Modularized electrical experiment teaching instrument based on internet of things and NFC identification

Abstract

This invention discloses a modular electrical experiment teaching instrument based on the Internet of Things (IoT) and NFC identification. The instrument comprises an intelligent core and multiple intelligent nodes. The intelligent nodes detect wire insertion / removal status via an NFC wire identification module and a hardware insertion detection module. The intelligent core periodically summarizes the insertion / removal status of each node and the NFC wire UID through a wireless IoT protocol, automatically reconstructing the real-time electrical topology of the physical circuit and using a large language model API via the Internet for intelligent reasoning. Compared with existing technologies, this invention features fully automatic, contactless detection of circuit connections. It utilizes a large AI model to provide multimodal voice correction and heuristic, personalized guidance for students' incorrect or missing connections in electrical experiments, significantly reducing the burden on experimental teachers and improving classroom teaching efficiency. While fully preserving students' traditional hands-on experience of connecting circuits, it guides students to independently explore electrical laws and cultivates independent thinking and hands-on practical abilities.

Description

Technical Field

[0001] This invention relates to the field of teaching instrument and smart hardware design technology, specifically a modular electrical experiment teaching instrument based on the Internet of Things and NFC identification. Background Technology

[0002] In physics experiments at universities and high schools (such as AC impedance experiments in RLC circuits and series resonance experiments), students often make mistakes such as incorrect connections, omissions, or poor contact due to a lack of experience in circuit wiring. To achieve automated detection and intelligent teaching guidance for circuit connections, existing technical solutions typically fall into two categories: one is to use image recognition technology (such as setting up a camera above the experimental platform to take pictures) and use computer vision algorithms to identify the color and direction of the wires; the other is to deploy a large-area matrix of switches under a breadboard or substrate and deduce the circuit topology by scanning the conduction state of the switch matrix.

[0003] However, image recognition solutions are highly susceptible to changes in lighting and obstruction by students' fingers or bodies, and cannot accurately determine whether the plug is actually in electrical contact; matrix switch solutions require custom-made, extremely complex substrate bases, which are expensive, difficult to maintain, and completely limit the experience of plugging and unplugging real wires in physics experiments, which does not meet the requirements for cultivating students' hands-on skills in actual teaching.

[0004] In summary, existing electrical experimental teaching instruments cannot accurately determine whether a plug is truly in electrical contact, and cannot guide students to independently explore electrical laws or cultivate their independent thinking and hands-on practical abilities. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by designing a modular electrical experiment teaching instrument based on the Internet of Things (IoT) and NFC identification. This instrument, employing a modular design combining infrared insertion detection and Near Field Communication (NFC) identification, achieves fully automatic, contactless detection of circuit connections and automatically reconstructs the real-time electrical topology of the physical circuit. The instrument uses miniature NFC tags affixed to the outside of ordinary wire plugs. An NFC wire identification module and a hardware insertion detection module detect wire insertion and removal. Combined with a coil antenna located around the core board / node board socket, and hardware insertion detection using an infrared photoelectric sensor and voltage comparator, the instrument periodically summarizes the insertion and removal status of each node and the NFC wire UID via a wireless IoT protocol. This automatically reconstructs the real-time electrical topology of the physical circuit and utilizes a large language model API via the internet for intelligent reasoning. It can accurately identify "which wire plug is inserted into which terminal of which component" in real time, while completely retaining the tactile feedback of traditional circuit insertion and removal, achieving near-100% accuracy in automatic topology recognition and multimodal voice AI teaching interaction. This invention utilizes a large AI model to provide precise multimodal voice error correction and heuristic personalized Q&A for students' faults such as incorrect or missing connections in electrical experiments. This not only greatly reduces the guidance burden of experimental teachers and improves classroom teaching efficiency, but also guides students to independently explore electrical laws and cultivate independent thinking and hands-on practical abilities while fully preserving students' traditional hands-on experience of connecting circuits. It is a highly efficient intelligent experimental teaching device.

[0006] The objective of this invention is achieved as follows: a modular electrical experiment teaching instrument based on the Internet of Things (IoT) and NFC identification. Its characteristic is that it is a modular electrical experiment teaching instrument constructed using a smart core and multiple smart nodes. The smart core and multiple smart nodes establish data communication via a wireless local area network to achieve fully automatic and contactless detection of circuit connections and automatically reconstruct the real-time electrical topology of the physical circuit. The smart core and smart nodes are equipped with a circuit connection detection structure, which includes a hardware insertion detection module and an NFC wire identification module. The hardware insertion detection module includes an infrared photoelectric sensor and a voltage comparator connected to the output of the infrared photoelectric sensor. The NFC wire identification module includes an NFC tag placed at the location of the external test wire plug and an NFC coil antenna integrated on the smart core or smart node. The NFC coil antenna is arranged around the insertion area surrounded by multiple sockets. When the hardware insertion detection module detects the insertion state, the main control chip drives the reading of the unique identification code (UID) of the NFC tag at the test wire plug and outputs a corresponding level signal through optical path switching to identify the insertion state of the external test wire plug into the smart core or smart node in real time.

[0007] The intelligent core and intelligent node are equipped with independent power boards that integrate power management chips and lithium batteries. The independent power boards are detachably electrically connected to the corresponding intelligent core board or intelligent node board through pin headers / female headers, thereby forming an integrated intelligent core and intelligent node. The independent power boards are connected to the intelligent core or intelligent node through a pin header / female header structure with a spacing of 1.27mm in multiple layers.

[0008] The main control MCU of the intelligent core is initialized with independent Web server tasks, NFC reading tasks, sensor acquisition tasks, power management tasks, circuit topology verification tasks, and voice interaction tasks based on the built-in real-time operating system. The circuit topology verification task includes dual-end mapping logic and periodically summarizes the local detection data of the intelligent core and the sensor data packets received from each intelligent node through multi-task mutexes.

[0009] The intelligent core is equipped with a multimodal voice AI teaching assistance button trigger module, a microphone, and a speaker. The button trigger module has a short-press topology broadcast mode and a long-press voice interaction mode. When a short press is triggered, the circuit topology verification task compares the current real-time electrical topology with the preset physical experiment theoretical topology, extracts abnormal information such as missing or incorrect connections, converts it into a text-based circuit state description, sends it to the cloud-based large language model API, obtains teaching guidance responses, and downloads the audio stream through the speech synthesis interface for playback by the speaker. When a long press is triggered, the microphone records audio and converts it into a text command through the speech recognition interface, submitting it along with the context information of the current real-time electrical topology to the cloud-based large language model API, realizing human-computer interactive physics experiment question answering.

[0010] The intelligent core and intelligent nodes use the ESP-NOW wireless communication protocol for low-latency one-way or two-way data stream interaction; the intelligent node board collects the test wire insertion status information, NFC tag identification code UID, and electrical parameters of each channel collected by voltage and current sensors in real time, and encapsulates them into a unified sensor data packet and sends it to the intelligent core.

[0011] The dual-end mapping logic retrieves a preset wire table to find the other end UID that has a pairing relationship with the current UID. If the other end UID is found in the terminal table, the two terminals are marked as electrically interconnected wires, thereby automatically constructing the real-time electrical topology of the current physics teaching experiment circuit. The wire table is a pre-existing mapping relationship between the two ends UIDs of a known wire in the intelligent core. The terminal table is a pre-existing mapping relationship between the physical terminals of a known circuit and the corresponding measurement channels in the intelligent core.

[0012] Compared with the prior art, the present invention has the following beneficial technical effects and significant technical progress:

[0013] 1) High recognition accuracy and strong anti-interference: Based on hardware infrared triggering and near field communication (NFC) technology, it is not affected by light or obstruction, and perfectly solves the problem of inaccurate image recognition.

[0014] 2) Compact structure and low power consumption: The power board utilizes a highly integrated solution, combining with the motherboard through fine pin headers, significantly reducing power consumption.

[0015] The instrument size has been reduced, and NFC reading is only enabled after infrared triggering, avoiding long-term high-power antenna transmission.

[0016] 3) It retains the real experimental experience: students still use traditional wires to connect the wires on the real experimental board, and the equipment completes the topology reconstruction in the background without being noticed. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the intelligent hardware topology system structure in Example 1;

[0018] Figure 2 A schematic diagram of the structure of an RLC experimental circuit after incorporating this invention;

[0019] Figure 3 This is a front view of the intelligent node in this invention;

[0020] Figure 4 This is a rear view of the intelligent node in this invention;

[0021] Figure 5 This is a left view of the smart node in this invention;

[0022] Figure 6 This is a top view of the smart node in this invention. Detailed Implementation

[0023] A modular electrical experiment teaching instrument based on the Internet of Things (IoT) and NFC identification includes: a smart core board and multiple smart node boards. The core board and node boards interact with each other via a low-power, high-real-time wireless protocol. The instrument's hardware architecture employs a split or multi-layered design: the main board includes the core board or node boards, connected to an independent power board via pin headers / sockets; the power board integrates battery charging management functions and deploys a hardware insertion detection module consisting of an infrared photoelectric sensor and a voltage comparator. The core board or node board has a test insertion area enclosed by multiple sockets, with the NFC antenna wound as a PCB printed coil around the outermost edge of the insertion area. When a test lead (with specific NFC tags affixed to both ends) is inserted into a socket, the infrared photoelectric sensor is first triggered to cut off the light path, and the voltage comparator outputs a level transition (hardware insertion detection) to the main control MCU. The main control MCU then drives the NFC chip to enable the antenna in a dedicated task, reading the unique UID of the test lead plug. The smart core board periodically receives data packets from each smart node board via a wireless network and internally runs circuit topology verification logic. By retrieving the preset wire table through the UID pairing mechanism at both ends, the system can automatically deduce the real-time global connection topology of the circuit and provide intelligent voice broadcast in conjunction with a large language model.

[0024] The technical solution of the present invention will be clearly and completely described below in conjunction with the hardware configuration, source code logic and specific RLC experimental operation process in the embodiments of the present invention.

[0025] Example 1

[0026] See Figure 1 The intelligent hardware topology system in this embodiment includes: an intelligent core (based on an ESP32-S3 main control chip, running a main control multi-tasking program) and several intelligent nodes (each based on a single-core ESP32-C3 chip, running a transmitter program; intelligent nodes can be added as needed for experiments). In the RLC experiment, four intelligent nodes are set up. These intelligent nodes, equipped with resistors, inductors, and capacitors, form a resistor node board (Node-R), an inductor node board (Node-L), and a capacitor node board (Node-C). Additionally, considering the special requirements of AC circuits for the ground terminal, a common ground terminal intelligent node board is provided.

[0027] See Figure 2 The RLC experimental circuit of this invention is equipped with external finished equipment used in the experiment, including a standard laboratory dual-channel digital oscilloscope and a function signal generator (hereinafter referred to as the signal source).

[0028] To achieve seamless topology reconfiguration of electrical connections to external finished devices, this embodiment employs a special configuration for the test leads used in the experiment:

[0029] 1) Component interconnection cable: Both ends are standard 2mm or 4mm banana plug wires, and its positive plug (usually...)

[0030] Both the red and negative plugs (usually black) have tiny circular NFC tags inside.

[0031] 2) Instrument Adapter Cables: Both the output terminal of the signal source and the channel input terminal of the oscilloscope are led out through dedicated BNC to banana plug test cables. The positive red connector of the signal source output cable is labeled UID_SIG_P, and the negative black connector is labeled UID_SIG_N. The red connector of the oscilloscope's first channel (CH1) probe is labeled UID_CH1_P, and the black ground connector is labeled UID_CH1_N. The unique identifier (UID) inside all connectors is pre-written into the intelligent core's preset wire mapping table (wireTable) during system initialization.

[0032] (a) Hardware connection and independent power management

[0033] Both the intelligent core and intelligent nodes employ a two-layer stacked hardware architecture. The bottom layer is the main control board (core board or node board), and the top layer is connected to an independent power supply board via 1.27mm pitch pin headers and sockets. The lithium battery is fixed on the power supply board and its charging and discharging are controlled by the high-performance power management chip IP5306. Since the main control board draws relatively little current when performing only topology scanning or low-power communication, to prevent the power management chip IP5306 from automatically entering a sleep / power-off state due to output current falling below its built-in threshold, a dedicated GPIO pin (e.g., GPIO9) of the main control MCU is directly connected to the KEY key of the IP5306 via a 1.27mm pin header. The main control program initializes an independent power management task (taskPowerManagementFunc), which runs as a periodic timer. Every 25000 milliseconds (IP5306_KEEP_ALIVE_INTERVAL), this task drives GPIO9 to send a low-level pulse for 100 milliseconds to the KEY key, forcibly refreshing the power management chip's automatic sleep timer, thereby maintaining continuous power supply to the entire system.

[0034] (II) Hardware Insertion Recognition and Dual-End NFC Tag Mapping Principle

[0035] Each smart node or core provides four physical channels, and the external contact surface of each channel is enclosed by four 2mm banana plugs to form a centralized test insertion area.

[0036] See Figures 3-6The infrared photoelectric sensor includes an infrared emitting tube and a receiving tube mounted on the power board. Its collimated optical path traverses the lower insertion path of the banana plug, and the output of the receiving tube is connected to an LM393 voltage comparator. When the wire plug is not inserted, the optical path is unobstructed, and the comparator outputs a low level to the input pin (insert_pins) of the main control MCU. Once the student inserts the test wire plug, the plug itself cuts off the infrared optical path, and the output of the LM393 comparator immediately flips to a high level. On the lower PCB of each channel, the near-field communication (NFC) antenna is a multi-layered dense microstrip coil that wraps around the entire insertion area consisting of the four banana plugs of that channel and is connected to the onboard MFRC522 near-field reader chip. Because the NFC antenna consumes a lot of power when emitting high-frequency electromagnetic waves, the system adopts an energy-saving logic of "infrared triggering and time-limited card reading," the specific steps of which are as follows:

[0037] Step 1: Hardware Awareness Jump

[0038] The sensor acquisition task in the main control MCU periodically scans the insert_pins of each channel at a high frequency (e.g., 10ms). When a change in the level of a certain channel from high to low is detected, it indicates that a plug has entered, and the corresponding inserted state is set to true.

[0039] Step Two: Dedicated Task Scheduling

[0040] When the circuit topology verification task detects a positive transition (from false to true) in the inserted state of the channel, it immediately activates the NFC reading task that was originally in a suspended or blocked state through the internal communication mechanism, enables the corresponding MFRC522 chip, and turns on the coil antenna around the channel.

[0041] Step 3: Timed UID Capture and Upload

[0042] The NFC coil antenna emits a magnetic field for a limited time (e.g., 2 seconds) to sense and read the unique identification code (UID) of the circular NFC tag affixed inside the test lead plug. Once capture is successful or the timeout occurs, the antenna is immediately turned off. If the UID is captured on the smart node board, the node board encapsulates the channel number and UID into a unified sensor data packet and transmits it unidirectionally or bidirectionally to the smart core via a dedicated communication channel using the built-in ESP-NOW wireless protocol. If captured locally on the smart core, it is directly sent to the topology buffer.

[0043] Step 4: Double-ended mapping topology reconstruction

[0044] After the intelligent core receives the inserted data from all nodes in the network, its internal circuit topology verification task (taskCircuitCheckFunc) initiates the two-terminal mapping logic. The core has a built-in wire table, which records, for example, "the positive UID of wire No. 101 is 044FF1..., and the negative UID is 043AE6...". When the verification task finds that channel 1 of the resistor node Node-R reads the positive UID of the wire, while channel 2 of the inductor node Node-L reads the negative UID, the algorithm immediately uses a mutex search to logically connect these two points, automatically reconstructing "Node-R [channel 1]" in the background. The real-time electrical topology path of “Node-L [Channel 2]”.

[0045] (III) Specific operating procedures for students to conduct RLC experiments using this device

[0046] In the RLC series resonance and AC impedance physics experiment, students use test leads equipped with double-ended NFC tags, an external signal generator, and an external oscilloscope. With the assistance of these smart instruments, they follow these steps:

[0047] Step 1: System startup and wireless network setup

[0048] Students sequentially press the touch buttons on the power boards of the intelligent core and the three node boards (resistor, inductor, and capacitor). The system starts with high-efficiency power supply via IP5306, and the onboard MCUs of each board power on and initialize. The intelligent core automatically establishes a Wi-Fi hotspot and runs the built-in web server task. Each intelligent node automatically scans and connects to the hotspot, locks onto the corresponding wireless channel, and then registers with the core via the ESP-NOW local area protocol. When the onboard WS2812 LED indicator changes from red to solid green, it indicates that the multi-node IoT topology network has been successfully established and the self-test is complete.

[0049] Step 2: Directly connect the external finished signal source to the component.

[0050] Students turn on the external pre-generated function signal generator and adjust it to the preset AC sine wave output mode (e.g., ...). Students take the dedicated test leads for the signal source:

[0051] 1) Direct positive connection to the first component: The student directly inserts the positive red plug of the signal source output test lead into the input channel socket of the Node-L inductor node board. At this time, the Node-L's infrared photoelectric sensor detects the plug obstruction, and the internal LM393 voltage comparator undergoes a level transition, quickly activating its local NFC coil. The coil senses and reads the tag UID_SIG_P inside the red plug. Node-L immediately transmits the "UID_SIG_P read from input channel" information wirelessly to the intelligent core via ESP-NOW.

[0052] 2) Negative terminal connected to common ground: Students insert the negative black plug of the signal source output test line into the corresponding "common ground (GND)" channel socket on the smart core board. The core board's local infrared sensor and NFC antenna work together to capture the tag UID_SIG_N inside the black plug.

[0053] 3) Circuit connection identification: The intelligent core searches the preset wire double-ended mapping table in the background, identifies that the pair of UIDs belongs to the external signal source, and then automatically establishes the initial topology node on the virtual topology map with "the positive end of the external signal source has been directly connected to the input end of the inductor Node-L, and the negative end has been connected back to the common ground of the core board".

[0054] Step 3: Complete the LCR series circuit construction

[0055] Following the standard physical experiment pathway, students continued to cascade the remaining components using interconnecting cables with dual-ended NFC tags:

[0056] 1) Connecting the inductor and capacitor: The student takes the first component interconnect wire, inserts the positive red plug into the output channel of the inductor node board Node-L, and inserts the negative black plug into the input channel of the capacitor node board Node-C. During this process, Node-L and Node-C read and upload the UIDs at both ends via infrared triggering, and the core board maps them as "inductor output connected to capacitor input".

[0057] 2) Connecting the capacitor and resistor: Students take the second component interconnect wire, insert the positive red plug into the output channel of the capacitor node board Node-C, and the negative black plug into the input channel of the resistor node board Node-R. The core board receives the dynamically uploaded data from both ends and maps it to "capacitor output connected to resistor input".

[0058] 3) Reconnecting the loop to common ground: The student takes the third component interconnect wire, inserts the positive red plug into the output channel of the resistor node board (i.e., the end of the series circuit), and inserts the negative black plug into the common GND channel socket of the intelligent core board. At this point, a tight physical LCR series closed loop is formed between the external signal source, inductor, capacitor, resistor and system common ground.

[0059] Step 4: Connect the external finished oscilloscope test points

[0060] To observe the total input voltage waveform of the circuit and the voltage waveforms of specific components in the loop, and thus analyze the phase difference and resonance state, students turned on an external commercial digital oscilloscope and used oscilloscope test leads modified with NFC tags to connect the test points:

[0061] 1) Channel 1 connected to the main input terminal: The student inserts the red connector (signal probe) of the oscilloscope's first channel (CH1) into the input channel of the Node-L inductor board (i.e., the positive terminal of the signal source), and inserts the black connector (grounding clip) of CH1 into the common GND socket of the core board. The NFC antennas of Node-L and the core board read UID_CH1_P and UID_CH1_N respectively and summarize them to the core. Based on this, the system determines that "oscilloscope CH1 is measuring the total input voltage of the circuit". ".

[0062] 2) Channel 2 connected to the third component: According to the experimental requirements, it is necessary to measure the voltage across the third component (i.e., resistor R) in the series circuit to indirectly obtain the phase information of the loop current. The student inserts the red plug of the oscilloscope's second channel (CH2) into the input channel of the resistor node board Node-R (i.e., the position where the capacitor output and resistor input are connected), and inserts the black plug of CH2 into the common GND socket of the core board (since the output of the resistor is already connected to GND, this connection method achieves equipotential measurement of the voltage across the resistor). The resistor node board and the core board capture UID_CH2_P and UID_CH2_N respectively. Based on this, the system accurately determines that "oscilloscope CH2 is measuring the voltage waveform across the resistor of the third component." ".

[0063] Step 5: Intelligent Topology Verification and Large Model Speech Error Correction Guidance

[0064] After the physical connections and measurement probes are set up, the student briefly presses the trigger button (GPIO1, press time less than 1 second) on the intelligent core board. The main control program of the intelligent core immediately releases the semaphore, activating the circuit topology verification task. This task compares the actual electrical topology reconstructed by multi-node wireless aggregation and dual-end NFC lookup with the preset "standard RLC series resonant experimental topology":

[0065] 1) If the circuit is completely correct: The core board drives the MAX98357 power amplifier and speaker through the I2S interface to automatically broadcast: "The circuit topology is connected correctly. The finished signal source and dual-channel oscilloscope are in place. The current circuit has formed an RLC series resonant circuit. Please start adjusting the signal source frequency and record the oscilloscope waveform."

[0066] 2) If there is an error in the circuit (for example, a student mistakenly connected the output of capacitor Node-C back to the input of inductor Node-L in step 3, causing a short circuit, or the oscilloscope's grounding plug being reversed, creating a potential short circuit): The topology analysis algorithm in the circuit topology verification task will immediately extract the feature information of these "missing connections" or "error loops," and call the buildCircuitDescription() function to assemble it into a structured plain text description of the circuit state (e.g., "Current Experiment: RLC Resonance; Fault: Capacitor Node-C output is incorrectly connected to resistor input, causing the LC loop to not be connected to common ground"). Subsequently, the core board submits this text to the cloud-based large language model API (such as the DeepSeek API) via the web network using an HTTP POST request. Based on its powerful contextual understanding capabilities, the large language model generates a highly targeted professional electrical teaching instruction of no more than 100 characters online, specifically addressing the student's current error. After receiving the text reply, the core board calls the Alibaba Cloud Text-to-Speech (TTS) interface to stream the audio stream and broadcasts a multimodal speech error correction message through the speaker: "Note that you have incorrectly connected the output of the capacitor back to the input of the resistor, which has caused the subsequent components to not form a closed loop. Please unplug the red and black wires and correctly connect the output of the capacitor back to the green GND socket on the core board."

[0067] Step 6: Human-computer interactive intelligent Q&A

[0068] After correcting the circuit according to the voice guidance, students observe the resonant waveforms of the resistor voltage and the total voltage using an external oscilloscope. If students have questions about the physical phenomena during the adjustment of the signal source frequency (e.g., not understanding why the two waveforms on the oscilloscope will coincide in phase at a specific frequency, or not understanding the physical meaning of the quality factor Q), they can press and hold the control button (GPIO1, press time greater than 1 second) on the core board. At this time, the system enters the long-press voice interaction mode, and the miniature PDM microphone (MP34DT05) on the core board starts high-fidelity audio recording. Students speak their questions into the microphone (e.g., "Teacher, why are the two waveforms completely coincident now?"). The main control chip streams the recorded audio data to the Alibaba Cloud Speech Recognition (ASR) interface to convert it into text, and then submits it along with the current perfect "RLC series resonant" circuit topology context to the cloud-based large language model API. The large language model performs intelligent reasoning based on the current physical circuit state, returning vivid Heuristic teaching solutions. These solutions are then translated by TTS and broadcast through a speaker: "This is because the frequency of the signal source you are currently adjusting has just reached the series resonant frequency of the circuit. At this time, the inductive reactance and capacitive reactance in the circuit completely cancel each other out, and the entire loop exhibits pure resistance. Therefore, the phases of the total voltage and the voltage across the resistor are completely coincident. You can try increasing the resistance and see what happens to the waveform amplitude." In this way, this device achieves fully automatic topology recognition and intelligent, personalized interactive teaching of physical experiments without interfering with the fundamental electrical measurement of traditional external instruments.

[0069] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A modular electrical experiment teaching instrument based on Internet of Things and NFC identification, characterized in that, This modular electrical experiment teaching instrument employs a single intelligent core and multiple intelligent nodes. The intelligent core and nodes communicate via a wireless local area network, enabling fully automatic, contactless detection of circuit connections and automatic reconstruction of the real-time electrical topology of the physical circuit. The intelligent core and nodes are equipped with a circuit connection detection structure, including a hardware insertion detection module and an NFC wire identification module. The hardware insertion detection module includes an infrared photoelectric sensor and a voltage comparator connected to the sensor's output. The NFC wire identification module includes an NFC tag positioned at the external test wire plug location and an NFC coil antenna integrated into the intelligent core or node. The NFC coil antenna surrounds the insertion area, which is enclosed by multiple sockets. When the hardware insertion detection module detects an insertion, the main control chip reads the unique identifier (UID) of the NFC tag at the test wire plug and outputs a corresponding level signal through optical path switching, thus identifying the insertion status of the external test wire plug into the intelligent core or node in real time. 2.The modularized electric experiment teaching instrument based on the internet of things and NFC identification according to claim 1, wherein The intelligent core and intelligent node are equipped with independent power boards that integrate power management chips and lithium batteries. The independent power boards are detachably electrically connected to the corresponding intelligent core or intelligent node through pin headers / female headers, thereby forming an integrated intelligent core and intelligent node. The independent power boards are connected to the intelligent core or intelligent node in multiple layers through pin headers / female headers with a spacing of 1.27mm. 3.The modularized electric experiment teaching instrument based on the internet of things and NFC identification of claim 1, wherein, The main control MCU of the intelligent core is initialized with independent Web server tasks, NFC reading tasks, sensor acquisition tasks, power management tasks, circuit topology verification tasks, and voice interaction tasks based on the built-in real-time operating system. The circuit topology verification task includes dual-end mapping logic and periodically summarizes the local detection data of the intelligent core and the sensor data packets received from each intelligent node board through multi-task mutexes. 4.The IoT and NFC identification based modular electrical experiment teaching instrument of claim 1 or claim 3, wherein, The intelligent core includes a multimodal voice AI teaching assistance button trigger module, a microphone, and a speaker. The button trigger module has a short-press topology broadcast mode and a long-press voice interaction mode. When a short press is triggered, the circuit topology verification task compares the current real-time electrical topology with the preset physical experiment theoretical topology, extracts abnormal information such as missing or incorrect connections, converts it into a text-based circuit state description, sends it to the cloud-based large language model API, obtains teaching guidance responses, and downloads the audio stream through the speech synthesis interface for playback by the speaker. When a long press is triggered, the microphone records audio and converts it into a text command through the speech recognition interface, submitting it along with the context information of the current real-time electrical topology to the cloud-based large language model API, realizing human-computer interactive physics experiment question answering. 5.The IoT and NFC identification based modular electrical experiment teaching instrument of claim 1 or claim 2, wherein, The intelligent core and intelligent nodes use the ESP-NOW wireless communication protocol for low-latency one-way or two-way data stream interaction; the intelligent node collects the test wire insertion status information, NFC tag identification code UID, and electrical parameters of each channel collected by voltage and current sensors in real time, and encapsulates them into a unified sensor data packet and sends it to the intelligent core. 6.The modularized electric experiment teaching instrument based on Internet of Things and NFC identification of claim 3, characterized in that, The dual-end mapping logic retrieves a preset wire table to find the other end UID that has a pairing relationship with the current UID. If the other end UID is found in the terminal table, the two terminals are marked as electrically interconnected wires, thereby automatically constructing the real-time electrical topology of the current physics teaching experiment circuit. The wire table is a pre-existing mapping relationship between the two ends UIDs of a known wire in the intelligent core. The terminal table is a pre-existing mapping relationship between the physical terminals of a known circuit and the corresponding measurement channels in the intelligent core.

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