Intelligent anodic protection acid cooler control system

Abstract

The invention provides an intelligent anodic protection acid cooler control system, which comprises an acquisition module, a transmission module, a processing module, a remote control module and a mobile monitoring terminal which are connected in sequence, and is characterized in that a data acquisition module acquires various multi-mode operation parameter data, transmits the acquired multi-mode operation parameter data to a data transmission module, and transmits the acquired multi-mode operation parameter data to a data processing module; the data processing module performs standardization processing on the multi-modal operation parameter data, and starts a multi-modal fault diagnosis model to perform analysis and prediction to obtain an analysis and prediction result; the remote control module starts a remote control model according to the analysis and prediction result and outputs a remote control strategy; and the mobile monitoring terminal receives the remote control strategy and enlightens the early warning of the corresponding level. The automatic and intelligent management level of the anode protection equipment is improved, remote monitoring, intelligent control and safety protection are achieved, and it is ensured that the anode protection equipment can operate safely and efficiently under the unattended condition.

Description

Technical Field

[0001] The present application relates to the technical field of concentrated sulfuric acid cooling, and in particular to an intelligent anode protection acid cooler control system. Background Art

[0002] Electrochemical protection is one of the most important methods to prevent metal corrosion. It uses external current to change the potential of the corroded metal to slow down or inhibit metal corrosion. When a metal with a passive tendency is subjected to anodic polarization, if the current reaches a sufficient value, a passivation film with high corrosion resistance can be formed on the metal surface to reduce the current, and the metal surface is in a passive state. Continuing to apply a small current can maintain this passivation state. The amount of dissolution on the passive metal surface is very small, thereby preventing the corrosion of the metal. This is anodic protection.

[0003] The anode-protected shell-and-tube stainless steel concentrated sulfuric acid cooler (hereinafter referred to as the acid cooler) was first introduced from abroad in the early 1980s and is used to cool concentrated sulfuric acid in the dry absorption section of various acid-making devices. With the advantages of long life, small footprint, high thermal efficiency, no pollution, low daily maintenance costs, and obvious water-saving effects, this equipment has played a positive role in the technological development of the domestic sulfuric acid industry and has replaced cast iron pipes to become the preferred product for concentrated sulfuric acid cooling.

[0004] In the dry absorption process of flue gas acid production in domestic copper smelters, concentrated sulfuric acid coolers with anode protection (including drying towers, first absorption towers, and second absorption tower acid coolers) are generally used. The monitoring of these equipment is mostly in the form of control and execution functions completed by constant potential instruments. Although this method has the characteristics of stable performance, short connection distance, and simple operation, it cannot be controlled on the monitoring interface of the production workshop such as the widely used DCS, which affects its adaptability and operational flexibility when the process conditions change greatly.

[0005] Therefore, how to improve the automation and intelligent monitoring and management level of the anode protection acid cooler to achieve remote monitoring, intelligent control and safety protection, and ensure that the anode protection acid cooler can operate safely and efficiently even without supervision is an important research topic for technicians in this field. Summary of the invention

[0006] In view of this, it is necessary to provide an intelligent anode protection acid cooler control system, which utilizes the system architecture of the distributed cloud platform and combines artificial intelligence technology to integrate the functions of operation monitoring, intelligent early warning, fault analysis, video monitoring and operation and maintenance management for the anode protection acid cooler system, so as to realize the unattended operation of the anode protection acid cooler and maximize the protection effect. It not only realizes the centralized monitoring of the anode protection acid cooler, but also manages its operation monitoring and maintenance. Through the cloud platform, remote tasks are issued, and the operation and maintenance personnel can also receive monitoring tasks through their mobile phones. For periodic tasks, they can also be issued regularly by appointment, which greatly improves the efficiency of monitoring and maintenance work and the maintenance and management level of anode protection equipment.

[0007] Can overcome at least one of the above defects.

[0008] In a first aspect, an embodiment of the present application provides an intelligent anode protection acid cooler control system, characterized in that the system comprises:

[0009] The data acquisition module, data transmission module, data processing module, remote control module and mobile monitoring terminal are connected in sequence, wherein:

[0010] The data acquisition module is used to collect various multi-modal operating parameter data of the acid cooler through sensors placed in the acid cooler, and transmit the collected multi-modal operating parameter data to the data transmission module;

[0011] The data transmission module is used to transmit the received multi-modal operation parameter data to the data processing module;

[0012] The data processing module is used to standardize the multi-modal operating parameter data and start the multi-modal fault diagnosis model to perform analysis and prediction to obtain analysis and prediction results;

[0013] The remote control module is used to start the remote control model according to the analysis and prediction results and output the remote control strategy;

[0014] The mobile monitoring terminal is used to receive remote control strategies and issue warnings of corresponding levels.

[0015] Optionally, in another implementation of the first aspect of the present invention, the data acquisition module is composed of an anode protection sensor, a liquid height detection sensor, a corrosion detection sensor, a temperature detection sensor, a pH detection sensor, and a sound sensor;

[0016] Among them, the anode protection sensor is used to monitor the potential of the equipment and adjust the current intensity and voltage when necessary;

[0017] The liquid height detection sensor is an optical liquid level sensor that uses a light source and a detector to monitor the liquid level in the acid cooler;

[0018] The corrosion detection sensor uses a piezoelectric ultrasonic pipeline corrosion detector, which uses the piezoelectric effect of the piezoelectric ultrasonic sensor to stimulate ultrasonic waves to perform corrosion detection and integrity evaluation on the pipeline;

[0019] Temperature sensing sensors provide temperature measurements in a readable form via electrical signals and include thermocouples or resistance temperature detectors;

[0020] The pH detection sensor includes a reference electrode and a measuring electrode, and calculates the pH value of the liquid by measuring the potential difference;

[0021] Piezoelectric sound sensor is a piezoelectric sound sensor that uses the piezoelectric effect to convert sound waves into voltage signals.

[0022] Optionally, in another implementation of the first aspect of the present invention, the multimodal operating parameter data includes text data and sound data.

[0023] Optionally, in another implementation of the first aspect of the present invention, the standardizing the multimodal operating parameter data includes:

[0024] Standardize the text data in the multimodal operating parameter data, including:

[0025] Cleaning dirty data in the text data;

[0026] Check outliers through box plots and remove them using the IQR rule;

[0027] View the distribution of missing data values ​​through visualization and perform filling processing;

[0028] The multi-modal operation parameter data is standardized, and the processing formula is:

[0029]

[0030] Where X is the original data, The mean value of the original data, σ is the standard deviation of the original data;

[0031] Standardize the sound data in the multi-modal operating parameter data, including:

[0032] Use the gradient descent method to denoise the original sound data;

[0033] Use the sound recognition model to recognize the denoised sound data;

[0034] Perform secondary denoising on the sound data based on the recognition results.

[0035] The gradient parameter expression is:

[0036]

[0037] F(x i )=A c [1+k(x i )]cos(2πf n x i +α i );

[0038]

[0039] Among them, F(x i ) is a function with parameter x i The amplitude modulated signal, k(x i ) is the signal to be modulated, x i is the observed signal sequence, A c is the amplitude of the carrier signal, f n is the frequency of the carrier signal, α i is the initial phase,

[0040] ▽ λ is the gradient and η is the learning rate.

[0041] Optionally, in another implementation of the first aspect of the present invention, the method for constructing the sound recognition model includes:

[0042] Acquire sound sampling data, wherein the sound sampling data includes equipment startup sound, operation sound, shutdown sound, silent sound and environmental noise;

[0043] The sound sampling data is segmented and labeled according to the amplitude spectrum, and the noise segment is pre-emphasized;

[0044] Use improved convolutional neural network to build a sound recognition model;

[0045] Use labeled sound data to train a sound recognition model.

[0046] Optionally, in another implementation of the first aspect of the present invention, the step of using an improved convolutional neural network to construct a sound recognition model includes:

[0047] The sound recognition model includes an amplitude spectrum input layer, multiple convolutional layers, a pooling layer, a fully connected layer, and an output layer in sequence;

[0048] The multiple convolutional layers respectively include convolution channels of multiple convolution kernels.

[0049] Optionally, in another implementation of the first aspect of the present invention, starting the multimodal fault diagnosis model to perform analysis and prediction to obtain analysis and prediction results includes:

[0050] Obtaining text data and sound classification result data in the sampled data as training samples;

[0051] Training a multimodal fault diagnosis model through training samples;

[0052] Analyze equipment status data through multimodal fault diagnosis models;

[0053] Output potential failure prediction results.

[0054] Optionally, in another implementation of the first aspect of the present invention, starting the remote control model according to the analysis and prediction results and outputting the remote control strategy includes:

[0055] Establish remote control models based on different strategic control algorithms;

[0056] The analysis and prediction results are input into the remote control model to output the control strategy, and fuzzy matching is performed to obtain the control strategy.

[0057] Optionally, in another implementation of the first aspect of the present invention, the receiving of the remote control strategy and the prompting of a warning of a corresponding level include:

[0058] Install remote monitoring applications on managers’ terminal devices;

[0059] The manager sends remote operation commands to the control end of the monitored control center through the remote monitoring application. After the security operation and maintenance protection system of the monitoring control center performs security authentication and authority authentication on the remote monitoring application and the manager, it accepts the control command to realize automatic control and optimization of the acid cooler equipment.

[0060] Optionally, in another implementation of the first aspect of the present invention, the method further includes:

[0061] The anode protection system is used to provide DC current and at the same time control, monitor and ensure the normal operation of the anode protection system.

[0062] In a second aspect, an embodiment of the present application provides an electronic device, including:

[0063] processor;

[0064] a memory for storing processor-executable instructions;

[0065] Wherein, the processor is configured to implement an intelligent anode protection acid cooler control system as described in the first aspect when executing the instructions.

[0066] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, comprising instructions, wherein the instructions instruct a device to execute an intelligent anode protection acid cooler control system as described in the first aspect.

[0067] In the technical solution provided by the present invention, a data acquisition module, a data transmission module, a data processing module, a remote control module, and a mobile monitoring terminal are connected in sequence, wherein: the data acquisition module is used to collect various multimodal operating parameter data of the acid cooler through a sensor installed in the acid cooler, and transmit the collected multimodal operating parameter data to the data transmission module; the data transmission module is used to transmit the received multimodal operating parameter data to the data processing module; the data processing module is used to standardize the multimodal operating parameter data, and start a multimodal fault diagnosis model for analysis and prediction to obtain analysis and prediction results; the remote control module is used to start a remote control model according to the analysis and prediction results and output a remote control strategy; the mobile monitoring terminal is used to receive the remote control strategy, prompt a warning of the corresponding level, and improve the accuracy of the semantic matching of the problem text.

[0068] The intelligent anode protection acid cooler control system provided in the embodiment of the present application can improve the automation and intelligent management level of the anode protection equipment to achieve remote monitoring, intelligent control and safety protection, and ensure that the anode protection equipment can operate safely and efficiently even when unattended. The beneficial effects of the system specifically include:

[0069] (1) Through real-time monitoring of anode protection equipment, including data acquisition, status monitoring and fault diagnosis.

[0070] (2) Conduct research on intelligent algorithms and control strategies to achieve automatic control and optimization of anode protection equipment.

[0071] (3) Use advanced communication technology to achieve high-speed, stable and secure data transmission between anode protection equipment and the control center.

[0072] (4) Through remote operation technology, managers can perform equipment operation and maintenance work remotely.

[0073] (5) Through the fault prediction technology of anode protection equipment, by analyzing the equipment status data, potential failures can be predicted and maintenance can be carried out in advance. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 A schematic diagram of the wiring of the anode protection acid cooler process flow and control system provided in another embodiment of the present application.

[0075] Figure 2A schematic diagram of functional modules of an intelligent anode protection acid cooler control system provided in one embodiment of the present application.

[0076] Figure 3 A schematic diagram of a sound recognition model provided in another embodiment of the present application.

[0077] Figure 4 A schematic diagram of an anode protection system provided in accordance with an embodiment of the present application.

[0078] Figure 5 A schematic diagram of an electronic terminal device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0079] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0080] It should be noted that, in the embodiments of the present application, "at least one" refers to one or more, and "more" refers to two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the present application. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0081] It should be noted that, in the embodiments of the present application, words such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying order. Features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way.

[0082] Based on the implementations in this application, all other implementations obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0083] With the rapid development of the anodic protection equipment industry, the operating environment of anodic protection equipment facilities has become increasingly complex, and equipment failures and defects have posed a severe challenge to the stability and safety of anodic protection equipment facilities. In particular, key facilities such as cooling equipment, once they fail, will not only affect the stable operation of the anodic protection equipment system, but may also lead to sulfuric acid production accidents, and even cause casualties and property losses. Therefore, how to promptly detect potential failures and defects during equipment operation has become an urgent problem to be solved in the anodic protection equipment industry.

[0084] At present, the traditional detection system of anodic protection equipment and facilities mostly relies on manual inspection and regular inspection. This system is not only labor-intensive, but also limited by the frequency and efficiency of manual inspection, making it difficult to detect minor defects in a timely manner. In addition, the disadvantage of the traditional system is that it lacks continuous monitoring and trend prediction of the equipment operation status, making it difficult to cope with the increasingly complex management needs of anodic protection equipment and facilities.

[0085] In view of this, the present application provides an intelligent anode protection acid cooler control system, which can improve the automation and intelligent management level of anode protection equipment to achieve remote monitoring, intelligent control and safety protection, and ensure that the anode protection equipment can operate safely and efficiently even without supervision.

[0086] Figure 1 A schematic diagram of the wiring of the anode protection acid cooler process flow and control system provided in another embodiment of the present application.

[0087] The anode protection acid cooler adopts the process of acid going through the shell (outside the heat exchange tube) and water going through the tube (inside the heat exchange tube). There are heat exchange tubes, baffles, tie rods, etc. inside the shell. The anode of the anode protection control system is connected to the shell, and the cathode penetrates the pipe box and is inserted into the shell. The electrode at the acid inlet is the control reference electrode, referred to as "control reference", which is the reference point of the anode protection system; the electrode at the acid outlet is the monitoring reference electrode, referred to as "monitoring reference", which plays the role of monitoring reference electrode.

[0088] The acid cooler with anode protection is used to cool the concentrated sulfuric acid in the dry absorption process. It adopts a fixed shell and tube structure, with acid on the shell side and water on the tube side. The cooling medium is industrial circulating cooling water. The main material of the anode-protected shell and tube concentrated acid cooler is 316L stainless steel, the shell is made of 314 stainless steel, and an anode protection device is attached. The post-pump cooling process is adopted, that is, the acid cooler is located after the pump outlet between the pump and the tower. The concentrated sulfuric acid cooler is pressurized and uses a plate heat exchanger for heat exchange.

[0089] The anode, sulfuric acid and cathode form a return circuit. After power is turned on, a passivation film is formed on the metal surface in contact with sulfuric acid, and the protective potential prevents sulfuric acid from corroding the metal surface, greatly extending the service life of the equipment.

[0090] Figure 2 This is a functional module diagram of an intelligent anode protection acid cooler control system provided by an embodiment of the present application. Figure 2 The intelligent anode protection acid cooler control system shown in the figure includes a data acquisition module 21, a data transmission module 22, a data processing module 23, a remote control module 24, and a mobile monitoring terminal 25 connected in sequence, wherein:

[0091] The data acquisition module 21 is used to collect various multi-modal operating parameter data of the acid cooler through sensors installed in the acid cooler, and transmit the collected multi-modal operating parameter data to the data transmission module.

[0092] It can be understood that a sensor is a component or device that has the function of sensing and detecting certain information of the object being measured and converts it into a corresponding useful signal according to a certain rule. It is usually composed of a sensitive element and a conversion element.

[0093] In the embodiment of the present application, for the anodic protection equipment, a variety of sensors are used to collect the operating parameters of the anodic protection equipment. The factors that mainly affect its corrosion resistance are anodic protection parameters (e.g., monitoring parameters, control parameters, current, tank pressure) and production process parameters (e.g., acid temperature, acid concentration), and temperature detection sensors, PH detection sensors; these parameters need to be monitored at all times. In addition, for the anodic protection acid distributor, one of the anodic protection equipment, its performance will also be affected by the sulfuric acid level, so it is also necessary to monitor the sulfuric acid level in the acid distributor. At the same time, since sulfuric acid is a highly corrosive liquid, it is also necessary to monitor the corrosion of the anodic protection equipment.

[0094] The parameters collected by the above sensors are transmitted and saved in text form. In addition to the above-mentioned influencing factors, fault detection methods based on sound recognition have gradually become a new research hotspot. The equipment will generate specific sound signals during operation, which can reflect the health status of the equipment and provide important clues for fault diagnosis.

[0095] The operating sound of the anode protection equipment is collected, and the audio signal is preprocessed, feature extracted and machine learning model trained to finally achieve accurate fault identification. Experiments have shown that this method has shown extremely high accuracy in the detection of various fault types, especially in the identification of transformer overheating faults, with an accuracy rate of up to 99.8%. This method provides an effective solution for intelligent fault detection of anode protection equipment.

[0096] In view of the above requirements, the data acquisition module 21 in this application mainly includes 6 types of sensors: anode protection sensor, liquid height detection sensor, corrosion detection sensor, temperature detection sensor, pH detection sensor, and sound sensor. When managing the anode protection equipment, these 6 types of sensors need to be set.

[0097] Specifically, in the embodiment of the present application, the data acquisition module is composed of an anode protection sensor, a liquid height detection sensor, a corrosion detection sensor, a temperature detection sensor, a PH detection sensor, and a sound sensor;

[0098] Specifically, anode protection sensors are used to monitor the electrical potential of the equipment and adjust the current intensity and voltage when necessary.

[0099] The liquid height detection sensor is an optical liquid level sensor that uses a light source and a detector to monitor the liquid level in the acid cooler.

[0100] The corrosion detection sensor adopts a piezoelectric ultrasonic pipeline corrosion detector, which uses the piezoelectric effect of the piezoelectric ultrasonic sensor to stimulate ultrasonic waves to perform corrosion detection and integrity evaluation on the pipeline.

[0101] The corrosion detection sensor uses a piezoelectric ultrasonic pipeline corrosion detector, which uses the piezoelectric effect of the piezoelectric ultrasonic sensor to excite ultrasonic waves to perform corrosion detection and integrity evaluation on the pipeline. The principle is that the ultrasonic waves excited by the sensor are reflected by the inner and outer walls of the pipeline during the radial propagation of the pipeline, and the reflected waves are received by the sensor. Since the propagation speed of the sound waves in the detected medium and the pipe wall is known, the lift-off value (the distance between the sensor and the inner wall of the pipeline) and the pipeline wall thickness value can be obtained by measuring the reflection time of the echo.

[0102] Temperature sensing sensors provide temperature measurements in a readable form via electrical signals and include thermocouples or resistance temperature detectors.

[0103] The pH detection sensor includes a reference electrode and a measuring electrode. By measuring the potential difference, the pH value of the liquid is calculated. The water quality pH sensor works based on the principle of electrochemistry. Its core components are a pair of special electrodes - a reference electrode and a measuring electrode (usually a glass electrode). When the sensor is immersed in the water to be tested, the sensitive membrane of the glass electrode will react with the hydrogen ions in the solution to produce a certain potential difference. This potential difference is logarithmically related to the activity of hydrogen ions in the solution (i.e., the pH value). By measuring this potential difference, the pH value of the water can be calculated.

[0104] The piezoelectric sound sensor is a piezoelectric sound sensor that uses the piezoelectric effect to convert sound waves into voltage signals. Further, in order to meet the advantages of the simple structure, small size, low power consumption and long service life of this product, the sound sensor is a piezoelectric sound sensor.

[0105] Furthermore, the data acquisition module 21 also includes a single chip microcomputer controller connected to each detection sensor, which is used to control the operation of each sensor and the collection of data, the conversion of data format, etc.

[0106] The data transmission module 22 is used to transmit the received multi-modal operating parameter data to the data processing module.

[0107] It is understandable that when the data transmission module 22 receives the multimodal operating parameter data, it can collect the data and convert the data format according to the communication protocol and send it to the data processing module.

[0108] The data processing module 23 is used to standardize the multi-modal operating parameter data and start the multi-modal fault diagnosis model to perform analysis and prediction to obtain analysis and prediction results.

[0109] Specifically, in the embodiment of the present application, the step of normalizing the multimodal operating parameter data includes:

[0110] Standardize the text data in the multimodal operating parameter data, including:

[0111] Cleaning dirty data in the text data;

[0112] Check outliers through box plots and remove them using the IQR rule;

[0113] View the distribution of missing data values ​​through visualization and perform filling processing;

[0114] The multi-modal operation parameter data is standardized, and the processing formula is:

[0115]

[0116] Where X is the original data, The mean value of the original data, σ is the standard deviation of the original data;

[0117] It is understandable that audio signal preprocessing is a key step in fault detection of anode protection equipment. Its main task is to remove noise interference to retain valid signals related to the operating status of the equipment, thereby providing high-quality data support for subsequent feature extraction and fault diagnosis. First of all, the original audio signal usually contains a lot of low-frequency noise and high-frequency interference, so a high-pass filter is needed to filter the signal to eliminate these unnecessary components.

[0118] It is understandable that the cutoff frequency range of the high-pass filter is usually set at 100 to 4000 Hz, which can effectively remove low-frequency noise and high-frequency interference, thereby retaining important frequency information in the operation of the device. Secondly, since the audio signal emitted by the device has a certain attenuation during the propagation process, especially in the high-frequency part, the high-frequency information in the signal is weakened. Therefore, pre-emphasis processing is required to compensate for this attenuation. The pre-emphasis coefficient is usually 0.97 to effectively enhance the high-frequency components in the signal and ensure that the high-frequency information of the signal will not be lost due to attenuation. Thirdly, the processed signal is framed, and the length of each frame is set to 25ms and the frame shift is 10ms. The framing strategy can smooth the signal in the time domain, making each frame signal more stable and complete in time, and reducing the information loss caused by too fast changes in the time domain. Finally, in order to further optimize the frequency domain analysis effect of the signal, the Hamming window is used to perform windowing processing on each frame of the signal. The Hamming window can effectively reduce the spectrum leakage phenomenon, improve the accuracy of frequency domain analysis, and ensure that the signal details are fully retained during feature extraction.

[0119] In the embodiment of the present application, the sound data in the multimodal operation parameter data is standardized, including:

[0120] Use the gradient descent method to denoise the original sound data;

[0121] Use the sound recognition model to recognize the denoised sound data;

[0122] The sound data is denoised twice according to the recognition results.

[0123] Due to the complexity of the noise problem in sound recognition in the operating parameter data, in order to overcome the limitations of the independent component analysis method in processing non-stationary signals, the gradient descent method is introduced for optimization.

[0124] The gradient descent method enables the independent component analysis method to adapt to the dynamic characteristics of the signal by iteratively optimizing the objective function. The basic idea is that in each iteration, the gradient of the objective function relative to the parameters is calculated, and then the parameters are adjusted in the opposite direction of the gradient to gradually approach the optimal solution. In the independent component analysis method, this means that the estimation of the mixing matrix can be updated in real time, so as to better capture the instantaneous characteristics of the signal.

[0125] The gradient parameter expression is:

[0126]

[0127] F(x i )=A c [1+k(x i )]cos(2πf n x i +α i );

[0128]

[0129] Among them, F(x i ) is a function with parameter x i The amplitude modulated signal, k(x i ) is the signal to be modulated, x i is the observed signal sequence, A c is the amplitude of the carrier signal, f n is the frequency of the carrier signal, α i is the initial phase,

[0130] ▽ λ is the gradient and η is the learning rate.

[0131] Specifically, in the embodiments of the present application, through these preprocessing steps, the signal-to-noise ratio of the audio signal is significantly improved, external interference and unnecessary components in the signal are removed, and a clearer and more reliable data basis is provided for subsequent feature extraction, thereby ensuring the efficiency and accuracy of fault detection.

[0132] It can be understood that after completing the preprocessing of the original anode protection equipment audio signal, a variety of technologies are used to extract audio features related to equipment failure from the time domain, frequency domain and time-frequency domain, mainly STFT and MFCC. Through STFT, the audio signal can be effectively converted from the time domain to the frequency domain, the spectrum can be obtained, and the frequency distribution of the signal in different time periods can be analyzed. In order to more accurately simulate the sensitivity of the human ear to different frequencies, the spectrum is filtered using a Mel filter. This is because the center frequency of the Mel filter is logarithmically distributed, which is consistent with the auditory characteristics of the human ear, and can extract frequency features that are closely related to the working status of the equipment.

[0133] After extracting the features of the audio signal of the anode protection equipment, the machine learning model is used to train and optimize the sound features to accurately identify substation equipment faults. Due to the complexity of anode protection equipment faults and the nonlinear characteristics of audio signals, two machine learning models, SVM and CNN, are used to fully mine the key information in the audio features and improve the accuracy of fault identification.

[0134] Specifically, in the embodiment of the present application, the method for constructing the sound recognition model includes:

[0135] Acquire sound sampling data, wherein the sound sampling data includes equipment startup sound, operation sound, shutdown sound, silent sound and environmental noise;

[0136] The sound sampling data is segmented and labeled according to the amplitude spectrum, and the noise segment is pre-emphasized;

[0137] Use improved convolutional neural network to build a sound recognition model;

[0138] Use labeled sound data to train a sound recognition model.

[0139] Specifically, the model training process mainly consists of four steps: 1. Pre-training stage; 2. Supervised fine-tuning, also called instruction fine-tuning stage; 3. Reward model training stage; 4. Reinforcement learning fine-tuning stage. The training steps specifically include: 1) Data collection and preprocessing: Collect and organize the data sets needed for training. 2) Model design and construction: Design and build a suitable model according to task requirements. 3) Model training: Use the training data set to train the model, and continuously adjust the model parameters so that the model can better fit the data. 4) Model evaluation and optimization: Use the test data set to evaluate the trained model, and optimize the model based on the evaluation results. 5) Model deployment and use: Deploy the trained model to the actual application scenario and apply it.

[0140] It is understandable that by analyzing a large amount of text data, potential trends, patterns or associations can be mined. By analyzing the text content, people's emotional tendencies towards specific topics, products or services, such as positive, negative or neutral, can be understood. Entity naming recognition and document classification can also be performed. The present invention uses text features combined with sound features to establish a multimodal fault diagnosis model to achieve fault prediction or classification.

[0141] Optionally, the multimodal fault diagnosis model can be a fusion of a text-type fault diagnosis model and a sound-type fault diagnosis model, which comprehensively considers the two recognition results, such as by weighted summing them according to a weight ratio, to obtain the final fault prediction or classification, thereby greatly improving the accuracy of fault prediction or classification.

[0142] Figure 3 This is a schematic diagram of a sound recognition model provided by another embodiment of the present application. Figure 3 As shown, in an embodiment of the present application, the sound recognition model is constructed using an improved convolutional neural network, including: the sound recognition model includes an amplitude spectrum input layer, multiple convolution layers, a pooling layer, a fully connected layer, and an output layer in sequence; wherein the multiple convolution layers respectively include convolution channels of multiple convolution kernels.

[0143] In an embodiment of the present application, the neural network comprises a total of 18 layers. It includes 15 convolutional layers, 1 pooling layer, 1 fully connected layer, and 1 output layer. The input of the deep convolutional neural network is a spectrogram signal of size 128×128, connected to a conventional convolutional layer. Then it is divided into conventional convolutional layer channels of different sizes according to the preset channel ratio. Because the channels use receptive fields of different sizes, they can perform all-round feature extraction. Optionally, the three channels use 3×3, 5×5 and 7×7 convolution kernels respectively. Finally, the feature maps output by the three groups of channels are spliced ​​and input into the global average pooling layer for dimensionality reduction, and the entire network model is structurally regularized.

[0144] The remote control module 24 is used to start the remote control model according to the analysis and prediction results and output the remote control strategy.

[0145] Specifically, in the embodiment of the present application, starting the remote control model according to the analysis and prediction results and outputting the remote control strategy includes:

[0146] Establish remote control models based on different strategic control algorithms;

[0147] The analysis and prediction results are input into the remote control model to output the control strategy, and fuzzy matching is performed to obtain the control strategy.

[0148] Specifically, different strategy control algorithms can establish control models of different strategy control algorithms according to different devices. According to the control model, it is judged whether the current monitoring data corresponding to the control model meets the adjustment conditions of the controlled device in the anode protection device; if so, the corresponding adjustment instructions are output to the controlled device, or the corresponding adjustment instructions are issued to the controlled device through the control center main station or the auxiliary equipment centralized monitoring system.

[0149] Specifically, in the embodiment of the present application, the multimodal fault diagnosis model is started to perform analysis and prediction to obtain analysis and prediction results, including:

[0150] Obtaining text data and sound classification result data in the sampled data as training samples;

[0151] Training a multimodal fault diagnosis model through training samples;

[0152] Analyze equipment status data through multimodal fault diagnosis models;

[0153] Output potential failure prediction results.

[0154] The mobile monitoring terminal 25 is used to receive remote control strategies and issue warnings of corresponding levels.

[0155] Specifically, in the embodiment of the present application, the receiving of the remote control strategy and the prompting of the corresponding level of warning include:

[0156] Install remote monitoring applications on managers’ terminal devices;

[0157] The manager sends remote operation commands to the control end of the monitored control center through the remote monitoring application. After the security operation and maintenance protection system of the monitoring control center performs security authentication and authority authentication on the remote monitoring application and the manager, it accepts the control command to realize automatic control and optimization of the acid cooler equipment.

[0158] Specifically, in addition to carrying out equipment operation and maintenance work, you can also set online monitoring levels, safety warnings, and maintenance work allocation, make relevant inspection or fault handling plans, arrange maintenance personnel to handle them, help maintenance personnel respond quickly, and reduce the risk of major failures caused by ignoring small defects.

[0159] Figure 4 A schematic diagram of an anode protection system provided in an embodiment of the present application. In the embodiment of the present application, the intelligent anode protection acid cooler control system further includes: an anode protection system for providing direct current and controlling, monitoring and ensuring the normal operation of the anode protection system.

[0160] The anode protection system consists of the concentrated sulfuric acid equipment body (anode), cathode, reference electrode (including control and monitoring parameters), constant potentiostat and wires and cables.

[0161] The anode refers to all parts that come into contact with sulfuric acid and need to be protected. The acid cooler includes the shell, heat exchange tubes, tube sheets, baffles, tie rods, connecting pipes, etc. The pipeline refers to the inner surface of all stainless steel pipelines; the acid distributor refers to all stainless steel surfaces that come into contact with the acid.

[0162] Acid cooler cathode: The cathode is used to conduct current. The cathode is a metal rod made of special material that passes through the tube sheet and baffle from the water tank and is parallel to the tube. Its outer surface is covered with a polytetrafluoroethylene tube with a certain number of small holes to control the distribution of current. The metal cathode used in the acid cooler is a consumable and needs to be replaced regularly. Its dissolution rate depends on the size of the current. The process of each factory is different, and its service life is also different. Generally speaking, the service life of the cathode is 4

[0163] Therefore, users should take out the steel pipes for inspection during the maintenance period after 4 years of use and decide whether they need to be replaced based on the degree of corrosion.

[0164] The absolute potential value cannot be measured directly. It must be measured with an electrode with a relatively stable potential as a reference. This electrode is called a "reference electrode". The potential of the reference electrode itself is required to be basically stable under operating conditions. The potential displayed by the instrument is relative to the potential of the reference electrode.

[0165] An electrode called "control reference electrode" is installed on the shell on the acid inlet side, and the measured anode potential is referred to as "control reference potential". The output current is changed by the constant potential instrument, so that the potential of the acid cooler is always in a stable passivation range. The reference electrode on the shell on the acid outlet side is called "monitoring reference electrode". The measured potential value is referred to as "monitoring reference potential", which plays a role in monitoring and auxiliary control.

[0166] The anode protection system is used to provide direct current, and plays a role in controlling, monitoring and ensuring the normal operation of the anode protection system. The potential value of the acid cooler, pipeline, and acid distributor is transmitted to the constant potential instrument, and this value is compared with the set control index. According to the difference between the measured value and the desired control value, the output of the thyristor rectifier power supply current is automatically adjusted, so as to achieve the control purpose of the measured value and the actual control value being always consistent. This instrument also has the auxiliary functions of alarm and current limiting protection. When a short circuit or open circuit occurs on the line, the anode protection system will send out a light alarm signal and cut off the current output.

[0167] It is understandable that the intelligent anode protection acid cooler control system provided in the embodiment of the present application can monitor the anode protection equipment in real time, including data collection, status monitoring and fault diagnosis. Research on intelligent algorithms and control strategies is carried out to achieve automatic control and optimization of anode protection equipment. Advanced communication technology is used to achieve high-speed, stable and secure data transmission between the anode protection equipment and the control center. Through remote operation technology, managers can remotely carry out equipment operation and maintenance work. Through the fault prediction technology of the anode protection equipment, by analyzing the equipment status data, potential faults can be predicted and maintenance can be performed in advance.

[0168] See also Figure 5 , Figure 5 This is an electronic terminal device 500 provided in an embodiment of the present application. Figure 5 The electronic terminal device 500 shown includes at least the following parts: one or more processors 501, one or more input devices 502, one or more output devices 503 and one or more memories 504. The processors 501, input devices 502, output devices 503 and memories 504 communicate with each other via a communication bus 505. The memory 504 is used to store computer programs, which include program instructions. The processor 501 is used to execute the program instructions stored in the memory 504. The processor 501 is configured to call the program instructions to perform the following operations to perform the functions of the modules / units in the above-mentioned device embodiments, such as Figure 1 Functionality of the modules shown.

[0169] In an embodiment of the present application, a computer-readable storage medium includes instructions, and the instructions instruct a device to execute the system of the first aspect. For example, the instructions instruct the device to execute Figure 1 An intelligent anode protection acid cooler control system is shown in the steps.

[0170] It should be understood that in the embodiment of the present invention, the processor 501 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. It should be noted that a part of the electronic device 500 of the above embodiment may also be implemented by a computer. In this case, the program for implementing the control function may be recorded in a computer-readable recording medium, and the program recorded in the recording medium may be read into a computer and executed.

[0171] The input device 502 may include a touch panel, a fingerprint sensor (for collecting the user's fingerprint information and fingerprint direction information), a microphone, etc., and the output device 503 may include a display (LCD, etc.), a speaker, etc.

[0172] It should be noted that the "computer" mentioned here refers to a computer built into the electronic device 500, and uses a computer including hardware such as an OS and peripheral devices. In addition, "computer-readable recording medium" refers to removable media such as a floppy disk, a magneto-optical disk, a ROM, a CD-ROM, and a storage device such as a hard disk built into the computer.

[0173] Furthermore, "computer-readable recording media" may include: media that dynamically store programs for a short period of time, such as communication lines when sending programs via networks such as the Internet or communication lines such as telephone lines; and media that store programs for a fixed period of time, such as volatile memories inside computers that serve as servers or clients in this case. In addition, the above-mentioned program may be a program for realizing a part of the above-mentioned functions, or a program that can realize the above-mentioned functions by combining with a program already recorded in a computer.

[0174] In addition, the electronic device 500 in the above-mentioned embodiment can also be implemented as a collection (device group) composed of multiple devices. Each device constituting the device group can have a part or all of the functions or functional blocks of the electronic device 500 in the above-mentioned embodiment. As a device group, it is sufficient to have all the functions or functional blocks of the electronic device 500.

[0175] It is understandable that the intelligent anode protection acid cooler control system, system, electronic device and storage medium provided by the embodiments of the present application can effectively improve the operation and maintenance level of anode protection equipment and facilities, and solve the problem that traditional manual inspections cannot identify facility defects in real time and comprehensively. Through the application of this system, potential hidden dangers of facility failures can be quickly discovered, and corresponding repair suggestions and optimization plans can be provided through intelligent analysis, thereby minimizing equipment downtime and maintenance costs. At the same time, the system's adaptive capabilities enable it to continuously learn and optimize, improve performance based on more data collected in real time, and further enhance its adaptability to different types of anode protection equipment and facilities and its ability to predict future defects.

[0176] Those skilled in the art should recognize that the above embodiments are only used to illustrate the present application and are not intended to be limiting of the present application. As long as they are within the spirit and scope of the present application, appropriate changes and modifications to the above embodiments are within the scope of protection claimed in the present application.

Claims

1. An intelligent anode protection acid cooler control system, characterized in that: The system includes a data acquisition module, a data transmission module, a data processing module, a remote control module, and a mobile monitoring terminal connected in sequence, wherein: The data acquisition module is used to collect various multi-modal operating parameter data of the acid cooler through sensors placed in the acid cooler, and transmit the collected multi-modal operating parameter data to the data transmission module; The data transmission module is used to transmit the received multi-modal operation parameter data to the data processing module; The data processing module is used to standardize the multi-modal operating parameter data and start the multi-modal fault diagnosis model to perform analysis and prediction to obtain analysis and prediction results; The remote control module is used to start the remote control model according to the analysis and prediction results and output the remote control strategy; The mobile monitoring terminal is used to receive remote control strategies and issue warnings of corresponding levels.

2. The intelligent anode protection acid cooler control system according to claim 1 is characterized in that: The data acquisition module is composed of an anode protection sensor, a liquid height detection sensor, a corrosion detection sensor, a temperature detection sensor, a pH detection sensor, and a sound sensor; Among them, the anode protection sensor is used to monitor the potential of the equipment and adjust the current intensity and voltage when necessary; The liquid height detection sensor is an optical liquid level sensor that uses a light source and a detector to monitor the liquid level in the acid cooler; The corrosion detection sensor uses a piezoelectric ultrasonic pipeline corrosion detector, which uses the piezoelectric effect of the piezoelectric ultrasonic sensor to stimulate ultrasonic waves to perform corrosion detection and integrity evaluation on the pipeline; Temperature sensing sensors provide temperature measurements in a readable form via electrical signals and include thermocouples or resistance temperature detectors; The pH detection sensor includes a reference electrode and a measuring electrode, and calculates the pH value of the liquid by measuring the potential difference; Piezoelectric sound sensor is a piezoelectric sound sensor that uses the piezoelectric effect to convert sound waves into voltage signals.

3. The intelligent anode protection acid cooler control system according to claim 1 is characterized in that: The multimodal operating parameter data includes text data and sound data.

4. The intelligent anode protection acid cooler control system according to claim 3 is characterized in that: The step of standardizing the multi-modal operation parameter data includes: Standardize the text data in the multimodal operating parameter data, including: Cleaning dirty data in the text data; Check outliers through box plots and remove them using the IQ R rule; View the distribution of missing data values ​​through visualization and perform filling processing; The multi-modal operation parameter data is standardized, and the processing formula is: Where X is the original data, The mean value of the original data, σ is the standard deviation of the original data; Standardize the sound data in the multi-modal operating parameter data, including: Use the gradient descent method to denoise the original sound data; Use the sound recognition model to recognize the denoised sound data; Perform secondary denoising on the sound data based on the recognition results. The gradient parameter expression is: F(x i )=A c [1+k(x i )]cos(2πf n x i +a i ); Among them, F(x i ) is a function with parameter x i The amplitude modulated signal, k(x i ) is the signal to be modulated, x i is the observed signal sequence, A c is the amplitude of the carrier signal, f n is the frequency of the carrier signal, α i is the initial phase, ▽ λ is the gradient and η is the learning rate.

5. The intelligent anode protection acid cooler control system according to claim 4 is characterized in that: The method for constructing the sound recognition model comprises: Acquire sound sampling data, wherein the sound sampling data includes equipment startup sound, operation sound, shutdown sound, silent sound and environmental noise; The sound sampling data is segmented and labeled according to the amplitude spectrum, and the noise segment is pre-emphasized; Use improved convolutional neural network to build a sound recognition model; Use labeled sound data to train a sound recognition model.

6. The intelligent anode protection acid cooler control system according to claim 5 is characterized in that: The improved convolutional neural network is used to construct a sound recognition model, including: The sound recognition model includes an amplitude spectrum input layer, multiple convolutional layers, a pooling layer, a fully connected layer, and an output layer in sequence; The multiple convolutional layers respectively include convolution channels of multiple convolution kernels.

7. The intelligent anode protection acid cooler control system according to claim 6 is characterized in that: The multi-modal fault diagnosis model is started to perform analysis and prediction to obtain analysis and prediction results, including: Obtaining text data and sound classification result data in the sampled data as training samples; Training a multimodal fault diagnosis model through training samples; Analyze equipment status data through multimodal fault diagnosis models; Output potential failure prediction results.

8. The intelligent anode protection acid cooler control system according to claim 7 is characterized in that: The step of starting the remote control model according to the analysis and prediction results and outputting the remote control strategy comprises: Establish remote control models based on different strategic control algorithms; The analysis and prediction results are input into the remote control model to output the control strategy, and fuzzy matching is performed to obtain the control strategy.

9. The intelligent anode protection acid cooler control system according to claim 8, characterized in that: The receiving remote control strategy and initiating a warning of a corresponding level include: Install remote monitoring applications on managers’ terminal devices; The manager sends remote operation commands to the control end of the monitored control center through the remote monitoring application. After the security operation and maintenance protection system of the monitoring control center performs security authentication and authority authentication on the remote monitoring application and the manager, it accepts the control command to realize automatic control and optimization of the acid cooler equipment.

10. The intelligent anode protection acid cooler control system according to claim 1, characterized in that: Also includes: The anode protection system is used to provide DC current and at the same time control, monitor and ensure the normal operation of the anode protection system.

Images