Electrical experiment monitoring system based on artificial intelligence and monitoring method thereof

Abstract

The invention discloses an electrical experiment monitoring system and method based on artificial intelligence, and the system comprises an intelligent instrument detection system, a network communication system, a cloud server (or a local server) and a client, and the intelligent instrument detection system is directly connected with a cloud server network through the network communication system. The cloud server is directly connected with the client, the implementation mode is that the intelligent instrument detection system collects detection parameters in the pointer type electrical instrument, processes the detection parameters and then transmits the detection parameters to the network communication system, and the network communication system safely uploads the collected data to the cloud server in real time through a Wi-Fi or Bluetooth communication protocol; the cloud server automatically analyzes experiment operation behaviors and carries out real-time scoring and abnormity prompting, and the client is a user terminal and comprises a student seat computer terminal and a teacher computer terminal. The monitoring result can be fed back to the user in real time, the user is helped to correct operation errors in time, and the experiment teaching effect and the classroom interaction efficiency are remarkably improved.

Description

Technical Field

[0001] This invention relates to the technical field of electrical experiment monitoring, specifically to an artificial intelligence-based electrical experiment monitoring system and its monitoring method. Background Technology

[0002] Traditional pointer-type electrical instruments, such as mechanical voltmeters and ammeters, are widely used in electrical experiment teaching due to their intuitive readability and ease of operation. These instruments play an important role in helping students intuitively understand the basic laws of electricity and improve their practical skills. However, with the increasing demands of modern education for classroom interactivity and data accuracy, traditional instruments have not yet effectively met the needs of real-time monitoring and evaluation of students' performance during experiments. Because with traditional mechanical voltmeters and ammeters, teachers can only check the values ​​of each instrument individually to ensure the measurements are correct, they cannot obtain real-time information on the experimental data (including key indicators such as voltage and current), experimental procedures, and whether the experimental trajectory meets requirements for each student. This results in slow teaching progress and fails to significantly improve the effectiveness of experimental teaching and classroom interaction. Therefore, improvements are needed. Summary of the Invention

[0003] The purpose of this invention is to provide an artificial intelligence-based electrical experiment monitoring system to solve the problems mentioned in the background art. In the operation of existing pointer-type electrical instruments, teachers can only check the values ​​of the corresponding mechanical measuring instruments one by one and whether the measurements are standardized. This makes it impossible for teachers to understand in real time whether the experimental data, experimental operation behavior and experimental trajectory of each student in the class meet the requirements, resulting in slow teaching progress and failure to significantly improve the experimental teaching effect and classroom interaction efficiency.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an artificial intelligence-based electrical experiment monitoring system, comprising an intelligent instrument detection system installed within a pointer-type electrical instrument, a network communication system, a cloud server, and a client. The intelligent instrument detection system is directly connected to the cloud server via the network communication system, and the cloud server is directly connected to the client. The system works by the intelligent instrument detection system collecting various detection parameters from the pointer-type electrical instrument in real time, performing preliminary processing, and then transmitting the data to the network communication system. The network communication system uploads the collected data to the cloud server in real time and securely via Wi-Fi or Bluetooth communication protocols. The cloud server automatically analyzes the experimental operation behavior and provides real-time scoring and anomaly alerts. The client includes a student computer and a teacher computer. The teacher computer is used to monitor the progress and scoring status of the entire class's experiments in real time. The student computer displays the experimental operation steps, data curves, scoring status, and anomaly alerts in real time using an intuitive graphical interface.

[0005] Preferably, the cloud server is based on a Node.js interface gateway and a PostgreSQL database, and integrates a real-time data analysis engine and a scoring module to automatically analyze experimental operations and provide real-time scoring and anomaly alerts.

[0006] Preferably, the intelligent instrument detection system includes a main control PCB board installed within the existing pointer-type electrical instrument and a power supply lithium battery. The main control PCB board is equipped with an instrument data acquisition unit. The power supply lithium battery is electrically connected to the instrument data acquisition unit to supply power. The instrument data acquisition unit includes a sensor sampling unit, a sampling data conversion module, an analog reference power output circuit for acquiring ±1.65V, and an MCU control unit. The sampling data conversion module includes an analog-to-digital converter chip U5 as the core sampling device and a differential input unit. The sensor sampling unit is connected in series within the existing pointer-type electrical instrument via a current branch to acquire current signals and a voltage signal via a voltage divider network. Multiple signals are then sent to the differential channel of the analog-to-digital converter chip U5 for 16-bit sampling after differential buffering. The analog-to-digital converter chip U5 is an ADS1115 16-bit converter. A high-precision analog-to-digital converter (ADC) chip, U5, has pin 1 connected to the I_VIN+ terminal via the eighteenth capacitor C18. The I_VIN+ terminal is also connected to pin 4 of the ADC chip U5. Pin 3 of the ADC chip U5 is connected to the I_VIN- terminal via the nineteenth capacitor C19. The I_VIN- terminal is also connected to pin 5 of the ADC chip U5. A fifteenth resistor R15 and a seventeenth capacitor C17 are connected in parallel between the I_VIN- and I_VIN+ terminals. Pin 1 of the ADC chip U5 is also connected to the... Replace pin 3 of chip U5. Pin 10 of the analog-to-digital converter chip U5 is connected to the +3.3V analog voltage terminal +3V3_AVDD through resistor R22 (22nd resistor). Pin 9 of the analog-to-digital converter chip U5 is connected to the +3.3V analog voltage terminal +3V3_AVDD through resistor R23 (23rd resistor). Pin 8 of the analog-to-digital converter chip U5 is connected to the +3.3V analog voltage terminal +3V3_AVDD. The I_VIN+ terminal is connected to the positive terminal of the original pointer-type electrical instrument, and the I_VIN- terminal is connected to the negative terminal of the original pointer-type electrical instrument.

[0007] Preferably, the differential input unit includes a dual operational amplifier chip integrating two operational amplifiers. The dual operational amplifier chip is model LMV358M. Pin 3 (non-inverting input) of the dual operational amplifier chip is connected to the 1.65V analog reference voltage pin VREF_1.65V via the fifteenth capacitor C15. Pin 3 (non-inverting input) is also connected to the S1_EXT_VIN pin via the sixteenth resistor R16. A seventeenth resistor R17 is connected in parallel with the fifteenth capacitor C15. Pin 2 (inverting input) of the dual operational amplifier chip is connected to pin 1 (output). Pin 1 (output) is connected to pin 7 of the analog-to-digital converter chip U5 via the twentieth resistor R20. The non-inverting input pin 5 of the dual operational amplifier chip is connected to the 1.65V analog reference voltage pin VREF_1.65V via the sixteenth capacitor C16. The non-inverting input pin 5 of the dual operational amplifier chip is also connected to the S2_EXT_VIN pin via the eighteenth resistor R18. A nineteenth resistor R19 is connected in parallel with the sixteenth capacitor C16. The inverting input pin 6 of the dual operational amplifier chip is connected to the output pin 7 of the dual operational amplifier chip. The output pin 7 of the dual operational amplifier chip is connected to pin 6 of the analog-to-digital converter chip U5 via the twenty-first resistor R21. The S1_EXT_VIN and S2_EXT_VIN pins serve as external measurement expansion input pins for connecting existing pointer-type electrical instruments.

[0008] Preferably, the analog reference power supply output circuit includes a 24th resistor R24, a 25th resistor R25, a 26th resistor R26, and a low-voltage precision regulator U6. The analog voltage terminal +3V3_AVDD is connected to the analog ground terminal via a series connection of the 24th resistor R24, the 25th resistor R25, and the 26th resistor R26. The common terminal connected to the 24th resistor R24 ​​and the 25th resistor R25 serves as the analog reference voltage pin VREF_1.65V. The common terminal of the 25th resistor R25 and the 26th resistor R26 is connected to pin 1 of the low-voltage precision regulator U6, and pin 2 of the low-voltage precision regulator U6 is connected to the analog reference voltage pin. The reference voltage pin VREF_1.65V is connected to the analog ground terminal. A 24th capacitor C24 is also connected between the analog reference voltage pin VREF_1.65V and the analog ground terminal. The 3.3V voltage input terminal +3V3 is connected to the analog voltage terminal +3V3_AVDD through inductor L1. A 12th capacitor C12, a 13th capacitor C13, and a 14th capacitor C14 are connected in parallel between the analog voltage terminal +3V3_AVDD and the analog ground terminal. A 14th resistor R14 is connected between the analog ground terminal and the ground terminal GND. The model of the low-voltage precision regulator U6 is TLV431BQDBZR.

[0009] Preferably, the non-inverting input pin 3 of the dual operational amplifier chip is connected to ground GND via the twentieth capacitor C20; the common terminal of the twentieth resistor R20 connected to pin 7 of the analog-to-digital converter chip U5 is connected to ground GND via the twentieth capacitor C22; the non-inverting input pin 5 of the dual operational amplifier chip is connected to ground GND via the twentieth capacitor C21; and the common terminal of the twentieth resistor R21 connected to pin 6 of the analog-to-digital converter chip U5 is connected to ground GND via the twentieth capacitor C23.

[0010] Preferably, the MCU control unit includes an MCU processor U4 as the core and peripheral circuits. The MCU processor is an ESP32 with a built-in 240MHz dual-core Xtensa processor. Pin 33 of the MCU processor U4 is connected to pin 10 of the analog-to-digital converter chip U5, and pin 36 of the MCU processor U4 is connected to pin 9 of the analog-to-digital converter chip U5. The MCU processor U4 will be directly connected to the network communication system in the future.

[0011] This invention also discloses an artificial intelligence-based method for monitoring electrical experiments, including the aforementioned artificial intelligence-based electrical experiment monitoring system, the specific steps of which are as follows: S1. First, the definition of the scoring rules. Developers create configuration content for each scoring point in the backend system of the cloud server. The configuration content includes detection variables (such as voltage, current, and sliding rheostat position), judgment conditions (greater than, less than, range, trend of change, etc.), error messages, and whether debouncing processing or parent-child scoring point dependency data is required. All configuration content is centrally managed by the system and can also be upgraded to version. S2, Data Acquisition The modified pointer-type electrical instrument is an existing pointer-type electrical instrument with an added intelligent instrument detection system. The intelligent instrument detection system uploads the current measurement data to the student's computer at fixed intervals (e.g., every 0.5 seconds). The collected measurement data includes voltage, current and instrument status information. The data acquisition is completed locally on the student's computer, ensuring real-time performance and stability. S3, Assessment of Conditions The cloud server's backend system retrieves the scoring point configuration from step S1, reads the scoring point configuration sequentially, and compares it with the current measurement data obtained in step S2. Then, the real-time data analysis engine and the real-time data analysis engine in the scoring module perform rule matching (threshold, interval, trend, etc.) on the sampling stream. Based on this, the real-time data analysis engine and the scoring module in the scoring module generate a "pass / fail" conclusion with an error code and prompt key. At this time, if the scoring point configuration set for a certain scoring point is inconsistent with the currently collected measurement data, the measurement scoring point is defined as not meeting the requirements and is immediately defined as "fail". S4, Debouncing and Dependency Control To avoid misjudgments caused by short-term current fluctuations or momentary poor contact, the back-end system enables a de-jitter mechanism for specific scoring points. That is, only when the measurement data is collected multiple times in a row and the results of each comparison do not meet the requirements is it defined as a failure and immediately defined as "failed". S5, Final Prompt Presentation When the real-time data analysis engine and scoring module determine that a scoring point has failed, the corresponding prompt will be highlighted and displayed on the software interface of the student's computer. The prompt is static text, using the language described in the textbook, and is accompanied by flashing and sound effects to remind the student to correct it in time. Moreover, all prompts are processed locally and do not need to be transmitted through the server. S6. Recording and Analysis The evaluation process and results of all scoring points (including the timestamp of each judgment, measurement data, and final status) are synchronously written to the log database of the backend system on the cloud server. Teachers can access the log database after class through their computer to view a student's score trajectory and error distribution at each step, which helps with teaching analysis and feedback.

[0012] Compared with existing technologies, the beneficial effects of this invention are as follows: While retaining the original structure and operating characteristics of mechanical pointer-type voltmeters and ammeters, this structure integrates a high-precision sampling data conversion module and utilizes a network communication system, cloud server, and client to successfully achieve real-time data acquisition and intelligent analysis of the teaching experiment process. Specifically, this system helps teachers understand the experimental data and experimental trajectory of each student in the class in real time, including key indicators such as voltage and current, thereby instantly grasping the overall progress and quality of classroom experiments. Simultaneously, based on preset scoring logic, the system automatically and accurately monitors and evaluates experimental operations in real time. Once an operational deviation is detected, it provides real-time feedback to the student, helping them correct operational errors promptly, significantly improving the effectiveness of experimental teaching and classroom interaction efficiency, and accelerating the teaching progress. Therefore, this invention achieves an intelligent upgrade of traditional instruments, combining real-time data monitoring and autonomous scoring feedback functions, and possesses high teaching promotion value and innovation. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of the system connection of the artificial intelligence-based electrical experiment monitoring system in Embodiment 1. Figure 2 This is a schematic diagram showing the connection between the main control PCB board and the power supply lithium battery in Embodiment 1. Figure 3 This is a circuit connection diagram for the instrument data acquisition unit; Figure 4 This is a schematic diagram of the circuit connection of the analog-to-digital converter chip in Embodiment 1. Figure 5 This is a schematic diagram of the circuit connection of the simulated reference power supply output circuit in Embodiment 1. Figure 6 This is a schematic diagram of the circuit connection of the MCU control unit in Embodiment 1. Figure 7 This is a schematic diagram of the circuit connection of the differential input unit in Embodiment 1. Figure 8 This is a reference diagram showing the student interface of the computer at the student's seat in this embodiment; Figure 9 This is a reference connection diagram for an existing digital ammeter and intelligent instrument detection system; Figure 10 The circuit schematic for the onboard charging / protection module.

[0014] In the diagram: 1. Intelligent instrument detection system; 2. Network communication system; 3. Cloud server; 4. Client; 5. Student computer terminal; 6. Teacher computer terminal; 7. Main control PCB board; 8. Power supply lithium battery; 9. Instrument data acquisition unit; 10. Sampling data conversion module; 10. Analog reference power output circuit; 10. MCU control unit; 10. Differential input unit; 10. Dual operational amplifier chip; 10. Sensor sampling unit; 10. Digital ammeter; 11. Detailed Implementation

[0015] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0016] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0017] Example 1 Please see Figures 1-6 As shown in this embodiment, an artificial intelligence-based electrical experiment monitoring system includes an intelligent instrument detection system 1 installed within a pointer-type electrical instrument, a network communication system 2, a cloud server 3, and a client 4. The intelligent instrument detection system 1 is directly connected to the cloud server 3 via the network communication system 2, and the cloud server 3 is directly connected to the client 4. The system works by the intelligent instrument detection system 1 collecting various detection parameters from the pointer-type electrical instrument in real time, performing preliminary processing, and then transmitting the data to the network communication system 2. The network communication system 2 uploads the collected data to the cloud server 3 in real time and securely via Wi-Fi or Bluetooth communication protocols. The cloud server 3 automatically analyzes the experimental operation behavior and provides real-time scoring and anomaly alerts. The client 4 includes a student-side computer terminal 401 and a teacher-side computer terminal 402. The teacher-side computer terminal 402 is used to monitor the experimental progress and scoring status of the entire class in real time. The student-side computer terminal 401 displays the experimental operation steps, data curves, scoring status, and anomaly alerts in real time using an intuitive graphical interface. In this embodiment, data transmission uses Wi-Fi. Wireless transmission can be achieved via Bluetooth communication protocol, but wired network transmission can also be used in actual data transmission, which is also within the scope of protection of this application. In addition, the communication server in this embodiment is a cloud server. In actual operation, it can also be replaced with a local server. Therefore, data communication and transmission after replacing the cloud server with a local server are also within the scope of protection of this application.

[0018] Preferably, the cloud server 3 is based on a Node.js interface gateway and a PostgreSQL database, and integrates a real-time data analysis engine and a scoring module 301 to automatically analyze experimental operation behavior and provide real-time scoring and anomaly alerts.

[0019] Preferably, the intelligent instrument detection system 1 includes a main control PCB board 101 installed in the original pointer-type electrical instrument and a power supply lithium battery 102. The main control PCB board 101 is equipped with an instrument data acquisition unit 103. The power supply lithium battery 102 is electrically connected to the instrument data acquisition unit 103 to supply power. The instrument data acquisition unit 103 includes a sensor sampling unit 108, a sampling data conversion module 104, an analog reference power output circuit 105 for acquiring ±1.65V (to realize the ±1.65V acquisition process), and an MCU control unit 106. The sampling data conversion module 104 includes an analog-to-digital converter chip U5 as the core sampling device and a differential input unit 107. The sensor sampling unit 108 is connected in series in the original pointer-type electrical instrument through a current branch to acquire current signals and acquires voltage signals through a voltage divider network. Then, multiple signals are sent to the differential channel of the analog-to-digital converter chip U5 after differential buffering for 16... Bit sampling; wherein, the analog-to-digital converter chip U5 is a 16-bit high-precision analog-to-digital converter chip of model ADS1115. Pin 1 of the analog-to-digital converter chip U5 is connected to the I_VIN+ terminal through the eighteenth capacitor C18. The I_VIN+ terminal is also connected to pin 4 of the analog-to-digital converter chip U5. Pin 3 of the analog-to-digital converter chip U5 is connected to the I_VIN- terminal through the nineteenth capacitor C19. The I_VIN- terminal is also connected to pin 5 of the analog-to-digital converter chip U5. The fifteenth resistor R15 and the seventeenth capacitor C17 are connected in parallel between the I_VIN- terminal and the I_VIN+ terminal. Pin 1 of the analog-to-digital converter chip U5 is also connected to the analog-to-digital converter... Replace pin 3 of chip U5. Pin 10 of the analog-to-digital converter chip U5 is connected to the +3.3V analog voltage terminal +3V3_AVDD through resistor R22. Pin 9 of the analog-to-digital converter chip U5 is connected to the +3.3V analog voltage terminal +3V3_AVDD through resistor R23. Pin 8 of the analog-to-digital converter chip U5 is connected to the +3.3V analog voltage terminal +3V3_AVDD. The I_VIN+ terminal is connected to the positive terminal of the original pointer-type electrical instrument, and the I_VIN- terminal is connected to the negative terminal of the original pointer-type electrical instrument. Preferably, the differential input unit 107 includes a dual operational amplifier chip 1071 integrating two operational amplifiers. The dual operational amplifier chip 1071 is an LMV358M. Pin 3 (non-inverting input) of the dual operational amplifier chip 1071 is connected to the 1.65V analog reference voltage pin VREF_1.65V via the fifteenth capacitor C15. Pin 3 (non-inverting input) is also connected to the S1_EXT_VIN pin via the sixteenth resistor R16. A seventeenth resistor R17 is connected in parallel with the fifteenth capacitor C15. Pin 2 (inverting input) of the dual operational amplifier chip 1071 is connected to pin 1 (output). Pin 1 (output) of the dual operational amplifier chip 1071 is connected to the analog-to-digital converter chip U via the twentieth resistor R20. Pin 7 of the dual operational amplifier chip 1071 is connected to the analog reference voltage pin VREF_1.65V via the sixteenth capacitor C16. The non-inverting input of pin 5 is also connected to the S2_EXT_VIN pin via the eighteenth resistor R18. A nineteenth resistor R19 is connected in parallel with the sixteenth capacitor C16. The inverting input of pin 6 of the dual operational amplifier chip 1071 is connected to the output of pin 7. The output of pin 7 of the dual operational amplifier chip 1071 is connected to pin 6 of the analog-to-digital converter chip U5 via the twenty-first resistor R21. The S1_EXT_VIN and S2_EXT_VIN pins serve as external measurement expansion input pins for connecting existing pointer-type electrical instruments. In this embodiment, the main control PCB board 101 and the power supply lithium battery 102 are installed inside the original pointer-type electrical instrument (reference connection diagram of digital ammeter 5). The I_VIN+ terminal is connected to the positive terminal of the original pointer-type electrical instrument, and the I_VIN- terminal is connected to the negative terminal of the original pointer-type electrical instrument. The specific installation method is as follows: A vertically mounted main control PCB board 101 is added to the upper part of the original pointer-type electrical instrument, and a single lithium battery, i.e., the power supply lithium battery 102, is embedded next to it. The power supply lithium battery 102 is connected to the onboard charging / protection module 6 via a ribbon cable and shares the power and signal channels with the four-pin aviation socket at the top. The connection structure of the onboard charging / protection module 6 is as follows. Figure 10 As shown, the onboard charging / protection module 6 is also connected to the MCU processor U4. To facilitate later USB interface charging, the onboard charging / protection module 6, i.e., the battery management circuit in the figure, is added. The onboard charging / protection module 6 is mounted on the main control PCB board 101, enabling the main control PCB board 101 to be powered by either a lithium battery or USB 5V. The specific circuit is shown below. Figure 10As shown, a push-button switch will be installed on the main control PCB board 101 to form a circuit with the onboard charging / protection module 6 and the lithium battery. Figure 10 In this implementation, a push-button switch is connected to the battery management chip U1 via a strip connector CN1, such as... Figure 10 As shown, the onboard charging / protection module 6 can be powered by a lithium battery when the button switch is pressed, and when both the circuit has a lithium battery plugged in and the USB interface has a USB cable plugged in, the button switch is in the off state and the USB power supply is used.

[0020] The bottom compartment of the pointer-type electrical instrument retains the original three brass terminals and shunt resistor, which serve as both the measured terminals and continue to support the meter coil circuit; specific connection details are as follows: Figure 9 As shown, the original internal structure of this digital ammeter 5 is as follows: it contains two shunt resistors: R3=0.1Ω (0.6A range) and R2=0.025Ω (3A range); the mechanical meter head has a full-scale deflection of 75 mV, and is connected in series with R1≈25Ω across the selected shunt resistors. The measured current forms the main circuit through either "positive 0.6A → positive (at the common terminal connected to R1 and R3)" or "positive 3A → negative (at the common terminal connected to R2 and R3)". The modified acquisition module adopts a parallel voltage measurement method: that is, "positive 0.6A" is used as the detection point of "voltage A positive" connected to the S1_EXT_VIN pin; "positive 3A" is used as the detection point of "voltage B". The positive terminal is connected to the S2_EXT_VIN pin; the original negative terminal of the digital ammeter 5 (i.e., the original black terminal "negative (COM)") is connected to the I_VIN- terminal to achieve a shared negative terminal; the other end of resistor R2 is led out as the positive terminal for current measurement and connected to the I_VIN+ terminal to form a sampling module, i.e., sensor sampling unit 108. This allows for real-time measurement of the voltages UA and UB. The terminal setting is determined based on whether the values ​​of UA and UB are equal, while maintaining the original mechanical meter function. Figure 9 The main control board is the MCU control unit 106, which makes the entire wiring short and direct to reduce impedance and mechanical interference, and realizes a compact structure of "mechanical pointer + digital measurement and control + built-in power supply".

[0021] The sampling data conversion module 104 of this technical solution uses the 16-bit high-precision analog-to-digital converter chip ADS1115 as the core sampling device, which fully digitizes voltage and current sampling. Utilizing the ADS1115's built-in programmable gain amplifier (PGA) and differential input structure, multiple high-resolution measurements are simultaneously completed within a single chip: ① For current measurement, a 100 milliohm precision sampling resistor (i.e., the fifteenth resistor R15) is connected in series in the circuit under test. A small voltage drop is introduced into the differential inputs AIN0 / AIN1 of the ADS1115 (i.e., the positive terminal of the current measurement in the original pointer-type electrical instrument). The PGA amplifies the signal to the full-scale range of ±3A, achieving bipolar current detection with microampere-level resolution; ② For voltage measurement, a 0.1% precision metal film resistor voltage divider network is used to linearly attenuate the measured voltage from a maximum of ±25V to the external reference voltage range of ±1.65V. Then, a low-noise and stable value is obtained through its maximum sampling rate of 860SPS (i.e.,...). Figure 8 Voltages A and B are introduced as measurement points; the entire circuit strictly isolates analog ground from digital ground, and an RC filter is added at the input to suppress surges and radio frequency interference, thereby significantly improving the signal-to-noise ratio and anti-interference capability of the measurement link. The module undergoes multi-point temperature drift calibration before leaving the factory and supports online zero-point self-calibration. Finally, the acquired data is transmitted through I... 2 The C bus transmits data at high speed to the MCU processor U4, providing reliable, low-latency raw data for data fusion, algorithm processing, and intelligent judgment.

[0022] Preferably, the analog reference power output circuit 105 includes a 24th resistor R24, a 25th resistor R25, a 26th resistor R26, and a low-voltage precision regulator U6. The analog voltage terminal +3V3_AVDD is connected to the analog ground terminal via a series connection of the 24th resistor R24, the 25th resistor R25, and the 26th resistor R26. The common terminal connected to the 24th resistor R24 ​​and the 25th resistor R25 serves as the analog reference voltage pin VREF_1.65V. The common terminal of the 25th resistor R25 and the 26th resistor R26 is connected to pin 1 of the low-voltage precision regulator U6, and pin 2 of the low-voltage precision regulator U6 is connected to the analog ground terminal. The reference voltage pin VREF_1.65V is connected to the analog ground terminal via pin 3 of the low-voltage precision regulator U6. A 24th capacitor C24 is also connected between the analog reference voltage pin VREF_1.65V and the analog ground terminal. The 3.3V voltage input terminal +3V3 is connected to the analog voltage terminal +3V3_AVDD via inductor L1. A 12th capacitor C12, a 13th capacitor C13, and a 14th capacitor C14 are connected in parallel between the analog voltage terminal +3V3_AVDD and the analog ground terminal. A 14th resistor R14 is connected between the analog ground terminal and the ground terminal GND. The model of the low-voltage precision regulator U6 is TLV431BQDBZR.

[0023] Preferably, the non-inverting input pin 3 of the dual operational amplifier chip 1071 is connected to ground GND through the twentieth capacitor C20; the common terminal of the twentieth resistor R20 connected to pin 7 of the analog-to-digital converter chip U5 is connected to ground GND through the twentieth capacitor C22; the non-inverting input pin 5 of the dual operational amplifier chip 1071 is connected to ground GND through the twentieth capacitor C21; and the common terminal of the twentieth resistor R21 connected to pin 6 of the analog-to-digital converter chip U5 is connected to ground GND through the twentieth capacitor C23.

[0024] Preferably, the MCU control unit 106 includes a core MCU processor U4 and peripheral circuitry. The MCU processor is an ESP32 with a built-in 240MHz dual-core Xtensa processor. Pin 33 of the MCU processor U4 is connected to pin 10 of the analog-to-digital converter chip U5, and pin 36 of the MCU processor U4 is connected to pin 9 of the analog-to-digital converter chip U5. The MCU processor U4 will later be directly connected to the network communication system 2. In this embodiment, the core controller of the MCU control unit 106 uses Espressif's latest ESP32 MCU, which has a built-in 240MHz dual-core Xtensa processor, 520 KBS RAM, and supports up to 16MB SPI Flash. The MCU processor U4 can be connected via I... 2The C 400kHz bus acquires multiple 16-bit data streams from the ADS1115 and uses DMA continuous buffer mode to write samples into a circular queue, ensuring no sample loss at 860SPS. Furthermore, the MCU processor U4's software stack is divided into a hardware abstraction layer, a data processing layer, and a communication service layer: the hardware abstraction layer is responsible for GPIO, power monitoring, and I / O. 2 C. UART driver; The data processing layer performs integer mean filtering, temperature compensation, and linear regression calibration, ultimately outputting voltage and current physical quantities; The communication service layer supports both 802.11b / g / n and Bluetooth LE 5.0, periodically pushing data packets to the campus IoT platform via the MQTT protocol, and supports HTTPS OTA upgrades to ensure firmware maintainability. For security, WPA2 PSK connection, TLS 1.2 encryption, and device-side SHA 256 signature are used to prevent malicious data theft or tampering.

[0025] This embodiment also discloses an artificial intelligence-based method for monitoring electrical experiments, including the aforementioned artificial intelligence-based electrical experiment monitoring system, the specific steps of which are as follows: S1. First, the definition of the scoring rules. Developers configure each scoring point in the backend system of cloud server 3. The configuration includes the detection variables (such as voltage, current, and the position of the sliding rheostat), judgment conditions (greater than, less than, range, trend, etc.), error messages, and whether debouncing or parent-child scoring point dependency data is required. All configurations are centrally managed by the system and can be upgraded to different versions. The "parent-child scoring point dependency" refers to the sequential constraint relationship between scoring points: the judgment of a child scoring point requires the parent scoring point to be "passed". During the evaluation, the system executes from upstream to downstream according to the dependency graph (which is set in advance). If the upstream fails, the judgment and prompt of the downstream scoring point will be blocked or delayed. S2, Data Acquisition The modified pointer-type electrical instrument is an existing pointer-type electrical instrument with an added intelligent instrument detection system 1. The intelligent instrument detection system 1 uploads the current measurement data to the student's computer terminal 401 at fixed intervals (e.g., every 0.5 seconds, or in a specific embodiment, every 0.4 seconds, 0.8 seconds, or 1 second). The collected measurement data includes voltage, current, and instrument status information. The data acquisition is completed locally on the student's computer, ensuring real-time performance and stability. S3, Assessment of Conditions The background system of cloud server 3 retrieves the scoring point configuration in step S1, reads the scoring point configuration sequentially, and compares it with the current measurement data obtained in step S2. Then, the real-time data analysis engine and the real-time data analysis engine in scoring module 301 perform rule matching (including threshold, interval, trend, etc.) on the sampling stream. Based on this, the real-time data analysis engine and the scoring module in scoring module 301 generate a "pass / fail" conclusion with an error code and prompt key. At this time, if the scoring point configuration set for a certain scoring point is inconsistent with the current collected measurement data, the measurement scoring point is defined as not meeting the requirements and is immediately defined as "failed". For example, if a scoring point is set to "voltage should be positive", but the current collection result is negative, then the scoring point is immediately marked as "failed". Step S3 supports multiple judgment methods, including single value comparison, threshold judgment, time series trend analysis, etc. S4, Debouncing and Dependency Control To avoid misjudgments caused by short-term current fluctuations or momentary poor contact, the back-end system enables a de-jitter mechanism for specific scoring points. That is, only when the measurement data is collected multiple times in a row and the results of each comparison do not meet the requirements is it defined as a failure and immediately defined as "failed". If there is a logical dependency between scoring points (e.g., an error in one step will affect the next step), the system will prioritize prompting errors in the upstream scoring points to avoid information confusion. Specifically, when multiple errors are detected at the same time and there is a dependency relationship, only the error of the most upstream (root cause) scoring point will be prompted. After it is corrected, the downstream scoring points will continue to be evaluated to avoid interference from multiple secondary error messages. S5, Final Prompt Presentation When the real-time data analysis engine and the scoring module 301 determine that a scoring point has failed, the corresponding prompt will be highlighted and displayed on the software interface of the computer at the student's seat 401. The prompt is static text, using the language described in the textbook, and is accompanied by flashing and sound effects to remind the student to correct it in time. All prompts are processed locally and do not need to be transmitted through the server. S6. Recording and Analysis The evaluation process and results of all scoring points (including the timestamp of each judgment, measurement data, and final status) are synchronously written to the log database of the background system on cloud server 3. Teachers can access the log database after class through the teacher's computer terminal 402 to view a student's score trajectory and error distribution at each step, which can help with teaching analysis and feedback.

[0026] The working principle of this structure is as follows: First, the developer establishes the configuration content for each scoring point in the backend system of the cloud server 3. The configuration content includes the detection variables (such as voltage, current, and sliding rheostat position), judgment conditions (greater than, less than, interval, trend, etc.), error messages, and whether anti-jitter processing or parent-child scoring point dependency data is required. When the student operates, the intelligent instrument detection system 1 uploads the current measurement data to the computer terminal 401 at the student's seat at fixed intervals. The collected measurement data includes voltage, current, and instrument status information. The backend system of the cloud server 3 retrieves the scoring point configuration in step S1, reads the scoring point configuration sequentially, and compares it with the current measurement data obtained in step S2 using the real-time data analysis engine and scoring module 301. If a certain scoring point is not properly configured, the system will automatically determine the scoring point configuration. When the scoring point configuration in the sub-point settings is inconsistent with the currently collected measurement data, the measurement scoring point is defined as not meeting the requirements and is immediately defined as "failed". For example, if a scoring point is set to "voltage should be positive", but the current collection result is negative, then the scoring point is immediately marked as "failed". When the real-time data analysis engine and scoring module 301 determine that a scoring point has failed, the corresponding prompt will be highlighted and popped up on the software interface of the student's computer terminal 401. The prompt is static text, using the language described in the textbook, and is accompanied by flashing and sound effects to remind students to correct it in time. Moreover, all prompts are processed locally and do not need to be transmitted through the server. Teachers can access the log database after class through the teacher's computer terminal 402 to view a student's score trajectory and error distribution in each step, which helps with teaching analysis and feedback.

[0027] Application Example Description Scenario Overview: After the teacher selects the "Investigating the Relationship Between Current and Voltage" template on the physics experiment cloud platform on the teacher's computer (402), the platform synchronously distributes the "experiment package" to the software side of each student's computer (401) and the corresponding smart instrument (i.e., the instrument with the smart instrument detection system 1) via HTTP / WS. Following the student interface on the student's computer (401) (see...), the experiment package is then distributed to the software side of each student's computer (401). Figure 8 (As shown) The left side lists the operations step by step, the right side displays the circuit diagram and sensor data stream in real time; the bottom area is used to pop up error messages.

[0028] The entire experiment was broken down into 16 scoring points, each bound to a triplet of "trigger condition - judgment criterion - prompt". Every 0.5 seconds, the system reads data such as line voltage U_A (read via a sensor inside the voltmeter), line current I (read via a sensor inside the ammeter), and switch voltage U_B (or voltage B), and combines this data with historical records, number of closing cycles, and other auxiliary quantities to complete the following judgment: 1. Scoring Point 1: Connect the experimental circuit as shown in the required circuit diagram. Detection timing: After the switch is closed for the first time and stable sampling begins; Judgment logic: If U_B≈0 (indicating that the loop is closed), but at the same time |U_A|<0.1 V or |I|<0.02 A, it indicates that there is an open circuit / series-parallel relationship error in the main circuit; or if the difference between the calculated |U_A / I| and the nominal resistance of 10 Ω exceeds ±0.5 Ω, it is determined that the wiring does not conform to the circuit diagram.

[0029] Examples of triggering causes: voltmeter mistakenly connected in series with the main circuit, ammeter connected in parallel, wires not connected in a clockwise direction causing the equivalent resistance of the circuit to be too large / too small.

[0030] Prompt: Prompt 1 "The experimental circuit was not connected as shown in the circuit diagram".

[0031] 2. Scoring point 2: The switch is in the off state (before the experiment begins). Timing of the test: Before the student clicks "Start Experiment / Self-Test".

[0032] Judgment logic: If the system does not record any sample of a "disconnected" state corresponding to a significant potential difference (|U_B|≈0) across the switch, it is considered that the switch preset is incorrect.

[0033] Typical trigger: Students forget to disconnect the switch before connecting the wire or close it directly.

[0034] Prompt message: Prompt 2 "The switch is not in the off state".

[0035] 3. Scoring Point 3: Voltmeter "+" and "-" terminal connection and range selection Detection timing: Continuous monitoring after the loop is closed, and 3 frames of jitter reduction are set for this point.

[0036] Polarity error (3.1): |U_A|≥0.1 V and U_A<0, indicating that the probes are reversed.

[0037] Range error (3.2): 0 < |U_A| < 0.6 V (below the selected range lower limit) or exceeds the set range upper limit.

[0038] Combination error (3.3): Conditions 3.1 and 3.2 are satisfied simultaneously.

[0039] Prompt message: Prompts 3.1 / 3.2 / 3.3 correspond to polarity, range, and combination errors, respectively.

[0040] 4. Scoring Point 4: Connection method of voltmeter in parallel across the resistor being measured Testing timing: When the circuit is closed and the power supply is outputting normally.

[0041] Judgment logic: The voltmeter should be connected in parallel across the fixed resistor R. If U_B≈0 (indicating the switch is closed), but |U_A|<0.1 V (almost zero), or |U_A|>3 V (far exceeding the supply voltage), or I<0.02 A (extremely small loop current) is detected, it indicates that the voltmeter is not connected in parallel to the correct position (it may be connected in series or across other components).

[0042] Prompt message: Prompt 4 "The connection method of the voltmeter to measure the voltage across the resistor is incorrect."

[0043] 5. Scoring Point 5: Ammeter "+" and "-" terminal connections and range selection Polarity error (5.1): |I|≥0.02 A and I<0, indicating that the current direction is opposite to the preset, and the test leads are reversed.

[0044] Range error (5.2): The system compares the voltage drop ratio of the dual shunt resistors in the current sensing module. When the ratio is close to 1 (0.9–1.1), it means that the student is still using a small range but a large current is flowing through it, which makes it impossible to distinguish the voltage drop of the two circuits. The range selection is therefore determined to be incorrect.

[0045] Combination error (5.3): Simultaneous occurrence of polarity and range abnormalities.

[0046] Prompt: Prompts 5.1 / 5.2 / 5.3 correspond to three different error scenarios.

[0047] 6. Scoring Point 6: Connection method of ammeter in series with the resistor being measured Judgment logic: When the switch is closed and U_B≈0, if I<0.02 A (almost no current) or I>0.6 A (significantly exceeding the experimental allowable range), it indicates that the ammeter is not connected in series to the resistor branch (it may be bypassed / connected in parallel), or the circuit is short-circuited.

[0048] Prompt message: Prompt 6 "The connection method of the ammeter for measuring resistance current is incorrect."

[0049] 7. Scoring Point 7: Connection Method of Sliding Rheostat Timing of detection: When the student begins adjusting the slider and clicks "Record". Judgment logic: If, after the slider position changes, the U_A and I detected by the sensor are within the set minimum change threshold (e.g., ΔU and ΔI are both less than 1%FS), it indicates that the sliding rheostat has not actually participated in the circuit or the connection is incorrect.

[0050] Prompt message: Prompt 7 "The sliding rheostat is not connected correctly".

[0051] 8. Scoring point 8: Before closing, the slider should be placed at the position of maximum resistance. Judgment logic: When the switch is actually closed for the first time, the system calculates the equivalent resistance R_calc = |(U_B−U_A)| / |I| using the instantaneously measured U_A, I and the switch terminal voltage U_B. If R_calc > 8 Ω (significantly greater than the set "maximum resistance judgment threshold"), it means that the slider was not placed at the maximum resistance end before closing.

[0052] Prompt message: Prompt 8 "The slider of the sliding rheostat is not in the maximum position".

[0053] 9. Scoring points 9 / 10 / 11 / 12 / 13 / 14 / 15 / 16: Consistency of data recording across four rounds. Specific scoring point requirements are as follows: Scoring point 9: Close the switch, move the slider of the sliding rheostat, read and record the voltage value for the first time; Scoring point 10: First reading and recording of the current value; Scoring point 11: Move the slider of the sliding rheostat, read the voltage value a second time, and record it; Scoring point 12: Read and record the current value a second time; Scoring point 13: Move the slider of the sliding rheostat, read the voltage value for the third time, and record it; Scoring point 14: Read and record the current value for the third time; Scoring point 15: Move the slider of the sliding rheostat, read the voltage value for the fourth time and record it; Scoring point 16: Read and record the current value for the fourth time; Analysis of experimental results for scoring point 17; Timing of detection: Each time a student clicks the "Record" button, the system checks the voltage and current records from the first to the fourth time.

[0054] Judgment logic: The manual value entered by the student must be within the allowable error range (e.g., ±1%FS) of the real-time value of the sensor at the moment of recording. If they are inconsistent, the corresponding prompt will be triggered.

[0055] Therefore, this invention proposes a complete electrical experiment system that integrates traditional mechanical pointer instruments and modern intelligent detection technology. While retaining the original structure and operating characteristics of mechanical pointer voltmeters and ammeters, this system integrates a high-precision sampling data conversion module 104 and utilizes a network communication system 2, a cloud server 3, and a client 4 to successfully achieve real-time data acquisition and intelligent analysis of the teaching experiment process. Specifically, this system helps teachers understand the experimental data and experimental trajectory of each student in the class in real time, including key indicators such as voltage and current, thereby instantly grasping the overall progress and quality of the classroom experiment. Simultaneously, based on a preset scoring logic, the system automatically and accurately monitors and evaluates experimental operations (such as instrument wiring, range selection, and sliding rheostat adjustment) in real time. Once an operational deviation is detected, it provides real-time feedback to the student, helping them correct operational errors promptly, significantly improving the effectiveness of experimental teaching and classroom interaction efficiency, and accelerating the teaching progress. Therefore, this invention achieves an intelligent upgrade of traditional instruments, combining real-time data monitoring and autonomous scoring feedback functions, and has high teaching promotion value and innovation.

[0056] It should also be noted that the network communication system 2, cloud server 3, client 4, student seat computer terminal 401, and teacher computer terminal 402 are all existing technologies. Furthermore, how the network communication system 2 acquires data, how it transmits data via Wi-Fi or Bluetooth communication protocols, how the cloud server 3's backend system acquires data, how it uses the real-time data analysis engine and scoring module 301 to judge the data, and the programming involved in traversing the data are all conventional technologies in this field, and therefore will not be described in detail.

[0057] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An electrical experiment monitoring system based on artificial intelligence, characterized in that: The system includes an intelligent instrument detection system (1) installed in a pointer-type electrical instrument, a network communication system (2), a cloud server (3), and a client (4). The intelligent instrument detection system (1) is directly connected to the cloud server (3) via the network communication system (2). The cloud server (3) is directly connected to the client (4). The intelligent instrument detection system (1) collects various detection parameters in the pointer-type electrical instrument in real time, performs preliminary processing, and then transmits the data to the network communication system (2). The network communication system (2) uploads the collected data to the cloud server (3) in real time and securely via Wi-Fi or Bluetooth communication protocols. The cloud server (3) automatically analyzes the experimental operation behavior and provides real-time scoring and abnormal prompts. The client (4) includes a student seat computer terminal (401) and a teacher computer terminal (402). The teacher computer terminal (402) is used to monitor the experimental progress and scoring status of the whole class in real time. The student seat computer terminal (401) displays the experimental operation steps, data curves, scoring status, and abnormal prompts in real time with an intuitive graphical interface.

2. The artificial intelligence-based electrical experiment monitoring system according to claim 1, characterized in that: The cloud server (3) is based on a Node.js interface gateway and a PostgreSQL database, and integrates a real-time data analysis engine and a scoring module (301) to automatically analyze experimental operation behavior and provide real-time scoring and abnormal prompts.

3. The artificial intelligence-based electrical experiment monitoring system according to claim 2, characterized in that: The intelligent instrument detection system (1) includes a main control PCB board (101) installed in the original pointer-type electrical instrument and a power supply lithium battery (102). The main control PCB board (101) is provided with an instrument data acquisition unit (103). The power supply lithium battery (102) is electrically connected to the instrument data acquisition unit (103) to supply power to the instrument data acquisition unit (103). The instrument data acquisition unit (103) includes a sensor sampling unit (108), a sampling data conversion module (104), and an analog reference voltage of ±1.65V. The source output circuit (105) and MCU control unit (106) are included. The sampling data conversion module (104) includes an analog-to-digital converter chip (U5) as the core sampling device and a differential input unit (107). The sensor sampling unit (108) is connected in series in the original pointer-type electrical instrument through a current branch to obtain the current signal, obtains the voltage signal through a voltage divider network, and then the multiple signals are sent to the differential channel of the analog-to-digital converter chip (U5) for 16-bit sampling after differential buffering. The analog-to-digital converter chip (U5) is an ADS1115 16-bit converter. A high-precision analog-to-digital converter (ADC) chip, U5, has pin 1 connected to the I_VIN+ terminal via the eighteenth capacitor (C18). The I_VIN+ terminal is also connected to pin 4 of the ADC chip (U5). Pin 3 of the ADC chip (U5) is connected to the I_VIN- terminal via the nineteenth capacitor (C19). The I_VIN- terminal is also connected to pin 5 of the ADC chip (U5). A fifteenth resistor (R15) and a seventeenth capacitor (C17) are connected in parallel between the I_VIN- and I_VIN+ terminals. Pin 1 of the ADC chip (U5) is also connected to the... The connection of pin 3 of the replacement chip (U5) is as follows: pin 10 of the analog-to-digital converter chip (U5) is connected to the +3.3V analog voltage terminal (+3V3_AVDD) through the 22nd resistor (R22); pin 9 of the analog-to-digital converter chip (U5) is connected to the +3.3V analog voltage terminal (+3V3_AVDD) through the 23rd resistor (R23); pin 8 of the analog-to-digital converter chip (U5) is connected to the +3.3V analog voltage terminal (+3V3_AVDD); the I_VIN+ terminal is connected to the positive terminal of the original pointer-type electrical instrument; and the I_VIN- terminal is connected to the negative terminal of the original pointer-type electrical instrument.

4. The artificial intelligence-based electrical experiment monitoring system according to claim 3, characterized in that: The differential input unit (107) includes a dual operational amplifier chip (1071) integrating two operational amplifiers. The dual operational amplifier chip (1071) is an LMV358M. The non-inverting input pin 3 of the dual operational amplifier chip (1071) is connected to the analog reference voltage pin (VREF_1.65V) with a voltage of 1.65V through the fifteenth capacitor (C15). The non-inverting input pin 3 of the dual operational amplifier chip (1071) is also connected to the S1_EXT_VIN pin through the sixteenth resistor (R16). The seventeenth resistor (R17) is connected in parallel with the fifteenth capacitor (C15). The inverting input pin 2 of the dual operational amplifier chip (1071) is connected to the output pin 1 of the dual operational amplifier chip (1071). The output pin 1 of the dual operational amplifier chip (1071) is connected to the analog-to-digital converter chip (U) through the twentieth resistor (R20). 5) The 7th pin of the dual operational amplifier chip (1071) is connected to the analog reference voltage pin (VREF_1.65V) of 1.65V through the sixteenth capacitor (C16). The 5th pin of the dual operational amplifier chip (1071) is also connected to the S2_EXT_VIN pin through the eighteenth resistor (R18). The nineteenth resistor (R19) is connected in parallel with the sixteenth capacitor (C16). The 6th pin of the dual operational amplifier chip (1071) is connected to the output pin of the dual operational amplifier chip (1071) through the seventeenth resistor (R21). The 7th pin of the dual operational amplifier chip (1071) is connected to the 6th pin of the analog-to-digital converter chip (U5) through the twenty-first resistor (R21). The S1_EXT_VIN pin and the S2_EXT_VIN pin are used as external measurement expansion input pins for connecting the original pointer-type electrical instruments.

5. The artificial intelligence-based electrical experiment monitoring system according to claim 4, characterized in that: The analog reference power output circuit (105) includes a 24th resistor (R24), a 25th resistor (R25), a 26th resistor (R26), and a low-voltage precision regulator (U6). The analog voltage terminal (+3V3_AVDD) and the analog ground terminal are connected in series with the 24th resistor (R24), the 25th resistor (R25), and the 26th resistor (R26). The common terminal connected to the 24th resistor (R24) and the 25th resistor (R25) serves as the analog reference voltage pin (VREF_1.65V). The common terminal of the 25th resistor (R25) and the 26th resistor (R26) is connected to pin 1 of the low-voltage precision regulator (U6), and pin 2 of the low-voltage precision regulator (U6) is connected to the analog ground terminal. The reference voltage pin (VREF_1.65V) is connected to the analog ground terminal via pin 3 of the low-voltage precision regulator (U6). The analog reference voltage pin (VREF_1.65V) is also connected to the analog ground terminal via a 24th capacitor (C24). The 3.3V voltage input terminal (+3V3) is connected to the analog voltage terminal (+3V3_AVDD) via inductor 1 (L1). The analog voltage terminal (+3V3_AVDD) and the analog ground terminal are connected in parallel with a 12th capacitor (C12), a 13th capacitor (C13), and a 14th capacitor (C14). The analog ground terminal and the ground terminal (GND) are connected with a 14th resistor (R14). The low-voltage precision regulator (U6) is model TLV431BQDBZR.

6. The artificial intelligence-based electrical experiment monitoring system according to claim 4, characterized in that: The non-inverting input pin 3 of the dual operational amplifier chip (1071) is connected to ground (GND) through the twentieth capacitor (C20). The common terminal of the twentieth resistor (R20) and pin 7 of the analog-to-digital converter chip (U5) is also connected to ground (GND) through the twenty-second capacitor (C22). The non-inverting input pin 5 of the dual operational amplifier chip (1071) is also connected to ground (GND) through the twenty-first capacitor (C21). The common terminal of the twenty-first resistor (R21) and pin 6 of the analog-to-digital converter chip (U5) is also connected to ground (GND) through the twenty-third capacitor (C23).

7. The artificial intelligence-based electrical experiment monitoring system according to claim 3, characterized in that: The MCU control unit (106) includes an MCU processor (U4) as the core and peripheral circuits. The MCU processor is an ESP32 built-in 240MHz dual-core Xtensa processor. Pin 33 of the MCU processor (U4) is connected to pin 10 of the analog-to-digital converter chip (U5), and pin 36 of the MCU processor (U4) is connected to pin 9 of the analog-to-digital converter chip (U5). The MCU processor (U4) will later be directly connected to the network communication system (2).

8. An artificial intelligence-based method for monitoring electrical experiments, comprising employing the artificial intelligence-based electrical experiment monitoring system as described in any one of claims 2-7, characterized in that, The specific steps are as follows: S1. First, the definition of the scoring rules. Developers establish configuration content for each scoring point in the background system of the cloud server (3). The configuration content includes detection variables, judgment conditions, error messages, and whether debouncing processing or parent-child scoring point dependency data is required. All configuration content is centrally managed by the system and can also be upgraded to version. S2, Data Acquisition The modified pointer electrical instrument is to add an intelligent instrument detection system (1) to the existing pointer electrical instrument. The intelligent instrument detection system (1) uploads the current measurement data to the computer terminal (401) at the student's seat at fixed intervals. The collected measurement data includes voltage, current and instrument status information. S3, Assessment of Conditions The background system of the cloud server (3) retrieves the scoring point configuration in step S1, reads the scoring point configuration in sequence, and compares it with the current measurement data obtained in step S2. Then, the real-time data analysis engine and the real-time data analysis engine in the scoring module (301) perform rule matching on the sampling stream. The real-time data analysis engine and the scoring module in the scoring module (301) generate a "pass / fail" conclusion with an error code and prompt key. If the scoring point configuration set for a certain scoring point is inconsistent with the current collected measurement data, the measurement scoring point is defined as not meeting the requirements and is immediately defined as "fail". S4, Debouncing and Dependency Control To avoid misjudgments caused by short-term current fluctuations or momentary poor contact, the back-end system enables a de-jitter mechanism for specific scoring points. That is, only when the measurement data is collected multiple times in a row and the results of each comparison do not meet the requirements is it defined as a failure and immediately defined as "failed". S5, Final Prompt Presentation When the real-time data analysis engine and the scoring module (301) determine that a scoring point has failed, the corresponding prompt will be highlighted in the software interface of the computer at the student's seat (401). The prompt is static text, using the language described in the textbook, and is accompanied by flashing and sound effects to remind the student to correct it in time. S6. Recording and Analysis The evaluation process and results of all scoring points will be synchronously written to the log database of the background system of the cloud server (3). Teachers can access the log database after class through the teacher's computer (402) to view a student's score trajectory and error distribution in each step, which can help with teaching analysis and feedback.

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