57 results about "Impedance imaging" patented technology

The invention relates to the technical field of material detection, in particular to a silver coating adhesive force detection method based on multi-frequency impedance imaging, which comprises the following steps: applying a dual-mode multi-frequency excitation signal to the surface of a silver coating through an attached microelectrode array, synchronously acquiring voltage-current response data, and generating a dual-mode multi-frequency impedance original data set; performing time-varying impedance phase-locked separation on the dual-mode multi-frequency impedance original data set, extracting a dynamic impedance component of a coating-matrix interface region, and reconstructing a three-dimensional sub-interface impedance distribution diagram; and outputting an adhesive force quantized value through an oscillation frequency-adhesive force conversion model according to the spatial oscillation frequency characteristics of the impedance phase in the three-dimensional sub-interface impedance distribution diagram. According to the invention, a double-phase-locked loop system is adopted to realize the time-varying component demodulation of the impedance response of the silver coating-matrix interface, the low-frequency dynamic impedance signal modulated by silver ion migration is effectively extracted, and the weak change of the interface bonding state is sensitively reflected.

The invention relates to an electrical impedance detection method and device, an electronic device and a storage medium, which are applied to the field of electrical impedance imaging, and the electrical impedance detection method comprises the following steps: obtaining an electrode signal of a target electrode in a measurement area; a plurality of preset electrodes are attached to the measurement area; the target electrode comprises any one of a plurality of preset electrodes; the electrode signal carries excitation electrode data and measurement electrode data generated by the target electrode; determining a fluctuation value of the target electrode according to the excitation electrode data and the measurement electrode data; when the fluctuation value is within a preset fluctuation threshold range, based on a preset electrode measurement method, electrode contact impedance of the measurement area is obtained; and generating an electrode adjustment result based on the electrode contact impedance and sending the electrode adjustment result to the client. According to the method and the device, the problem that the efficiency of electrical impedance measurement is relatively low due to the fact that whether the electrode falls off or is in poor contact cannot be quickly judged during bioelectrical impedance imaging is solved.

The invention relates to the field of intelligent medical treatment, in particular to an electrical impedance imaging method and device based on mixed excitation and a program product. Comprising the steps that an initial current vector and an initial boundary voltage vector of mixed excitation are obtained, the mixed excitation comprises L kinds of excitation, and L is a natural number larger than 1; performing weighted fusion on the L excitation initial current vectors to obtain a mixed excitation current vector, and performing weighted fusion on the L excitation initial boundary voltages to obtain a mixed excitation voltage; performing electrical impedance imaging optimization function calculation based on the mixed excitation current vector and the mixed excitation voltage to obtain conductivity change; the optimization function is used for carrying out regularization calculation on regional potential distribution and conductivity variation based on the mixed excitation current vector and the mixed excitation voltage; an electrical impedance distribution image is generated based on the conductivity change. According to the invention, the resolution of EIT imaging is improved, and the method has good clinical value.

The invention discloses an electrical impedance tomography method, which integrates a physical model and deep learning to construct a deep learning framework with physical constraints so as to improve physical consistency and reconstruction precision. According to the method, a neural network based on an encoder-decoder structure is adopted, and measured boundary voltage is used as input to predict conductivity distribution of a target object; in order to improve the performance of the neural network, a physical Jacobian matrix is introduced, and the sensitivity of boundary voltage to conductivity is described; and constructing a physical regularization loss function, comparing a physically calculated Jacobi matrix with a neural network predicted Jacobi matrix, and combining a final total loss function with data error loss and physical consistency loss. According to the method, data driving model training is guided through physical constraints, combination of physical information and deep learning is achieved, the precision of electrical impedance reconstruction and the generalization ability of the model are remarkably improved, and a new solution thought is provided for the fields of medicine, flexible electronics, environment monitoring and the like.

The present invention discloses a method for detecting cancer by bioelectrical impedance based on deep learning, comprising the following steps: S1: obtaining the electrical signal data of benign and malignant samples of cancer tissues by using an electrical impedance imaging system and combining it with physiological data; S2: extracting global features of the electrical signals by using a matrix decomposition module; S3: taking the result of global feature extraction and the original electrical signal data together as new inputs and further extracting signal features by using data dependency; S4: integrating the extracted feature maps into a final classification result. The present invention uses an electrical impedance imaging system to obtain the electrical signal data of cancer tissues, performs global feature extraction through a matrix decomposition module, and at the same time takes the result of global feature extraction and the original electrical signal data together as new inputs, and further extracts signal features by using data dependency, which can improve the utilization rate of data and further enhance the accuracy and reliability of cancer detection.

The invention belongs to the technical field of respiratory therapy equipment control, and discloses a multi-band airway vibration sputum excretion adaptive optimization system, which obtains acoustic impedance distribution data of each micro-section of the inner wall of the airway of a patient through an acoustic impedance imaging technology, accurately recognizes an abnormal region of a tissue interface, and improves the accuracy of sputum excretion. And calculating a reflection enhancement coefficient and a transmission attenuation coefficient of the vibration wave in the areas. And based on the multi-path interference model and the sputum excretion demand priority matrix, a multi-band vibration wave composite field with phase modulation characteristics is generated, and effective compensation of the energy transfer efficiency of the abnormal region of the tissue interface is realized. A closed-loop feedback control system is formed by monitoring spectrum characteristics of pressure pulsation signals in an airway in real time, identifying characteristic harmonic peaks and dynamically adjusting vibration wave parameters according to the characteristic harmonic peaks. The sputum excretion parameters can be optimized in real time according to individual differences of patients and sputum characteristics, the sputum excretion efficiency is remarkably improved, and discomfort of the patients is relieved.

The invention relates to an electrical impedance tomography data processing method and device and a storage medium. The impedance imaging data processing method comprises the following steps: acquiring a plurality of channel data of a plurality of electrodes in a measurement area; calculating a signal disturbance comprehensive index based on the channel data; under the condition that the data state is determined to be an abnormal state based on the signal disturbance comprehensive index, filtering and baseline correction are carried out on the channel data based on the signal disturbance comprehensive index to obtain optimized data; obtaining tissue transmission impedance based on the channel data; on the basis of tissue transmission impedance, respiratory impedance physiological characteristic indexes are obtained; determining a mutation frame in the optimization data based on the respiratory impedance physiological characteristic index; and obtaining valid data based on the mutation frame. According to the invention, the problem of low data validity in the electrical impedance imaging system is solved.

The present application relates to a kind of electrode belt and electrode assembly for electrical impedance imaging, it is related to biomedical electrical impedance imaging technical field.The electrode belt for electrical impedance imaging of the present application, including band body, the band body includes multiple electrode segments arranged along length, and elastic segment between adjacent electrode segments, the electrode segment can be used to set electrode assembly, the thickness of the electrode segment is greater than the thickness of the elastic segment, so that the elastic segment is more likely to occur elastic deformation compared with the electrode segment.Band body can be more conveniently fixed around the body of the object to be measured, and the fixing mode is simple, can make electrode and the surface of the object to be measured always keep good contact.

The invention discloses a bioelectrical impedance cancer detection method based on deep learning, and the method comprises the following steps: S1, obtaining electrical signal data of a benign sample and a malignant sample of a cancer tissue through an electrical impedance imaging system, and combining the electrical signal data with physiological data; s2, performing global feature extraction on the electric signal by using a matrix decomposition module; s3, taking a global feature extraction result and original electric signal data as new input, and further extracting signal features by using a data dependency relationship; and S4, mapping and integrating the extracted features into a final classification result. The electrical signal data of the cancer tissue is acquired by using the electrical impedance imaging system, global feature extraction is performed through the matrix decomposition module, the result of global feature extraction and the original electrical signal data are used as new input, and the signal features are further extracted by using the data dependency relationship, so that the utilization rate of the data can be improved; and the accuracy and reliability of cancer detection are further improved.

The utility model relates to the technical field of electrical impedance imaging, in particular to an electrical impedance imaging signal acquisition circuit and an acquisition device, the electrical impedance imaging signal acquisition circuit comprises an excitation signal generation circuit and a measurement signal detection circuit, a first voltage follower circuit is arranged in the measurement signal detection circuit in series, and a second voltage follower circuit is arranged in the measurement signal detection circuit. The first voltage following circuit can increase the input impedance of the measurement signal detection circuit, so that the measurement signal detection circuit is equivalent to an ideal voltmeter, and almost does not draw current from a motor of the excitation signal generation circuit, thereby enabling the voltage of a motor signal not to be remarkably changed, reducing the influence on the motor signal, and improving the reliability of the motor. And the authenticity and integrity of the measurement signal are ensured.

The invention provides a floating photovoltaic power station grounding resistor fault positioning method and device, the method is applied to a controller in a fault positioning system, and the fault positioning system further comprises a grounding grid of a floating photovoltaic power station, a sensing network formed by deploying sensors at key nodes of the grounding grid, and a signal generator. The method comprises the following steps: in response to a received fault positioning instruction, triggering a signal generator to inject a high-frequency test current signal into a grounding grid; acquiring a current-voltage data set through a voltage response signal of each key node acquired by a sensing network; based on the current-voltage data set, resistivity distribution reconstruction is carried out by adopting an impedance imaging algorithm, and a three-dimensional resistivity distribution diagram of the grounding grid is generated; and according to the three-dimensional resistivity distribution diagram, positioning an abnormal region with a resistance value exceeding a preset threshold value in the grounding grid. According to the method, three-dimensional visual imaging of resistivity distribution in the grounding grid and accurate positioning of an abnormal area are realized.

The invention discloses a meat quality detection method and a meat quality detection system based on flexible biological impedance imaging. The meat quality detection method comprises the following steps: acquiring impedance original values, PH, chromatic aberration and tenderness of a to-be-detected meat sample measured according to a preset measurement principle at a plurality of time points; correcting the impedance original value of the to-be-detected meat sample by using the correction coefficient; reconstructing the corrected impedance original value to obtain a reconstructed impedance value; obtaining the time point of meat deterioration according to the PH, color difference and tenderness at different time points; obtaining the average impedance value of all the reconstructed impedance values of the meat sample to be measured at the deterioration time point and the initial time point, calculating the impedance change rate of the meat sample to be measured at the deterioration time point and the initial measurement time point by using the reconstructed average impedance value, and comparing the impedance change rate with the pre-calculated impedance change rate of the frozen meat sample, if the impedance change rate of the to-be-detected meat sample is greater than the freezing impedance change rate, the to-be-detected meat sample is an unfrozen fish, otherwise, the to-be-detected meat sample is an iced fish. The method is simple in step, easy to detect and accurate in detection result.

This invention discloses a method for measuring cranial thickness based on bioelectrical impedance analysis (BIA), comprising: dividing the target region into subdivisions using the finite element method (FEM) to establish a finite element model; acquiring surface voltage signals of the skull using a four-electrode method on a brain electrical impedance imaging platform; obtaining the conductivity distribution of each node of the triangular element by solving the inverse voltage problem; inputting the conductivity distribution into the finite element model and calculating the distance between the node with the largest resistivity change and the corresponding boundary voltage node by solving the forward problem, thus obtaining the skull width at a set of signal locations; measuring the node voltages and calculating the corresponding skull thickness using a sixteen-electrode cyclic excitation method; and finally, assembling all node voltages and skull thicknesses into a plane to obtain the overall skull thickness. This invention designs a method for measuring cranial thickness based on bioelectrical impedance analysis (BIA), providing an accurate method for calculating cranial thickness.

The present invention belongs to the field of respiratory therapy equipment control technology. The present invention discloses a multi-band airway vibration expectoration adaptive optimization system. The acoustic impedance distribution data of each micro-segment of the patient's airway inner wall is obtained through acoustic impedance imaging technology, the abnormal tissue interface area is accurately identified, and the reflection enhancement coefficient and transmission attenuation coefficient of the vibration wave in these areas are calculated. Based on the multi-path interference model and the expectoration demand priority matrix, a multi-band vibration wave composite field with phase modulation characteristics is generated to achieve effective compensation for the energy transfer efficiency of the abnormal tissue interface area. By real-time monitoring of the spectral characteristics of the pressure pulsation signal in the airway, the characteristic resonance peak is identified and the vibration wave parameters are dynamically adjusted accordingly to form a closed-loop feedback control system. The present invention can optimize the expectoration parameters in real time according to the individual differences of the patients and the characteristics of the sputum, significantly improve the expectoration efficiency, and reduce the discomfort of the patients.

The invention belongs to the technical field of medical rehabilitation assessment, and provides a urinary surgery postoperative wound rehabilitation assessment method based on artificial intelligence, electronic equipment and a storage medium. The method comprises the steps of multi-source data acquisition, data preprocessing, spatial alignment, normalization processing, three-branch network feature extraction, feature integration, feature compression, feature mapping and wound rehabilitation grade matching. Through three-mode objective data of microwave imaging, electrical impedance imaging and bioelectrical impedance, the whole data acquisition process is free of invasion and interference, and errors caused by individual differences are reduced; through a three-branch feature extraction network and a deep fusion process, different types of data characteristics are adapted, cross-modal information complementation is realized, and wound rehabilitation multi-dimensional key signals are captured.

The invention provides wearable non-invasive lung function monitoring equipment, system and device based on impedance imaging and a medium. The wearable non-invasive lung function monitoring equipment, system and device based on impedance imaging have the advantages that limitation of traditional equipment is overcome, complex operation of a patient is not needed, subjective interference is reduced, and imaging and function testing can be integrated to complete comprehensive lung function evaluation; 2, the device is highly portable and wearable, is extremely miniaturized and is suitable for multiple scenes, and the flexible sensor belt is comfortable and stable to wear; intelligence and interconnection are achieved, real-time remote medical treatment can be achieved through cloud data sharing, and the early diagnosis and management efficiency of diseases is improved in combination with the AI technology; the method can be applied to multiple scenes, and a high-value scheme is provided for chronic disease management, postoperative rehabilitation, health screening and the like; intelligent interaction and platform integration are supported, real-time online communication between doctors and patients is facilitated, and the hospital is assisted to improve diagnosis and treatment efficiency; and 6, personalized health management and diagnosis optimization are promoted, precise health prediction and intervention suggestions are given by depending on an AI algorithm, and a dynamic analysis tool is also provided to jointly manage the health trend and improve the treatment effect.

The invention relates to the technical field of biomedicine, and provides an electromagnetic imaging method, device, system and equipment and a medium, and the method comprises the steps: constructing an electrical impedance imaging model and an electromagnetic imaging model; generating a source domain data set by using the electrical impedance imaging model, and generating a target domain data set by using the electromagnetic imaging model; training a preset auxiliary model according to the source domain data set to obtain an improved auxiliary model; performing adaptation layer training and whole network fine tuning on the improved auxiliary model by using the target domain data set to obtain an electromagnetic imaging optimization model; and inputting to-be-tested data in the target domain data set into the electromagnetic imaging optimization model to output a reconstructed image of craniocerebral lesion signal distribution. By means of the scheme, the advantage that a large number of data sets are easy to generate during electrical impedance imaging model construction is utilized, under limited resources, the optimal balance between calculation time and imaging precision is achieved, and generalization of the model in similar problems is verified.

The invention relates to the technical field of electrical impedance imaging, and discloses a high-frame-rate electrical impedance imaging method and system based on sparse representation. The method comprises the steps of collecting original electrical impedance data, and performing preprocessing to obtain a training sample matrix; carrying out dictionary learning algorithm training by utilizing the training sample matrix to obtain an optimized dictionary matrix; setting a mask matrix for electrical impedance data acquisition, and performing sparse sampling on the original electrical impedance data according to the mask matrix to obtain incomplete data frames; and carrying out sparse representation on the incomplete data frame by using the optimized dictionary matrix, solving a corresponding sparse vector, and recovering the original electrical impedance data by using the dictionary matrix and the sparse vector. The method can effectively improve the system frame rate, is suitable for different electrical impedance imaging platforms, and does not depend on specific hardware.

The utility model relates to an electrode band which is used for application scenes such as human body electrical impedance imaging needing a multi-point monitoring human body impedance imaging technology, due to the fact that an avoiding section is arranged between a first electrode set and a second electrode set, contact between electrodes and the spine is avoided through the design of the avoiding section; in this way, signal interference caused by poor attaching contact between the protruding uneven structures of the spine of the body and the electrodes is reduced, and the problem that signal transmission is unstable or insufficient in accuracy is solved. Moreover, the electrodes of the electrode bands protrude towards the side, making contact with the skin of the patient, of the flexible band body, so that stable contact with the skin is ensured, and the quality and stability of signal collection are further improved.

The present application relates to the field of electrical impedance imaging, and discloses a regional lung perfusion delay time analysis method, an electronic device and a storage medium. A dynamic image of conductivity change caused by blood perfusion in a human thoracic cavity is obtained. A blood perfusion image of a heart region and a blood perfusion image of a lung region are separated. A reference curve of conductivity change of the heart region is obtained according to the dynamic image of blood perfusion conductivity change and the blood perfusion image of the heart region. A conductivity change curve of each pixel point in the blood perfusion image of the lung region is obtained according to the dynamic image of blood perfusion conductivity change and the blood perfusion image of the lung region. The conductivity change curve is divided by a sliding time window, and a regional lung perfusion delay time is calculated in each time window. The time resolution advantage of electrical impedance imaging technology is fully utilized, and the time delay of the time when the local blood volume of the lung reaches the maximum relative to the end of the ventricular diastole is reflected by calculating the regional lung perfusion delay time.

The present invention relates to an electrical impedance imaging assembly (10), an electrical impedance imaging device, an electrical impedance imaging set, and a method for manufacturing the electrical impedance imaging assembly. The electrical impedance imaging assembly (10) includes at least one electrode patch (11, 21) externally applied to a patient. The electrode patch includes multiple electrodes (12, 22) for measuring the impedance of a target object located between the multiple electrodes (12, 22). The electrical impedance imaging assembly further includes a first interface (13) for transmitting electrical signals between the electrode patches (11, 21) and an external device (100). The external device calculates a feature of the target object, particularly a three-dimensional tomographic image (101) of the target object, based on the electrical signals from the electrodes (12, 22). At least the first electrode patch (11) includes at least 12 electrodes (12), preferably 16 to 32 electrodes, and the electrodes (12) are arranged in a two-dimensional pattern, preferably a matrix, on a substrate (14) of the first electrode patch. The first electrode patch (11) comprises an adhesive (15) for fixing the first electrode patch (11) to the patient's skin, and preferably the ratio of the area of ​​the adhesive surface (16) for contact with the skin to the area of ​​the total surface (17) of the electrode (12) facing the skin is between 0.5 and 2.

The present invention relates to a magnetic resonance passive high-impedance imaging gain system. A transmitting coil is used to transmit a magnetic resonance signal to a target site to be imaged, and a receiving coil is used to receive an electromagnetic signal returned from the target site to be imaged. At a target location to be imaged, a passive high-impedance gain coil is disposed, a single-channel high-impedance coil being in parallel connection with a resonant circuit and a detuning circuit, and the single-channel high-impedance coil being used to limit the flow of internal current. When a magnetic resonance signal is received, both the resonant circuit and the detuning circuit operate simultaneously. Due to the effect of the detuning circuit, the passive high-impedance gain coil does not affect the magnetic resonance signal at this time. Meanwhile, when an electromagnetic signal is received, only the resonant circuit operates in the circuit. Under the action of the resonant circuit, the passive high-impedance gain coil enters a resonant state, thereby enhancing the electromagnetic signal. The magnetic resonance passive high-impedance imaging gain system disclosed in the present application can achieve the objective of improving imaging quality.

This application discloses a cross-hole acoustic wave reflection imaging method, which relates to the field of geotechnical engineering exploration. The method includes: using a coded acoustic wave transmitting assembly to generate a set of coded acoustic wave signals of different frequencies and propagating them to the strata surrounding the transmitting borehole; using a coded acoustic wave receiving assembly to receive all direct transmitted wave signals of the coded acoustic waves propagating through the strata to the receiving borehole, as well as all coded reflected wave signals reflected by geological bodies within the stratum, and sensing corresponding coded electrical signals. The coded acoustic wave transmitting assembly converts these signals into coded acoustic wave digital signals, and data analysis software analyzes the transmitted and reflected acoustic wave signals. If the total number of coded acoustic wave detections reaches a preset number, a three-dimensional formation wave impedance imaging map of the strata beyond the line connecting the transmitting and receiving boreholes is determined based on the time and amplitude of the reflected acoustic wave signals corresponding to any frequency. This application can achieve large-span, high-resolution reflection imaging.

The invention belongs to the field of respiratory driving evaluation, and provides a respiratory driving state evaluation method and system based on lung electrical impedance tomography aiming at the problem that traditional inspiration effort measurement operation steps are complex. The method comprises the steps that the inspiration flow rate of a patient to be monitored is obtained based on EIT equipment, and a respiratory flow rate time curve is obtained; determining a nonlinear model of the inspiration flow velocity changing along with the time waveform based on the inspiration flow velocity time curve; determining the value of a concavity index of the respiratory flow rate time curve according to the nonlinear model; and evaluating the inspiration effort degree of the to-be-monitored patient according to the value of the concavity index. According to the method, on the basis of the EIT technology, insertion of any catheter is not needed, the concavity of the flow velocity-time curve is analyzed, evaluation of the respiratory driving force is achieved, and a basis is provided for precise treatment.

The application discloses a high-temperature embedded electric impedance imaging system and a use method thereof, and belongs to the technical field of composite material nondestructive testing. The system comprises an embedded controller, a current excitation module, a channel switching module, a voltage signal acquisition module, a communication module, a high-temperature electrode array, a power supply and an upper computer system. The embedded controller is connected with the upper computer system through the communication module. The current excitation module is connected with the embedded controller and the channel switching module respectively. The channel switching module is connected with the current excitation module and the high-temperature electrode array respectively. The voltage signal acquisition module is connected with the current excitation module, the channel switching module and the embedded controller in signal. The embedded controller can automatically adjust the excitation current size to obtain a suitable excited voltage difference value. In combination with a filtering algorithm, abnormal voltage signals under the influence of high temperature and high change load can be eliminated. The high-temperature electrode manufacturing method and the cooling scheme are used to realize electric impedance detection in a high-temperature gas environment.

The invention discloses an airway clearance equipment control method based on a lung electrical impedance imaging system. The airway clearance equipment control method comprises the following steps that S1, threshold values of monitoring parameters in airway clearance equipment are set; s2, real-time numerical values of the physiological parameters are obtained through a lung electrical impedance imaging system; s3, calculating the real-time numerical values of the physiological parameters obtained in the step S2, comparing the real-time numerical values with the threshold values of the monitoring parameters set in the step S1, and adjusting the operation parameters of the airway clearance equipment according to the comparison result; and S4, circularly executing the steps S2 and S3. According to the invention, automatic control based on objective data is realized, and subjectivity caused by human judgment is avoided.

The invention provides a pseudo-ginseng digging shovel digging control system based on electrical impedance imaging, which relates to the field of intelligent digging and comprises an electrical impedance imaging component (10), a controllable digging component (20), an industrial computer, a PID (Proportion Integration Differentiation) controller (30) and a high-pressure air pump (40), the electrical impedance imaging assembly (10) is arranged in front of the controllable digging assembly (20) and comprises an electrode plate (11), an electrical impedance undercarriage (12), an electrode bar (13), an electrode slice (14) and an air injection cleaning head (15), and the controllable digging assembly (20) comprises a digging support (21), a digging shovel (22), an angle control hydraulic rod (23) and a depth control undercarriage (24); and the industrial computer, the PID controller (30) and the high-pressure air pump (40) are arranged behind the controllable excavating assembly (20). By means of the system, precise and lossless excavation of pseudo-ginseng can be achieved, and damage in the excavation process is effectively avoided.

The invention provides an FPGA-based multi-frequency electrical impedance imaging automatic compensation device and a measurement method, the device comprises an FPGA master controller, a compensation path and an electrical impedance excitation and measurement path, and the device aims to solve the problem that in the process that a voltage-controlled current source rises along with the frequency of a multi-frequency signal, the signal amplitude is reduced due to the fact that the current output stability is reduced due to output impedance attenuation. Amplitude compensation is carried out on a multi-frequency signal by using an AGC module, the amplitude of the multi-frequency signal is automatically compensated, a framework of alternating calibration and measurement is adopted, and under the action of a control signal of an FPGA, calibration is carried out firstly and then measurement is carried out through an analog switch. By means of the design, automatic amplitude compensation of multi-frequency signals is achieved, the current stability is improved, and the measurement precision of a multi-frequency electrical impedance imaging system is improved.

The invention discloses a grounding grid electrical impedance imaging method based on an adaptive smooth Newton method. The method comprises the following steps: S1, constructing a mathematical model of grounding grid electrical impedance imaging; s2, solving a correction problem: S21, dispersing a grounding grid field domain into triangular unit grids; s22, converting the Laplacian equation into a weak form by adopting a Galerkin method; s23, potential distribution is solved; s3, solving the inverse problem; S31, constructing a target function; s32, constructing a smooth functional, and performing continuous differentiable smooth approximation on a total variation regularization item in the target function; s33, dynamically adjusting the smoothing parameter by adopting a homotopy continuation strategy, and performing iterative solution based on the smoothed target function to obtain reconstructed conductivity distribution; and S4, generating an electrical impedance image of the grounding grid. The grounding grid electrical impedance imaging method effectively improves the accuracy and stability of grounding grid electrical impedance imaging, and is suitable for solving inverse problems such as grounding grid electrical impedance imaging.

The invention discloses an electrical impedance imaging system which comprises at least two electrode bands and a controller, and every two adjacent electrode bands are elastically connected. The electrode band comprises a plurality of electrodes arranged in an array; the controller is electrically connected with each electrode; the controller is used for providing electric signals for the electrodes or receiving feedback electric signals fed back by the electrodes; when the electrode bands are fixed to the body surface to be detected, the controller is configured to determine excitation electric signals provided for the electrodes according to the band spacing between every two adjacent electrode bands. According to the technical scheme provided by the invention, the imaging resolution and the imaging definition of the electrical impedance imaging system can be improved, and the imaging quality is improved.