Tailing density visual monitoring device based on ERT technology and control method

Abstract

The invention relates to the technical field of tailing filling, and discloses a tailing thickening visual monitoring device based on the ERT technology and a control method.The tailing thickening visual monitoring device comprises a thickener and further comprises an ERT electrode sensor system, a data collecting and processing unit and an image reconstruction unit; stimulation current is applied to the tailing slurry, boundary voltage is stimulated, slurry electric potential distribution changes are converted into voltage signals, and the voltage signals are captured through the first electrode and the second electrode; and the image reconstruction unit is used for reconstructing a conductivity distribution diagram of the tailing slurry by using a Landweber algorithm according to the received signal, converting the conductivity distribution diagram into a visual image sequence and reconstructing a medium distribution image in the thickener. The electrode array is adopted to cover multiple positions of the thickener, electrical information of the tailings is obtained comprehensively, the overall state can be reflected more accurately compared with traditional manual sampling and pressure sensors, and judgment errors are avoided; the resistivity is directly measured, and compared with a pressure sensor which needs to indirectly calculate and speculate the concentration and height of tailings, errors are reduced.

Description

Technical Field

[0001] The present invention relates to the field of tailings filling, and in particular to a tailings density visualization monitoring device and a control method based on ERT technology. Background Art

[0002] In the green transformation of mines, the treatment and innovative use of tailings play a key role. Tailings filling technology not only cleverly copes with the environmental problems of tailings storage, but also prevents surface subsidence. Among them, tailings thickening is a key step in the filling process. As the grinding particle size of ore is gradually refined by mineral processing, the difficulty of thickening tailings has increased significantly. As the key equipment for tailings thickening, vertical sand silos often encounter problems such as poor flocculation effect and unstable bottom sand concentration, which causes the slurry to compact at the bottom of the sand silo, making it difficult to clean, affecting the slurry making efficiency, and having a major impact on the continuous production of the mine.

[0003] Monitoring the tailings thickening process is crucial to achieving good tailings thickening effects. Traditional tailings thickening monitoring methods cannot meet the needs of modern mining development due to their high labor intensity, poor real-time performance, limited accuracy, and lack of intuitive display of the tailings thickening process.

[0004] The existing tailings density monitoring technology mainly comes from long-term mining industry production practice, the research results of relevant scientific research institutions on tailings treatment, and the exploration of production process optimization by the research and development departments of mining enterprises themselves. At present, there are two main ways to monitor tailings density: one is to determine the key parameters through experimental analysis after manual sampling, which has the disadvantages of long time consumption and poor real-time performance; the other is to use pressure sensors to calculate the height and concentration distribution of tailings according to their weight, but with poor accuracy and lack of visualization. Summary of the invention

[0005] The purpose of the present invention is to provide a tailings density visualization monitoring device and control method based on ERT technology, aiming to solve the problems of poor real-time performance, incomplete monitoring, and inability to intuitively display the density status in the existing tailings density monitoring technology, and to provide a solution for real-time, accurate, continuous and visual monitoring of the tailings density process.

[0006] The present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a visual monitoring device for tailings thickening based on ERT technology, which includes a thickener for accelerating the gravity thickening of tailings, and also includes an ERT electrode sensor system, a data acquisition and processing unit, and an image reconstruction unit, wherein the ERT electrode sensor system includes a first electrode evenly layered and arranged around the cylindrical inner wall of the thickener and a second electrode on the wall of the lower conical bin, wherein the first and second electrodes form an electrode array in the thickener; a stimulation current is applied to the tailings slurry in the thickener to excite a boundary voltage, and changes in the slurry potential distribution are converted into a voltage signal, which is captured by the first and second electrodes;

[0008] A data acquisition and processing unit, each of the first and second electrodes is connected to the data acquisition and processing unit through a signal line, and the voltage signal is excited and collected by the data acquisition and processing unit, and is sent to the image reconstruction unit after analog-to-digital conversion;

[0009] The image reconstruction unit is used to reconstruct the conductivity distribution map of the tailings slurry using the Landweber algorithm according to the received signal, convert it into a visual image sequence, and reconstruct the medium distribution image in the thickener.

[0010] In the above implementation, the inverse problem of ERT is image reconstruction. Based on all capacitance measurement values ​​combined with the sensitivity matrix, the image reconstruction unit inverts the distribution images of different media in the measured field, thereby realizing visual measurement. Compared with the direct problem, the inverse problem has a larger amount of calculation, and the key to solving it is to select a suitable calculation method.

[0011] According to some embodiments, the thickener comprises a cylindrical thickener body and a conical bin connected at the bottom, the thickener body has a diameter of 12 to 15 m and a height of 10 to 12 m; the conical bin wall has a height of 2 to 3 m.

[0012] According to some embodiments, the cone angle of the cone bin below the thickener is selected from 30°, 60°, 90° or 120°.

[0013] According to some embodiments, the number of the first electrodes or the second electrodes in each layer is preferably 8 or 16.

[0014] According to some embodiments, the material used to make the first electrode and the second electrode is selected from copper.

[0015] According to some embodiments, the first electrode and the second electrode are both rectangular electrodes.

[0016] In a second aspect, the present invention further provides a control method for the tailings density visualization monitoring device based on the ERT technology, comprising the following steps:

[0017] S1, turning on the ERT electrode sensing system;

[0018] First, the ERT electrode sensing system is calibrated, and a bipolar pulse excitation is generated by a DC current source signal generator. The ERT electrode sensing system receives the excitation signal from the DC current source, applies an excitation current to the first electrode and the second electrode through a signal line, and the electrode array conducts the excitation current to the tailings slurry, and introduces the current into the tailings thickening area of ​​the thickener cavity to stimulate the boundary voltage. The change in the slurry potential distribution is converted into a voltage signal, and the first and second electrodes capture the voltage signal and input it into the data acquisition and processing unit through the signal line;

[0019] S2, the data acquisition and processing unit receives the voltage signal transmitted by the ERT electrode sensing system and processes it using an adaptive adjustment mechanism;

[0020] The data acquisition and processing unit includes a data acquisition and excitation module, an A / D conversion and control module data acquisition, and a communication module; the data acquisition and excitation module is used to receive a voltage signal, and can output an excitation current, and is electrically connected to the A / D conversion and control module; the A / D conversion and control module converts the original analog signal into a digital signal that is easy to process, and dynamically adjusts the excitation current according to demand, so as to optimize the performance of the ERT electrode sensing system; the communication module receives the digital signal calculated by the A / D conversion and control module, optimizes the signal, and transmits the signal to the image reconstruction unit in a wired manner;

[0021] S3, the image reconstruction unit receives the signal processed by the data acquisition and processing unit, and uses the Landweber algorithm to reconstruct the conductivity distribution map of the tailings slurry and convert it into a visual image sequence;

[0022] The image reconstruction unit receives the signal transmitted by the data acquisition and processing unit, divides the measurement area of ​​the thickener cavity into different finite elements by using the finite element method, and then establishes the unit equation for the finite element by using the linear interpolation method to solve the overall equation to obtain the discrete solution in the thickener; the image reconstruction unit uses these data to use the Landweber algorithm to reconstruct the conductivity distribution map of the tailings slurry, converts it into a medium distribution image, and further converts it into a visualized image sequence and video to show the medium distribution in the thickener cavity. The operator adjusts the process parameters and optimizes the tailings thickening according to the real-time visualization system monitoring data and reconstructed images.

[0023] According to some implementations, the Landweber algorithm working process in step S3 includes:

[0024] S301, the image reconstruction unit pre-processes the data to ensure that the generated information is stable and reliable:

[0025] Signal processing technology is used to filter and denoise the collected boundary voltage data to eliminate noise interference and improve the signal-to-noise ratio of the data. A low-pass filter is used to remove high-frequency noise while retaining the useful components of the signal to ensure the accuracy and reliability of subsequent processing;

[0026] S302, the image reconstruction unit divides the subspace and estimates the subspace:

[0027] Through the SVD technology, the data matrix is ​​decomposed into signal subspace and noise subspace. The signal subspace contains the conductivity change, while the noise subspace represents the noise component that is irrelevant to the measurement. Through this decomposition, the signal is extracted from the noise, providing a clear data basis for image reconstruction.

[0028] S303, normalizing the signal subspace and performing mathematical transformation to eliminate data correlation, thereby ensuring signal orthogonality and independence;

[0029] The Landweber algorithm is used to further normalize the signal subspace. Normalization is a mathematical transformation of the signal subspace, which eliminates the correlation between data and ensures the orthogonality and independence of the signal, thereby obtaining the orthogonal basis of the signal subspace to improve the image accuracy; normalization helps to reduce the sensitivity of the algorithm to the initial conditions, thereby obtaining consistent reconstruction results under different measurement conditions;

[0030] S304, perform image reconstruction, use regularization technology to overcome the problem of ill-posedness, and use the Landweber algorithm to find the conductivity distribution that best matches the measurement in the canonical signal space to improve the image spatial resolution;

[0031] The Tikhonov regularization technique is applied to solve the ill-posed problem in the image reconstruction process, and the conductivity distribution that best matches the measured data is found in the normalized signal subspace through the Landweber algorithm.

[0032] S305 and Landweber algorithms optimize images through iteration and edge enhancement technology, refine conductivity details, improve contrast and clarity, and provide high-quality visual information for diagnostic monitoring; they use an iterative algorithm to optimize the reconstructed image to more accurately reflect the actual conductivity distribution.

[0033] According to some implementations, the working process of the adaptive adjustment mechanism of the data acquisition and processing unit in step S2 includes:

[0034] S201, the data acquisition and processing unit evaluates the signal quality according to the real-time signal transmitted by the signal line;

[0035] Analyze the quality of real-time signals, adopt a multi-parameter real-time monitoring strategy, and conduct in-depth analysis of the data collected from each electrode, including but not limited to signal strength, signal-to-noise ratio, stability and consistency indicators;

[0036] S202, screening the numerous signals evaluated to intelligently identify the electrodes with problems;

[0037] First, the noise source is intelligently located. Through the signal processing algorithm, the electrodes significantly affected by the noise are identified. These electrodes may suffer from signal quality attenuation due to poor contact, corrosion or external environmental interference. Then, the signal distortion is detected. Through in-depth analysis of the signal waveform characteristics and frequency response, the electrodes with signal distortion or abnormality are identified to ensure the purity and accuracy of the signal.

[0038] S203, according to the detection results and data, dynamic excitation parameter adjustment is performed through the data acquisition and excitation module to optimize and improve the effect of signal monitoring;

[0039] First, the excitation signal is adaptively adjusted to automatically adjust the excitation voltage or current of the problematic electrode, and the signal acquisition is intelligently optimized according to the monitoring results. Then, the electrode configuration is intelligently optimized based on real-time monitoring data to automatically adjust the electrode configuration, including the optimization of the electrode arrangement and working mode, to maximize signal coverage and significantly improve signal quality.

[0040] S204, the data acquisition and processing unit verifies the signal quality and provides closed-loop feedback according to the quality of the received signal, and repeats the process until the signal meets the expected requirements;

[0041] The data acquisition and excitation module, and the A / D conversion and control module re-evaluate the signal quality after each parameter adjustment to ensure the effectiveness of the optimization measures and the continuous improvement of signal acquisition. For adjustments that fail to achieve the expected performance, the feedback loop is automatically started to re-identify the problem and make targeted adjustments until the signal quality is stable and meets the preset standards.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The tailings density visualization monitoring device and control method based on ERT technology provided by the present invention make the monitoring comprehensive and direct; the electrode array is used to cover multiple positions of the thickener, and the ERT technology obtains comprehensive electrical information of the tailings, which can more accurately reflect the overall state and avoid misjudgment than traditional manual sampling and pressure sensors; the ERT technology directly measures resistivity, and compared with pressure sensors, it needs to indirectly calculate and infer the tailings concentration and height, reducing errors in intermediate links.

[0044] Visual operation is performed through the image reconstruction unit. Visualization facilitates operators to observe and make decisions more intuitively. Operation control can be quantified and the control is more precise: the visual monitoring device presents the tailings resistivity distribution in the form of images. Operators can intuitively judge the thickening problem and make timely adjustments, which is different from the traditional method. The data acquisition and excitation module of the data acquisition and processing unit applies excitation current to the electrode array to establish a sensitive field, and measures the voltage value at the boundary of the sensitive field. The measured data is transmitted to the image reconstruction unit through the communication module, and the conductivity distribution inside the object is reconstructed with an appropriate algorithm, thereby obtaining a medium distribution image. Based on the direct feedback of the tailings status information in the thickener using the ERT technology, the operating parameters can be adjusted to make the thickening process more stable and efficient.

[0045] The control method provided by the present invention adopts ERT technology for real-time monitoring and precise control to keep the tailings thickening process in an ideal state, reduce interruptions and improve efficiency, which is conducive to stable production and accident prevention; visual monitoring can detect potential problems in advance, handle them in time to avoid safety accidents and improve safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of the structure of a tailings density visualization monitoring device based on ERT technology provided in an embodiment of the present invention.

[0047] Figure 2 A side view of the distribution of electrodes on the thickener wall and the conical bin wall in the thickener provided in an embodiment of the present invention.

[0048] Figure 3 A distribution diagram of electrodes on the conical bin wall of a thickener provided in an embodiment of the present invention.

[0049] Figure 4 A schematic diagram of the structure of a data acquisition and processing unit provided in an embodiment of the present invention.

[0050] Figure 5 A flow chart of a control method of a tailings density visualization monitoring device based on ERT technology provided in an embodiment of the present invention.

[0051] Figure 6 A sensitive field is established for adjacent stimulation modes provided in an embodiment of the present invention.

[0052] Figure 7 A reconstructed medium distribution image in a thickener provided by an embodiment of the present invention.

[0053] Figure 8 The embodiments of the present invention provide 8 electrodes, 16 electrodes and 32 electrodes to respectively reconstruct images of the simulated flow patterns.

[0054] Fig. 9The equipotential line distribution diagram of different numbers of electrodes in a uniform medium provided by an embodiment of the present invention.

[0055] In the figure:

[0056] 1-1, first electrode; 1-2, second electrode; 1-3, signal line protection housing; 1-4, signal line; 2, thickener body; 2-1, thickener wall; 2-2, conical bin wall; 2-3, conical bin discharge port; 2-4, thickener inner cavity. DETAILED DESCRIPTION

[0057] In recent years, sensor technology and image processing technology have developed rapidly. Electrical resistivity imaging technology (ERT) has been widely used in many fields due to its advantages such as non-invasiveness, strong real-time performance and high resolution. ERT technology measures the conductivity difference of different media in the thickener and is suitable for monitoring different media in multiphase fluids. In the field of tailings density monitoring in mine filling, the appropriate application of ERT technology can realize real-time, continuous and visual process monitoring of tailings density. Compared with traditional monitoring methods, ERT technology has significant advantages in tailings density monitoring: (1) Real-time continuity. ERT technology can realize continuous monitoring of the tailings density process and provide real-time tailings density status information, which is helpful to adjust parameters in time and optimize the thickening effect. (2) Visual monitoring. The images generated by ERT can intuitively show the changes in tailings density in the thickener, provide data support for in-depth understanding of the thickening process, and help improve the scientificity and accuracy of tailings treatment. (3) Non-invasiveness. This feature of ERT technology avoids interference with the tailings thickening process and ensures the objectivity and reliability of monitoring. Therefore, it is of great significance to study and apply ERT technology in the visualization of mine tailings density.

[0058] The present invention is described in detail below in conjunction with the embodiments and drawings, but it should be understood that the embodiments and drawings are only used to exemplify the present invention and do not constitute any limitation on the protection scope of the present invention. All reasonable changes and combinations within the scope of the inventive concept of the present invention fall within the protection scope of the present invention.

[0059] The present invention will be further described below in conjunction with the accompanying drawings.

[0060] Example 1

[0061] This embodiment provides a tailings density visualization monitoring device based on ERT technology (Electrical Resistance Tomography, resistivity imaging technology), hereinafter referred to as the monitoring device. The design of the monitoring device is to achieve real-time and accurate monitoring of tailings distribution and density changes during the tailings density process.

[0062] like Figure 1 and Figure 2 As shown, the monitoring device includes a thickener, an ERT electrode sensing system, a data acquisition and processing unit, and an image reconstruction unit.

[0063] Among them, the ERT electrode sensing system is arranged in the thickener body 2, applies stimulation current and converts it into an acceptable electrical signal, which includes a first electrode 1-1 that is evenly layered and embedded in the cylindrical inner wall of the deep cone thickener and a second electrode 1-2 embedded in the conical bin wall 2-2 of the deep cone thickener. The first electrodes 1-1 are 16 in annular direction, with a total of 7 layers; the second electrodes 1-2 are 16 in annular direction, with a total of 3 layers; a total of 160 electrodes; also includes signal lines 1-4 distributed in each layer of the thickener body and connecting the data acquisition and processing unit to transmit information, and a signal line protection shell 1-3 for protecting the signal lines of each layer of electrodes in the thickener wall 2-1; the thickener body 2 is a device for accelerating the gravity thickening of tailings, and the internal stirring provides a detectable environment. It includes a thickener wall 2-1 for wrapping the slurry to provide a thickening environment and support the thickening of the slurry, a conical bin wall 2-2 for facilitating the gravity sedimentation, thickening and collection of tailings, a conical bin outlet 2-3 for concentrated discharge of the thickened tailings, and a thickener inner cavity 2-4 for providing a thickening space; a data acquisition and processing unit, which is connected to each first or second electrode in the thickener wall 2-1 through a signal line 1-4. The unit receives voltage signals from 160 electrodes through the signal line 1-4 and converts them into digital signals for processing, thereby improving signal quality; an image reconstruction unit, which uses an algorithm to convert digital signals into intuitive medium distribution images. By calculating the slight difference in boundary voltages between front and rear measuring points, the unit accurately reconstructs the medium layout of the measuring domain, providing data visualization support for dynamic monitoring.

[0064] The ERT electrode sensing system comprises a first electrode 1-1, a second electrode 1-2, a signal line protection shell 1-3 and a signal line 1-4; wherein the first electrode 1-1 and the second electrode 1-2 work synchronously, and these electrodes are embedded in the wall of the deep cone thickener to form a dense electrode array, forming a three-dimensional monitoring network, and by applying a stimulation current to the tailings slurry, these electrodes can capture the slight change of the slurry resistivity and convert the physical phenomenon into a quantifiable electrical signal. The layout of these electrodes ensures the comprehensive monitoring of the tailings thickening distribution state inside the thickener, even in the cone where the slurry is gradually thickened and the fluidity is reduced. The signal wires 1-4 are used to connect the first electrode 1-1, the second electrode 1-2 and the data acquisition and processing unit. These signal wires 1-4 are responsible for efficiently transmitting the electrical signals collected by all electrodes to the data acquisition and processing unit, and ensuring the real-time and accuracy of the data; the signal wire protection shell 1-3 is used to protect the signal wires from being damaged by the harsh environment outside the thickener. The special signal wire protection shell 1-3 is installed outside the thickener wall 2-1, wrapping the signal wires of each layer of electrodes, ensuring the stable connection between the electrodes and the data acquisition and processing unit 3, and maintaining the continuity and safety of signal transmission. At the same time, the data acquisition and processing unit is connected to the image reconstruction unit. The monitoring system also has an adaptive adjustment mechanism, which is based on the weight adjustment between neurons of the existing computer neural network, and automatically optimizes the system performance according to the data to adapt to different working conditions; the image reconstruction unit can dynamically adjust the working state of the electrode according to the resistivity distribution changes in the tailings thickening process monitored in real time. This adjustment may include changing the excitation voltage or current of the electrode to optimize signal acquisition and improve the quality of image reconstruction. Through adaptive adjustment, the monitoring system can more accurately reflect the actual situation of tailings thickening, thereby achieving more effective process control and optimization.

[0065] like Figures 2-3The thickener body 2 includes a thickener wall 2-1, a conical bin wall 2-2, a conical bin outlet 2-3 and a thickener inner cavity 2-4; the thickener wall 2-1 and the conical bin wall 2-2 constitute the peripheral structure of the thickener, providing a closed and thickening-supporting thickener inner cavity 2-4 for the tailings slurry. The layout of the internal ERT electrode sensor system ensures effective interaction between the electrode and the tailings. The material and design of the machine wall ensure stable storage of the slurry inside, while supporting the entire thickening process and providing the necessary physical boundaries for the sedimentation and thickening of the tailings. With support, the conical bin wall 2-2 utilizes its unique geometric shape to effectively guide the gravity sedimentation of tailings, while facilitating the collection and subsequent treatment of dense tailings, ensuring that the tailings are smoothly concentrated to the bottom of the conical bin under the action of gravity, providing convenience for subsequent discharge; the conical bin discharge port 2-3 is located at the very bottom of the conical bin, and is the key outlet for the tailings to be discharged from the thickener after being thickened by the conical bin wall 2-2. The design of the discharge port fully considers the characteristics of the dense tailings, ensuring its smooth and controllable discharge, avoiding blockage or overflow, and ensuring the continuity and efficiency of the thickening process.

[0066] The data acquisition and processing unit works in coordination with the first electrode 1-1 and the second electrode 1-2 in the thickener body 2 through the signal line 1-4 to monitor and control the tailings thickening process in real time; the data acquisition and processing unit includes a data acquisition and excitation module, an A / D conversion and control module, and a communication module. The data acquisition and excitation module is used for data acquisition excitation and ensures seamless connection with the A / D conversion and control module through the transmission of analog signals and control signals. The function of the A / D conversion and control module is to convert analog signals into digital signals, and further improve the signal quality and monitoring accuracy through internal signal processing logic, such as filtering and amplification, and adjust the excitation current according to control requirements to optimize the performance of the ERT electrode sensing system; the communication module, as the output interface of the data processing unit, is responsible for transmitting data to an external monitoring system or data processing center in a wireless or wired form to realize remote monitoring and analysis of data.

[0067] The tailings density visualization monitoring device can realize automatic non-intrusive real-time observation of the tailings density process, ensure the smoothness and safety of the production process, significantly improve the accuracy of dynamic intelligent monitoring of tailings distribution, optimize the working parameters of the thickener, and realize remote transmission and analysis of data through the communication module to enhance management efficiency, effectively avoid emergency repairs, and significantly save maintenance expenses, which plays a significant role in promoting green mine construction.

[0068] like Figures 2-3As shown, the first and second electrodes are arranged at equal intervals in the thickener wall 2-1 and the conical bin wall 2-2, and a sensitive field is produced by periodically applying an excitation current to the thickener inner cavity 2-4 to form a tailings thickening field. The first electrode 1-1 and the second electrode 1-2 keenly capture these voltage changes and provide original signals for subsequent data analysis. At the same time, the ERT electrode sensing system also has an adaptive adjustment mechanism, which can adjust the signal according to the resistivity change during the tailings thickening process; the ERT electrode sensing system adopts an adjacent stimulation mode to establish a sensitive field, and constructs a sensitive field by applying an excitation current between each group of adjacent electrodes;

[0069] For example, a layer of the first or second electrode is numbered from 001 to 016, current is excited between electrodes 001 and 002, and boundary voltage is obtained synchronously at electrodes 003, 004, and 005 to 016; then, current is excited again between electrodes 002 and 003, and voltage is measured at electrodes 004, 005, 006 to 001, and this process is repeated until all electrode pairs are excited; in the ERT electrode system with 16 electrodes in each layer, this process accumulates 224 boundary voltage readings and extracts a total of 104 independent potential values.

[0070] The image reconstruction unit uses the finite element method (FEM), that is, the conductivity distribution σ in the sensitive field Ω is known, and after applying a low-frequency excitation signal, the potential distribution Φ is solved to obtain the boundary voltage value. The measurement area 2-4 in the thickener cavity is divided into multiple finite elements, and the unit equation is constructed with the help of linear interpolation. When solving the units in different areas of the thickener, the finer the unit division, the closer the result is to the real solution. With the help of the Landweber algorithm, high-quality image reconstruction can be achieved. The conversion of digital signals into images includes five aspects: data preprocessing, subspace estimation, normalization processing, image reconstruction, and result optimization. By analyzing the voltage difference at the boundary of the thickener, the image reconstruction unit can construct the medium distribution status in the thickener and provide visual support for monitoring work.

[0071] In this embodiment, the diameter d of the thickener body 2 is 12 to 15 m, the lower conical bin wall 2-2 is 2 to 3 m high, and the upper thickener wall 2-1 is 10 to 12 m high; the first electrodes 1-1 are uniformly arrayed and embedded in the upper thickener wall 2-1, and the electrode array is arranged in 7 layers, with 16 electrodes in each layer, a total of 112 electrodes, and a spacing of 0.7 m between each layer. The thickness of the first electrode 1-1 is the same as the thickness of the container wall, with a height of 0.3 m and a width of 0.4 m; the second electrodes 1-2 are uniformly arrayed and embedded in the lower conical bin wall 2-2, and the second electrode array is arranged in 3 layers, with 16 electrodes in each layer. Electrodes, a total of 48 electrodes, the second electrode 1-2 is made of stainless steel, the distance between each layer is 0.7m, the thickness of the second electrode is the same as the conical warehouse wall 2-2, 0.3m high and 0.4m wide; one end of the signal line 1-4 is connected to 160 ERT electrodes, and the other end is connected to the data acquisition and processing unit to ensure that the analog signals received by the first and second electrodes can be safely and effectively transmitted to the data acquisition and processing unit for digitization and further data processing; the signal line protection shell 1-3 is wrapped around the outside of the signal line 1-4 to protect the integrity of the signal line and the stability of data transmission.

[0072] The ERT image reconstruction resolution depends on the number of finite element subdivision units. The more units there are, the higher the resolution. In view of this, in order to obtain higher image resolution, the number of electrodes can be appropriately increased when the number of subdivision units is fixed. Here, 8 electrodes, 16 electrodes, and 32 electrodes are used to reconstruct the image of the simulated flow pattern, and the results are presented in order. Figure 8 .a. Figure 8 .b. Figure 8 .c.

[0073] The arrangement of the first and second electrode arrays is preferably 8 electrodes or 16 electrodes per layer. In the adjacent excitation mode, the more electrodes there are, the smaller the distance between the excitation electrode pairs will be. The excitation current will flow more toward the field boundary and increase the current density, and the sensitivity distribution inhomogeneity in the field will also increase. Fig. 9 As shown in Figures 9a, 9b and 9c, the equipotential line distributions of the 8-electrode, 16-electrode and 32-electrode ERT systems show that the equipotential line distributions of the 8-electrode and 16-electrode ERT systems are more uniform, while the equipotential line distribution of the 32-electrode ERT system is uneven, and the current density is large at the field boundary.

[0074] The material of the electrode must meet the following requirements: 1. Good electrical conductivity. The conductivity of the electrode must be much higher than that of the medium being measured, and the current density must be perpendicular to the electrode surface and evenly distributed. 2. Stable chemical properties. When the tailings slurry is corrosive or an electrolyte solution, the electrode should be prevented from being corroded by electrochemical reactions. 3. Good wear resistance. Tailings particles can cause wear to the electrode, so the electrode material must have a certain degree of wear resistance. Therefore, this embodiment uses metallic copper as the electrode.

[0075] There are three types of electrodes commonly used in ERT systems: point electrodes, rectangular electrodes, and point and rectangular composite electrodes.

[0076] The rectangular electrode has a significantly better effect on improving the uniformity of current density distribution in the field than the point electrode. In the tailings slurry, the current density distribution is similar to the distribution of electric lines in the parallel plate capacitor, the electric field distribution is more uniform, and the sensitivity distribution is also more uniform than the point electrode, which is more in line with the two-dimensional field model. Therefore, this embodiment uses a rectangular electrode.

[0077] In summary, the arrangement of the sensitive fields in this embodiment has good uniformity.

[0078] The conical bin wall 2-2 is made of welded steel plates and is designed to be connected to the thickener wall 2-1. There is no nozzle configuration inside. The cone angle can be selected to be 30°, 60°, 90° or 120°, which can be flexibly adapted to different needs. Experimental verification shows that the 30° cone angle configuration significantly improves the thickening efficiency, while avoiding the problems of compaction and rake pressure, and can provide a good environment for thickening process monitoring, facilitating the collection and subsequent treatment of thickened tailings. The conical bin discharge port 2-3 is located at the bottom of the conical bin and is the outlet for the thickener after the tailings are thickened. The diameter of the conical bin discharge port 2-3 is set to 0.7m, which ensures the continuity and efficiency of the thickening process.

[0079] Example 2

[0080] This embodiment also provides a control method for the tailings density visualization monitoring device based on the ERT technology provided in Embodiment 1, such as Figure 5 The following steps are shown:

[0081] S1. Turn on the ERT electrode sensing system.

[0082] When the system is started, the ERT electrode sensor system is first calibrated, and the DC current source signal generator generates a bipolar pulse excitation. The ERT electrode sensor system receives the excitation signal from the DC current source, and applies an excitation current to the first electrode 1-1 and the second electrode 1-2 through the signal line 1-4. At this time, the electrode array conducts the excitation current to the tailings slurry, and introduces the current into the tailings thickening area 2-4 of the thickener cavity to stimulate the boundary voltage, and the change in the slurry potential distribution is converted into a voltage signal; the signals captured by the first and second electrodes are sent to the data acquisition and processing unit through the signal line 1-4;

[0083] S2. The data acquisition and processing unit receives the voltage signal transmitted by the ERT electrode sensing system and processes it.

[0084] The data acquisition and processing unit receives the voltage signals of the first electrode 1-1 and the second electrode 1-2 through the signal line 1-4;

[0085] like Figure 4The data acquisition and processing unit includes a data acquisition and excitation module, an A / D conversion and control module, and a communication module. The data acquisition and excitation module is described as follows:

[0086] 1. Amplify the collected weak voltage signal and perform other pre-processing, and pass the analog signal to the A / D conversion and control module for further processing.

[0087] 2. Start the excitation module to adjust the excitation level according to the control signal of the A / D conversion and control module.

[0088] 3. It can realize synchronous data acquisition of multiple electrode channels to ensure the consistency and accuracy of data.

[0089] 4. Provide appropriate excitation signals to the electrode array to make the tailings produce corresponding electrical responses, so that the state of the tailings can be reflected by measuring the voltage signal.

[0090] The data acquisition and excitation module transmits signals to ensure the accurate transmission of analog signals and control instructions, and establishes seamless connection with the A / D conversion and control module. The A / D conversion and control module converts the original analog signal into a digital signal that is easy to process, and dynamically adjusts the excitation current according to system requirements to optimize the performance of the ERT electrode sensing system. The communication module receives the digital signal calculated by the A / D conversion and control module, and further improves the signal purity and monitoring accuracy through internal signal optimization processes such as signal filtering and enhancement. The optimized digital signal is then transmitted to the image reconstruction unit by the module in a wired manner. At the same time, the ERT electrode sensing system can respond to the dynamic evolution of the resistivity spectrum in the tailings concentration process monitored in real time, and improve the signal acquisition efficiency and image reconstruction accuracy by intelligently adjusting the excitation parameters of each electrode, such as voltage or current intensity. The data acquisition and processing unit adjusts the electrode operation state adaptively based on real-time data feedback, through the analog signal and feedback control signal adjustment between the data acquisition and excitation module and the A / D conversion and control module, to ensure that the acquired signal is more in line with the actual resistivity distribution characteristics of the tailings slurry, thereby optimizing the process monitoring and control strategy.

[0091] The data acquisition and excitation module, A / D conversion and control module, and communication module can all be selected from existing electronic modules. For example, the data acquisition and excitation module includes a multiplexer module, an excitation current source, and an acquisition current signal generator of the analog module EM231. The multiplexer module acts on the ERT wireless sensor node to control the channels for excitation current and boundary voltage acquisition. This module uses the high-precision, 16-channel, low-voltage analog multiplexer MAX396 produced by Maxim Semiconductor. The excitation current source uses a bipolar pulse excitation mode, which is simulated and implemented with the help of a DC current source signal generator and a high-speed analog switch MAX4583. The MAX4583 chip contains multiple double-pole single-throw switches; the A / D conversion and control module includes a controller and an A / D conversion module. The controller uses the STM32F030x4 microcontroller; the controller is the core unit of the ERT wireless sensor node hardware, responsible for the selection control of MAX396, the polarity control of MAX4583, the control of the A / D conversion module and the reading of data as well as the processing and communication of data. The A / D conversion module uses the AD7663 model; the communication module uses the STM32WB5MMG 2.4G wireless module.

[0092] S3, the image reconstruction unit receives the data processed by the data acquisition and processing unit, and uses the Landweber algorithm to reconstruct the conductivity distribution map of the tailings slurry and convert it into a visual image sequence.

[0093] The image reconstruction unit receives the data describing the physical state of the thickener cavity 2-4 after being processed by the data acquisition and processing unit, and uses the finite element method (FEM) to divide the measurement area of ​​the thickener cavity 2-4 into different finite units. Then, the linear interpolation method is used to establish the unit equation for the finite unit to solve the overall equation to obtain the discrete solution in the thickener. The more subdivided units, the closer the solution is to the real solution. The image reconstruction unit uses these data to reconstruct the conductivity distribution map of the tailings slurry using the Landweber algorithm, converts it into an intuitive medium distribution image, and further converts it into a visual image sequence and video. These images can intuitively display the medium distribution in the thickener cavity 2-4, providing an instant and easy-to-understand monitoring interface. The operator adjusts the process parameters and optimizes the tailings thickening according to the real-time visualization system monitoring data and reconstructed images. The operation of the image reconstruction unit is crucial to improving the thickening efficiency, reducing energy consumption and maintaining the stable operation of the equipment. Through real-time image reconstruction, changes in the thickening process can be identified and responded to more quickly, thereby achieving more efficient tailings thickening.

[0094] The description of the data acquisition and processing unit is as follows:

[0095] 1. Generate stimulation signals through the set signal generator and amplifier to stimulate the tailings to produce electrical effects.

[0096] 2. Perform pre-processing such as amplification on the collected weak voltage signal to improve the signal quality.

[0097] 3. Convert analog voltage signals into digital signals for computer processing and analysis.

[0098] 4. In conjunction with the image reconstruction unit, with the help of filtering algorithms (existing algorithms), a large number of evaluation signals can be analyzed to accurately locate the noise source, thereby identifying the electrodes significantly affected by the noise.

[0099] 5. The data acquisition and processing unit can detect electrodes with distorted or abnormal signals by deeply analyzing the waveform characteristics and frequency response of the signal to ensure the purity and accuracy of the signal.

[0100] 6. The data acquisition and excitation module automatically adjusts the excitation voltage or current of the problematic electrode according to the monitoring results to optimize signal acquisition. When facing environmental noise, the system will increase the excitation intensity to overcome noise interference; when there is a risk of signal saturation, the excitation will be reduced to ensure that the signal acquisition is in the optimal state.

[0101] 7. After each parameter adjustment, the data acquisition and excitation module and the A / D conversion and control module will re-evaluate the signal quality to ensure the effectiveness of the optimization measures and the continuous improvement of signal acquisition.

[0102] The working process of the adaptive adjustment mechanism of the data acquisition and processing unit in step S2 includes:

[0103] S201. The data acquisition and processing unit evaluates the quality of the real-time signal transmitted by the signal line.

[0104] To analyze the quality of the integrated signal, the system uses a multi-parameter real-time monitoring strategy to conduct in-depth analysis of the data collected from each electrode, including but not limited to signal strength, signal-to-noise ratio, stability and consistency indicators. This comprehensive evaluation is designed to ensure the high quality and reliability of signal acquisition.

[0105] S202. Screening the numerous signals to be evaluated can be used to intelligently identify the electrodes with problems.

[0106] First, the noise source is intelligently located. Relying on the signal processing algorithm, the system can identify electrodes that are significantly affected by noise. These electrodes may suffer from signal quality attenuation due to poor contact, corrosion or external environmental interference. Then the signal distortion is detected. By deeply analyzing the waveform characteristics and frequency response of the signal, the system can identify electrodes with distorted or abnormal signals to ensure the purity and accuracy of the signal.

[0107] S203. According to the detection results and data, the data acquisition and excitation module performs dynamic excitation parameter adjustment to optimize and improve the effect of signal monitoring.

[0108] The data acquisition and processing unit first performs adaptive adjustment of the excitation signal. The system can automatically adjust the excitation voltage or current of the problem electrode and intelligently optimize signal acquisition based on the monitoring results. For example, by enhancing the excitation intensity to overcome environmental noise, or reducing the excitation to avoid signal saturation, the optimal signal can be collected. Then, by intelligently optimizing the electrode configuration and based on real-time monitoring data, the electrode configuration is automatically adjusted, including the optimization of the electrode arrangement and working mode, to maximize signal coverage and significantly improve signal quality.

[0109] S204, the data acquisition and processing unit verifies the signal quality and provides closed-loop feedback according to the quality of the received signal, and repeats the process until the signal meets the expected requirements.

[0110] The data acquisition and excitation module, and the A / D conversion and control module will re-evaluate the signal quality after each parameter adjustment to ensure the effectiveness of the optimization measures and continuous improvement of signal acquisition; for adjustments that fail to achieve expected performance, the system will automatically start a feedback loop, intelligently re-identify the problem and make targeted adjustments until the signal quality is stable and meets the preset standards.

[0111] The image reconstruction unit then performs high-precision image reconstruction and process optimization based on the optimized signals.

[0112] The image reconstruction unit uses the optimized signal data to generate high-precision tailings distribution images, providing intuitive and detailed visual information for monitoring tailings density. Then, based on the high-precision images, the system or operator can adjust the thickening process parameters, such as stirring speed, flocculant dosage, etc., to achieve significant optimization of the thickening effect and intelligent upgrade of process control, thereby improving the overall processing efficiency and environmental friendliness.

[0113] Through the above intelligent adjustments, the ERT electrode sensing system can not only significantly improve the accuracy of signal acquisition and the clarity of image reconstruction, but also gain in-depth insights into subtle changes in the concentration process, providing data support for the continuous optimization of tailings treatment technology. This mechanism achieves refinement and intelligence of concentration process control by accurately matching signal acquisition with actual working conditions, providing a strong technical guarantee for improving concentration efficiency and optimizing process parameters.

[0114] The Landweber algorithm working process in step S3 includes:

[0115] S301, the image reconstruction unit pre-processes the data to ensure that the generated information is stable and reliable:

[0116] The Landweber algorithm uses signal processing technology to filter and denoise the collected boundary voltage data to eliminate noise interference and improve the signal-to-noise ratio of the data. This step usually involves the application of a low-pass filter to remove high-frequency noise while retaining the useful components of the signal to ensure the accuracy and reliability of subsequent processing.

[0117] S302, the image reconstruction unit divides the subspace and estimates the subspace:

[0118] By applying the singular value decomposition (SVD) technique, the data matrix can be decomposed into signal and noise subspaces. The signal subspace contains the conductivity changes, while the noise subspace represents the noise components that are not related to the measurement. Through this decomposition, the algorithm can effectively extract the signal from the noise and provide a clear data basis for image reconstruction.

[0119] By establishing a data matrix and using SVD technology, the data matrix can be decomposed into a signal subspace containing conductivity change information and a noise subspace containing noise components that are irrelevant to the measurement. The algorithm can effectively extract signals from background noise and provide a clear data basis for image reconstruction;

[0120] S303, Landweber algorithm normalizes the signal subspace and implements mathematical transformation to eliminate data correlation and ensure signal orthogonality and independence:

[0121] The Landweber algorithm is used to further normalize the signal subspace. Normalization is a mathematical transformation of the signal subspace, which eliminates the correlation between data and ensures the orthogonality and independence of the signal, thereby obtaining the orthogonal basis of the signal subspace to improve the image accuracy; normalization helps to reduce the sensitivity of the algorithm to the initial conditions, thereby obtaining consistent reconstruction results under different measurement conditions;

[0122] S304. Perform image reconstruction, use regularization technology to overcome the problem of ill-posedness, capture the conductivity distribution that is conducive to measurement in the standardized signal space, and improve the image spatial resolution.

[0123] This step applies regularization techniques to solve the ill-posed problem in the image reconstruction process and find the conductivity distribution that best matches the measured data in the normalized signal subspace; in this process, the introduction of regularization terms helps to stabilize the calculation of the solution, especially when the data is incomplete or the noise level is high; Tikhonov regularization is one of the commonly used regularization methods, which improves the spatial resolution of the image by introducing a smoothing term to suppress the oscillation of the solution.

[0124] The S305 and Landweber algorithms optimize images through iteration and edge enhancement techniques, refine conductivity details, improve contrast and clarity, and provide high-quality visual information for diagnostic monitoring.

[0125] To further improve the image quality, the Landweber algorithm also includes a result optimization step, which involves an iterative algorithm. By optimizing the reconstructed image, it can more accurately reflect the actual conductivity distribution and provide high-quality intuitive information for tailings density monitoring.

[0126] Example 3

[0127] In this example, the ERT image reconstruction in step S304 adopts the Landweber algorithm in the iterative algorithm, which reduces the error between the measured data and the reconstructed data through iteration. The target equation (existing equation) is:

[0128]

[0129] Where f(g) is a function of the conductivity distribution in the target field; λ is the voltage measurement at the boundary; S is the sensitivity matrix; and g is the grayscale value vector.

[0130] The ERT electrode sensing system uses adjacent stimulation patterns to establish a sensitive field such as Figure 6 As shown:

[0131] After adding the tailings slurry into the thickener, it is gently stirred and detected with the help of the ERT electrode sensing system. Figure 7 The color distribution of each reconstructed image corresponds to the normalized conductivity value. For details, please refer to the scale on the right side of the figure. The blank area shows the color corresponding to the background conductivity value, and the blue part represents the area with lower conductivity, which reflects the higher concentration of tailings slurry.

[0132] The above embodiments are only preferred implementations of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A visual monitoring device for tailings thickening based on ERT technology, comprising a thickener for accelerating the gravity thickening of tailings, characterized in that: It also includes an ERT electrode sensor system, a data acquisition and processing unit, and an image reconstruction unit. The ERT electrode sensor system includes a first electrode uniformly layered and arranged around the cylindrical inner wall of the thickener and a second electrode on the wall of the lower conical bin. The first and second electrodes form an electrode array in the thickener. A stimulation current is applied to the tailings slurry in the thickener to excite the boundary voltage. The change in the slurry potential distribution is converted into a voltage signal and captured by the first and second electrodes. A data acquisition and processing unit, each of the first and second electrodes is connected to the data acquisition and processing unit through a signal line, and the voltage signal is excited and collected by the data acquisition and processing unit, and is sent to the image reconstruction unit after analog-to-digital conversion; The image reconstruction unit is used to reconstruct the conductivity distribution map of the tailings slurry using the Landweber algorithm according to the received signal, convert it into a visual image sequence, and reconstruct the medium distribution image in the thickener.

2. According to claim 1, the tailings density visualization monitoring device based on ERT technology is characterized by: The thickener comprises a cylindrical thickener body and a conical bin connected at the bottom. The thickener body has a diameter of 12 to 15 m and a height of 10 to 12 m. The wall of the conical bin is 2 to 3 m high.

3. According to claim 1, the tailings density visualization monitoring device based on ERT technology is characterized by: The cone angle of the cone bin below the thickener is selected from 30°, 60°, 90° or 120°.

4. According to claim 1, the tailings density visualization monitoring device based on ERT technology is characterized by: The number of the first electrodes or the second electrodes in each layer is preferably 8 or 16.

5. According to claim 4, the tailings density visualization monitoring device based on ERT technology is characterized by: The material used to make the first electrode and the second electrode is selected from copper.

6. The tailings density visualization monitoring device based on ERT technology according to claim 5 is characterized by: The first electrode and the second electrode are both rectangular electrodes.

7. A control method for the tailings density visualization monitoring device based on ERT technology according to any one of claims 1 to 6, characterized in that: The following steps are involved: S1, turning on the ERT electrode sensing system; First, the ERT electrode sensing system is calibrated, and a bipolar pulse excitation is generated by a DC current source signal generator. The ERT electrode sensing system receives the excitation signal from the DC current source, applies an excitation current to the first electrode and the second electrode through a signal line, and the electrode array conducts the excitation current to the tailings slurry, and introduces the current into the tailings thickening area of ​​the thickener cavity to stimulate the boundary voltage. The change in the slurry potential distribution is converted into a voltage signal, and the first and second electrodes capture the voltage signal and input it into the data acquisition and processing unit through the signal line; S2, the data acquisition and processing unit receives the voltage signal transmitted by the ERT electrode sensing system and processes it using an adaptive adjustment mechanism; The data acquisition and processing unit includes a data acquisition and excitation module, an A / D conversion and control module data acquisition, and a communication module; the data acquisition and excitation module is used to receive a voltage signal, and can output an excitation current, and is electrically connected to the A / D conversion and control module; the A / D conversion and control module converts the original analog signal into a digital signal that is easy to process, and dynamically adjusts the excitation current according to demand, so as to optimize the performance of the ERT electrode sensing system; the communication module receives the digital signal calculated by the A / D conversion and control module, optimizes the signal, and transmits the signal to the image reconstruction unit in a wired manner; S3, the image reconstruction unit receives the signal processed by the data acquisition and processing unit, and uses the Landweber algorithm to reconstruct the conductivity distribution map of the tailings slurry and convert it into a visual image sequence; The image reconstruction unit receives the signal transmitted by the data acquisition and processing unit, solves the ERT positive problem by using the finite element method, divides the measurement area of ​​the thickener cavity into different finite elements, and then uses the linear interpolation method to establish the unit equation for the finite element to solve the overall equation to obtain the discrete solution in the thickener; the image reconstruction unit uses the data to reconstruct the conductivity distribution map of the tailings slurry using the Landweber algorithm, converts it into a medium distribution image, and further converts it into a visualized image sequence and video to display the medium distribution in the thickener cavity. The operator adjusts the process parameters and optimizes the tailings thickening according to the real-time visualization system monitoring data and reconstructed images.

8. The control method according to claim 6, characterized in that: The Landweber algorithm working process in step S3 includes: S301, the image reconstruction unit pre-processes the data to ensure that the generated information is stable and reliable: The Landweber algorithm uses signal processing technology to filter and denoise the collected boundary voltage data to eliminate noise interference and improve the signal-to-noise ratio of the data. A low-pass filter is used to remove high-frequency noise while retaining the useful components of the signal to ensure the accuracy and reliability of subsequent processing; S302, the image reconstruction unit divides the subspace and estimates the subspace: Through the SVD technology, the data matrix is ​​decomposed into signal subspace and noise subspace. The signal subspace contains the conductivity change, while the noise subspace represents the noise component that is irrelevant to the measurement. Through this decomposition, the signal is extracted from the noise, providing a clear data basis for image reconstruction. S303, Landweber algorithm normalizes the signal subspace and implements mathematical transformation to eliminate data correlation and ensure signal orthogonality and independence; The Landweber algorithm is used to further normalize the signal subspace. Normalization is a mathematical transformation of the signal subspace, which eliminates the correlation between data and ensures the orthogonality and independence of the signal, thereby obtaining the orthogonal basis of the signal subspace to improve the image accuracy; normalization helps to reduce the sensitivity of the algorithm to the initial conditions, thereby obtaining consistent reconstruction results under different measurement conditions; S304, perform image reconstruction, use regularization technology to overcome the problem of ill-posedness, and use the Landweber algorithm to find the conductivity distribution that best matches the measurement in the canonical signal space to improve the image spatial resolution; The Tikhonov regularization technique is applied to solve the ill-posed problem in the image reconstruction process, and the conductivity distribution that best matches the measured data is found in the normalized signal subspace through the Landweber algorithm. S305 and Landweber algorithms optimize images through iteration and edge enhancement technology, refine conductivity details, improve contrast and clarity, and provide high-quality visual information for diagnostic monitoring; they use an iterative algorithm to optimize the reconstructed image to more accurately reflect the actual conductivity distribution.

9. The control method according to claim 6, characterized in that: The working process of the adaptive adjustment mechanism of the data acquisition and processing unit in step S2 includes: S201, the data acquisition and processing unit evaluates the signal quality according to the real-time signal transmitted by the signal line; Analyze the quality of real-time signals, adopt a multi-parameter real-time monitoring strategy, and conduct in-depth analysis of the data collected from each electrode, including but not limited to signal strength, signal-to-noise ratio, stability and consistency indicators; S202, screening the numerous signals evaluated to intelligently identify the electrodes with problems; First, the noise source is intelligently located. Through the signal processing algorithm, the electrodes significantly affected by the noise are identified. These electrodes may suffer from signal quality attenuation due to poor contact, corrosion or external environmental interference. Then, the signal distortion is detected. Through in-depth analysis of the signal waveform characteristics and frequency response, the electrodes with signal distortion or abnormality are identified to ensure the purity and accuracy of the signal. S203, according to the detection results and data, dynamic excitation parameter adjustment is performed through the data acquisition and excitation module to optimize and improve the effect of signal monitoring; First, the excitation signal is adaptively adjusted to automatically adjust the excitation voltage or current of the problematic electrode, and the signal acquisition is intelligently optimized according to the monitoring results. Then, the electrode configuration is intelligently optimized based on real-time monitoring data to automatically adjust the electrode configuration, including the optimization of the electrode arrangement and working mode, to maximize signal coverage and significantly improve signal quality. S204, the data acquisition and processing unit verifies the signal quality and provides closed-loop feedback according to the quality of the received signal, and repeats the process until the signal meets the expected requirements; The data acquisition and excitation module, and the A / D conversion and control module re-evaluate the signal quality after each parameter adjustment to ensure the effectiveness of the optimization measures and the continuous improvement of signal acquisition. For adjustments that fail to achieve the expected performance, the feedback loop is automatically started to re-identify the problem and make targeted adjustments until the signal quality is stable and meets the preset standards.

Images