Intelligent control method for grille cleaner

Abstract

The invention provides an intelligent control method for a bar screen machine, and relates to the technical field of sewage treatment, and the method comprises the steps: building a prediction model which is used for carrying out the trend prediction of the future trend of the data of the bar screen machine in a past period of time; cameras are additionally arranged inside and around the grille cleaner to shoot video images, and pictures of fault phenomena are collected from the video images; a fault early warning system is constructed, and alarm event information is generated in time by adopting a means of combining video real-time monitoring with mutual verification of a prediction model so as to feed back an equipment operation state and potential risks; and according to alarm event information fed back by the fault early warning system, the self-cleaning capability of the grille cleaner is enhanced. According to the method, the alarm event information is generated in time by adopting the means of combining video real-time monitoring with the prediction model to feed back the equipment operation state and the potential risk, the operation state of the bar screen machine is controlled, the self-cleaning capability of the bar screen machine is enhanced, the operation and maintenance efficiency is enhanced, and the maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of sewage treatment, and in particular to an intelligent control method for a grid dirt remover. Background Art

[0002] Before entering the secondary treatment structure of a sewage treatment plant, wastewater is typically pre-treated by screens (or screen scrubbers) to remove as much material as possible that is unfavorable to subsequent treatment due to its nature or size. When using traditional secondary sewage treatment processes (primarily AAO, oxidation ditch, and BR processes, as well as their improved versions), screens primarily separate and remove coarse matter. When using membrane and filter tank processes (primarily MBR membrane treatment processes), screens place even higher demands on the removal of fine fibers such as hair.

[0003] In view of the harsh environment of sewage treatment sites and the easy breeding of microorganisms harmful to the human body, engineers have developed an intelligent system suitable for the control and management of screen decontamination systems. It uses cloud computing, intelligent control management algorithms and other technologies to intelligently manage various equipment in the sewage treatment workshop. The staff can remotely query and adjust the real-time operation status of the equipment and processes in the sewage workshop through the cloud computing platform, thereby realizing the intelligent operation of the sewage treatment process.

[0004] However, the management system currently used controls the overall process operation of the screen decontamination system and is mostly used to adjust the system's process parameters, such as adjusting the operating parameters of equipment such as pumps and fans in the system. However, there is no complete intelligent control system for monitoring and intelligent control of the operating status of individual equipment such as the screen decontamination machine in the screen decontamination system. It is also impossible to judge the operating status and potential risks of the screen decontamination machine, which has caused difficulties in the operation and maintenance of the screen decontamination machine and made the pretreatment effect of the water plant unstable.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0006] In response to the above situation, the present invention provides an intelligent control method for a screen sewage remover, aiming to solve the problem that the currently used management system controls the overall process operation of the screen sewage removal system, and is mostly used to adjust the process parameters of the system, such as adjusting the operating parameters of equipment such as pumps and fans in the system. However, there is no complete intelligent control system for monitoring and intelligent control of the operating status of individual equipment such as the screen sewage remover in the screen sewage removal system, and it is also impossible to judge the operating status and potential risks of the screen sewage remover, which creates difficulties for the operation and maintenance of the screen sewage remover and makes the pretreatment effect of the water plant unstable.

[0007] To achieve the above object, the present invention provides the following technical solutions: The present invention provides an intelligent control method for a grid dirt remover, comprising: Step 1: Establish a prediction model, which is used to predict the future trend of the data of the screen decontamination machine over a period of time; Step 2: Conduct real-time video monitoring; install cameras inside and around the grille decontamination machine to capture video images, and collect photos of the fault phenomenon from the video images; Step 3: Build a fault warning system that uses real-time video monitoring combined with predictive model verification to generate alarm event information in a timely manner to provide feedback on equipment operating status, potential risks, and faults. Step 4: According to the alarm event information fed back by the fault warning system, the operating state of the screen cleaner is controlled to enhance the self-cleaning capability of the screen cleaner.

[0008] In some embodiments of the present invention, in step 4, the operation state of the screen cleaner is controlled according to the alarm event information fed back by the fault warning system to enhance the self-cleaning capability of the screen cleaner, including: Build a grid intelligent control cloud platform to remotely control the operating status of the grid decontamination machine based on the alarm event information fed back by the fault warning system, and enhance the self-cleaning ability of the grid decontamination machine.

[0009] In some embodiments of the present invention, in step 4, after the fault warning system returns alarm event information, detailed alarm content is retrieved through human-computer interaction and corresponding processing actions are taken. Processing actions include review, ignore, and accept. If ignoring, the reason for ignoring is entered. If accepting, the processing is carried out. After the specific processing content is entered, the closed-loop processing is completed and the alarm event is resolved.

[0010] In some embodiments of the present invention, the collected video images and monitoring data are used to build an equipment operation and maintenance knowledge base, which uses large-model natural language processing and machine learning technologies to provide intelligent retrieval services to improve the efficiency of human-computer interaction.

[0011] In some embodiments of the present invention, step 1 includes: Step 11: Establish a monitoring point at a designated location of the moving component of the grid decontamination system, and install a monitoring sensor at the monitoring point. The monitoring sensor is used to monitor the movement state of the moving component.

[0012] Step 12: Combine the monitoring data fed back by the monitoring sensor to obtain equipment information data; the equipment information data includes three types of information data: basic equipment information data, equipment stage adjustment information data, and equipment operation status data.

[0013] Step 13: Use the equipment information data to build a database, build a time domain waveform diagram, frequency spectrum diagram and envelope spectrum through the monitoring data fed back by each monitoring sensor, and form a data model based on the equipment information data to analyze abnormal data and determine the equipment fault point.

[0014] Step 14: Based on the monitoring data fed back by the monitoring sensors, analyze the changes in the characteristics of the equipment status data over time, thereby obtaining historical status monitoring data and storing it in a database. The historical status monitoring data includes the degradation trend of moving parts and the changes in the equipment operation stability; By performing trend analysis and curve fitting on historical status monitoring data, the changing trend of relevant characteristic quantities is obtained, and then a prediction model is established based on the data model using the data changing trend characteristics; and the future trend of the data is predicted by the prediction model.

[0015] In some embodiments of the present invention, the screen cleaner is a perforated screen cleaner; in step 11, monitoring points are established at the following locations, and vibration sensors are installed at each monitoring point: The output end bearing position of the main motor, the input end bearing position of the gearbox, and the bearing positions at both ends of the main drive shaft; the output end bearing position of the pump motor; the output end bearing position of the reduction motor, the input end bearing position of the pressing gearbox, and the bearing positions at both ends of the screw shaft.

[0016] In some embodiments of the present invention, step 4 also includes: establishing a pump pressure curve, a liquid level difference curve, and a water quality and water quantity change curve through a database; if at least one of the above three curves is abnormal, increasing the instantaneous speed of the main drive shaft of the orifice plate screen cleaner and increasing the water flux of the orifice plate screen cleaner.

[0017] In some embodiments of the present invention, the equipment information data includes factory data such as equipment specifications, models, power, etc.; the equipment stage adjustment information data includes adjustment information such as parameter adjustment records and maintenance records; the equipment operation status data includes the remaining life of the equipment and the monitoring data.

[0018] In some embodiments of the present invention, in step 13, the time domain waveform describes the fault characteristics as follows: When there is dynamic imbalance, it manifests as a typical sine wave in one cycle, which indicates that among the moving parts of the screen decontamination system, the main transmission structure of the orifice plate screen decontamination machine has parts worn or loose transmission; Poor alignment will manifest as a time domain waveform of a 1x or 2x sine wave, or in severe cases, a 3x to 5x sine wave. The superimposed time domain waveform will resemble an "M" or "W" waveform, with stable and repeatable waveforms. If these waveforms appear, it is initially suspected that the equipment has a deformation or looseness in the transmission component, or that the equipment is not level with the ground. Subsequent inspection and verification will confirm whether the fault is caused by a deformation or looseness in the transmission component, or by the equipment being not level with the ground. When friction occurs between the main drive shaft and the orifice plate during transmission, the waveform will be rough, unstable or clipped; When a fault occurs in the bearings at the output end of the main motor, the input end of the gearbox, or the bearings at both ends of the main drive shaft, the time domain waveform shows regular impact signals, with the signal interval being the defect frequency of the inner and outer rings, rolling elements, and cages of the bearings or an impact signal at 1 times the rotational frequency; When a fault occurs in the gears at the output end bearing position of the reduction motor, the input end bearing position of the pressing gear box, or the bearing positions at both ends of the screw shaft, a regular rotation frequency impact signal will appear in the time domain waveform.

[0019] In some embodiments of the present invention, in step 13, the spectrum diagram can intuitively express which frequency components the signal is composed of and the amplitude of each component, analyze the relationship between the signals, and then find the source of vibration noise to perform automatic diagnosis and analysis of the faulty equipment, and timely discover potential faults of the equipment; and / or, in step 13, the envelope spectrum is sensitive to events related to impact force, and is used to diagnose early defects of components such as the inner ring, outer ring, rolling element and cage of the bearing at each monitoring point, or effectively identify damaged teeth on the gear through the pulse signal of the gear meshing frequency. The embodiments of the present invention have at least the following advantages or beneficial effects: 1. This application adopts a monitoring method that combines real-time video monitoring with a predictive model to generate alarm event information in a timely manner to feedback the equipment's operating status and potential risks and faults; based on the alarm event information feedback from the fault warning system, the operating status of the screen cleaner is controlled to enhance the self-cleaning ability of the screen cleaner, thereby improving operation and maintenance efficiency, reducing downtime, and reducing maintenance costs.

[0020] 2. Real-time video monitoring and predictive models can verify each other to ensure the accuracy of the maintenance measures taken.

[0021] Other features and advantages of the present invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 It is a structural diagram of a perforated plate type screen decontamination machine; Figure 2 This is a schematic diagram of the monitoring point distribution of the main transmission structure of the perforated plate screen decontamination machine; Figure 3 This is a schematic diagram of the monitoring point distribution of the grid decontamination system; Figure 4 This is a schematic diagram of the monitoring point distribution of the pressing device; Figure 5 Schematic diagram of the flow chart of the intelligent control method for the screen decontamination machine.

[0024] Icons: 1-bearing position of the output end of the main motor, 2-bearing position of the input end of the gearbox, 3-bearing position of one end of the main drive shaft, 4-bearing position of the other end of the main drive shaft, 5-bearing position of the output end of the pump motor, 6-bearing position of the output end of the reduction motor, 7-bearing position of the input end of the pressing gearbox, 8-bearing position of one end of the screw shaft, 9-bearing position of the other end of the screw shaft. DETAILED DESCRIPTION

[0025] In the following, only certain exemplary embodiments are briefly described. As those skilled in the art would realize, the described embodiments may be modified in various different ways without departing from the spirit or scope of the embodiments of the present invention.

[0026] In the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "installed," "connected," "connected," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; they may refer to direct connection or indirect connection through an intermediate medium; they may refer to internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on specific circumstances.

[0027] The embodiments of the present invention are described in detail below.

[0028] Example 1

[0029] First, see Figures 1 to 4 , this embodiment introduces a grille dirt removal system, which includes a perforated plate grille dirt remover, a flushing device and a pressing device.

[0030] The perforated plate screen cleaner is used to filter impurities from sewage, removing water-containing impurities. It consists of a main drive mechanism and a transmission mechanism. The main drive mechanism includes a main motor, a gearbox, and a main drive shaft, which are sequentially connected. The transmission mechanism connects the main drive shaft to the perforated plate via a chain or track.

[0031] More specifically, from a holistic perspective, the orifice plate screen cleaner consists of a continuously rotating orifice plate driven by a motor placed in a fixed frame; the sewage to be filtered enters from the central opening of the equipment, passes through the orifice plates on both sides from the inside to the outside to filter out impurities, and then flows out. The orifice plate rotates, and the impurities deposited on the inside of the orifice plate are lifted by the lifting plate on the orifice plate to the top of the screen discharge area, and are washed into the screen collection tank by the flushing water, and are discharged to the pressing device by the spiral conveying device built into the screen collection tank.

[0032] The flushing device is used for cleaning the perforated plate type grille decontamination machine. The flushing device comprises a flushing pump and a flushing nozzle connected thereto, wherein the flushing pump has a pump motor.

[0033] The squeezing device is used to achieve solid-liquid separation of water-containing impurities. It comprises a reduction motor, a squeezing gearbox, and a screw shaft, all connected in sequence. Specifically, the output of the reduction motor is connected to the input of the squeezing gearbox, which in turn is connected to one end of the screw shaft. The reduction motor drives the screw shaft, which has a spiral structure that pushes and squeezes water-containing impurities entering the squeezing device, achieving solid-liquid separation.

[0034] It should be noted that the screen cleaning system has moving parts, and the above-mentioned main engine drive mechanism, transmission mechanism, pump motor, reduction motor, pressing gear box and screw shaft are part of the moving parts; the perforated screen cleaning machine is a type of screen cleaning machine, and other types of screen cleaning machines can also be used in the screen cleaning system.

[0035] Second, see Figures 1 to 5 This embodiment provides an intelligent control method for a grid dirt remover, comprising the following steps: Step 1: Establish a prediction model. The prediction model is used to predict the future trend of the screen decontamination machine data over a period of time.

[0036] Step 2: Conduct real-time video monitoring; install cameras inside and around the grille cleaner to capture video images, and collect photos of the fault phenomenon from the video images.

[0037] Step 3: Build a fault warning system that uses real-time video monitoring combined with predictive model verification to generate alarm event information in a timely manner to provide feedback on equipment operating status, potential risks, and faults. The way in which real-time video monitoring and prediction models verify each other is as follows: Method 1: When the prediction model predicts that the data will reach the alarm value within a certain period of time in the future, the alarm is triggered through video verification if there is indeed a problem; Method 2: When a fault phenomenon is detected based on the video image, if the prediction model also predicts that the corresponding data will reach the alarm value within a period of time in the future, an alarm is triggered.

[0038] Step 4: According to the alarm event information fed back by the fault warning system, the operating state of the grille cleaner is controlled to enhance the self-cleaning capability of the grille cleaner; After the fault warning system reports an alarm event, human-computer interaction retrieves detailed alarm information and takes appropriate action. Actions include review, ignore, and accept. If the action is ignored, a reason is entered. If the action is accepted, the action is taken. Entering the specific action completes the closed-loop process and the alarm is resolved. A knowledge base for equipment operation and maintenance is constructed using collected video images and monitoring data. This knowledge base utilizes large-scale natural language processing and machine learning technologies to provide intelligent retrieval services, improving the efficiency of human-computer interaction.

[0039] In step 4, according to the alarm event information fed back by the fault warning system, the operating state of the grille dirt remover is controlled and the self-cleaning ability of the grille dirt remover is enhanced, including: building a grille intelligent control cloud platform, remotely controlling the operating state of the grille dirt remover according to the alarm event information fed back by the fault warning system, and enhancing the self-cleaning ability of the grille dirt remover.

[0040] In this embodiment, step 1 includes: Step 11: Establish a monitoring point at a designated location of the moving component of the grid decontamination system, and install a monitoring sensor at the monitoring point. The monitoring sensor is used to monitor the movement state of the moving component.

[0041] Step 12: Combine the monitoring data fed back by the monitoring sensor to obtain equipment information data; the equipment information data includes three types of information data: basic equipment information data, equipment stage adjustment information data, and equipment operation status data.

[0042] Step 13: Use the equipment information data to build a database, build a time domain waveform diagram, frequency spectrum diagram and envelope spectrum through the monitoring data fed back by each monitoring sensor, and form a data model based on the equipment information data to analyze abnormal data and determine the equipment fault point.

[0043] Step 14: Based on the monitoring data fed back by the monitoring sensors, analyze the changes in the characteristics of the equipment status data over time, thereby obtaining historical status monitoring data and storing it in a database. The historical status monitoring data includes the degradation trend of moving parts and the changes in the equipment operation stability; By performing trend analysis and curve fitting on historical status monitoring data, the changing trend of relevant characteristic quantities is obtained, and then a prediction model is established based on the data model using the data changing trend characteristics; and the future trend of the data is predicted by the prediction model.

[0044] This embodiment implements predictive maintenance and intelligent control of the grille through hardware operating status monitoring, video image fault diagnosis, and a back-end intelligent operation platform (the grille intelligent control cloud platform), thereby improving the operation and maintenance efficiency of the grille decontamination machine and the stability of the equipment operation. In addition, this embodiment has at least the following beneficial effects: (1) Improve operation and maintenance efficiency: Establish a prediction model based on the database to accurately locate the equipment's fault points and trace the root causes of the faults, provide a scientific basis for equipment maintenance decisions, reduce the workload of maintenance personnel, improve efficiency, reduce costs and increase efficiency, and gradually achieve less-manned or even unmanned operation; (2) Reduce downtime: Use scientific condition monitoring methods to understand the equipment operating status in real time, predict equipment failures in advance, monitor equipment failure deterioration trends, and effectively reduce equipment downtime, especially the huge losses caused by unplanned downtime; (3) Reduce maintenance costs: avoid "under-maintenance" and "over-maintenance", reduce unnecessary disassembly work, and reduce maintenance costs; (4) Maintenance quality assessment: After maintenance, the movement status of moving parts can continue to be monitored to help evaluate the quality of the equipment after maintenance and ensure the reliability of the equipment maintenance quality; (5) Extending equipment life: Monitor and predict the entire life cycle of the equipment and its key components, and take appropriate minor and medium repair maintenance measures to effectively extend the service life of the equipment and improve equipment utilization (simple repairs are sufficient before a breakdown, while major repairs are required after a breakdown).

[0045] Example 2

[0046] In this embodiment, the above-mentioned perforated plate type grille decontamination machine is used in the grille decontamination system.

[0047] See also Figures 1 to 5 In step 11, establish monitoring points at the following locations and install vibration sensors at each monitoring point: The output end bearing position of the main motor, the input end bearing position of the gearbox, and the bearing positions at both ends of the main drive shaft; the output end bearing position of the pump motor; the output end bearing position of the reduction motor, the input end bearing position of the pressing gearbox, and the bearing positions at both ends of the screw shaft.

[0048] In step 12, the equipment information data specifically includes factory data such as equipment specifications, model, and power; equipment phase adjustment information data includes adjustment information such as parameter adjustment records and maintenance records; and equipment operating status data includes the remaining equipment life and the aforementioned monitoring data. The method for obtaining the basic equipment information data, the factory data of the equipment phase adjustment information data, and the remaining equipment life is not limited.

[0049] In step 13, the time domain waveform describes the fault characteristics as follows: 1) When dynamic imbalance occurs, it manifests as a typical sine wave within one cycle, which indicates that there is wear or loosening of components in the moving parts of the screen decontamination system, such as the main transmission structure of the orifice screen decontamination machine; 2) Poor alignment (used to determine the levelness and stability of the equipment's shaft transmission) manifests itself as a time domain waveform of a 1x sinusoidal wave, a 2x sinusoidal wave, and in severe cases, a 3x to 5x sinusoidal wave. The superimposed time domain waveform resembles an "M" or "W" waveform, with stable and repeatable waveforms. If these waveforms appear, it is preliminarily determined that the equipment has a problem with deformation or loosening of transmission components, or that the equipment as a whole is not level with the ground. Subsequent verification through manual inspection and other methods will confirm whether the equipment fault is a problem with deformation or loosening of transmission components, or that the equipment as a whole is not level with the ground. 3) When friction occurs between the main drive shaft and the orifice plate during transmission, the waveform will be rough, unstable or clipped; 4) When a bearing fault occurs at the output end bearing position of the main motor, the input end bearing position of the gearbox, or the bearing positions at both ends of the main drive shaft, a regular impact signal will appear in the time domain waveform. The signal interval is mostly the defect frequency of the inner and outer rings, rolling elements, cages, etc. of the bearing, or an impact signal of 1 times the rotational frequency (the rotational frequency is the shaft speed of the damaged gear); 5) When a fault occurs in the gears at the output bearing position of the reduction motor, the input bearing position of the pressing gearbox, or the bearing positions at both ends of the screw shaft, a regular frequency shock signal will appear in the time domain waveform (the frequency is the shaft speed where the damaged gear is located).

[0050] In step 13, the spectrum diagram can intuitively express which frequency components the signal is composed of and the amplitude of each component, analyze the relationship between the signals, and then find the source of vibration noise to perform automatic diagnosis and analysis of the faulty equipment, and timely discover potential faults of the equipment.

[0051] In step 13, the envelope spectrum is primarily sensitive to events related to impact force and can be used to diagnose early defects in bearing (rolling bearing) inner rings, outer rings, rolling elements, cages, and other components at each monitoring point. It can also effectively identify damaged teeth such as cracks on gears through pulse signals of the gear meshing frequency.

[0052] In step 2, the fault phenomena include: garbage overflow, orifice plate blockage, etc.; Garbage overflow refers to the overflow of garbage from the entire machine chassis and the screen slag discharge area, indicating that the equipment is entangled; Orifice plate blockage refers to the blockage of the orifice plate.

[0053] Example 3

[0054] See also Figures 1 to 5 The difference between this embodiment and embodiment 2 is that, in step 4, in addition to using the above-mentioned prediction model to achieve predictive maintenance, the following method is also adopted to adjust the operating state of the perforated plate type screen decontamination machine by judging the working condition of the equipment to improve its self-cleaning ability: Establish pump pressure curve, liquid level difference curve, water quality and quantity change curve through database; The pump pressure curve reflects whether the flushing pump or flushing nozzle is blocked by the change in the pump pressure of the flushing pump. If the pump pressure increases, it means that there is a blockage. The liquid level difference curve is used to reflect the blockage of the orifice plate. If the liquid level difference decreases to the alarm value, it means that the orifice plate is blocked. The water quality and water quantity change curve can reflect the changes in the water quality and water quantity of the water to be filtered at the front end of the orifice plate grille decontamination machine. If the water quality or water quantity indicators increase significantly, it means that the orifice plate is at risk of being blocked. If at least one of the above three curves is abnormal (reflecting abnormal operating conditions of the equipment), the pre-built intelligent operation status control module is used to increase the instantaneous speed of the main drive shaft of the orifice plate screen cleaner, increase the water flux of the orifice plate screen cleaner, reduce the accumulation of dirt on the orifice plate, and improve the self-cleaning ability of the orifice plate screen cleaner.

[0055] This embodiment adjusts the operating state of the perforated plate type screen cleaner from two aspects: equipment failure and equipment abnormality, improves its self-cleaning ability, and can achieve better predictive maintenance effects.

[0056] Finally, it should be noted that the above are merely preferred embodiments of the present application and are not intended to limit the present application. Persons skilled in the art will readily appreciate that the present application is susceptible to various modifications and variations. The embodiments and features of the embodiments may be combined arbitrarily without conflict. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. An intelligent control method for a grille decontamination machine, characterized in that: include: Step 1: Establish a prediction model, which is used to predict the future trend of the data of the screen decontamination machine over a period of time; Step 2: Conduct real-time video monitoring; install cameras inside and around the grille decontamination machine to capture video images, and collect photos of the fault phenomenon from the video images; Step 3: Build a fault warning system that uses real-time video monitoring combined with predictive model verification to generate alarm event information in a timely manner to provide feedback on equipment operating status, potential risks, and faults. Step 4: According to the alarm event information fed back by the fault warning system, the operating state of the screen cleaner is controlled to enhance the self-cleaning capability of the screen cleaner.

2. The intelligent control method for a grille decontamination machine according to claim 1, characterized in that: In step 4, according to the alarm event information fed back by the fault warning system, the operating state of the screen cleaner is controlled to enhance the self-cleaning capability of the screen cleaner, including: Build a grid intelligent control cloud platform to remotely control the operating status of the grid decontamination machine based on the alarm event information fed back by the fault warning system, and enhance the self-cleaning ability of the grid decontamination machine.

3. The intelligent control method for a grille dirt remover according to claim 1, characterized in that: In step 4, after the fault warning system feeds back the alarm event information, it retrieves the detailed alarm content through human-computer interaction and takes corresponding processing actions; the processing actions include viewing, ignoring and accepting; if ignoring, enter the reason for ignoring, if accepting, process it, and after entering the specific processing content, it means that the closed-loop processing is completed and the alarm event is released.

4. The intelligent control method for a grille dirt remover according to claim 3, characterized in that: In step 4, the collected video images and monitoring data are used to build an equipment operation and maintenance knowledge base. The equipment operation and maintenance knowledge base uses large-model natural language processing and machine learning technologies to provide intelligent retrieval services to improve the efficiency of human-computer interaction.

5. The intelligent control method for a grille dirt remover according to claim 1, characterized in that: Step 1 includes: Step 11: Establish a monitoring point at a designated location of a moving component of the grid decontamination system, and install a monitoring sensor at the monitoring point, the monitoring sensor being used to monitor the motion state of the moving component; Step 12: Combine the monitoring data fed back by the monitoring sensor to obtain equipment information data; the equipment information data includes three types of information data: basic equipment information data, equipment stage adjustment information data, and equipment operation status data; Step 13: Use the equipment information data to build a database, build a time domain waveform, spectrum diagram, and envelope spectrum based on the monitoring data fed back by each monitoring sensor, and combine the equipment information data to form a data model to analyze abnormal data and determine the equipment fault point; Step 14: Based on the monitoring data fed back by the monitoring sensors, analyze the changes in the characteristics of the equipment status data over time, thereby obtaining historical status monitoring data and storing it in a database. The historical status monitoring data includes the degradation trend of moving parts and the changes in the equipment operation stability; By performing trend analysis and curve fitting on historical status monitoring data, the changing trend of relevant characteristic quantities is obtained, and then a prediction model is established based on the data model using the data changing trend characteristics; and the future trend of the data is predicted by the prediction model.

6. The intelligent control method for a grille dirt remover according to claim 5, characterized in that: The screen cleaner is a perforated plate screen cleaner. In step 11, monitoring points are established at the following locations, and vibration sensors are installed at each monitoring point: The output end bearing position of the main motor, the input end bearing position of the gearbox, and the bearing positions at both ends of the main drive shaft; the output end bearing position of the pump motor; the output end bearing position of the reduction motor, the input end bearing position of the pressing gearbox, and the bearing positions at both ends of the screw shaft.

7. The intelligent control method for a grille dirt remover according to claim 6, characterized in that: Step 4 also includes: establishing a pump pressure curve, a liquid level difference curve, and a water quality and water quantity change curve through a database; if at least one of the above three curves is abnormal, increasing the instantaneous speed of the main drive shaft of the orifice plate screen cleaner and increasing the water flux of the orifice plate screen cleaner.

8. The intelligent control method for a grille dirt remover according to claim 6, characterized in that: The equipment information data includes factory data such as equipment specifications, models, and power; the equipment stage adjustment information data includes adjustment information such as parameter adjustment records and maintenance records; and the equipment operation status data includes the remaining life of the equipment and the monitoring data.

9. The intelligent control method for a grille dirt remover according to claim 6, characterized in that: In step 13, the time domain waveform describes the fault characteristics as follows: When there is dynamic imbalance, it will show a typical sine wave in one cycle, which indicates that among the moving parts of the grille decontamination system, the main engine transmission structure has component wear or transmission looseness; Poor alignment will manifest as a time domain waveform showing a 1x or 2x sine wave, or in severe cases, a 3x to 5x sine wave. The superimposed time domain waveform will appear as an "M" or "W" shape. If any of these waveforms appear, it is initially suspected that the equipment has a deformed or loose transmission component, or that the equipment is not level with the ground. Subsequent inspection and verification will confirm whether the fault is caused by a deformed or loose transmission component, or by the equipment being not level with the ground. When friction occurs between the main drive shaft and the orifice plate during transmission, the waveform will be rough, unstable or clipped; When a fault occurs in the bearings at the output end of the main motor, the input end of the gearbox, or the bearings at both ends of the main drive shaft, the time domain waveform shows regular impact signals, with the signal interval being the defect frequency of the inner and outer rings, rolling elements, and cages of the bearings or an impact signal at 1 times the rotational frequency; When a fault occurs in the gears at the output end bearing position of the reduction motor, the input end bearing position of the pressing gear box, or the bearing positions at both ends of the screw shaft, a regular rotation frequency impact signal will appear in the time domain waveform.

10. The intelligent control method for a grille dirt remover according to claim 6, characterized in that: In step 13, the frequency components of the signal and the amplitude of each component are obtained through the spectrum diagram, and the relationship between the signals is analyzed to find the source of vibration noise and perform automatic diagnosis and analysis of the faulty equipment, so as to timely discover potential faults of the equipment; and / or, In step 13, the envelope spectrum is sensitive to events related to impact force and is used to diagnose early defects of components such as the inner ring, outer ring, rolling elements, and cage of the bearing at each monitoring point, or to effectively identify damaged teeth on the gear through the pulse signal of the gear meshing frequency.

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