Intelligent detector for tomography and material identification and working method thereof

Abstract

The invention discloses an intelligent detector for tomography and material recognition. The intelligent detector comprises a shell and a sensor module in the shell. The signal processing module comprises an analog switch matrix, a multi-frequency excitation signal generation unit and a response signal acquisition unit; the calculation control module is used for controlling the channel switching of the analog switch matrix, the generation of a multi-frequency excitation signal and the acquisition of a response signal, and processing the obtained transfer impedance data; the interface module receives an input instruction of a user and transmits the input instruction to the calculation control module; and outputting an image and a material identification result obtained by processing of the calculation control module. Therefore, through the technical means of applying multi-frequency current, measuring the electrical impedance distribution of an object under different frequencies and further identifying the material type according to the difference of the electrical impedance characteristics of different materials, uniform detection response is achieved to eliminate the end effect, high-precision material type identification is achieved, and wood, metal, PVC and other materials are accurately distinguished.

Description

Technical Field

[0001] This application relates to the technical field of building inspection, and in particular to an intelligent detector for tomography and material identification and its working method. Background Technology

[0002] In construction, renovation, and building inspection, accurate detection of the internal features of walls and components, such as framing, pipelines, and materials, is crucial for ensuring construction safety and quality. Intelligent detectors are smart devices used to detect internal or hidden features of objects, helping people identify internal structures and material distribution in fields such as building inspection and industrial flaw detection.

[0003] Existing intelligent detectors have certain shortcomings. Currently, the main technologies used for detecting internal features in buildings on the market are as follows: 1. Magnetic induction detectors: These locate wooden frames or metal components by detecting changes in the magnetic field generated by ferromagnetic materials such as metal nails and screws. However, this technology is ineffective for detecting pure wood structures without metal fixings, limiting its application scenarios. 2. Ultrasonic detectors: These detect internal structures by utilizing the differences in the propagation characteristics of ultrasound in different media. However, they are greatly affected by surface flatness and material density; detection accuracy is difficult to guarantee when the surface is uneven or the material density is uneven. 3. Infrared thermal imagers: These identify internal structures by detecting differences in surface temperature. However, this equipment requires significant temperature differences and is expensive, hindering large-scale application.

[0004] Meanwhile, the existing patent US20200256817A1 has the following technical problems: Regarding electric field distribution, the electric field geometry formed by the end sensor plate and the middle sensor plate is different, resulting in inconsistent response characteristics and affecting the consistency and accuracy of detection. Regarding surface dependence, detection accuracy is significantly affected by surface thickness, material, and humidity, requiring frequent calibration, which increases usage costs and operational complexity. Regarding material identification capabilities, it can mainly distinguish between "featured" and "unfeatured" materials, making it difficult to accurately identify specific material types such as wood, metal, and PVC.

[0005] Therefore, there is an urgent need for an intelligent detector to solve the above-mentioned technical problems to some extent. Summary of the Invention

[0006] This application aims to at least partially address one of the technical problems in the related art.

[0007] Therefore, one objective of this application is to propose an intelligent detector for tomographic scanning and material identification.

[0008] To achieve the above objectives, a first aspect of this application proposes an intelligent detector for tomography and material identification, comprising: a housing, and a sensor module inside the housing, including an electrode array arranged in a predetermined structure, the electrode array containing multiple electrodes; a signal processing module, including an analog switch matrix, a multi-frequency excitation signal generation unit, and a response signal acquisition unit, wherein the analog switch matrix is ​​connected to each electrode in the electrode array; wherein the analog switch matrix is ​​used to dynamically configure the multiple electrodes in the electrode array as driving electrodes or measuring electrodes; the multi-frequency excitation signal generation unit is used to output multi-frequency AC excitation signals to a designated driving electrode; the response signal acquisition unit acquires response signals to a designated measuring electrode to obtain transfer impedance data characterizing the electrical characteristics of the object under test; a calculation control module, connected to the signal processing module, for controlling the channel switching of the analog switch matrix, the generation of multi-frequency excitation signals, and the acquisition of response signals, and processing the acquired transfer impedance data; and an interface module, connected to the calculation control module, for accepting user input commands and transmitting them to the calculation control module; and outputting the image and material identification results processed by the calculation control module.

[0009] The intelligent detector and its working method for tomography and material identification in this application embodiment utilizes a technique that applies multi-frequency currents to measure the impedance distribution of objects at different frequencies, and then identifies material types based on the differences in impedance characteristics of different materials. This achieves uniform detection response to eliminate end effects, enabling high-precision material type identification and accurately distinguishing materials such as wood, metal, and PVC. Simultaneously, it enhances deep feature detection capabilities, provides intuitive internal structure imaging, reduces sensitivity to surface conditions and environmental factors, and can stably detect on different surfaces and in various environments. This effectively overcomes the shortcomings of existing detection technologies such as capacitive, magnetic induction, ultrasonic, and infrared thermal imaging in terms of detection uniformity, material identification accuracy, deep detection, imaging, and environmental adaptability.

[0010] In addition, the intelligent detector and its working method for tomography and material identification proposed in this application may also have the following additional technical features: In one embodiment of this application, the specific steps of dynamically configuring the electrode array are as follows: S1, configuring the j-th electrode and the (j+1)-th electrode in the electrode array as a driving electrode pair, where j is the initial sequence number of the electrode; S2, keeping the pair of driving electrodes unchanged, sequentially configuring other adjacent electrode pairs in the electrode array, excluding the driving electrode pair, into a pair of measurement electrodes, and performing an impedance measurement after each configuration; S3, after completing the cyclic measurement in step S2, updating the driving electrode pair to the (j+1)-th electrode and the (j+2)-th electrode; S4, repeating steps S2 and S3 until the driving electrode pair traverses all adjacent electrode pair combinations in the electrode array.

[0011] In one embodiment of this application, the outer shell includes a detection part and a gripping part. The detection part includes an electrode plate layer, a PCB fixing layer, an electromagnetic shielding layer, and an outer shell arranged sequentially from the outside to the inside. The electrode is mounted on the electrode plate layer through an elastic conductive element, and the detection end of the elastic conductive element extends out of the electrode plate layer to contact the object being measured. The surface of the electrode is coated with a contact layer. A PCB board is mounted on the PCB fixing layer. A physical control switch is provided on the gripping part.

[0012] In one embodiment of this application, the number of electrodes in the electrode array is 16, the electrode array is a 4x4 grid layout, and the electrode array includes 12 electrodes surrounding the outer layer and 4 electrodes in the center of the inner layer.

[0013] In one embodiment of this application, when outputting a multi-frequency AC excitation signal, for each combination of driving electrode and measuring electrode, the excitation signal sequentially sweeps the driving electrode from five characteristic frequencies of 10kHz, 50kHz, 100kHz, 500kHz, and 1MHz, and obtains the corresponding transfer impedance data.

[0014] In one embodiment of this application, the calculation control module processes the acquired transfer impedance data as follows: it generates multiple one-dimensional column vectors from all driving measurement electrode pairs and transfer impedance data acquired at all frequencies, based on their corresponding electrode positions, driving pairs, and frequencies, and uses these vectors as input data; it detects outliers in the input data and removes them; it reconstructs impedance tomography based on the input data and transmits the results to the interface module.

[0015] In one embodiment of this application, the specific method for reconstructing impedance tomography is as follows: In a laboratory setting, the impedance spectrum of the comparative materials is measured in advance to establish a material database; A two-dimensional planar finite element model is pre-built to simulate the detection area of ​​the object under test. This model contains multiple triangular elements, and each element is assigned an initial complex impedance value σ. o Then, a sensitivity matrix is ​​generated through calculation. ; The calculation is performed using the Tikhonov-regularized Newton-Raphson iterative algorithm, and the formula is as follows: ; Where Δσ is the updated conductivity distribution, For the sensitivity matrix, For regularization parameters, For regularization matrix, This is the measured voltage. For analog voltage; A one-step Gauss-Newton method is used for real-time imaging optimization to obtain reconstructed images; The measured spectrum of each pixel in the reconstructed image is matched with the impedance spectrum in the material database, and the material label and confidence level are obtained based on the matching results.

[0016] The working method of an intelligent detector for tomographic scanning and material identification includes the following steps: First, place the detector head of the detector smoothly onto the surface of the object being measured, ensuring full contact between the contact layer and the object surface. Then, press the physical control switch on the grip to activate the detector. The internal computing and control module of the detector controls the analog switch matrix, dynamically configures the electrode array, and performs electrode switching; the multi-frequency excitation signal generation unit outputs the set AC excitation signals in sequence, and the response signal acquisition unit synchronously acquires the voltage response signals on the measuring electrodes; The transfer impedance dataset collected in the above steps is processed, and the identification results and confidence level are displayed in real time through the interface module.

[0017] Compared with existing technologies, the present invention has at least the following beneficial effects: The present invention can achieve uniform detection response to eliminate end effect, achieve high-precision material type identification, and accurately distinguish materials such as wood, metal, and PVC; at the same time, it enhances the detection capability of deep features, provides intuitive internal structure imaging, reduces the sensitivity to surface conditions and environmental factors, and can stably detect on different surfaces and in different environments, effectively overcoming the shortcomings of existing detection technologies such as capacitive, magnetic induction, ultrasonic, and infrared thermal imaging in terms of detection uniformity, material identification accuracy, deep detection, imaging, and environmental adaptability.

[0018] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the electrode arrangement of the intelligent detector for tomography and material identification in this application; Figure 2 This is a system diagram of the intelligent detector for tomography and material identification according to this application; Figure 3 This is a flowchart illustrating the working method of the intelligent detector for tomography and material identification in this application. Detailed Implementation

[0020] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0021] The following description, in conjunction with the accompanying drawings, describes an intelligent detector for tomography and material identification, and its working method, according to embodiments of this application.

[0022] like Figure 2 As shown, the intelligent detector for tomography and material identification and its working method according to embodiments of this application may include: a housing, and an internal component of the housing. The sensor module includes an electrode array arranged in a predetermined structure, the electrode array containing multiple electrodes; it should be noted that the sensor module may also integrate a temperature sensor and a pressure sensor, used to test the ambient temperature and the pressing state of the electrodes, respectively.

[0023] The signal processing module includes an analog switch matrix, a multi-frequency excitation signal generation unit, and a response signal acquisition unit. The analog switch matrix is ​​connected to each electrode in the electrode array. The analog switch matrix is ​​used to dynamically configure multiple electrodes in the electrode array as driving electrodes or measuring electrodes. It should be noted that the analog switch matrix described in the above embodiment is a 4x16 analog switch matrix composed of 8 ADG1404 chips. The 8 ADG1404 chips jointly control 16 electrodes, forming a switch network that can be flexibly addressed by the computational control module.

[0024] The multi-frequency excitation signal generation unit is used to output multi-frequency AC excitation signals to the designated driving electrode; the response signal acquisition unit acquires the response signal to the designated measuring electrode to obtain transfer impedance data characterizing the electrical characteristics of the object under test; it should be noted that the multi-frequency excitation signal generation unit and the response signal acquisition unit can adopt a multi-excitation and measurement circuit based on the AD5940 impedance chip.

[0025] The AD5940 chip features a built-in waveform generator that can output a sinusoidal excitation signal in the range of 10kHz-1MHz via register configuration, supporting rapid switching between multiple frequency points. After being converted by the chip's internal DAC, the signal is amplified by a buffer amplifier and then sent to an analog switch matrix via a differential output circuit, providing stable multi-frequency excitation for 16 probe electrodes. Simultaneously, the impedance response signal from the electrodes is selected by the analog switch matrix and input to the AD5940 via a differential input circuit. The signal is first adjusted in amplitude by a programmable gain amplifier (PGA) to match the chip's ADC input range, and then filtered by an anti-aliasing filter to remove high-frequency noise and ensure signal integrity.

[0026] Meanwhile, the AD5940 chip's built-in ADC converts analog signals into digital signals, and the on-chip impedance measurement engine extracts the real and imaginary parts and calculates the impedance value. The chip supports single / continuous measurement modes and can communicate with the MCU via the SPI interface to read measurement parameter configuration data and integrate multi-frequency data.

[0027] The computational control module, connected to the signal processing module, is used to control the channel switching of the analog switch matrix, the generation of multi-frequency excitation signals, and the acquisition of response signals, and to process the acquired transfer impedance data. It should be noted that the computational control module can adopt a heterogeneous computing architecture of STM32H7 main controller and FPGA coprocessor, equipped with 4GB LPDDR4 memory and 64MB QSPIFlash memory, and implement real-time task scheduling based on FreeRTOS, which has the advantages of high-efficiency computing, sufficient storage and real-time response capabilities.

[0028] An interface module, connected to the computing control module, is used to accept user input commands and transmit them to the computing control module; and to output the image and material recognition results processed by the computing control module. It should be noted that the interface module may be equipped with an LCD touchscreen.

[0029] The detector also includes a power module to supply power to the aforementioned modules, ensuring their normal operation and cooperation. As this technology is existing, it will not be elaborated upon here.

[0030] In one embodiment of this application, the specific steps of dynamically configuring the electrode array are as follows: S1, configure the j-th electrode and the (j+1)-th electrode in the electrode array as a driving electrode pair, where j is the initial sequence number of the electrode; S2, keeping the pair of driving electrodes unchanged, sequentially configure the other adjacent electrode pairs in the electrode array, excluding the driving electrode pair, into a pair of measuring electrodes, and perform an impedance measurement after each configuration; S3, after completing the cyclic measurement in step S2, update the driving electrode pair to the (j+1)th electrode and the (j+2)th electrode; S4. Repeat steps S2 and S3 until the driving electrode pair traverses all adjacent electrode pair combinations in the electrode array.

[0031] In one embodiment of this application, the outer shell includes a detection part and a gripping part, wherein the detection part includes an electrode plate layer, a PCB fixing layer, an electromagnetic shielding layer and an outer shell arranged sequentially from the outside to the inside; The electrode is mounted on the electrode plate layer via an elastic conductive element, the probe end of the elastic conductive element extends out of the electrode plate layer to contact the object being measured, and the electrode surface is coated with a contact layer.

[0032] It should be noted that the elastic conductive element described in the above-mentioned embodiments can be a spring-pin contact mechanism. When the detector is working, the tip of the spring pin is in close contact with the electrode, and the spring compression generates continuous pressure to ensure the transmission of electrical signals from the electrode. After the detection is completed, the spring resets, and the tip of the pin springs back. This mechanism has the characteristics of high fault tolerance and vibration and shock resistance, and can work stably in frequent use or complex environments. At the same time, through precise structural design, it meets the requirements of multi-frequency resistance impedance tomography for electrode contact stability and signal accuracy.

[0033] A PCB board is mounted on the PCB fixing layer, and the aforementioned chips and electronic components are mounted on the PCB board. A physical control switch is provided on the gripping part.

[0034] It should be noted that the electromagnetic shielding layer described in the above embodiments is disposed outside the PCB layer and is connected to the device housing through vias, which can effectively shield external electromagnetic noise.

[0035] In one embodiment of this application, such as Figure 1 As shown, the electrode array contains 16 electrodes in a 4x4 grid layout, comprising 12 outer electrodes and 4 inner center electrodes.

[0036] The outer layer has 12 electrodes arranged around the periphery of the probe, used for most of the driving and measurement. The inner layer has 4 electrodes arranged in the central region of the probe, used for deep detection and to increase the measurement density in the central region. The advantage of this double-layer staggered arrangement is that the outer layer electrodes can cover a larger area, suitable for detecting features over a wider range, while the inner layer electrodes can enhance the measurement sensitivity in the central region, improving the detection capability for small features.

[0037] It should be noted that the 4x4 grid layout described in the above embodiments can acquire richer three-dimensional spatial information than linear arrays, which is the basis for imaging.

[0038] Meanwhile, each electrode can be a 5mm diameter circular gold-plated copper pad, which can reduce contact resistance and oxidation, and the electrode spacing is 10mm to ensure sufficient resolution in a compact space.

[0039] In one embodiment of this application, when outputting a multi-frequency AC excitation signal, for each combination of driving electrode and measuring electrode, the excitation signal sequentially sweeps the driving electrode from five characteristic frequencies of 10kHz, 50kHz, 100kHz, 500kHz, and 1MHz, and obtains the corresponding transfer impedance data.

[0040] It is understandable that for each combination of driving and measuring electrodes, after sweeping the frequency through five characteristic frequencies of the excitation signal (10kHz, 50kHz, 100kHz, 500kHz, and 1MHz), a large amount of raw data will be generated.

[0041] In one embodiment of this application, the step of the calculation control module processing the acquired transfer impedance data is as follows: All transfer impedance data acquired from all driving measurement electrode pairs and all frequencies are used as input data by forming multiple one-dimensional column vectors based on their corresponding electrode positions, driving pairs, and frequencies. The system detects and removes outliers from the input data. It should be noted that filtering can be used to remove abnormal noise points.

[0042] Based on the input data, the impedance tomography image is reconstructed, and the result is transmitted to the interface module.

[0043] In one embodiment of this application, such as Figure 3 As shown, the specific method for reconstructing impedance tomography is as follows: In a laboratory setting, the impedance spectrum of the comparative materials is measured in advance to establish a material database; A two-dimensional planar finite element model is pre-built to simulate the detection area of ​​the object under test. This model contains multiple triangular elements, and each element is assigned an initial complex impedance value σ.o Then, a sensitivity matrix is ​​generated through calculation. It should be noted that the sensitivity matrix The effect of conductivity changes in each unit on the measured values ​​of the electrode pair is described, and the sensitivity matrix can be pre-calculated and stored in the device.

[0044] The calculation is performed using the Tikhonov-regularized Newton-Raphson iterative algorithm, and the formula is as follows: ; Where Δσ is the updated conductivity distribution, For the sensitivity matrix, For regularization parameters, This is the regularization matrix (usually the Laplacian operator is chosen to smooth the image). This is the measured voltage. For analog voltage; A one-step Gauss-Newton method is used for real-time imaging optimization to obtain the reconstructed image. It should be noted that the one-step Gauss-Newton method described in the above embodiments is for real-time imaging (>5fps) on embedded devices, performing only one iteration. Although the image quality is slightly reduced, the speed is extremely fast and can meet the user's real-time scanning needs. The measured spectrum of each pixel in the reconstructed image is matched with the impedance spectrum in the material database, and the material label and confidence level are obtained based on the matching results. It should be noted that the impedance spectra of various common building materials (such as dry wood, wet wood, metal, PVC, gypsum, concrete, etc.) are pre-measured in the material database.

[0045] It should be noted that each pixel in the reconstructed image has an impedance spectrum at 5 frequency points. When the device is running, the measured spectrum of each pixel is matched with the spectrum in the material database (e.g., using algorithms such as K-nearest neighbor or spectral angle mapping). Then, each pixel is assigned a material label and confidence level based on the matching results.

[0046] Specifically, the reconstructed conductivity distribution map is displayed in pseudo-color, with highly conductive areas (such as metal) shown in red, low-conductivity areas (such as wood) shown in green, and air shown in blue. Material identification results are superimposed on the impedance image using different icons or boundary lines. For example: a brown solid box: identified as "dry wood" with high confidence; a red dashed box: identified as "metal" with a lightning bolt icon warning; and a blue dashed box: identified as "PVC". By analyzing the differences in signal penetration depth at different frequencies, the algorithm can roughly estimate the burial depth of the target and display it numerically on the screen (e.g., dry wood, depth: 15mm).

[0047] Specifically, when relevant personnel need to detect and measure the internal structure of a wall, after the equipment is started, open-circuit / short-circuit calibration is performed on all electrodes. The detector part is then smoothly placed against the surface of the object being measured, ensuring full contact between the contact layer and the object surface. The physical control switch on the grip is pressed to activate the detector. Subsequently, the internal computing and control module of the detector controls the analog switch matrix, dynamically configuring the electrode array and switching electrodes. The multi-frequency excitation signal generation unit sequentially outputs the set AC excitation signals, and the response signal acquisition unit synchronously acquires the voltage response signals on the measuring electrodes. The transfer impedance dataset acquired in the above steps is processed, and the identification results and confidence level are displayed in real time through the interface module.

[0048] In summary, the intelligent detector for tomography and material identification, and its working method, as described in this application, have the following beneficial effects: 1. It can not only detect metals, but also effectively identify non-metallic materials such as wood and PVC, thus overcoming the limitations of magnetic induction technology.

[0049] 2. It is significantly less dependent on the flatness of the object surface and the temperature difference of the environment than ultrasonic and infrared technologies, and has a wider range of applications.

[0050] 3. Electrical impedance tomography (EIT) technology can reconstruct images of internal structures, enhancing the ability to detect deep features.

[0051] 4. It can directly output material type identification results and confidence levels, and generate intuitive images, reducing the professional threshold and judgment difficulty for operators.

[0052] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0053] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0054] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An intelligent probe for tomography and material identification, characterized in that, The application relates to a portable material identification device, which comprises a shell and a sensor module inside the shell. The sensor module comprises an electrode array arranged in a predetermined structure, and the electrode array comprises a plurality of electrodes. The signal processing module comprises an analog switch matrix, a multi-frequency excitation signal generation unit and a response signal acquisition unit, and the analog switch matrix is connected with each electrode in the electrode array. The analog switch matrix is used for dynamically configuring the plurality of electrodes in the electrode array as driving electrodes or measuring electrodes; the multi-frequency excitation signal generation unit is used for outputting multi-frequency alternating excitation signals to the specified driving electrodes; and the response signal acquisition unit acquires response signals from the specified measuring electrodes to obtain transfer impedance data representing the electrical characteristics of the measured object. The computing control module is connected with the signal processing module, is used for controlling the channel switching of the analog switch matrix, the multi-frequency excitation signal generation and the acquisition of the response signals, and processes the acquired transfer impedance data. The interface module is connected with the computing control module, is used for accepting the input instructions of a user and transmitting the input instructions to the computing control module, and outputs images and material identification results processed by the computing control module. The specific steps for dynamically configuring the electrode array are as follows:

2. The intelligent probe of tomography and material identification according to claim 1, characterized in that, S1, the jth electrode and the j+1th electrode in the electrode array are configured as a driving electrode pair, wherein j is the initial serial number of the electrode; S2, one pair of the driving electrodes is kept unchanged, other adjacent electrode pairs in the electrode array except the driving electrode pair are sequentially configured as a pair of measuring electrodes, and impedance measurement is performed once after each configuration; S3, after the cyclic measurement in step S2 is completed, the driving electrode pair is updated to the j+1th electrode and the j+2th electrode; S4, steps S2 and S3 are repeatedly performed until the driving electrode pair traverses all adjacent electrode pair combinations in the electrode array. The shell comprises a detection part and a holding part, and the detection part comprises an electrode plate layer, a PCB fixing layer, an electromagnetic shielding layer and an outer shell arranged in sequence from outside to inside.

3. The intelligent probe of tomographic and material identification according to claim 1, characterized in that, The electrode is mounted on the electrode plate layer through an elastic conductive element, the detection end of the elastic conductive element extends out of the electrode plate layer to contact the measured object, and the electrode surface is coated with a contact layer. The PCB fixing layer is provided with a PCB, and the holding part is provided with a physical control switch. The number of electrodes in the electrode array is 16, the electrode array is arranged in a 4X4 grid shape, and the electrode array comprises 12 electrodes surrounded by an outer layer and four electrodes in the center of an inner layer.

4. The intelligent probe of claim 1, wherein, When the multi-frequency alternating excitation signals are output, for each electrode pair combination of the driving electrode and the measuring electrode, the excitation signal sequentially sweeps the driving electrode from five characteristic frequencies of 10 kHz, 50 kHz, 100 kHz, 500 kHz and 1 MHz, and corresponding transfer impedance data is obtained.

5. The intelligent probe of tomographic and material identification according to claim 1, characterized in that, The computing control module processes the acquired transfer impedance data in the following steps:

6. The intelligent probe of tomographic and material identification according to claim 1, characterized in that, ​ The transfer impedance data collected by all driving measurement electrode pairs at all frequencies are formed into multiple one-dimensional column vectors as input data according to the corresponding electrode positions, driving pairs and frequencies; Anomalies in the input data are detected and abnormal data is removed; Impedance tomography is reconstructed according to the input data, and the result is transmitted to the interface module.

7. The intelligent probe of tomographic and material identification according to claim 6, characterized in that, The specific method for reconstructing impedance tomography is: In a laboratory environment, the impedance spectrum of the contrast material is measured in advance to establish a material database; a two-dimensional plane finite element model is established in advance to simulate the detection area of the measured object, containing multiple triangular elements, and each element is assigned an initial complex impedance value σ o , and then a sensitivity matrix is generated by calculation ; The Newton-Raphson iterative algorithm with Tikhonov regularization is used for calculation, and the formula is ; where Δσ is the updated conductivity distribution, is the sensitivity matrix, is the regularization parameter, is the regularization matrix, is the measured voltage, is the simulated voltage; A one-step Gauss-Newton method is used to optimize real-time imaging to obtain a reconstructed image; The measured frequency spectrum of each pixel in the reconstructed image is matched with the impedance frequency spectrum in the material database, and the material label and confidence are obtained according to the matching result.

8. A method of operating a smart probe for tomography and material identification according to any of claims 1-7, characterized in that, The method comprises the following steps: First, the detection part of the detector is smoothly attached to the surface of the measured object, so that the contact layer and the object surface are in full contact, and the physical control switch on the holding part is pressed to issue an instruction to start the detector; The calculation control module inside the detector controls the analog switch matrix to dynamically configure the electrode array and switch the electrodes; the multi-frequency excitation signal generation unit outputs the set AC excitation signal in sequence, and the response signal acquisition unit synchronously acquires the voltage response signal on the measurement electrode; The transfer impedance data set collected in the above steps is processed, and the recognition result and the confidence are displayed in real time through the interface module.

Images