monitoring sensor; all data collected by the data monitoring module are recorded and transmitted to the signal processing module for preprocessing.

[0016] Preferably, the signal processing module converts all the data collected by the data monitoring module into digital signals through an analog-to-digital converter, and uses a built-in signal filtering algorithm to filter out high-frequency interference, suppress low-frequency noise, and compensate for errors on the digital signals; subsequently, the digital signal after filtering is preprocessed using digital computing technology, including signal standardization, abnormal data removal, and data smoothing operations, and the preprocessed digital signal is partitioned and archived for storage;

[0017] Finally, the processed status-related data, high temperature environment-related data, electromagnetic interference intensity-related data, and dust density-related data in the pipe are summarized and stored to construct a sub-area clearing data set.

[0018] Preferably, the fault prediction analysis module includes a fault prediction ratio calculation unit, an environmental feature extraction unit, a fault warning generation unit, a blockage clearing efficiency calculation unit and a blockage clearing effectiveness calculation unit;

[0019] The fault prediction ratio calculation unit calculates the fault prediction ratio coefficient Pfb by extracting the state-related data in the sub-area clearing data set, including the equipment operation time Rti, the sensor data fluctuation rate Sde, the motor temperature rise rate Mte and the battery voltage decay rate Vde. The specific calculation formula is as follows: .

[0020] Preferably, the environmental feature extraction unit is used to extract feature data from the sub-region clearing data set, including high temperature environment related data, electromagnetic interference intensity related data and dust accumulation density related data in the pipe;

[0021] Finally, the temperature threshold Tyz, electromagnetic interference intensity Edy and dust density Gdm in the corresponding partition are calculated by the following formulas: ; ; ;

[0022] In the formula, Hri represents the thermal radiation intensity in the sub-region clearing data set, Twf represents the temperature fluctuation frequency in the sub-region clearing data set, and Lht represents the local hot spot temperature increment in the sub-region clearing data set; ln represents the natural logarithm, which is used to compress the range of the local hot spot temperature increment Lht in the formula;

[0023] Efp represents the peak value of the electric field intensity in the sub-region clearing data set, End represents the electromagnetic noise density in the sub-region clearing data set, Fvr represents the frequency change rate in the sub-region clearing data set, and Mwa represents the fluctuation amplitude of the magnetic field intensity in the sub-region clearing data set;

[0024] Pmc represents the concentration of microparticles in the sub-region clearing data set, Psr represents the particle deposition rate in the sub-region clearing data set, and Psa represents the particle adhesion index in the sub-region clearing data set.

[0025] Preferably, the fault warning generation unit is used to associate the fault prediction ratio coefficient Pfb with the corresponding temperature threshold Tyz and the electromagnetic interference intensity Edy, and obtain the fault prediction value Fyz by calculating through the following formula: ;

[0026] The preset fault tolerance threshold C and fault prediction value Fyz are used for evaluation, and a fault warning instruction is issued. The specific evaluation contents are as follows:

[0027] If the fault prediction value Fyz>fault tolerance threshold C, it means that the current environment and equipment status are abnormal. At this time, the system starts the fault warning mechanism, automatically issues a fault warning instruction, and notifies the operator to take preventive measures;

[0028] If the fault prediction value Fyz ≤ the fault tolerance threshold C, it means that the current environment and equipment status are normal, the system maintains a normal monitoring state, and no additional intervention measures are required.

[0029] Preferably, the blockage clearing efficiency calculation unit extracts the blockage clearing time Ct, the blockage clearing pressure Cp and the residual dust accumulation rate Rd in the sub-region blockage clearing data set based on the dust accumulation density Gdm, and obtains the blockage clearing efficiency coefficient Qxcs by the following formula: .

[0030] Preferably, the congestion clearing effectiveness calculation unit is used to associate the fault prediction value Fyz in the monitoring sub-area with the congestion clearing efficiency coefficient Qxcs, and obtain the congestion clearing effectiveness index Cjxzb in the corresponding monitoring partition by fitting, which is specifically disclosed as follows; .

[0031] Preferably, the intelligent management module is used to compare and evaluate the clearing effectiveness index Cjxzb with the management threshold Qj, and take management intervention measures within the monitoring sub-area, and the specific contents are as follows:

[0032] When the clearing effectiveness index Cjxzb ≥ the management threshold Qj, it means that the efficiency and safety of the clearing process in the current monitoring sub-area under high temperature and electromagnetic interference environment have met the requirements, and the system does not need further intervention and only maintains the normal monitoring state;

[0033] When the clearing effectiveness index Cjxzb is less than the management threshold Qj, it means that the efficiency and safety of the clearing process in the current monitoring sub-area under high temperature and electromagnetic interference environment do not meet the requirements. At this time, the system initiates management intervention measures, including enhancing the monitoring accuracy by 10% on the original basis, adjusting the clearing strategy, adjusting the dynamic fault tolerance threshold, and adjusting the clearing robot to intermittent clearing mode, and adjusting the load of the clearing robot under high temperature or high interference conditions during the rest time.

[0034] The present invention provides an intelligent monitoring and management system for a dust removal duct blockage clearing robot. It has the following beneficial effects:

[0035] (1) The intelligent monitoring and management system of a dust removal duct blockage clearing robot converts all analog data collected by the data monitoring module into digital signals through the signal processing module using an analog-to-digital converter, and uses a signal filtering algorithm to filter out high-frequency interference, suppress low-frequency noise and compensate for errors in the converted digital signals. The digital computing technology is used to standardize the filtered signals, eliminate abnormal data and perform data smoothing preprocessing to ensure that the blockage clearing robot can achieve real-time monitoring in a high temperature or strong electromagnetic interference environment, reduce data transmission delays, and improve the timeliness of operation feedback, thereby improving the blockage clearing efficiency and operation accuracy. In addition, the sensor design in the data monitoring module includes thermal sensors, electromagnetic sensors and laser particle sensors, which respectively collect data related to the high temperature environment, electromagnetic interference intensity and dust density in the duct. These diverse fine sensor combinations and the multiple data filtering and preprocessing technologies of the signal processing module improve the accuracy of the sensors in complex power environments, reduce monitoring false alarms or missed alarms, and ensure the accuracy and reliability of the blockage clearing effect.

[0036] (2) This kind of dust removal duct blockage clearing robot intelligent monitoring and management system, secondly, the fault prediction analysis module can calculate the fault prediction ratio coefficient Pfb for the key data from the sub-area blockage clearing data set, and extract the fault prediction ratio coefficient Pfb from the equipment operation time Rti, sensor data fluctuation rate Sde, motor temperature rise rate Mte and battery voltage decay rate Vde through the fault prediction ratio calculation unit, and then associate it with the temperature threshold Tyz and electromagnetic interference intensity Edy in the environmental feature extraction unit, and calculate the fault prediction value Fyz in real time through the fault warning generation unit; if the fault prediction value Fyz exceeds the fault tolerance threshold C, the system will issue a fault warning instruction to notify the operator to take preventive measures, so as to ensure timely prediction and fault avoidance before the failure occurs in the key task of the system, effectively improving the continuity of the blockage clearing process and the reliability of equipment operation.

[0037] (3) An intelligent monitoring and management system for a dust removal duct clearing robot. The intelligent management module sets a management threshold Qj to dynamically compare and evaluate the clearing effectiveness index Cjxzb in the monitoring sub-area. If the clearing effectiveness index Cjxzb is lower than the management threshold Qj, the system will adjust the clearing strategy according to the preset management intervention measures, including improving the monitoring accuracy by 10%, adjusting the clearing pressure Cp or the clearing time Ct, or dynamically adjusting the fault tolerance threshold C and the clearing effectiveness management threshold Qj. At the same time, the clearing robot is set to intermittent clearing mode in a timely manner to reduce the load in a high temperature or electromagnetic interference environment. This management method ensures the stable operation of the clearing robot in a high-difficulty environment, further optimizes the clearing effect, and improves the continuity of the overall dust removal process and the intelligence level of the management system. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention is a schematic diagram of the framework structure of an intelligent monitoring and management system for a dust removal and air duct clearing robot. DETAILED DESCRIPTION

[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Example 1

[0041] See also Figure 1 , the present invention provides an intelligent monitoring and management system for a dust removal air duct clearing robot, including a region division module, a data monitoring module, a signal processing module, a fault prediction and analysis module and an intelligent management module;

[0042] The area division module is used to pre-select a specific air duct area in the power plant, divide the specific air duct area into a plurality of monitoring sub-areas, and set monitoring points in the monitoring sub-areas;

[0043] The data monitoring module is used to monitor and record the high temperature environment related data, electromagnetic interference intensity related data and dust density related data in each monitoring sub-area; at the same time, the state related data of the clearing robot in each monitoring sub-area is monitored and collected and recorded in real time;

[0044] The signal processing module is used to convert all the data recorded in the data monitoring module into digital signals, process the digital signals through a signal filtering algorithm, perform preprocessing and storage through digital computing technology, and construct and store them into a sub-area clearing data set;

[0045] The fault prediction analysis module is used to respectively construct a fault prediction ratio coefficient Pfb, a temperature threshold Tyz, an electromagnetic interference intensity Edy, and a dust accumulation density Gdm; then the fault prediction value Fyz is obtained by calculation and evaluated, and a fault warning instruction is issued externally; secondly, according to the dust accumulation density Gdm in the monitoring sub-area, the clearing efficiency coefficient Qxcs is calculated, and the fault prediction value Fyz in the monitoring sub-area is associated with the clearing efficiency coefficient Qxcs, and the clearing effectiveness index Cjxzb in the corresponding monitoring sub-area is obtained by fitting;

[0046] The intelligent management module is used to pre-set the management threshold Qj, and compare and evaluate the clearing effectiveness index Cjxzb in the monitoring sub-area with the management threshold Qj. According to the evaluation results, management intervention measures are taken for the current monitoring sub-area to adjust the monitoring and management accuracy of the clearing robot in high temperature and electromagnetic interference environments.

[0047] In this embodiment, the area division module divides the specific air duct area in the power plant into several monitoring sub-areas and sets monitoring points, thereby realizing accurate monitoring of different blockage risk areas and improving the pertinence and efficiency of the clearing operation;

[0048] The data monitoring module monitors and records the data related to the high temperature environment, electromagnetic interference intensity, dust density in the pipe and the status of the clearing robot in real time. The data related to the high temperature environment include the thermal radiation intensity Hri, the temperature fluctuation frequency Twf and the local hot spot temperature increment Lht; the data related to the electromagnetic interference intensity include the electric field intensity peak Efp, the electromagnetic noise density End, the frequency change rate Fvr and the magnetic field intensity fluctuation amplitude Mwa; the data related to the dust density in the pipe include the microparticle concentration Pmc, the particle deposition rate Psr and the particle adhesion index Psa; the data related to the clearing robot status include the equipment operation time Rti, the sensor data fluctuation rate Sde, the motor temperature rise rate Mte and the battery voltage decay rate Vde; it ensures the comprehensive monitoring and data support of the clearing process and significantly improves the monitoring accuracy and data reliability;

[0049] The signal processing module uses analog-to-digital conversion and signal filtering algorithms to filter out high-frequency interference, suppress low-frequency noise, and compensate for errors in environmental data, effectively reducing data transmission delays in high-temperature or strong electromagnetic interference environments, and ensuring the real-time and stability of the operation feedback of the blockage clearing robot;

[0050] The fault prediction analysis module constructs the fault prediction ratio coefficient Pfb, and combines the temperature threshold Tyz, electromagnetic interference intensity Edy and dust density Gdm to dynamically generate the fault prediction value Fyz, and issue fault warning instructions in advance, effectively improving the system's fault prediction capability and the safety of the clearing process;

[0051] The intelligent management module dynamically adjusts the monitoring accuracy, clearing strategy, fault-tolerant threshold and clearing mode by comparing and evaluating the clearing effectiveness index Cjxzb and the management threshold Qj, thereby achieving precise management in complex high temperature and electromagnetic interference environments, ensuring the clearing efficiency of the system and the reliability of long-term operation.

[0052] Example 2

[0053] The area division module divides a specific air duct area into several groups of monitoring sub-areas based on the structural characteristics of the air duct, dust accumulation and blockage removal requirements; determines the distribution position and boundary range of each monitoring sub-area by preliminarily analyzing the dust accumulation distribution and blockage risk of the air duct, and divides the air duct into easy-to-block, relatively easy-to-block and regular areas according to the blockage possibility, temperature fluctuation and wind pressure difference information; then, deploys monitoring points in each monitoring sub-area.

[0054] The sensors deployed by the data monitoring module at the monitoring points in each monitoring sub-area include thermal sensors, electromagnetic sensors and laser particle sensors, which collect real-time data related to high temperature environment, electromagnetic interference intensity and dust density in the pipe; finally, the state-related data of the blockage clearing robot in each monitoring sub-area, including the robot motor operation data, are collected through the built-in state monitoring sensor; all data collected by the data monitoring module are recorded and transmitted to the signal processing module for preprocessing.

[0055] The signal processing module converts all the data collected by the data monitoring module into digital signals through an analog-to-digital converter, and uses a built-in signal filtering algorithm to filter out high-frequency interference, suppress low-frequency noise, and compensate for errors in the digital signals; subsequently, the digital signal after filtering is preprocessed using digital computing technology, including signal standardization, abnormal data removal, and data smoothing operations, and the preprocessed digital signal is partitioned and archived for storage;

[0056] Finally, the processed status-related data, high temperature environment-related data, electromagnetic interference intensity-related data, and dust density-related data in the pipe are summarized and stored to construct a sub-area clearing data set.

[0057] In this embodiment, the coordinated operation of various modules fully improves the real-time performance, stability and clearing efficiency of the system in complex environments; the area division module divides the specific air duct area into several monitoring sub-areas according to the structural characteristics of the air duct, dust accumulation and clearing requirements, realizes accurate clearing area division and monitoring, and improves the pertinence and efficiency of clearing work; the data monitoring module deploys thermal sensors, electromagnetic sensors and laser particle sensors at the monitoring points in each monitoring sub-area, collects high temperature environment related data, electromagnetic interference intensity related data and dust density related data in the pipe in real time, and records the clearing robot status related data through the status monitoring sensor, including the robot motor status data. The operation data provides the system with accurate and comprehensive monitoring information; the signal processing module converts all analog data into digital signals through an analog-to-digital converter, and uses a signal filtering algorithm to filter out high-frequency interference and low-frequency noise, and combines digital computing technology to perform pre-processing operations such as signal standardization, abnormal data elimination and data smoothing, and finally constructs a sub-area clearing data set to ensure the integrity and accuracy of the clearing data in each sub-area; through the collaborative work of various modules, the system realizes efficient monitoring and data processing during the operation of the clearing robot, effectively improving the intelligence level of dust removal duct clearing and the safety of equipment operation, ensuring the overall clearing efficiency and long-term operation of the system.

[0058] Example 3

[0059] The fault prediction analysis module includes a fault prediction ratio calculation unit, an environmental feature extraction unit, a fault warning generation unit, a blockage clearing efficiency calculation unit and a blockage clearing effectiveness calculation unit;

[0060] The fault prediction ratio calculation unit calculates the fault prediction ratio coefficient Pfb by extracting the state-related data in the sub-area clearing data set, including the equipment operation time Rti, the sensor data fluctuation rate Sde, the motor temperature rise rate Mte and the battery voltage decay rate Vde. The specific calculation formula is as follows: .

[0061] The environmental feature extraction unit is used to extract feature data from the sub-region clearing data set, including high temperature environment related data, electromagnetic interference intensity related data and dust accumulation density related data in the pipe;

[0062] Finally, the temperature threshold Tyz, electromagnetic interference intensity Edy and dust density Gdm in the corresponding partition are calculated by the following formulas: ; ; ;

[0063] In the formula, Hri represents the thermal radiation intensity in the sub-region clearing data set, Twf represents the temperature fluctuation frequency in the sub-region clearing data set, and Lht represents the local hot spot temperature increment in the sub-region clearing data set; ln represents the natural logarithm, which is used to compress the range of the local hot spot temperature increment Lht in the formula;

[0064] Efp represents the peak value of the electric field intensity in the sub-region clearing data set, End represents the electromagnetic noise density in the sub-region clearing data set, Fvr represents the frequency change rate in the sub-region clearing data set, and Mwa represents the fluctuation amplitude of the magnetic field intensity in the sub-region clearing data set;

[0065] Pmc represents the concentration of microparticles in the sub-region clearing data set, Psr represents the particle deposition rate in the sub-region clearing data set, and Psa represents the particle adhesion index in the sub-region clearing data set.

[0066] The fault warning generation unit is used to associate the fault prediction ratio coefficient Pfb with the corresponding temperature threshold Tyz and the electromagnetic interference intensity Edy, and obtain the fault prediction value Fyz by calculating the following formula: ;

[0067] The preset fault tolerance threshold C and fault prediction value Fyz are used for evaluation, and a fault warning instruction is issued. The specific evaluation contents are as follows:

[0068] If the fault prediction value Fyz>fault tolerance threshold C, it means that the current environment and equipment status are abnormal. At this time, the system starts the fault warning mechanism, automatically issues a fault warning instruction, and notifies the operator to take preventive measures;

[0069] If the fault prediction value Fyz ≤ the fault tolerance threshold C, it means that the current environment and equipment status are normal, the system maintains a normal monitoring state, and no additional intervention measures are required.

[0070] The blockage clearing efficiency calculation unit extracts the blockage clearing time Ct, blockage clearing pressure Cp and residual dust accumulation rate Rd in the sub-region blockage clearing data set based on the dust accumulation density Gdm, and obtains the blockage clearing efficiency coefficient Qxcs through the following formula: .

[0071] The blockage clearing effectiveness calculation unit is used to associate the fault prediction value Fyz in the monitoring sub-area with the blockage clearing efficiency coefficient Qxcs, and obtain the blockage clearing effectiveness index Cjxzb in the corresponding monitoring sub-area by fitting, which is specifically shown as follows; .

[0072] In this embodiment, the coordinated operation of each unit significantly improves the fault prediction capability and blockage clearing efficiency of the system in complex environments; the fault prediction ratio calculation unit extracts the equipment operation time Rti, sensor data fluctuation rate Sde, motor temperature rise rate Mte and battery voltage decay rate Vde, and calculates the fault prediction ratio coefficient Pfb, ensuring that the potential possibility of faults is accurately reflected when monitoring the key operating state of the blockage clearing robot, and providing a comprehensive equipment operation trend analysis;

[0073] The environmental feature extraction unit extracts data related to high temperature environment, electromagnetic interference intensity and dust density in the pipe, and calculates temperature threshold Tyz, electromagnetic interference intensity Edy and dust density Gdm from fine parameters such as thermal radiation intensity Hri, temperature fluctuation frequency Twf, local hot spot temperature increment Lht, electric field intensity peak Efp, electromagnetic noise density End, frequency change rate Fvr, magnetic field intensity fluctuation amplitude Mwa, microparticle concentration Pmc, particle deposition rate Psr and particle adhesion index Psa, so as to accurately analyze the impact of various environmental variables on equipment failure;

[0074] The fault warning generation unit associates the fault prediction ratio coefficient Pfb with the temperature threshold Tyz and the electromagnetic interference intensity Edy, calculates the fault prediction value Fyz, and compares it with the preset fault tolerance threshold C. When the fault prediction value Fyz exceeds the tolerance threshold C, the system automatically issues a warning instruction to achieve early identification and effective prevention of fault risks;

[0075] The clearing efficiency calculation unit extracts the clearing time Ct, clearing pressure Cp and residual dust accumulation rate Rd based on the dust density Gdm, and calculates the clearing efficiency coefficient Qxcs through the formula, which clearly measures the efficiency level of clearing operations under different dust accumulation conditions;

[0076] The clearing effectiveness calculation unit obtains the clearing effectiveness index Cjxzb by fitting the fault prediction value Fyz and the clearing efficiency coefficient Qxcs. Based on this, the system comprehensively evaluates the overall effectiveness of the clearing process under specific environmental conditions to ensure the accuracy, stability and safety of the clearing operation in a complex power environment.

[0077] Example 4

[0078] The intelligent management module is used to compare and evaluate the clearing effectiveness index Cjxzb with the management threshold Qj, and take management intervention measures within the monitoring sub-area. The specific contents are as follows:

[0079] When the clearing effectiveness index Cjxzb ≥ the management threshold Qj, it means that the efficiency and safety of the clearing process in the current monitoring sub-area under high temperature and electromagnetic interference environment have met the requirements, and the system does not need further intervention and only maintains the normal monitoring state;

[0080] When the clearing effectiveness index Cjxzb is less than the management threshold Qj, it means that the efficiency and safety of the clearing process in the current monitoring sub-area under high temperature and electromagnetic interference environment do not meet the requirements. At this time, the system initiates management intervention measures, including enhancing the monitoring accuracy by 10% on the original basis, adjusting the clearing strategy, adjusting the dynamic fault tolerance threshold, and adjusting the clearing robot to intermittent clearing mode, and adjusting the load of the clearing robot under high temperature or high interference conditions during the rest time.

[0081] In this embodiment, by real-time comparative evaluation of the clearing effectiveness index Cjxzb and the management threshold Qj, the clearing efficiency and safety of the system in high temperature and electromagnetic interference environments are comprehensively improved; when the clearing effectiveness index Cjxzb reaches or exceeds the management threshold Qj, the system maintains a normal monitoring state, saving resources and avoiding unnecessary intervention; when the clearing effectiveness index Cjxzb is lower than the management threshold Qj, the intelligent management module immediately starts management intervention measures, including enhancing the monitoring accuracy by 10%, adjusting the clearing strategy, dynamically adjusting the fault tolerance threshold, and setting an intermittent clearing mode, thereby effectively reducing the workload of the clearing robot under high temperature or high interference conditions and ensuring the long-term stability of the equipment; the significance of this module in the system is to realize the refined management of the clearing process, ensure the stable operation of the equipment in a complex power environment, and improve the reliability and efficiency of the clearing work through reasonable dynamic adjustment measures, which is conducive to the intelligence and efficiency of the system.

[0082] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring and management system for a dust removal duct clearing robot, characterized in that: It includes area division module, data monitoring module, signal processing module, fault prediction and analysis module and intelligent management module; The area division module is used to pre-select a specific air duct area in the power plant, divide the specific air duct area into a plurality of monitoring sub-areas, and set monitoring points in the monitoring sub-areas; The data monitoring module is used to monitor and record the high temperature environment related data, electromagnetic interference intensity related data and dust density related data in each monitoring sub-area; at the same time, the state related data of the clearing robot in each monitoring sub-area is monitored and collected and recorded in real time; The signal processing module is used to convert all the data recorded in the data monitoring module into digital signals, process the digital signals through a signal filtering algorithm, perform preprocessing and storage through digital computing technology, and construct and store them into a sub-area clearing data set; The fault prediction analysis module is used to respectively construct a fault prediction ratio coefficient Pfb, a temperature threshold Tyz, an electromagnetic interference intensity Edy, and a dust accumulation density Gdm; then the fault prediction value Fyz is obtained by calculation and evaluated, and a fault warning instruction is issued externally; secondly, according to the dust accumulation density Gdm in the monitoring sub-area, the clearing efficiency coefficient Qxcs is calculated, and the fault prediction value Fyz in the monitoring sub-area is associated with the clearing efficiency coefficient Qxcs, and the clearing effectiveness index Cjxzb in the corresponding monitoring sub-area is obtained by fitting; The intelligent management module is used to pre-set the management threshold Qj, and compare and evaluate the clearing effectiveness index Cjxzb in the monitoring sub-area with the management threshold Qj. According to the evaluation results, management intervention measures are taken for the current monitoring sub-area to adjust the monitoring and management accuracy of the clearing robot in high temperature and electromagnetic interference environments.

2. According to claim 1, the intelligent monitoring and management system for dust removal and air duct clearing robot is characterized by: The area division module divides a specific air duct area into several groups of monitoring sub-areas based on the structural characteristics of the air duct, dust accumulation and blockage removal requirements; determines the distribution position and boundary range of each monitoring sub-area by preliminarily analyzing the dust accumulation distribution and blockage risk of the air duct, and divides the air duct into easy-to-block, relatively easy-to-block and regular areas according to the blockage possibility, temperature fluctuation and wind pressure difference information; then, deploys monitoring points in each monitoring sub-area.

3. According to claim 1, the intelligent monitoring and management system for dust removal and air duct clearing robot is characterized by: The sensors deployed by the data monitoring module at the monitoring points in each monitoring sub-area include thermal sensors, electromagnetic sensors and laser particle sensors, which collect real-time data related to high temperature environment, electromagnetic interference intensity and dust density in the pipe; finally, the state-related data of the blockage clearing robot in each monitoring sub-area, including the robot motor operation data, are collected through the built-in state monitoring sensor; all data collected by the data monitoring module are recorded and transmitted to the signal processing module for preprocessing.

4. According to claim 1, the intelligent monitoring and management system for dust removal and air duct clearing robot is characterized by: The signal processing module converts all the data collected by the data monitoring module into digital signals through an analog-to-digital converter, and uses a built-in signal filtering algorithm to filter out high-frequency interference, suppress low-frequency noise, and compensate for errors in the digital signals; subsequently, the digital signal after filtering is preprocessed using digital computing technology, including signal standardization, abnormal data removal, and data smoothing operations, and the preprocessed digital signal is partitioned and archived for storage; Finally, the processed status-related data, high temperature environment-related data, electromagnetic interference intensity-related data, and dust density-related data in the pipe are summarized and stored to construct a sub-area clearing data set.

5. According to claim 1, the intelligent monitoring and management system for dust removal and air duct clearing robot is characterized by: The fault prediction analysis module includes a fault prediction ratio calculation unit, an environmental feature extraction unit, a fault warning generation unit, a blockage clearing efficiency calculation unit and a blockage clearing effectiveness calculation unit; The fault prediction ratio calculation unit calculates the fault prediction ratio coefficient Pfb by extracting the state-related data in the sub-area clearing data set, including the equipment operation time Rti, the sensor data fluctuation rate Sde, the motor temperature rise rate Mte and the battery voltage decay rate Vde. The specific calculation formula is as follows: 。 6. According to claim 5, the intelligent monitoring and management system for dust removal and air duct clearing robot is characterized by: The environmental feature extraction unit is used to extract feature data from the sub-region clearing data set, including high temperature environment related data, electromagnetic interference intensity related data and dust accumulation density related data in the pipe; Finally, the temperature threshold Tyz, electromagnetic interference intensity Edy and dust density Gdm in the corresponding partition are calculated by the following formulas: ； ； ； In the formula, Hri represents the thermal radiation intensity in the sub-region clearing data set, Twf represents the temperature fluctuation frequency in the sub-region clearing data set, and Lht represents the local hot spot temperature increment in the sub-region clearing data set; ln represents the natural logarithm, which is used to compress the range of the local hot spot temperature increment Lht in the formula; Efp represents the peak value of the electric field intensity in the sub-region clearing data set, End represents the electromagnetic noise density in the sub-region clearing data set, Fvr represents the frequency change rate in the sub-region clearing data set, and Mwa represents the fluctuation amplitude of the magnetic field intensity in the sub-region clearing data set; Pmc represents the concentration of microparticles in the sub-region clearing data set, Psr represents the particle deposition rate in the sub-region clearing data set, and Psa represents the particle adhesion index in the sub-region clearing data set.

7. The intelligent monitoring and management system for dust removal and air duct clearing robot according to claim 6 is characterized by: The fault warning generation unit is used to associate the fault prediction ratio coefficient Pfb with the corresponding temperature threshold Tyz and the electromagnetic interference intensity Edy, and obtain the fault prediction value Fyz by calculating the following formula: ； The preset fault tolerance threshold C and fault prediction value Fyz are used for evaluation, and a fault warning instruction is issued. The specific evaluation contents are as follows: If the fault prediction value Fyz>fault tolerance threshold C, it means that the current environment and equipment status are abnormal. At this time, the system starts the fault warning mechanism, automatically issues a fault warning instruction, and notifies the operator to take preventive measures; If the fault prediction value Fyz ≤ the fault tolerance threshold C, it means that the current environment and equipment status are normal, the system maintains a normal monitoring state, and no additional intervention measures are required.

8. The intelligent monitoring and management system for dust removal and air duct clearing robot according to claim 7 is characterized by: The blockage clearing efficiency calculation unit extracts the blockage clearing time Ct, blockage clearing pressure Cp and residual dust accumulation rate Rd in the sub-region blockage clearing data set based on the dust accumulation density Gdm, and obtains the blockage clearing efficiency coefficient Qxcs through the following formula: 。 9. The intelligent monitoring and management system for dust removal and air duct clearing robot according to claim 8 is characterized by: The blockage clearing effectiveness calculation unit is used to associate the fault prediction value Fyz in the monitoring sub-area with the blockage clearing efficiency coefficient Qxcs, and obtain the blockage clearing effectiveness index Cjxzb in the corresponding monitoring sub-area by fitting, which is specifically shown as follows; 。 10. The intelligent monitoring and management system for dust removal and air duct clearing robot according to claim 1, characterized in that: The intelligent management module is used to compare and evaluate the clearing effectiveness index Cjxzb with the management threshold Qj, and take management intervention measures within the monitoring sub-area. The specific contents are as follows: When the clearing effectiveness index Cjxzb ≥ the management threshold Qj, it means that the efficiency and safety of the clearing process in the current monitoring sub-area under high temperature and electromagnetic interference environment have met the requirements, and the system does not need further intervention and only maintains the normal monitoring state; When the clearing effectiveness index Cjxzb is less than the management threshold Qj, it means that the efficiency and safety of the clearing process in the current monitoring sub-area under high temperature and electromagnetic interference environment do not meet the requirements. At this time, the system initiates management intervention measures, including enhancing the monitoring accuracy by 10% on the original basis, adjusting the clearing strategy, adjusting the dynamic fault tolerance threshold, and adjusting the clearing robot to intermittent clearing mode, and adjusting the load of the clearing robot under high temperature or high interference conditions during the rest time.

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