Smart classroom multimedia equipment networking debugging management system

Abstract

The invention belongs to the technical field of smart classroom equipment debugging, and discloses a smart classroom multimedia equipment networking debugging management system, which can automatically identify access equipment through a dynamic service discovery unit of a protocol adaptation module and obtain information such as protocol types and functional parameters of the access equipment without manual input; for a special protocol which is not preset, an adaptive plug-in can be generated through sample collection, format analysis and logic learning, the adaptive plug-in is put into use after being verified by a virtual presetting test, meanwhile, various heterogeneous protocol data are converted into a unified standard format, and a data transmission strategy is optimized; the pre-debugging monitoring module simulates an equipment linkage scene in a virtual environment, simulates various faults and outputs and optimizes a debugging scheme, on-site repeated trial and error are avoided, an automatic and manual dual-mode execution mode of the intelligent debugging module is combined, the debugging process is simplified, manual intervention is reduced, and the debugging efficiency is improved. And the multi-device linkage debugging precision and the system operation stability are improved.

Description

Technical Field

[0001] This invention belongs to the field of smart classroom equipment debugging technology, specifically a smart classroom multimedia equipment network debugging and management system. Background Technology

[0002] As educational informatization continues to deepen, smart classrooms have become the core carrier of modern teaching activities. These classrooms typically integrate various multimedia and sensing devices such as projectors, interactive smart panels, sound systems, recording and broadcasting equipment, and environmental sensors. Currently, the networking, debugging, and daily management of multimedia equipment in smart classrooms still rely mainly on manual operation or simple management tools, and the following technical problems remain:

[0003] Multimedia devices in smart classrooms come from different manufacturers and use different network communication protocols. Existing debugging solutions are mostly developed for single-protocol devices and lack a unified adaptation and collaborative debugging mechanism for heterogeneous protocols. When debugging multiple devices in a coordinated manner, different protocol devices need to be configured and debugged separately, which not only increases the complexity of the debugging process, but also easily causes device linkage failures due to protocol adaptation deviations. Furthermore, the inability to perceive the interaction between the network status of devices with different protocols in real time leads to low debugging efficiency and insufficient system stability.

[0004] Existing technologies, during equipment network debugging, can only collect basic data such as network status and whether the equipment is online, without systematically storing and deeply analyzing all debugging data. When network failures occur later, it is impossible to trace the root cause of the failure based on historical debugging data, resulting in a long troubleshooting cycle. On the other hand, the lack of a correlation analysis model between debugging data and equipment operation data makes it difficult to predict potential failure risks in the equipment network link in advance, and can only passively repair after a failure occurs, which is insufficient to meet the actual needs of continuous teaching activities in smart classrooms. Summary of the Invention

[0005] The purpose of this invention is to provide a smart classroom multimedia equipment network debugging and management system to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a smart classroom multimedia equipment network debugging and management system, including a pre-debugging monitoring module, a protocol adaptation module, a status perception module, an intelligent debugging module, a data storage module, a fault prediction module, and a remote operation and maintenance module; Preferably, the pre-debugging monitoring module includes a twin modeling unit, a virtual pre-debugging unit, a virtual-real linkage unit, a twin panoramic monitoring unit, and a teaching interference early warning unit; The twin modeling unit is based on laser scanning and equipment 3D modeling technology to replicate the classroom spatial layout, equipment installation location and network topology. It simultaneously imports equipment hardware parameters, protocol specifications, historical fault data and other information to build a twin that corresponds one-to-one with the physical entity and the virtual model, and supports the intensive management of twins in multiple classrooms. Laser scanning accuracy is controlled within ±2mm. The 3D modeling of the equipment is output in OBJ format. Imported data is uniformly in JSON format and the field integrity must be ≥98%. The centralized management of multiple classrooms adopts a three-level tree structure of "campus-floor-classroom", which supports the simultaneous management of ≤500 classroom twins. The loading time of a single classroom twin model is ≤3 seconds.

[0007] The virtual pre-debugging unit receives device access information from the protocol adaptation module, simulates multi-device linkage scenarios in a virtual environment, performs pre-debugging strategy simulations, simulates various faults such as protocol incompatibility, IP conflicts, and link congestion, and outputs a pre-debugging report. The fault simulation covers 12 core fault types, including protocol incompatibility, IP conflict, link congestion, port occupation, parameter mismatch, device offline, insufficient bandwidth, latency exceeding limits, packet loss rate exceeding standards, abnormal permissions, firmware version mismatch, and linkage logic conflict. The pre-debugging report must include 5 core modules: device adaptation status, fault simulation results, optimal debugging parameter combination, risk level assessment, and execution step suggestions.

[0008] The virtual-physical linkage unit realizes real-time data synchronization between the virtual model and the physical device through edge computing nodes. After the virtual pre-debugging is passed, the optimized debugging instructions are sent to the intelligent debugging module and the physical device with one click, while the real-time operating data of the physical device is fed back to the virtual model. The data synchronization frequency is set to 10Hz, and the debugging commands are transmitted using the MQTT protocol with a command issuance delay of ≤20ms. The feedback data includes three core types of data: device operating status code, actual parameter values, and link transmission quality. The virtual model completes the status update within 100ms after receiving the feedback.

[0009] The twin panoramic monitoring unit integrates the operational data of each module to present information such as equipment operating status, network link quality, and debugging progress, and supports switching between multiple perspectives such as classrooms, floors, and campuses; The visualization interface offers three display modes: 3D panorama, 2D topology, and data list. The data update frequency is synchronized with the virtual-real linkage unit at 10Hz, and the viewpoint switching response time is ≤500ms. It supports quick retrieval of the target device's operating data by device ID and fault type.

[0010] The teaching interference early warning unit monitors the impact of debugging operations on teaching activities in real time. When the impact exceeds the preset threshold, it automatically pauses debugging and switches to teaching priority mode, while simultaneously feeding back the early warning signal to the remote operation and maintenance module.

[0011] Noise monitoring during debugging uses data from a sound sensor located in the center of the classroom. Equipment brightness change monitoring focuses on the screen brightness of the interactive smart flat panel and projector. In teaching priority mode, the power consumption of the equipment is controlled within 60% of that in normal debugging mode. The trigger condition for resuming debugging is the end of teaching activities or manual confirmation that there is no interference. The preset thresholds are as follows: ambient noise generated during debugging ≤45dB, and screen brightness variation ≤200cd / m². 2 Network bandwidth usage ≤ 30% of total bandwidth, equipment vibration ≤ 0.1g; in teaching priority mode, equipment parameter adjustment and link reconstruction debugging operations are suspended, while equipment status monitoring functions are retained to prioritize the stable operation of projectors, interactive smart flat panels, and audio systems, and debugging is automatically resumed after the teaching activities are over.

[0012] Preferably, the protocol adaptation module includes a dynamic service discovery unit, an AI autonomous learning adaptation unit, and a protocol conversion and optimization unit; The dynamic service discovery unit is based on the MCP protocol to realize automatic device discovery and capability reporting. When a device connects, it actively pushes information such as supported protocol types, API interfaces, and functional parameters, eliminating the need for manual entry of device information.

[0013] The device detection timeout is set to 30 seconds. If there is no response after the timeout, it is marked as pending access and will re-probe every 10 seconds. The repeated reporting process adopts the latest data overwrite combined with the historical data archiving mechanism, which supports the automatic identification of mainstream multimedia devices such as projectors, interactive smart panels, speakers, recording and broadcasting equipment, as well as environmental sensors.

[0014] For special protocols that are not pre-defined, the AI ​​autonomous learning adaptation unit automatically generates adaptation plugins through sample data collection, protocol format parsing, and interaction logic learning, and verifies the stability of the adaptation through the virtual pre-debugging unit of the pre-debugging monitoring module.

[0015] Special protocols refer to protocols that are not included in the default protocol list and cannot complete capability reporting and basic adaptation through the dynamic service discovery unit (based on the MCP protocol). These include customized protocols from niche manufacturers, non-standard extended protocols, and special protocols for older equipment.

[0016] Sample data collection must meet the following requirements: a single protocol sample size of ≥500 records, including protocol headers, data fields, and complete link data of interactive responses, and the format must be uniformly JSON. Protocol parsing adopts a field extraction algorithm based on BPE. Interaction logic learning is based on an unsupervised learning combination of K-means clustering and hidden Markov model, with a learning cycle of ≤30 minutes and an iteration count of ≥50 rounds. Adaptation plugin verification must run continuously for 2 hours in virtual pre-debugging without adaptation errors.

[0017] The protocol conversion and optimization unit converts heterogeneous protocol data into a unified standard format for the system. At the same time, it dynamically optimizes the data transmission strategy based on AI algorithms to reduce data transmission latency and packet loss rate. The converted standardized data is then pushed to the status awareness module.

[0018] The system adopts the JSON-LD standard format, with fields including device ID, protocol type, data content, timestamp, and checksum. The AI ​​optimization algorithm uses an adaptive bandwidth allocation algorithm to dynamically adjust the data transmission priority based on network jitter, aiming to achieve a transmission latency of ≤30ms and a packet loss rate of ≤0.3% during the data protocol conversion stage. When the network bandwidth is lower than 1Mbps, data compression transmission is automatically triggered with a compression ratio of ≤3:1.

[0019] Preferably, the state perception module includes a multi-dimensional data acquisition unit, a data fusion and cleaning unit, and a real-time early warning unit; The multi-dimensional data acquisition unit collects various types of data, such as network status, device parameters, device operation audio and video data, and environmental data. At the same time, it receives device protocol data transmitted by the protocol adaptation module and realizes local real-time data processing through edge computing nodes. The network status is collected at a frequency of 5Hz, including three types of parameters: bandwidth utilization, latency, and packet loss rate; the device parameters are collected at a frequency of 2Hz, with an accuracy of ≤±0.5%; audio and video data are collected using H.265 encoding, with a frame rate of 15fps and a resolution of 720P; environmental data includes temperature with an accuracy of ±0.2℃, humidity of ±2%RH, and electromagnetic interference of ≤-80dBm, all collected at a frequency of 1Hz.

[0020] The data fusion and cleaning unit adopts a multimodal data fusion algorithm to eliminate misjudgments from a single data dimension. At the same time, it uses AI algorithms to denoise, deduplicatize, and format the data, generating a standardized data set, which is then pushed to the intelligent debugging module and the fault prediction module respectively. The standardized data output frequency after data fusion and cleaning is uniformly synchronized with the highest acquisition frequency of 5Hz to ensure that it matches the data processing rhythm of subsequent modules.

[0021] Multimodal data fusion employs an attention-based weighted algorithm, with network state data weighted at 0.4, device parameter weight at 0.3, environmental data weight at 0.2, and audio / video data weight at 0.1. This weight can be dynamically adjusted based on device type. Noise reduction uses a lightweight CNN model with an input dimension of 256×256, two hidden layers, and a ReLU activation function. The noise filtering threshold is set to ±3σ. Deduplication uses a cosine similarity AI model, where data with a similarity ≥ 0.95 is considered duplicate and removed.

[0022] The real-time early warning unit triggers a local early warning for the collected abnormal data and synchronizes it to the pre-debugging monitoring module for fault tracing simulation. At the same time, it transmits the early warning information to the fault prediction module.

[0023] The local early warning system uses a combination of a buzzer and a flashing red indicator light. The warning information includes five core fields: abnormal data type, collection time, device ID, current value, and threshold range. Low-level warnings only trigger local notifications, while medium- and high-level warnings are simultaneously pushed to the fault prediction module for in-depth analysis.

[0024] Preferably, the intelligent debugging module includes a debugging strategy generation unit, a dual-mode execution unit, and a debugging effect verification unit; The debugging strategy generation unit generates the optimal debugging scheme based on the virtual pre-debugging report output by the pre-debugging monitoring module and combined with real-time perceived device status data. It uses a reinforcement learning algorithm to generate the optimal debugging scheme, which includes protocol adaptation parameters, network configuration, and device linkage logic. It supports setting corresponding debugging strategies for different teaching scenarios. This unit uses reinforcement learning algorithms to build a decision model. The state space includes 3 types of parameters for device operation status, 2 types of parameters for network link quality, and 3 types of teaching scenarios, totaling 8-dimensional state vectors. The action space covers 12 types of adjustable and debuggable parameters, 4 types of protocol adaptation parameters, 3 types of network configuration, and 5 types of device linkage logic. The reward function is set to achieve the target of +10 points, latency ≤50ms is added by an extra 5 points, fault triggering deducts 20 points, and teaching interference deducts 15 points. The number of training iterations is ≥1000 rounds, and the convergence condition is that the average reward value of 50 consecutive rounds is ≥8 points. The training batch size is 32. In the live class scenario, the network latency weight is increased to 0.5 and the bandwidth allocation parameter is adjusted first. In the recorded class scenario, the image quality parameter adjustment weight is increased to 0.4.

[0025] The automatic execution mode of the dual-mode execution unit can remotely issue debugging commands to the target device to achieve unattended debugging; the manual intervention mode provides a visual interface that synchronizes virtual model operation with physical equipment. Managers can adjust debugging parameters by dragging and dropping virtual devices, and the debugging process data is synchronized to the data storage module in real time. Both modes of execution are adapted to scenario-based input and output associations. For live classes, new input parameters such as real-time online users and bandwidth fluctuation values ​​are added, and the output scheme prioritizes locking low-latency protocol configuration and bandwidth reservation strategy. For recorded classes, new input parameters such as image quality requirement level and remaining storage capacity are added, and the output scheme focuses on encoding format optimization and caching strategy adjustment.

[0026] After debugging is completed, the debugging effect verification unit verifies the debugging effect by comparing the virtual model pre-simulation results with the actual operating data of the physical equipment. If the results do not meet expectations, it automatically returns to the pre-debugging monitoring module to re-optimize the debugging strategy. If the debugging is successful, it sends the debugging completion signal to the remote operation and maintenance module and pushes the debugging result data to the data storage module for archiving.

[0027] The core verification indicators must meet the following requirements: protocol adaptation success rate ≥ 98%, device linkage response time ≤ 100ms, overall network transmission latency after device linkage ≤ 50ms, and failure rate ≤ 0.5%. At the same time, similarity matching rules are used to assist in the verification. The similarity of core parameters (corresponding to the above core indicators) is ≥ 95%, and the similarity of non-core parameters is ≥ 90%. If both are met, it is considered that the single round of verification is passed. If the expected results are not met, the debugging parameters associated with the unmet indicators should be re-optimized first. If the results are still not met after two optimizations, manual intervention and reminders will be triggered.

[0028] Preferably, the data storage module includes a distributed encrypted storage unit, a blockchain traceability unit, and an access control unit; The distributed encrypted storage unit encrypts the debugging data using AES-256 and stores it synchronously to the local edge node and the blockchain network, thereby achieving decentralized data management. The blockchain traceability unit writes key data such as basic equipment information, debugging parameters, fault handling records, and operation and maintenance logs into the blockchain, generating an immutable timestamp, and supports traceability by multiple dimensions such as equipment type, debugging time, and fault type. The traceability query response time is ≤3 seconds, and a composite index method of device ID and timestamp is used to improve query efficiency; the blockchain block size is set to 2MB, the block generation time is 10 seconds, and each traceability data is associated with a unique hash value to ensure that the traceability results cannot be tampered with.

[0029] The permission management unit implements permission control based on blockchain smart contracts. Different roles can only access data with corresponding permissions, and the permission verification results are synchronized to the remote operation and maintenance module.

[0030] Preferably, the fault prediction module includes a prediction model construction unit, a hierarchical early warning unit, and a model self-optimization unit; The predictive model building unit is based on a multi-algorithm fusion model such as LSTM neural network and random forest. It takes historical debugging fault data, equipment operation data, digital twin simulation fault data and environmental data as input to achieve accurate prediction of five types of potential faults, including network link aging, protocol adaptation deviation, hardware performance degradation, abnormal port occupation and firmware version incompatibility. The multi-algorithm fusion model includes an LSTM neural network and a random forest. The LSTM network has a 16-dimensional input layer containing 8 types of equipment operation data, 4 types of environmental data, and 4 types of historical fault features; 2 hidden layers; and a 5-dimensional output layer corresponding to 5 types of faults. The activation function is tanh and the dropout rate is 0.2. The random forest is set with 100 decision trees, a maximum depth of 15, and uses Gini coefficients to select features with a minimum number of sample splits of 5. The prediction results of the two algorithms are weighted and voted on with LSTM weight of 0.6 and random forest weight of 0.4 to output the final result. The model training is divided into training and test sets in a 7:3 ratio, with a batch size of 64, a learning rate of 0.001, and 50 training epochs. The target accuracy is ≥92% and the recall is ≥90%.

[0031] The graded early warning unit pushes early warning information through various means such as audible and visual alarms, SMS notifications, and system pop-ups based on the fault risk level. At the same time, it generates fault tracing paths and preliminary handling suggestions by linking with the pre-debugging monitoring module and synchronizes them to the remote operation and maintenance module. The warning judgment logic is as follows: when the input data contains a combination of conditions that may cause multi-device linkage failure, affect teaching activities or equipment safety, such as equipment temperature ≥60℃ and network packet loss rate ≥5%, it is judged as a high-risk failure. The fault tracing path is associated with the equipment topology structure of the twin modeling unit. The physical location of the faulty equipment and the associated links are marked first. The handling suggestions include three types of directly executable operation instructions: emergency shutdown, parameter fine-tuning, and component replacement.

[0032] The specific criteria for classifying fault risk levels are as follows: Low risk is when equipment parameters deviate slightly from the threshold (≤5%), which does not affect core functions and is only warned through system pop-up windows; Medium risk is when a single device malfunctions and has not spread to the linkage system, which is warned through system pop-up windows and audible and visual alarms, with a buzzer frequency of 1kHz and a yellow flashing indicator light; High risk is when multiple devices fail together, affecting teaching activities or equipment safety, which is warned through system pop-up windows, audible and visual alarms, and SMS notifications.

[0033] The model self-optimization unit periodically retrieves newly added debugging data and fault handling data from the data storage module to train the model, and expands the training set by combining virtual fault data generated by digital twin simulation. The optimized model parameters are then synchronized to the pre-debugging monitoring module.

[0034] The self-optimization cycle is once every 200 valid debugging / fault data points or once every 7 days. Data screening retains only valid data with complete annotations and no outliers, and removes duplicate and false alarm data. Virtual fault data is generated using the SMOTE algorithm based on the distribution characteristics of real data, with the number of generated data in a 1:1 ratio with real data. The optimization iteration is performed by loading the historical model, replacing the training set with a new one, retraining for 10 epochs, and evaluating the validation set. If the accuracy is ≥93%, the model is updated; otherwise, the parameters are adjusted.

[0035] Preferably, the remote operation and maintenance module includes an edge local operation and maintenance unit, a cloud collaborative operation and maintenance unit, and an operation and maintenance log blockchain storage unit; The edge local operation and maintenance unit is deployed locally in the classroom. It receives handling suggestions from the fault prediction module, realizes millisecond-level response of basic operation and maintenance operations, and synchronizes operation and maintenance data to the data storage module in real time. Supported basic operation and maintenance operations include five categories: device local restart, parameter initialization, port self-test, link repair, and firmware rollback. The response time is controlled within 50-200 milliseconds. If an operation fails, it will automatically retry twice with a retry interval of 5 seconds. If it still fails, it will be marked as an operation and maintenance anomaly and synchronized to the cloud collaborative operation and maintenance unit.

[0036] The cloud-based collaborative operation and maintenance unit integrates the operation and maintenance data of each edge node with the monitoring data of the pre-debugging monitoring module through the cloud platform, realizing the centralized management of equipment in multiple campuses and classrooms. It supports operations such as equipment grouping, batch debugging, and remote firmware updates, and generates global operation and maintenance reports to push to the pre-debugging monitoring module.

[0037] Device grouping supports three dimensions: campus, classroom type, and device brand. The maximum concurrent batch debugging capacity is ≤100 classrooms. Firmware updates adopt a differential upgrade method, and the upgrade package size is ≤30% of the complete package. The update can be automatically resumed if the network is interrupted during the update process. The global operation and maintenance report includes four core indicators: device online rate, debugging pass rate, failure rate, and operation and maintenance response time. It is automatically generated on a daily / weekly / monthly basis, with a data latency of ≤1 hour.

[0038] The operation and maintenance log blockchain storage unit writes all operation and maintenance logs to the blockchain traceability unit of the data storage module in real time, ensuring that operation and maintenance behavior is traceable and auditable, and providing operation and maintenance data support for the fault prediction module.

[0039] The beneficial effects of this invention are as follows: 1. This invention can automatically identify access devices and obtain their protocol types, functional parameters, and other information through the dynamic service discovery unit of the protocol adaptation module, eliminating the need for manual input. For special protocols that are not preset, it can generate adaptation plugins through sample collection, format parsing, and logic learning, which can be put into use after virtual pre-debugging verification. At the same time, it converts various heterogeneous protocol data into a unified standard format and optimizes data transmission strategies. In conjunction with the pre-debugging monitoring module, it simulates device linkage scenarios and various faults in a virtual environment and outputs optimized debugging solutions, avoiding repeated trial and error on site. Combined with the automatic and manual dual-mode execution of the intelligent debugging module, it simplifies the debugging process, reduces manual intervention, and improves the accuracy of multi-device linkage debugging and system operation stability.

[0040] 2. The status perception module of this invention comprehensively collects network status, equipment operation data, environmental data, and equipment protocol information. After data fusion and cleaning, it eliminates misjudgments based on a single dimension and generates reliable data support. The fault prediction module integrates historical fault data, equipment operation data, and virtual simulation fault data. Through a multi-algorithm fusion model, it accurately identifies potential faults such as network link aging and hardware performance degradation, and provides early warnings and disposal suggestions according to risk levels. At the same time, the data storage module adopts distributed encrypted storage and blockchain technology to securely store and tamper-proof record key data such as debugging, faults, and maintenance. It supports multi-dimensional traceability, and can quickly locate the root cause when a fault occurs, shortening the investigation time and shifting from passive repair to proactive prevention, ensuring that smart classroom teaching activities are not affected by equipment failures.

[0041] 3. The teaching interference early warning unit of this invention monitors factors that may affect teaching during the debugging process in real time. When the impact exceeds a preset threshold, it automatically pauses debugging and switches to teaching priority mode to reduce interference with teaching activities. The remote operation and maintenance module constructs an edge and cloud collaborative architecture. Edge nodes deployed locally in the classroom can quickly respond to basic operation and maintenance needs without relying on the cloud network. The cloud platform integrates data from various edge nodes to achieve centralized management of equipment in multiple campuses and classrooms, supporting operations such as device grouping, batch debugging, and remote firmware updates. At the same time, based on the permission control mechanism of blockchain smart contracts, corresponding data access permissions are assigned to different roles to ensure data security and compliance. The operation and maintenance logs are traceable and auditable through blockchain notarization. Combined with automatically generated multi-dimensional operation and maintenance reports, it provides data reference for school academic affairs management, improving operation and maintenance efficiency and standardization. Attached Figure Description

[0042] Figure 1 This is a flowchart of the overall system of the present invention; Figure 2 This is a flowchart illustrating the protocol adaptation and virtual pre-debugging process of the present invention. Figure 3 This is a flowchart of the intelligent debugging closed-loop process of the present invention; Figure 4 This is a flowchart of the fault prediction and remote operation and maintenance process of this invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] like Figures 1 to 4 As shown in the figure, this embodiment of the invention provides a smart classroom multimedia equipment network debugging and management system, including a pre-debugging monitoring module, a protocol adaptation module, a status perception module, an intelligent debugging module, a data storage module, a fault prediction module, and a remote operation and maintenance module. The specific implementation of each module is as follows: The pre-debugging monitoring module includes a twin modeling unit, a virtual pre-debugging unit, a virtual-real linkage unit, a twin panoramic monitoring unit, and a teaching interference early warning unit. The twin modeling unit is based on laser scanning and equipment 3D modeling technology. It accurately replicates the classroom spatial layout, equipment installation location and network topology, and synchronously imports equipment hardware parameters, protocol specifications, historical fault data and other information to build a twin that corresponds one-to-one between the physical entity and the virtual model. It supports the intensive management of twins in multiple classrooms. The virtual pre-debugging unit receives device access information from the protocol adaptation module, simulates multi-device linkage scenarios in a virtual environment, performs debugging strategy rehearsals, simulates various faults such as protocol incompatibility, IP conflicts, and link congestion, and outputs a pre-debugging report, including optimal debugging parameter suggestions and potential risk points, to avoid repeated trial and error in on-site debugging. The virtual-physical linkage unit realizes real-time data synchronization between the virtual model and the physical device through edge computing nodes, with a synchronization delay of ≤50ms. After the virtual pre-debugging is passed, the optimized debugging instructions are sent to the intelligent debugging module and the physical device with one click. At the same time, the real-time running data of the physical device is fed back to the virtual model to continuously optimize the simulation accuracy of the twin. The twin panoramic monitoring unit integrates the operating data of each module in real time through a 3D visualization interface, presenting information such as equipment operating status, network link quality, and debugging progress, and supports switching between multiple perspectives such as classrooms, floors, and campuses; The teaching interference early warning unit monitors the impact of debugging operations on teaching activities in real time, such as debugging noise, changes in equipment brightness, network bandwidth usage, and equipment vibration. When the impact exceeds the preset threshold, the debugging is automatically paused and switched to the teaching priority mode, while the early warning signal is fed back to the remote operation and maintenance module.

[0045] The protocol adaptation module includes a dynamic service discovery unit, an AI autonomous learning adaptation unit, and a protocol conversion and optimization unit. The dynamic service discovery unit is based on the MCP protocol to realize automatic device discovery and capability reporting. When a device connects, it actively pushes information such as supported protocol types, API interfaces, and functional parameters, eliminating the need for manual entry of device information. For special protocols that are not pre-set, the AI ​​autonomous learning adaptation unit automatically generates adaptation plugins through sample data collection, protocol format parsing, and interaction logic learning. The adaptation cycle (including sample learning and virtual verification) is ≤2.5 hours, and the adaptation stability is verified through the virtual pre-debugging unit of the pre-debugging monitoring module. The protocol conversion and optimization unit converts heterogeneous protocol data into a unified standard format for the system. At the same time, it dynamically optimizes data transmission strategies based on AI algorithms, such as automatically switching between TCP and UDP protocols according to network bandwidth to reduce data transmission latency and packet loss rate. The converted standardized data is then synchronously pushed to the status awareness module.

[0046] The state perception module includes a multi-dimensional data acquisition unit, a data fusion and cleaning unit, and a real-time early warning unit. The multi-dimensional data acquisition unit collects environmental data such as network status, device parameters, device operation audio and video data, temperature, humidity, and electromagnetic interference. At the same time, it receives device protocol data transmitted by the protocol adaptation module and realizes local real-time data processing through edge computing nodes, reducing the transmission pressure on the cloud. The data fusion and cleaning unit adopts a multimodal data fusion algorithm to eliminate misjudgments based on a single data dimension. For example, it combines network latency data and equipment temperature data to determine the cause of the fault. At the same time, it uses AI algorithms to denoise, deduplicatize, and format the data to generate a standardized data set, which is then pushed to the intelligent debugging module and the fault prediction module respectively. The real-time early warning unit immediately triggers a local early warning for any abnormal data collected, such as a sudden increase in link packet loss rate or excessive equipment temperature. It also synchronizes the warning information to the pre-debugging monitoring module for fault tracing simulation and transmits the warning information to the fault prediction module for in-depth analysis.

[0047] The intelligent debugging module includes a debugging strategy generation unit, a dual-mode execution unit, and a debugging effect verification unit. The debugging strategy generation unit generates the optimal debugging scheme based on the virtual pre-debugging report output by the pre-debugging monitoring module and combined with real-time perceived device status data. It uses a reinforcement learning algorithm to generate the optimal debugging scheme, which includes protocol adaptation parameters, network configuration, device linkage logic, etc. It supports setting corresponding debugging strategies for different teaching scenarios such as live classes and recorded classes. The automatic execution mode of the dual-mode execution unit can remotely issue debugging commands to the target device to achieve unattended debugging; the manual intervention mode provides a visual interface that synchronizes virtual model operation with physical equipment. Managers can adjust debugging parameters by dragging and dropping virtual equipment, reducing the difficulty of operation. Debugging process data is synchronized to the data storage module in real time. After debugging is completed, the debugging effect verification unit verifies the debugging effect by comparing the virtual model pre-simulation results with the actual operating data of the physical equipment. If the results do not meet expectations, it automatically returns to the pre-debugging monitoring module to re-optimize the debugging strategy. If the debugging is successful, it sends the debugging completion signal to the remote operation and maintenance module and pushes the debugging result data to the data storage module for archiving.

[0048] Data comparison uses similarity matching rules. A single round of verification is considered passed if the similarity of core parameters (corresponding to protocol adaptation success rate, device linkage response time, and other core indicators) is ≥95% and the similarity of non-core parameters is ≥90%. The verification time window is set to 30 seconds. Passing three consecutive rounds of verification is considered as overall debugging success. If the expected results are not achieved, the debugging strategy corresponding to the three parameters with the lowest similarity will be optimized first.

[0049] The data storage module includes a distributed encrypted storage unit, a blockchain traceability unit, and an access control unit. The distributed encrypted storage unit encrypts the debugging data using AES-256 and stores it synchronously to the local edge node and the blockchain network, realizing decentralized data management and avoiding data loss due to single point of failure; Data is categorized and stored in segments according to debugging data, fault data, and operation and maintenance data, with a single segment size ≤100MB. Local edge node storage capacity is ≥1TB, with 30% redundancy reserved. The number of blockchain network nodes is ≥5, distributed in different physical locations. Data synchronization adopts Byzantine fault tolerance mechanism to ensure data consistency. The data retention period is ≥3 years, and data exceeding the retention period is automatically archived to cold storage.

[0050] The blockchain traceability unit writes key data such as basic equipment information, debugging parameters, fault handling records, and operation and maintenance logs into the blockchain, generates an immutable timestamp, supports multi-dimensional traceability by equipment type, debugging time, fault type, etc., and provides historical data query services for the model optimization of the fault prediction module. The access control unit uses blockchain smart contracts to achieve fine-grained access control. Different roles, such as administrators, maintenance personnel, and teachers, can only access data with corresponding permissions. At the same time, the access verification results are synchronized to the remote maintenance module to ensure the compliance of maintenance operations.

[0051] The fault prediction module includes a prediction model construction unit, a hierarchical early warning unit, and a model self-optimization unit. The predictive model building unit is based on a multi-algorithm fusion model, including LSTM neural network and random forest. It takes historical debugging fault data, equipment operation data, digital twin simulation fault data, and environmental data as input to achieve accurate prediction of five types of potential faults, such as network link aging, protocol adaptation deviation, hardware performance degradation, abnormal port occupation, and firmware version incompatibility. The graded early warning unit pushes early warning information through various means such as audible and visual alarms, SMS notifications, and system pop-ups, based on the three risk levels of low, medium, and high faults. At the same time, it connects with the pre-debugging monitoring module to generate fault tracing paths and preliminary handling suggestions, and synchronizes them to the remote operation and maintenance module. The model self-optimization unit periodically retrieves newly added debugging data and fault handling data from the data storage module to train the model. It also expands the training set by combining virtual fault data generated by digital twin simulation, continuously improving the prediction accuracy. The optimized model parameters are synchronized to the pre-debugging monitoring module to improve simulation accuracy.

[0052] The remote operation and maintenance module includes an edge local operation and maintenance unit, a cloud collaborative operation and maintenance unit, and an operation and maintenance log blockchain storage unit. The edge local operation and maintenance unit is deployed locally in the classroom. It receives handling suggestions from the fault prediction module and achieves millisecond-level response for basic operation and maintenance operations such as device restart, parameter fine-tuning, and simple fault repair. It does not rely on the cloud network, reducing the impact of network interruption on operation and maintenance. Operation and maintenance operation data is synchronized to the data storage module in real time. The cloud-based collaborative operation and maintenance unit integrates the operation and maintenance data of each edge node with the monitoring data of the pre-debugging monitoring module through the cloud platform, realizing the centralized management of equipment in multiple campuses and classrooms. It supports operations such as equipment grouping, batch debugging, and remote firmware updates, and generates global operation and maintenance reports to push to the pre-debugging monitoring module.

[0053] The operation and maintenance log blockchain storage unit writes all operation and maintenance logs to the blockchain traceability unit of the data storage module in real time, ensuring that operation and maintenance behavior is traceable and auditable, avoiding equipment failures caused by violations, and providing operation and maintenance data support for the fault prediction module.

[0054] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart classroom multimedia equipment network debugging and management system, characterized in that, It includes a pre-debugging monitoring module, a protocol adaptation module, a status awareness module, an intelligent debugging module, a data storage module, a fault prediction module, and a remote operation and maintenance module; Pre-debugging monitoring module: Constructs a digital twin of the smart classroom and multimedia equipment to realize virtual pre-drilling of the debugging process, fault simulation, and real-time linkage between virtual data and physical data, and provides visualized monitoring of equipment operation status and early warning of teaching interference. Protocol adaptation module: Enables dynamic discovery and autonomous protocol adaptation of heterogeneous devices, and builds a standardized data conversion and transmission optimization mechanism; Status Awareness Module: It receives standardized equipment data after adaptation, collects multi-dimensional equipment operation data and environmental status data, and realizes data fusion processing and anomaly early warning; Intelligent debugging module: It takes over the pre-debugging results and status perception data of digital twin, builds intelligent debugging strategies and realizes closed-loop debugging of virtual pre-play, physical execution and effect feedback. It supports dual-mode debugging of automatic execution and manual intervention, and completes debugging effect verification and result archiving. Data storage module: Adopts a distributed encrypted storage architecture to achieve secure storage and full lifecycle traceability of system operation data, and configures an access control mechanism to ensure data trustworthiness and compliance; Fault prediction module: Integrates multi-source data to build a fault prediction engine, realizes the prediction and graded early warning of potential equipment faults, and generates fault tracing paths and handling suggestions; Remote Operation and Maintenance Module: Constructs a collaborative operation and maintenance architecture between the edge and the cloud, realizes local response and centralized cloud management, and completes the execution of operation and maintenance operations and log storage.

2. The smart classroom multimedia equipment network debugging and management system according to claim 1, characterized in that, The pre-debugging monitoring module includes a twin modeling unit, a virtual pre-debugging unit, a virtual-real linkage unit, a twin panoramic monitoring unit, and a teaching interference early warning unit; The twin modeling unit replicates the classroom spatial layout, equipment installation location, and network topology, and associates core equipment information to construct a twin that corresponds one-to-one between the physical entity and the virtual model; The virtual pre-debugging unit simulates multi-device linkage scenarios in a virtual environment based on device access information, performs debugging strategy pre-rehearsals and fault simulations, and outputs a pre-debugging report. The virtual-real linkage unit realizes real-time data synchronization between the virtual model and the physical device, and sends the optimized debugging instructions to the intelligent debugging module and the physical device. The twin panoramic monitoring unit integrates the operating data of each module to present the equipment operating status, network link quality and debugging progress, and supports multi-dimensional perspective switching; The teaching interference early warning unit monitors the impact of debugging operations on teaching activities in real time. When the impact exceeds the preset threshold, it automatically pauses debugging and switches to teaching priority mode.

3. The smart classroom multimedia equipment network debugging and management system according to claim 2, characterized in that, The protocol adaptation module includes a dynamic service discovery unit, an AI autonomous learning adaptation unit, and a protocol conversion and optimization unit. The dynamic service discovery unit enables dynamic discovery of devices and reporting of capabilities, eliminating the need for manual input of device information; The AI ​​autonomous learning adaptation unit automatically generates adaptation plugins for special protocols that are not preset, through sample data collection, protocol parsing, and interaction logic learning, and verifies the adaptation stability through the virtual pre-debugging unit of the pre-debugging monitoring module. The protocol conversion and optimization unit converts heterogeneous protocol data into a unified standard format of the system, simultaneously optimizes data transmission performance, and pushes the converted standardized data to the status awareness module.

4. The intelligent classroom multimedia equipment network debugging and management system according to claim 3, characterized in that, The state perception module includes a multi-dimensional data acquisition unit, a data fusion and cleaning unit, and a real-time early warning unit. The multi-dimensional data acquisition unit collects network status, device parameters, device operation audio and video data, and environmental data, and combines them with device protocol data to achieve localized real-time data processing. The data fusion and cleaning unit uses a multimodal data fusion algorithm to eliminate misjudgments in a single data dimension, while simultaneously performing data denoising, deduplication, and formatting to generate a standardized data set that is pushed to the intelligent debugging module and the fault prediction module. The real-time early warning unit triggers a local early warning for abnormal data and synchronizes it to the pre-debugging monitoring module for fault tracing simulation. At the same time, it transmits the early warning information to the fault prediction module.

5. The intelligent classroom multimedia equipment network debugging and management system according to claim 4, characterized in that, The intelligent debugging module includes a debugging strategy generation unit, a dual-mode execution unit, and a debugging effect verification unit. The debugging strategy generation unit generates the optimal debugging scheme based on the virtual pre-debugging report and real-time device status data, which supports adaptation to different teaching scenarios. The dual-mode execution unit's automatic execution mode enables unattended debugging; the manual intervention mode provides a visual interface that synchronizes virtual model operation with physical equipment, allowing administrators to adjust debugging parameters. The debugging effect verification unit compares the virtual model pre-simulation results with the actual operating data of the physical equipment to verify the debugging effect. If the results do not meet expectations, the unit returns to the virtual pre-debugging unit of the pre-debugging monitoring module to optimize the debugging strategy. After the debugging is successful, the unit feeds back the debugging completion signal to the remote operation and maintenance module and archives the debugging result data.

6. The intelligent classroom multimedia equipment network debugging and management system according to claim 5, characterized in that, The data storage module includes a distributed encrypted storage unit, a blockchain traceability unit, and an access control unit. The distributed encrypted storage unit enables decentralized and secure storage of debugging data; The blockchain traceability unit writes key data, including basic equipment information, debugging parameters, fault handling records, and operation and maintenance logs, into the blockchain to generate an immutable timestamp, supporting multi-dimensional traceability. The permission management unit implements permission control based on blockchain smart contracts. Different roles can only access data with corresponding permissions, and the permission verification results are synchronized to the remote operation and maintenance module.

7. The intelligent classroom multimedia equipment network debugging and management system according to claim 6, characterized in that, The fault prediction module includes a prediction model construction unit, a hierarchical early warning unit, and a model self-optimization unit; The prediction model construction unit is based on a multi-algorithm fusion model, and takes into account historical debugging fault data, equipment operation data, digital twin simulation fault data, and environmental data to predict potential faults. The graded early warning unit pushes early warning information through various means according to the fault risk level, and generates fault tracing paths and handling suggestions in conjunction with the pre-debugging monitoring module; The model self-optimization unit periodically retrieves newly added debugging data and fault handling data to train the model, expands the training set by combining virtual fault data generated by digital twin simulation, and synchronizes the optimized model parameters to the pre-debugging monitoring module.

8. The smart classroom multimedia equipment network debugging and management system according to claim 7, characterized in that, The remote operation and maintenance module includes an edge local operation and maintenance unit, a cloud collaborative operation and maintenance unit, and an operation and maintenance log blockchain storage unit. The edge local operation and maintenance unit receives handling suggestions from the fault prediction module to achieve rapid response to basic operation and maintenance operations; The cloud-based collaborative operation and maintenance unit integrates the operation and maintenance data and monitoring data of each edge node to achieve centralized management of devices. It supports operations including device grouping, batch debugging, and remote firmware updates, and generates global operation and maintenance reports to be pushed to the pre-debugging monitoring module. The operation and maintenance log blockchain storage unit writes the operation and maintenance logs into the blockchain traceability unit of the data storage module in real time, ensuring that the operation and maintenance behavior is traceable and auditable, and providing operation and maintenance data support for fault prediction.

9. The intelligent classroom multimedia equipment network debugging and management system according to claim 8, characterized in that, The laser scanning accuracy of the twin modeling unit is controlled within ±2mm. The 3D modeling of the device is output in OBJ format, and the imported data is uniformly in JSON format with field integrity ≥98%. The network status acquisition frequency of the multi-dimensional data acquisition unit is 5Hz, and the temperature acquisition accuracy in the environmental data is ±0.2℃ and the humidity acquisition accuracy is ±2%RH.

10. The intelligent classroom multimedia equipment network debugging and management system according to claim 9, characterized in that, The cloud-based collaborative operation and maintenance unit supports grouping by three dimensions: campus, classroom type, and equipment brand. The maximum concurrent batch debugging capacity is ≤100 classrooms.

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