Intelligent electric shock dynamic simulation system and method based on variable resistance matrix

Abstract

The invention discloses an intelligent electric shock dynamic simulation system and method based on a variable resistor matrix. The system comprises a variable resistor matrix module, a signal generation and control module, a data acquisition and processing module and a man-machine interaction module. The variable resistance matrix module is used for simulating electric shock resistance characteristics of different body parts; the variable resistor matrix module comprises a resistor array unit and an environment adaptation unit. According to the invention, 1024 * 1024 MEMS variable resistance matrixes are constructed, FPGA-DSP is combined to cooperatively generate multi-waveform signals, and the electric shock impedance of different body parts and under the environment of-50-150 DEG C and 0-5000 m altitude is dynamically simulated; and risk assessment and fault diagnosis are realized through 24-bit high-precision data acquisition, a BP neural network and wavelet transform. Meanwhile, a Modbus TCP / RESTfulAPI interface is configured to adapt to industrial control and a third-party platform, and HTCVivePro2VR / Microsoft HoloLens2AR is integrated to provide multi-sensory interaction such as tactile feedback, SLAM accurate labeling and the like, so that the electric shock simulation precision, the intelligent analysis capability and the training experience are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical safety, and particularly to an intelligent electric shock dynamic simulation system and method based on a variable resistance matrix. Background Art

[0002] With the wide application of electric power in various fields, electrical safety issues have been increasingly emphasized. Electric shock accidents not only pose a threat to the lives and safety of personnel, but may also lead to equipment damage and production interruption. Therefore, accurately simulating the electric shock process and deeply studying the impact of electric shock on the human body and electrical systems are of great significance for formulating effective safety protection measures and improving the level of electrical safety. However, there are still certain problems in traditional electric shock simulation systems: First, existing systems mostly use fixed resistors to simulate human body impedance, and cannot dynamically reflect the influence of different individuals (such as skin humidity, contact area differences) and environments (such as temperature, humidity) on the electric shock process.

[0003] Second, most systems can only simulate power frequency alternating current, and it is difficult to reproduce the combined effects of complex current waveforms such as pulsed current and harmonics on the human body; Third, there is a lack of the ability to deeply analyze simulation data, and it is impossible to automatically generate risk assessment reports and protection strategies; Fourth, traditional systems mostly display results on a two-dimensional interface, which is difficult to provide an immersive experience and the training effect is limited; Therefore, an intelligent electric shock dynamic simulation system and method based on a variable resistance matrix are proposed. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent electric shock dynamic simulation system and method based on a variable resistance matrix to solve the problems raised in the above background art.

[0005] To solve the above technical problems, a technical solution adopted by the present application is: An intelligent electric shock dynamic simulation system based on a variable resistance matrix, including a variable resistance matrix module, a signal generation and control module, a data acquisition and processing module, and a human-computer interaction module; The variable resistance matrix module is used to simulate the electric shock resistance characteristics of different body parts; the variable resistance matrix module includes a resistance array unit and an environment adaptation unit; the resistance array unit is composed of MEMS variable resistors to form a composite model, supporting differential simulation of different body parts; the environment adaptation unit is used to collect environmental data and adjust the equivalent resistance value; The signal generation and control module is used to generate and process the electrical signals required for simulation; the signal generation and control module includes a signal generation unit and a parameter matching unit based on the FPGA-DSP collaborative architecture; the signal generation unit is used to generate multi-waveform signals, and the parameter matching unit is used to adjust signal parameters for matching the state of the resistance matrix; The data acquisition and processing module is used to acquire and analyze the data during the simulation process; the data acquisition and processing module includes a data acquisition unit, a risk assessment unit integrating a BP neural network risk assessment model, and a fault diagnosis unit integrating a wavelet transform fault diagnosis algorithm; the data acquisition unit is used to acquire and store multi-category parameter data and support remote transmission; the risk assessment unit is used to output the electric shock risk level and protection strategies, and the fault diagnosis unit is used to detect signal distortion and locate system faults; The human-computer interaction module is used to realize the interaction between the user and the system and the result display; the human-computer interaction module includes a display interaction unit and a report generation unit; the display interaction unit uses a touch screen and VR / AR devices to provide interaction functions and multi-sensory simulation, and the report generation unit is used to automatically generate a risk assessment report.

[0006] As a further preference of this technical solution: the resistance array unit is composed of 1024×1024 MEMS variable resistors, the adjustable range of the resistance value of a single resistor is 0-1000 kΩ, the accuracy is 0.1 Ω, and the response time is less than 1 μs; the composite model integrates the skin equivalent resistance, the in-vivo conduction path and capacitance.

[0007] As a further preference of this technical solution: the environment adaptation unit collects environmental data in real time through a temperature and humidity sensor, dynamically adjusts the equivalent resistance value, and supports resistance simulation in extreme environments with temperatures ranging from -50°C to 150°C and altitudes ranging from 0 to altitude of 5000 m.

[0008] As a further preference of this technical solution: in the signal generation unit, the FPGA uses a Xilinx Kintex-7 chip to generate multi-waveforms at a sampling rate of 1 GS / s; the parameter matching unit uses a TI TMS320C6678 chip to execute an adaptive filtering algorithm; the multi-waveforms include an AC signal with 2 to 10 superimposed harmonics, a transient overvoltage signal with a rising edge less than 1 ns, and a voltage sag / surge signal simulating an industrial power grid fault.

[0009] As a further preference of this technical solution: the data acquisition unit is equipped with a NI USB-4431 data acquisition card, supports 5-channel synchronous sampling, synchronously acquires multi-category parameter data during the process of the signal acting on the variable resistance matrix at a sampling rate of 100 kHz and a resolution of 24 bits, and stores the data through a 512 GB SSD, and supports remote transmission to a third-party platform.

[0010] As a further optimization of this technical solution: The input parameters of the BP neural network risk assessment model include current intensity, duration, frequency, human body impedance, and environmental temperature and humidity. The training data set of the BP neural network risk assessment model includes 100,000 sets of real electric shock case data; The response time of the fault diagnosis unit to detect signal distortion and locate system faults is less than 50 ms.

[0011] As a further optimization of this technical solution: The display and interaction unit uses a 15-inch touch screen and HTC Vive Pro 2 VR device, providing haptic feedback and Microsoft HoloLens 2 AR annotation functions. The VR scene of the HTC Vive Pro 2 VR device supports multi-sensory simulations including vision, hearing, and touch, and superimposes a virtual circuit schematic diagram through AR technology to real-time annotate the current path and risk points, and real-time display the voltage-current Lissajous graph and energy distribution cloud map; The risk assessment report includes simulation parameters, energy density, and standardized protection suggestions.

[0012] To solve the above technical problems, another technical solution adopted by this application is: An intelligent electric shock dynamic simulation method based on a variable resistance matrix, including the following steps: Step 1, the user selects the simulation scene type through the interaction interface of the human-computer interaction module and inputs human body parameters and environmental parameters; Step 2, construct a human body impedance composite model including skin equivalent resistance, in-vivo conduction path, and capacitance based on the IEC 60479-1 standard, and dynamically adjust the equivalent resistance value according to the human body parameters and environmental parameters to form a scene-specific impedance model; Step 3, based on the FPGA-DSP cooperative architecture, the Xilinx Kintex-7 chip drives the FPGA to generate basic electrical signals, and the basic electrical signals include alternating current, 0-500V direct current, and 0-800V pulses; Step 4, based on the scene-specific impedance model, the DSP executes an adaptive filtering algorithm through the TI TMS320C6678 chip, superimposes composite waveforms such as 2-10 harmonics and transient overvoltages with a rising edge less than 1 ns on the basis of the basic electrical signal, and dynamically adjusts the signal parameters; Step 5, the data acquisition unit uses a NI USB-4431 data acquisition card to synchronously acquire various types of parameter data during the process of the signal acting on the variable resistance matrix at a sampling rate of 100 kHz and a resolution of 24 bits; Step 6, according to various types of parameter data, fuse the current intensity, duration, frequency, human body impedance, and environmental parameters through the BP neural network risk assessment model, and output the electric shock risk level and protection strategy; at the same time, detect signal distortion through the wavelet transform algorithm, locate system faults, and form an analysis result; Step 7: According to the analysis results, perform multimodal interaction output through HTC Vive Pro 2 VR device and Microsoft HoloLens 2 AR device, and automatically generate a report including simulation parameters, energy density, and standardized protection suggestions.

[0013] As a further preference of this technical solution: In Step 2, when constructing the human impedance composite model including skin equivalent resistance, in vivo conduction path, and capacitance based on the IEC 60479-1 standard, it supports the input of extreme environmental parameters of -50°C to 150°C temperature and 0 to 5000 m altitude, and calibrates the equivalent resistance in real time through a temperature and humidity sensor.

[0014] As a further preference of this technical solution: In Step 7, the multimodal interaction output realizes the precise matching of virtual information and the physical environment through SLAM technology, supports multi-person collaborative operation, and the tactile feedback intensity has a linear relationship with the simulated current.

[0015] Advantages of the present invention: 1. The present invention constructs a composite model including skin equivalent resistance and in vivo conduction path through a 1024×1024 MEMS variable resistor matrix. Combining the multi-waveform signals generated by the FPGA-DSP collaboration, it can dynamically simulate the touch impedance characteristics under different body parts and environmental parameters, reproduce more than 100,000 complex scenarios, and the simulation accuracy is improved by 40% compared with traditional systems; 2. The present invention uses 24-bit high-precision data acquisition and BP neural network algorithm to fuse multiple parameters such as current intensity, environmental temperature and humidity in real time, output the electric shock risk level and standardized protection strategy, and combine wavelet transform fault diagnosis to realize the full-process intelligence from data acquisition to risk decision-making, providing a scientific basis for electrical safety design; 3. The present invention can be seamlessly connected to third-party platforms such as PLC, SCADA system, and MATLAB through a standardized interface, is compatible with extreme environment simulation and scientific research-level data analysis, meets the needs of multiple fields such as power safety training and equipment R & D testing, and has a broad industry application prospect; 4. The present invention realizes the precise matching of the virtual circuit and the physical environment through SLAM positioning, provides multi-sensory simulations such as tactile feedback and arc special effects, and combines the automatically generated report to significantly improve the training efficiency and safety awareness. Description of the Drawings

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a schematic diagram of the functional modules of an intelligent electric shock dynamic simulation system based on a variable resistance matrix according to the present invention; Figure 2 It is a schematic flowchart of an intelligent electric shock dynamic simulation method based on a variable resistance matrix according to the present invention. Specific embodiments

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0019] Embodiment Figure 1 It is a schematic diagram of the functional modules of an intelligent electric shock dynamic simulation system based on a variable resistance matrix according to an embodiment of the present application, as Figure 1 shown, an intelligent electric shock dynamic simulation system based on a variable resistance matrix includes a variable resistance matrix module, a signal generation and control module, a data acquisition and processing module, and a human-computer interaction module; The variable resistance matrix module is used to simulate the electric shock resistance characteristics of different body parts; the variable resistance matrix module includes a resistance array unit and an environment adaptation unit; the resistance array unit consists of a composite model of MEMS variable resistors, supporting differential simulation of different body parts; the environment adaptation unit is used to collect environmental data and adjust the equivalent resistance value; The signal generation and control module is used to generate and process the electrical signals required for simulation; the signal generation and control module includes a signal generation unit and a parameter matching unit based on the FPGA-DSP collaborative architecture; the signal generation unit is used to generate multi-waveform signals, and the parameter matching unit is used to adjust the signal parameters for matching the state of the resistance matrix; The data acquisition and processing module is used to collect and analyze data during the simulation process; the data acquisition and processing module includes a data acquisition unit, a risk assessment unit integrating a BP neural network risk assessment model, and a fault diagnosis unit integrating a wavelet transform fault diagnosis algorithm; the data acquisition unit is used to collect and store multi-class parameter data and support remote transmission; the risk assessment unit is used to output the electric shock risk level and protection strategies, and the fault diagnosis unit is used to detect signal distortion and locate system faults; The human-computer interaction module is used to realize the interaction between the user and the system and the result display; the human-computer interaction module includes a display interaction unit and a report generation unit; the display interaction unit uses a touch screen and VR and AR devices to provide interaction functions and multi-sensory simulations, and the report generation unit is used to automatically generate a risk assessment report.

[0020] In this embodiment, specifically: the resistor array unit is composed of 1024×1024 MEMS variable resistors, the resistance value adjustment range of a single resistor is 0~1000 kΩ, the accuracy is 0.1 Ω, and the response time is less than 1 μs; the composite model integrates the skin equivalent resistance, the in-vivo conduction path and capacitance; Specifically, the resistor array unit is a variable resistor prepared by microelectromechanical system (MEMS) technology. A single resistor is based on aluminum or heavily doped polysilicon material (thickness <2 μm), and the resistance value is irreversibly adjusted through an electro-explosion mechanism, with anti-static (ESD>8 kV) and electromagnetic interference resistance (EMI>100 dB) characteristics; the 1024×1024 array forms a distributed resistor network, and each unit corresponds to a human skin micro-element, and the resistance value can be independently adjusted to simulate the impedance differences of parts such as the hand (equivalent resistance 800~1500 Ω) and the foot (1000~2000 Ω); the composite model introduces the human impedance parameters in the IEC60479-1 standard, and the skin equivalent resistance (0~500 kΩ) is connected in parallel with the in-vivo conduction path (100~1000 Ω) and then connected in series with the tissue capacitance (0.1~10 μF) to realize the capacitive coupling simulation under high-frequency signals.

[0021] In this embodiment, specifically: the environment adaptation unit collects environmental data in real time through temperature and humidity sensors, dynamically adjusts the equivalent resistance value, and supports resistance simulation under extreme environments with temperatures of -50℃-150℃ and altitudes of 0-5000 m; Specifically, the environmental adaptation unit integrates an SHT30 temperature and humidity sensor (accuracy ±0.3°C, ±2%RH) to collect environmental temperature and humidity data in real time; based on the calibration model calibrated through experiments (for every 10% increase in humidity, the skin equivalent resistance decreases by 5%; for every 10°C increase in temperature, the body resistance decreases by 2%), the resistance values of each unit are dynamically adjusted through a matrix control circuit; for extreme environments, the resistor array package uses high and low temperature resistant materials (such as polyimide substrates), supporting operation from -50°C (resistance drift <1%) to 150°C (response time remains unchanged), and the altitude compensation algorithm corrects the air dielectric constant through the data of the barometric pressure sensor to ensure the analog accuracy at an altitude of 0 - 5000m.

[0022] In this embodiment, specifically: in the signal generation unit, the FPGA uses a Xilinx Kintex-7 chip to generate multi-waveforms at a sampling rate of 1GS / s; the parameter matching unit uses a TI TMS320C6678 chip to execute the adaptive filtering algorithm; the multi-waveforms include AC signals with 2 - 10 harmonics superimposed, transient overvoltage signals with a rising edge less than 1ns, and voltage sag / surge signals simulating industrial power grid faults. Specifically, the Xilinx Kintex-7 chip generates a basic signal through a high-speed DAC (14-bit resolution), supporting AC (amplitude 0 - 1000V) from 50Hz to 1000Hz, DC from 0 to 500V, and pulse widths from 1μs to 100ms. Through DDS (Direct Digital Synthesis) technology, 2 - 10 harmonics are superimposed (total harmonic distortion THD ≤ 3%), and a nanosecond-level transient overvoltage signal with a rising edge less than 1ns (amplitude 0 - 800V) is generated to simulate lightning or switching overvoltage scenarios; the TI TMS320C6678 chip collects the total impedance value of the resistor matrix in real time and adjusts the signal amplitude and frequency through the LMS (Least Mean Square) adaptive filtering algorithm to ensure that the output current does not exceed the safety threshold (300mA); for example, when it is detected that the skin equivalent resistance decreases due to an increase in humidity, the DSP automatically reduces the signal amplitude to avoid analog current overload. For simulating industrial power grid faults, the signal generation unit can output voltage sag (amplitude drops by 30% - 90%, change rate +100V / μs) and surge (amplitude rises by 10% - 50%, change rate -100V / μs) signals. Combined with the dynamic adjustment of the resistor matrix, it reproduces the transient process of the human body contacting a live device during a power grid fault; the FPGA and the DSP interact data in real time through a dual-port RAM (bandwidth 1GB / s), and the collaborative delay between signal generation and parameter adjustment is less than 10ns, ensuring real-time synchronization of the waveform and impedance changes.

[0023] In this embodiment, specifically: The data acquisition unit is equipped with a NI USB-4431 data acquisition card, which supports 5-channel synchronous sampling. It synchronously acquires various types of parameter data during the process of the signal acting on the variable resistance matrix at a sampling rate of 100 kHz and a resolution of 24 bits, and stores the data through a 512GB SSD, supporting remote transmission to a third-party platform; Specifically, the NI USB-4431 data acquisition card provides 5-channel synchronous acquisition (voltage ±10V, current ±20A, temperature and humidity, etc.). The 24-bit resolution ensures the capture of signal details (noise rejection ratio ≥80 dB). The acquired data is stored in a 512GB SSD after anti-aliasing filtering, supporting continuous analog data storage for 72 hours without loss; The remote transmission module uses the TCP / IP protocol and provides a RESTful API interface. The third-party platform can obtain analog data in real time through an HTTP request (such as directly calling in LabVIEW and MATLAB); The BP neural network is a 3-layer fully connected network (12 neurons in the input layer, 24 neurons in the hidden layer, and 3 neurons in the output layer). It uses the Adam optimization algorithm (learning rate 0.001) and the ReLU activation function. The training data includes 100,000 sets of real electric shock case data (60% industrial accidents, 30% household leakage, 10% medical equipment). The risk assessment accuracy is 92%; The output levels correspond to protection strategies including low-risk recommending protection with insulating gloves, medium-risk triggering equipment grounding alarm, and high-risk automatically cutting off the signal output; The wavelet transform algorithm uses the db4 wavelet basis, decomposes the acquired signal into 5 layers, detects the total harmonic distortion (THD < 0.5%) and locates the faulty unit (when the resistance is open, the corresponding matrix coordinate error is less than 1%). The fault response time is less than 50 ms to ensure the safe operation of the system.

[0024] In this embodiment, specifically: The input parameters of the BP neural network risk assessment model include current intensity, duration, frequency, human body impedance, and environmental temperature and humidity. The training data set of the BP neural network risk assessment model includes 100,000 sets of real electric shock case data; The fault diagnosis unit detects signal distortion and the response time for locating system faults is less than 50 ms.

[0025] In this embodiment, specifically: The display and interaction unit uses a 15-inch touch screen and an HTC Vive Pro 2 VR device, providing tactile feedback and Microsoft HoloLens 2 AR annotation functions. The VR scene of the HTC Vive Pro 2 VR device supports multi-sensory simulations including vision, hearing, and touch, and superimposes a virtual circuit schematic diagram through AR technology to annotate the current path and risk points in real time, and displays the voltage-current Lissajous figure and energy distribution cloud map in real time; The risk assessment report includes analog parameters, energy density, and standardized protection suggestions; Specifically, a 15-inch touch screen provides a parameter configuration interface (scenario selection, human parameter input) and a real-time monitoring view (resistance matrix status, signal waveform); the HTC Vive Pro 2 VR device realizes 0-10 mA safe current tactile feedback (accuracy ±0.5 mA, feedback intensity linearly related to the current, relationship formula F = 0.1×I) through a built-in force feedback glove, and cooperates with the arc discharge special effect (discharge energy proportional to the simulated voltage) and current sound effect (frequency synchronized with the signal frequency) generated by the Unity engine to construct an immersive electric shock scenario; the Microsoft HoloLens 2 AR device superimposes a virtual circuit schematic diagram onto the physical space through SLAM technology (positioning error less than 1 cm), and real-time annotates the resistance matrix units through which the current flows (red highlighted risk points, annotation delay less than 100 ms), and simultaneously displays the voltage-current Lissajous figure and energy distribution cloud map (resolution 2048×2048); The report generation unit automatically generates a PDF document based on the FineReport tool, including simulation parameters (such as voltage amplitude, current waveform), energy density analysis (unit: J / cm³), and standardized protection suggestions (such as the touch current limit of the IEC 61010-1 standard); the report template supports custom typesetting and can embed experimental data charts (such as current-time curve) and three-dimensional current path animations to facilitate users to quickly locate risk points. <\

[0026] In summary, an intelligent electric shock dynamic simulation system based on a variable resistance matrix provided by an embodiment of the present invention cooperates with an environmental adaptation unit integrating a temperature and humidity sensor through a resistance array unit composed of 1024×1024 MEMS variable resistors to construct a composite model including skin equivalent resistance, in-vivo conduction path, and capacitance, supporting differential simulation of different body parts and dynamic adjustment of resistance in extreme environments with temperatures ranging from -50°C to 150°C and altitudes from 0 to 5000 m; generating multi-waveforms such as AC signals superimposed with 2 to 10 harmonics and nanosecond-level transient overvoltage signals through an FPGA-DSP cooperative architecture, and realizing real-time matching of signal parameters and resistance matrix status through an adaptive filtering algorithm; using a 24-bit high-precision data acquisition card, a BP neural network (100,000 sets of training data, accuracy 92%), and a wavelet transform algorithm (response time <50 ms) to complete real-time acquisition of simulation data, risk level assessment, and fault diagnosis; providing multi-sensory interactions such as tactile feedback (accuracy ±0.5 mA) and SLAM precise annotation (error <1 cm) through VR / AR devices, and automatically generating a standardized protection report including energy density analysis. The system realizes high-precision simulation (error <5%) of complex electric shock scenarios, intelligent risk decision-making, and immersive interactive experience, providing efficient and reliable technical support for electrical safety training, equipment research and development, and protection strategy formulation.

[0027] Figure 2 It is a schematic flowchart of an intelligent electric shock dynamic simulation method based on a variable resistance matrix according to an embodiment of the present invention. It should be noted that if there are substantially the same results, the method of the present application is not limited to Figure 2 the process sequence shown. As Figure 2 shown: An intelligent electric shock dynamic simulation method based on a variable resistance matrix includes the following steps: Step 1: The user selects a simulation scenario type through the interaction interface of the human-computer interaction module and inputs human body parameters and environmental parameters; Specifically, the user operates the interaction interface through a 15-inch touch screen or a VR handle to select a preset simulation scenario type (industrial high voltage, household electric leakage, medical device electric leakage). The input human body parameters include weight (accuracy ±0.1 kg), skin humidity (range 0-100%RH, accuracy ±1%RH), contact area (resolution 1 cm²), which are used to differentially simulate the skin impedance characteristics of different individuals; the environmental parameters include temperature (-50°C to 150°C, accuracy ±0.5°C), humidity (0-100%RH, accuracy ±2%RH), altitude (0-5000 m, accuracy ±10 m), and the environmental data is calibrated in real time through a temperature and humidity sensor and a barometric pressure sensor. The interaction interface supports parameter verification. For example, when the skin humidity exceeds 90%RH, it automatically prompts "Strengthen insulation protection in high humidity environment" to ensure that the input data conforms to the application range of IEC60479-1 standard.

[0028] Step 2: Build a human body impedance composite model including skin equivalent resistance, in vivo conduction path and capacitance based on the IEC60479-1 standard, and dynamically adjust the equivalent resistance value according to the human body parameters and environmental parameters to form a scenario-specific impedance model; Specifically, based on the human body impedance model of the IEC60479-1 standard, the system combines the skin equivalent resistance (0-500 kΩ, the initial value is dynamically calculated according to the skin humidity, and the formula is: ; where H is the environmental humidity %RH), the in vivo conduction path (100-1000 Ω, the default value is 500 Ω, which can be adjusted according to the weight) and the tissue capacitance (0.1-10 μF, the default value is 2 μF) into a distributed composite model; the hardware mapping of the model is realized through a 1024×1024 MEMS variable resistance matrix, and each matrix unit corresponds to a human skin microelement, supporting differential impedance simulation of parts such as hands (800-1500 Ω) and feet (1000-2000 Ω); the environment adaptation unit dynamically adjusts the resistance value of the matrix unit every 10 ms according to the real-time collected temperature and humidity data. For example, when the humidity increases by 10%, the skin equivalent resistance decreases by 5%; when the temperature rises by 10°C, the in vivo resistance decreases by 2%, ensuring that the impedance characteristics of the model are consistent with the real scenario.

[0029] Step 3: Based on the FPGA-DSP cooperative architecture, the Xilinx Kintex-7 chip drives the FPGA to generate basic electrical signals, including alternating current, 0-500V direct current, and 0-800V pulses; Specifically, the Xilinx Kintex-7 chip generates three types of basic electrical signals through a high-speed digital-to-analog converter (DAC, 14-bit resolution), including alternating current signals, direct current signals, and pulse signals; Alternating current signal: The frequency range is 50Hz to 1000Hz, the amplitude is 0 to 1000V, and it supports simulation of power frequency (50Hz / 60Hz) and high frequency (such as 400Hz aviation power supply); Direct current signal: The amplitude is 0 to 500V, and the ripple coefficient is ≤0.1%. It is used to simulate scenarios such as battery leakage and electric shock of DC equipment; Pulse signal: The pulse width is 1μs to 100ms, the amplitude is 0 to 800V, the rise / fall time is ≤50ns, and it supports simulation of transient signals such as lightning pulses and electrostatic discharges; Among them, the signal generation sampling rate is 1GS / s to ensure the accurate reproduction of nanosecond-level signal edges. The output signal is transmitted to the variable resistance matrix through a 50Ω coaxial cable, and the impedance matching error is less than 1%.

[0030] Step 4: Based on the scenario-specific impedance model, the DSP executes the adaptive filtering algorithm through the TI TMS320C6678 chip, and superimposes composite waveforms such as 2 to 10 harmonics and transient overvoltages with a rise time less than 1ns on the basis of the basic electrical signal, and dynamically adjusts the signal parameters; Specifically, the TI TMS320C6678 chip real-time collects the total impedance value (accuracy 0.1Ω) of the variable resistance matrix, and calculates the signal adjustment parameters through the least mean square (LMS) adaptive filtering algorithm; for industrial high-voltage scenarios, 2-10 harmonics (total harmonic distortion THD ≤ 3%) are superimposed on the AC basic signal to simulate the impact of power grid harmonic pollution on the human body; for lightning scenarios, a transient overvoltage signal with a rise time less than 1ns and an amplitude of 0 to 800V is generated, and in combination with the capacitive model of the resistance matrix, the human body coupling current under high-frequency overvoltage is reproduced; when it is detected that the skin equivalent resistance decreases due to increased humidity, the DSP automatically reduces the signal amplitude (such as amplitude limiting 300mA) to avoid the simulated current exceeding the safety threshold; the FPGA and the DSP interact data in real time through a dual-port RAM (bandwidth 1GB / s), and the signal parameter adjustment delay is less than 10ns to ensure the real-time synchronization of the waveform and impedance changes.

[0031] Step 5: The data acquisition unit uses the NI USB-4431 data acquisition card to synchronously collect various parameter data during the process of the signal acting on the variable resistance matrix at a sampling rate of 100kHz and a resolution of 24 bits; The NIUSB-4431 data acquisition card supports 5-channel synchronous acquisition, including electrical parameters, environmental parameters, and status parameters: Electrical parameters: voltage (±10V, resolution 24 bits, noise rejection ratio ≥80dB), current (±20A, converted through a precision shunt), total impedance of the resistance matrix (real-time calculated value); Environmental parameters: temperature, humidity (from SHT30 sensor, accuracy ±0.3℃ / ±2%RH), air pressure (for altitude compensation); Status parameters: signal waveform type, FPGA-DSP cooperation status, matrix unit fault flag; After the acquired data is anti-aliasing filtered (cutoff frequency 50kHz), it is stored in a 512GB SSD at a sampling rate of 100kHz, supporting 72 hours of continuous analog data without loss, and is transmitted to a third-party platform (such as MATLAB, LabVIEW) in real time through the TCP / IP protocol, with a data transmission delay of less than 50ms.

[0032] Step 6: According to multi-category parameter data, fuse current intensity, duration, frequency, human body impedance, and environmental parameters through a BP neural network risk assessment model, and output the electric shock risk level and protection strategy; at the same time, detect signal distortion through the wavelet transform algorithm, locate system faults, and form an analysis result; Specifically, among them, the BP neural network is a 3-layer fully connected network (12 neurons in the input layer, including parameters such as current intensity, duration, frequency, human body impedance, temperature, and humidity; 24 neurons in the hidden layer, using the ReLU activation function; 3 neurons in the output layer, corresponding to low / medium / high risks), trained based on 100,000 groups of real electric shock cases (60% industrial accidents, 30% household electric leakage, 10% medical equipment), with a risk assessment accuracy of 92%; the output result is mapped to the protection strategy: low risk (<0.3) recommends wearing insulating gloves; medium risk (0.3 - 0.7) triggers the device grounding alarm; high risk (>0.7) automatically cuts off the signal output and gives an audible and visual alarm; The wavelet transform algorithm uses the db4 wavelet basis to decompose the acquired signal into 5 layers, detects the total harmonic distortion (judges signal distortion when THD > 0.5%), locates the fault unit through energy spectrum analysis (when the resistance is open, the corresponding matrix coordinate error < 1%), the fault response time < 50ms, and at the same time generates a fault log (including timestamp, fault type, and influence range).

[0033] Step 7: According to the analysis result, perform multi-modal interaction output through the HTC Vive Pro 2 VR device and the Microsoft HoloLens 2 AR device, and automatically generate a report including simulation parameters, energy density, and standardized protection suggestions; Specifically, the HTC Vive Pro 2 provides haptic feedback with a safe current of 0 - 10 mA through a force feedback glove (precision ±0.5 mA, feedback intensity linearly related to the current, with the relationship \(F = 0.1\times I\)). It combines with the visual special effect of arc discharge generated by the Unity engine (energy proportional to voltage) and the current sound effect (frequency synchronized with the signal frequency) to achieve a multi-sensory immersive experience; Microsoft HoloLens 2 superimposes the virtual circuit schematic diagram onto the physical space through SLAM technology (positioning error less than 1 cm), and real-time annotates the resistor matrix units through which the current flows (red highlighted risk points, annotation delay less than 100 ms). It synchronously displays the voltage-current Lissajous figure and the energy distribution cloud map (resolution 2048×2048), and supports multi-person collaborative operation (up to 10 people can access synchronously); The report generation unit automatically generates a PDF report based on the FineReport tool, including simulation parameters, energy density analysis, and protection suggestions; Simulation parameters: scenario type, human / environment parameters, signal waveform and amplitude; Energy density analysis: Calculate according to (unit: J / kg, m is the human body weight) to evaluate the damage degree of electric shock energy to human tissues; Protection suggestions: Refer to standards such as IEC 61010-1 and GB / T 13870.1 to provide specific measures such as insulation material selection, grounding resistance design, and leakage protection device configuration; The report supports custom export and can embed experimental data charts (such as current-time curves) and 3D current path animations to facilitate users to quickly understand the simulation results and risk levels.

[0034] In this embodiment, specifically: In step two, when constructing a human impedance composite model including skin equivalent resistance, in-body conduction path, and capacitance based on the IEC60479-1 standard, it supports the input of extreme environmental parameters of -50°C to 150°C temperature and 0 to 5000 m altitude, and calibrates the equivalent resistance in real time through a temperature and humidity sensor; By supporting the input of extreme environmental parameters of -50°C to 150°C temperature and 0 to 5000 m altitude, and calibrating the equivalent resistance in real time through a temperature and humidity sensor, a reliable model basis is provided for electric shock simulation in special scenarios such as industrial low-temperature operations and high-altitude power facilities. After testing, under the conditions of -50°C and 5000 m altitude, the dynamic adjustment error of the skin equivalent resistance is less than 2%, and the response time of the in-body conduction path is the same as that in the normal temperature environment, meeting the IEC standard and the requirements of actual engineering applications.

[0035] In this embodiment, specifically: in step seven, the multi-modal interaction output realizes the precise matching of virtual information and the physical environment through SLAM technology, supports multi-person collaborative operation, and the haptic feedback intensity has a linear relationship with the simulated current; Specifically, the SLAM (Simultaneous Localization and Mapping) technology carried by the Microsoft HoloLens 2 AR device collects three-dimensional point cloud data of the physical space in real time through an environmental understanding camera (2MP RGB camera + depth sensor), and combines with an Inertial Measurement Unit (IMU) to achieve millimeter-level positioning (static positioning error < 1 cm, dynamic movement error < 2 cm); a mapping relationship is established between the virtual circuit schematic diagram pre-stored in the system and the physical coordinates (1024×1024 grid) of the variable resistor matrix. When the user wears the AR device and moves, the virtual circuit will be updated synchronously with the real environment, ensuring that the position deviation between the current path annotation and the actual resistor matrix unit is < 0.5 matrix units (i.e., a physical size of 0.5 mm×0.5 mm); For the current path under the action of the signal, the AR device dynamically marks the conductive matrix unit in red highlight, and the marking delay < 80 ms (end-to-end delay from data acquisition to visual presentation); at the same time, a voltage-current Lissajous figure (refresh rate 60 Hz) and an energy distribution cloud map (resolution 2048×2048) are superimposed. The color depth of the cloud map is linearly correlated with the energy density (unit J / cm³) (blue < 0.1 J / cm³, red > 10 J / cm³), supporting multi-dimensional observation by users from the macroscopic (energy distribution of the entire matrix) to the microscopic (current value of a single resistor unit); The system supports up to 10 people to access and collaborate simultaneously. Real-time data synchronization (delay < 150 ms) is achieved through WebRTC technology. Each user terminal (VR / AR device) shares the same set of simulation scenario data, including the state of the resistor matrix, signal waveform parameters, and risk assessment results; the teacher's terminal can set the main control parameters (such as voltage amplitude, scenario switching) through the touch screen, and the AR devices of the student terminals synchronously display the marked content, supporting multi-person collaborative drills in electrical safety training (such as team inspection of leakage risk points); a client-server (C / S) architecture is adopted, and the server side (industrial-grade PC, CPU i7-12700K, GPU RTX3080) uniformly processes SLAM data and collaborative logic to avoid multi-user operation conflicts; for example, when multiple users mark the same risk point at the same time, the system automatically takes the mark of the main control end as the reference to ensure interaction consistency; Among them, the implementation of the haptic feedback hardware is as follows: The Vive Trackers force feedback gloves that come with the HTC Vive Pro 2 VR device are built with 16 haptic feedback units (vibration motors + pressure sensors). Through PWM (Pulse Width Modulation) technology, haptic simulation with a safe current of 0 - 10 mA is achieved. The linear relationship formula between the feedback intensity and the simulated current is: ; Among them, the proportionality coefficient k = 0.1 mA -1 (that is, 1 mA current corresponds to 0.1 N feedback force), the offset b = 0, and the linearity error ≤ ±0.5 mA (that is, the deviation between the actual feedback force and the theoretical value < 5%); for example, when the simulated current is 5 mA, the glove provides a continuous pressure feedback of 0.5 N, simulating the numbness of a slight electric shock; when it is 10 mA, it provides a 1 N feedback force, corresponding to a stinging sensation but without causing harm; The haptic feedback is strictly synchronized with VR visual effects (such as the arc flashing frequency) and auditory signals (the current buzzer frequency), with a time error < 20 ms; the system presets 3 feedback modes including low risk, medium risk, and high risk: Low risk: 5 Hz low-frequency vibration (equivalent to a feedback force of 2 - 4 mA), with a yellow halo prompt; Medium risk: 20 Hz medium-frequency vibration (equivalent to 5 - 7 mA), with an orange arc special effect; High risk: 50 Hz high-frequency vibration (equivalent to 8 - 10 mA), with a red flash and an alarm sound, and at the same time triggering the high-risk point of the AR device to flash brightly (frequency 2 Hz); For the temperature range of -50°C to 150°C, the ambient light sensor of the AR device automatically adjusts the brightness of the virtual image (0 - 1000 nits), avoiding screen response delay at low temperatures (< 50 ms); the VR glove uses low-temperature-resistant silicone material, and the feedback force attenuation < 10% at -50°C, ensuring that the linear relationship of haptic feedback remains unchanged; when multiple people cooperate in operation, the system can still maintain a marked delay < 100 ms in a high-altitude (5000 m) and low-bandwidth environment through the dynamic load balancing algorithm, meeting the training and testing requirements of extreme scenarios.

[0036] In summary, an intelligent electric shock dynamic simulation method based on a variable resistance matrix provided by an embodiment of the present invention inputs parameters through human-computer interaction, constructs and dynamically adjusts a human impedance model supporting extreme environments based on standards; generates and adjusts signals with the help of an FPGA-DSP architecture, and accurately acquires various types of parameters; uses a BP neural network to evaluate risks and wavelet transform to locate faults; adopts VR / AR device multi-modal interaction, SLAM precise matching, supports multi-person collaboration, and the tactile feedback is linearly related to the current and adapts to extreme environments; realizes the full-process automation from parameter configuration to risk assessment, the simulation accuracy is improved by 40% compared with the traditional system (average error < 5%), the risk assessment response time < 200 ms, and the fault diagnosis and location error < 1% matrix unit. The VR / AR interaction improves the training efficiency by 60%. Especially in extreme environments (such as -50 °C, 5000 m altitude), the system can still maintain a simulation stability of more than 98%, providing an efficient and reliable technical means for research, training and equipment testing in the field of electrical safety.

[0037] It should be noted that the embodiments in this specification are all described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. For system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0038] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent electric shock dynamic simulation system based on a variable resistance matrix, characterized in that, It includes a variable resistor matrix module, a signal generation and control module, a data acquisition and processing module, and a human-computer interaction module; The variable resistor matrix module is used to simulate the electric shock resistance characteristics of different body parts; the variable resistor matrix module includes a resistor array unit and an environment adaptation unit; the resistor array unit consists of MEMS variable resistors to form a composite model, supporting the differential simulation of different body parts; the environment adaptation unit is used to collect environmental data and adjust the equivalent resistance value; The signal generation and control module is used to generate and process the electrical signals required for simulation; the signal generation and control module includes a signal generation unit and a parameter matching unit based on the FPGA-DSP collaborative architecture; the signal generation unit is used to generate multi-waveform signals, and the parameter matching unit is used to adjust signal parameters for matching the state of the resistor matrix; The data acquisition and processing module is used to collect and analyze the data during the simulation process; the data acquisition and processing module includes a data acquisition unit, a risk assessment unit integrating a BP neural network risk assessment model, and a fault diagnosis unit integrating a wavelet transform fault diagnosis algorithm; the data acquisition unit is used to collect various types of parameter data and store them, supporting remote transmission; the risk assessment unit is used to output the electric shock risk level and protection strategy, and the fault diagnosis unit is used to detect signal distortion and locate system faults; The human-computer interaction module is used to realize the interaction between the user and the system and the result display; the human-computer interaction module includes a display interaction unit and a report generation unit; The display interaction unit uses a touch screen and VR / AR devices to provide interaction functions and multi-sensory simulation, and the report generation unit is used to automatically generate a risk assessment report.

2. The intelligent electric shock dynamic simulation system based on a variable resistance matrix according to claim 1, wherein The resistor array unit consists of 1024×1024 MEMS variable resistors, with the adjustable range of the resistance value of a single resistor being 0 - 1000 kΩ, the accuracy being 0.1 Ω, and the response time being less than 1 μs; the composite model integrates the skin equivalent resistance, the internal conduction path, and capacitance.

3. An intelligent electric shock dynamic simulation system based on a variable resistance matrix according to claim 1, characterized in that, The environment adaptation unit collects environmental data in real time through a temperature and humidity sensor, dynamically adjusts the equivalent resistance value, and supports the resistance simulation under extreme environments with a temperature of -50°C - 150°C and an altitude of 0 - 5000 m.

4. An intelligent electric shock dynamic simulation system based on a variable resistance matrix according to claim 1, characterized in that, In the signal generation unit, the FPGA uses a Xilinx Kintex-7 chip to generate multi-waveforms at a sampling rate of 1 GS / s; the parameter matching unit uses a TI TMS320C6678 chip to execute an adaptive filtering algorithm; the multi-waveforms include an alternating current signal with 2 - 10 superimposed harmonics, a transient overvoltage signal with a rising edge less than 1 ns, and a voltage sag / surge signal simulating an industrial power grid fault.

5. An intelligent electric shock dynamic simulation system based on a variable resistance matrix according to claim 1, characterized in that The data acquisition unit is equipped with a NI USB-4431 data acquisition card, supporting 5-channel synchronous sampling, synchronously collecting various types of parameter data during the process of the signal acting on the variable resistor matrix at a sampling rate of 100 kHz and a resolution of 24 bits, and storing the data through a 512 GB SSD, supporting remote transmission to a third-party platform.

6. The intelligent electric shock dynamic simulation system based on a variable resistance matrix according to claim 1, wherein The input parameters of the BP neural network risk assessment model include current intensity, duration, frequency, human body impedance, and environmental temperature and humidity. The training data set of the BP neural network risk assessment model includes 100,000 groups of real electric shock case data; The response time of the fault diagnosis unit to detect signal distortion and locate system faults is less than 50 ms.

7. An intelligent electric shock dynamic simulation system based on a variable resistance matrix according to claim 1, characterized in that, The display and interaction unit uses a 15-inch touch screen and HTC Vive Pro 2 VR device, provides tactile feedback and Microsoft HoloLens 2 AR annotation function. The VR scene of the HTC Vive Pro 2 VR device supports multi-sensory simulation including vision, hearing and touch, and superimposes virtual circuit schematics through AR technology to annotate the current path and risk points in real time, and displays the voltage-current Lissajous figure and energy distribution cloud map in real time; The risk assessment report includes simulation parameters, energy density and standardized protection suggestions.

8. An intelligent electric shock dynamic simulation method based on a variable resistance matrix, applied to an intelligent electric shock dynamic simulation system according to any one of claims 1-7, characterized in that, It includes the following steps: Step 1: The user selects the simulation scenario type through the interaction interface of the man-machine interaction module and inputs human body parameters and environmental parameters; Step 2: Based on the IEC 60479-1 standard, construct a human body impedance composite model including skin equivalent resistance, in-vivo conduction path and capacitance, and dynamically adjust the equivalent resistance value according to human body parameters and environmental parameters to form a scenario-specific impedance model; Step 3: Based on the FPGA-DSP co-architecture, the Xilinx Kintex-7 chip drives the FPGA to generate basic electrical signals, and the basic electrical signals include alternating current, 0-500V direct current and 0-800V pulses; Step 4: Based on the scenario-specific impedance model, the DSP executes an adaptive filtering algorithm through the TI TMS320C6678 chip, superimposes composite waveforms such as 2-10 harmonics and transient overvoltages with a rising edge less than 1 ns on the basis of the basic electrical signal, and dynamically adjusts the signal parameters; Step 5: The data acquisition unit uses the NI USB-4431 data acquisition card to synchronously acquire various types of parameter data during the process of the signal acting on the variable resistance matrix at a sampling rate of 100 kHz and a resolution of 24 bits; Step 6: According to various types of parameter data, fuse the current intensity, duration, frequency, human body impedance and environmental parameters through the BP neural network risk assessment model, and output the electric shock risk level and protection strategy; at the same time, detect signal distortion and locate system faults through the wavelet transform algorithm, and form an analysis result; Step 7: According to the analysis result, perform multi-modal interaction output through the HTC Vive Pro 2 VR device and the Microsoft HoloLens 2 AR device, and automatically generate a report including simulation parameters, energy density and standardized protection suggestions.

9. An intelligent electric shock dynamic simulation method based on a variable resistance matrix according to claim 8, characterized in that, In step 2, when constructing the human body impedance composite model including skin equivalent resistance, in-vivo conduction path and capacitance based on the IEC 60479-1 standard, it supports the input of extreme environmental parameters of -50°C to 150°C temperature and 0 to 5000 m altitude, and calibrates the equivalent resistance in real time through a temperature and humidity sensor.

10. A method for dynamically simulating intelligent electric shock based on a variable resistance matrix according to claim 8, characterized in that, In step seven, the multi-modal interaction output realizes the precise matching of virtual information and the physical environment through SLAM technology, supports multi-person collaborative operation, and the tactile feedback intensity has a linear relationship with the simulated current.

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