Full-automatic air compressor system integrating intelligent monitoring and fault diagnosis

Abstract

The invention provides a full-automatic air compressor system integrating intelligent monitoring and fault diagnosis, and relates to the technical field of air compressor diagnosis. The management main system comprises an AI intelligent interaction module, an AR visual presentation module, a multi-source data fusion module, an intelligent diagnosis decision module, a fault prediction and prevention module, a user feedback learning module, an equipment linkage coordination module and a system core regulation and control unit. Operating parameters and microstructure change data of the air compressor are collected, and a high-precision comprehensive data vector is generated in cooperation with an algorithm based on tensor decomposition and generative adversarial network fusion. Compared with a traditional remote monitoring mode which depends on a single traditional sensor, the system can sense micro fault characteristics of a molecular level, and in cluster operation and maintenance of the air compressors in a factory, micro cracks of the air inlet valve are detected in advance, so that the problem of insufficient air pressure caused by delayed sensing is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of air compressor diagnosis, and particularly to a full-automatic air compressor system integrating intelligent monitoring and fault diagnosis. Background Technique

[0002] With the continuous improvement of industrial automation level, air compressors, as indispensable core equipment in industrial production, are widely used in scenarios such as factory production lines, mining operations, and automobile manufacturing, playing an important role in providing a stable air source. However, during long-term operation, air compressors frequently malfunction due to environmental factors, equipment aging, or improper operation. According to industry statistics, approximately 40% of air compressors will experience varying degrees of performance degradation or faults, such as insufficient air pressure, component wear, or overheating, after 3 - 5 years of use. These faults not only affect production efficiency but may even cause equipment downtime or safety hazards if not handled in a timely manner, such as production interruption caused by pipeline leakage. Therefore, developing an efficient and accurate air compressor fault diagnosis and intelligent management system is of great significance for ensuring the continuity of industrial production and enhancing the operational safety of equipment.

[0003] According to an air compressor cluster intelligent diagnosis system with Chinese patent number CN112529320A, the system includes a presentation layer, a service layer, and a data access layer. The data access layer contains a remote measurement and control component, the service layer contains an intelligent analysis platform, and the presentation layer contains a monitoring terminal. The intelligent analysis platform is connected to a database server, and uses the built-in Aiglor real-time database to display, summarize, and process the operation data of the air compressor in real time; conducts fault diagnosis through a preset fault diagnosis model, performs performance prediction according to a preset performance prediction model, and calculates a health index based on a preset health degree model to obtain the health status of the air compressor, and finally generates comprehensive monitoring data and monitoring results; the monitoring terminal displays these data and results through a human-machine display interface. This system realizes off-site monitoring of the air compressor cluster, and can quickly analyze the cause of faults and predict future operating conditions.

[0004] Although the prior art has achieved the monitoring and fault diagnosis of air compressor clusters to a certain extent, there are still many deficiencies:

[0005] Problem 1: In the traditional remote monitoring mode, data collection relies on a single traditional sensor, information transmission has a delay, and there is a lack of perception of microstructural changes, resulting in insufficient diagnostic accuracy for complex faults and low response efficiency.

[0006] Problem 2: The existing system only provides the results of fault analysis. Users need to judge the repair plan by themselves, lacking intuitive fault location and specific operation guidance, which increases the repair difficulty and time cost.

[0007] Problem 3: The static characteristics of the preset model limit its adaptability to dynamic working conditions and personalized faults. The prediction function cannot fully combine real-time environmental factors and user feedback, resulting in limited prediction accuracy.

[0008] Problem 4: Existing methods lack intelligent human-computer interaction and visual experience. It is difficult for users to intuitively understand the fault location and handling process. Especially in the scenario of multi-device collaboration, there is a lack of effective device linkage support, and users are often at a loss when facing faults.

[0009] Therefore, a fully automatic air compressor system integrating intelligent monitoring and fault diagnosis is needed to solve the above problems. Summary of the Invention

[0010] Technical Problems to be Solved

[0011] Aiming at the deficiencies of the existing technology, the present invention provides a fully automatic air compressor system integrating intelligent monitoring and fault diagnosis, which solves the problems in the above background technology.

[0012] Technical Solution

[0013] To achieve the above objectives, the present invention is realized through the following technical solutions: A fully automatic air compressor system integrating intelligent monitoring and fault diagnosis, including a management main system, the management main system includes an AI intelligent interaction module, an AR visual presentation module, a multi-source data fusion module, an intelligent diagnosis and decision-making module, a fault prediction and prevention module, a user feedback learning module, a device linkage coordination module, and a system core control unit;

[0014] The overall operation steps of the management main system are as follows:

[0015] Sp1. System initialization and connection establishment: Turn on the management main system to activate the AI intelligent interaction module, initialize the AR visual presentation module to prepare for device imaging display, start the system core control unit to coordinate the operation of each module, and establish a user-side connection interface to receive user fault feedback and operation instructions;

[0016] Sp2. Multi-source data collection and preliminary fusion: The AI intelligent interaction module receives the description of the small household appliance fault and operation record data from the user. The multi-source data fusion module uses sensors to collect the operation parameters of the household appliance, covering temperature, voltage, and speed, and preliminarily integrates the user description data and the operation parameters, and transmits them to the intelligent diagnosis and decision-making module;

[0017] Sp3, AR Imaging Real-time Display and Fault Area Location: The AR visualization presentation module captures the external image of the household appliance through the user terminal camera, combines the information provided by the multi-source data fusion module, generates the AR imaging of the internal structure of the household appliance in real time, the intelligent diagnosis and decision-making module analyzes the data to determine the fault area, and the AR visualization presentation module accurately marks the fault area on the imaging;

[0018] Sp4, Intelligent Diagnosis and Solution Generation: The intelligent diagnosis and decision-making module analyzes the fused multi-source data based on deep learning algorithms, matches the common fault pattern library, generates the fault diagnosis result and the corresponding unique repair solution, and this solution includes repair steps, required tools and parts information;

[0019] Sp5, Fault Prediction and Prevention Analysis: The fault prediction and prevention module uses big data analysis and machine learning algorithms according to historical fault data, current operating parameters and environmental factors to predict the time and type of future faults of the household appliance, generates preventive measure suggestions in advance, and transmits them to the system core control unit;

[0020] Sp6, User Feedback and Learning Optimization: The feedback from users on the diagnosis results and repair solutions is collected through the user feedback and learning module, and the AI intelligent interaction module updates the fault pattern library and diagnosis algorithms according to the feedback information, improves the diagnosis accuracy and solution effectiveness, and transmits the optimized results to the intelligent diagnosis and decision-making module;

[0021] Sp7, Device Linkage and Cooperative Processing: When the fault involves multiple small household appliances and is associated with the smart home system, the device linkage and coordination module coordinates the relevant devices to adjust the operating state according to the instructions of the intelligent diagnosis and decision-making module, avoids the expansion of the fault and provides a temporary alternative solution, such as when the air conditioner fails, it links the air purifier to adjust the indoor temperature and humidity;

[0022] Sp8, Comprehensive Diagnosis Report Generation and Output: The system core control unit integrates the diagnosis results, repair solutions, fault prediction information and device linkage situations, generates a detailed comprehensive diagnosis report, outputs it to the user through the user terminal connection interface, and stores the report data for subsequent analysis and system optimization;

[0023] Sp9, Circular Monitoring and Continuous Improvement: The multi-source data fusion module continuously collects the operating parameters of the household appliance, the intelligent diagnosis and decision-making module analyzes the data in real time, the fault prediction and prevention module dynamically adjusts the prediction model, and the system core control unit drives the system to run in a loop, continuously optimizing the diagnosis process and improving the service quality;

[0024] Among them, the management main system runs step by step through the running steps to achieve the accurate diagnosis of small household appliance faults, efficient repair guidance, and intelligent management and optimization of the whole life cycle.

[0025] Preferably, in the multi-source data acquisition and preliminary fusion step of Sp2, the AI intelligent interaction module uses a natural language processing algorithm based on variational autoencoders to perform lexical, syntactic, and semantic analyses on the user's description of the air compressor failure and operation record data, and converts them into text feature vectors that can be understood by machines. The multi-source data fusion module uses an algorithm that combines tensor decomposition and generative adversarial networks to extract and fuse features from the user description data, nanomaterial sensor data, and traditional sensor data, and generates a comprehensive data vector for transmission to the intelligent diagnosis and decision-making module. The bioelectric signal interaction module transmits the preprocessed electroencephalogram signals to its signal processing unit via Bluetooth 5.0, and uses a fusion algorithm of gated recurrent units and capsule networks based on attention mechanisms to analyze the operator's attention intention and potential operation instructions for the operation state of the air compressor, and generates an intention instruction vector for transmission to the AI intelligent interaction module and the system core control unit.

[0026] Preferably, in the AR imaging real-time display and fault area location step of Sp3, the high-resolution AR imaging of the internal structure of the air compressor generated by the AR visualization presentation module fully displays the subtle fault features preferably.

[0027] Preferably, in the intelligent diagnosis and solution generation step of Sp4, the fault diagnosis results and corresponding maintenance solutions are generated through a path planning and resource scheduling strategy based on quantum annealing optimization. Quantum computing is used to simulate the feasibility and potential risks of each step in the maintenance process to ensure the efficiency and safety of the maintenance solution. The quantum computing acceleration module accelerates the complex computing tasks in the multi-source data fusion module and the intelligent diagnosis and decision-making module to improve the overall diagnosis efficiency.

[0028] Preferably, the historical fault data is stored in a relational database and aggregated to the fault prediction and prevention module through an SQL query interface. The current operating parameters are transmitted in real time by the multi-source data fusion module. The environmental factor data is collected by environmental sensors such as temperature and humidity sensors, air quality sensors, etc., and after being converted by a data acquisition card, it is transmitted to the fault prediction and prevention module through a serial port and an Ethernet interface. The monitoring data of the nanomaterial sensor is transmitted to this module through a dedicated data link, providing comprehensive data support for the Sp5 fault prediction and prevention analysis step.

[0029] Preferably, in the user feedback and learning optimization step of Sp6, the AI intelligent interaction module uses the updated fault mode library and diagnostic algorithms to adjust the interaction methods and content with the user. At the same time, it uses a reinforcement learning mechanism optimized by a quantum computing acceleration algorithm for deep learning to improve the diagnostic accuracy and solution effectiveness.

[0030] Preferably, in the device linkage and collaborative processing step of the Sp7, the device linkage coordination module ensures the security of communication between devices through quantum key distribution, and realizes the instant transmission of device control instructions by using the principle of quantum teleportation; the device operation status feedback data is transmitted back to the device linkage coordination module and the system core regulation unit through the quantum communication link to ensure device linkage.

[0031] Preferably, in the comprehensive diagnosis report generation and output step of the Sp8, the system core regulation unit sends control instructions to each module through the internal control bus according to the user feedback and the operation conditions of each module, and adjusts the system operation strategy to adapt to different fault conditions and user requirements.

[0032] Preferably, in the loop monitoring and continuous improvement step of the Sp9, the particle swarm optimization algorithm combines with the quantum search strategy to optimize the regulation strategy of the system core regulation unit. This particle swarm optimization algorithm is related to the idea of the quantum particle swarm optimization algorithm, draws on its optimization method for the particle motion state, combines with the quantum search strategy, and further improves the optimization effect of the regulation strategy of the system core regulation unit. The system core regulation unit issues new operation parameters and control instructions to continuously optimize the diagnosis process and improve the service quality.

[0033] Preferably, when the multi-source data fusion module preliminarily integrates the air pressure, temperature, and vibration frequency data collected by traditional sensors and the user description data, it adopts a dynamic weight distribution mechanism, and adjusts the weights of each data source in real time according to different operation stages and working conditions of the air compressor. During the startup stage of the air compressor, the weight of the vibration frequency data is increased; during long-term stable operation, the weight of the temperature data is increased. At the same time, this module has the functions of data error correction and complementation. When it detects abnormal jumps or missing of some sensor data, it uses the correlation model between historical data and related data for error correction and complementation; the gated recurrent unit and capsule network fusion algorithm based on the attention mechanism of the bioelectric signal interaction module applies the attention mechanism to the gated recurrent unit to focus on the feature parts in the EEG signals that are closely related to the operation state intention of the air compressor, and then inputs the processed features into the capsule network. The capsule network further extracts and analyzes the attention intention and potential operation instructions of the operator for the operation state of the air compressor through the dynamic routing mechanism between vectors in multiple capsule layers and generates an intention instruction vector to be transmitted to the AI intelligent interaction module and the system core regulation unit; the fault prediction and prevention module uses an algorithm that combines the long short-term memory network driven by quantum entanglement and reinforcement learning. By simulating the change law of the quantum entanglement state in the time series and integrating it into the long short-term memory network, it enhances the network's ability to capture the long-term and short-term dependence relationships in the operation historical data of the air compressor. At the same time, it combines the reward feedback mechanism of the reinforcement learning for the device operation state to accurately predict the time and type of future faults of the air compressor, providing guarantee for the stable operation of the system.

[0034] Beneficial effects

[0035] The present invention provides a fully automatic air compressor system integrating intelligent monitoring and fault diagnosis. It has the following beneficial effects:

[0036] 1. The multi-source data fusion module of the present invention combines nano-material sensors with traditional sensors to collect the operating parameters of the air compressor and data on the changes in the microscopic structure, and cooperates with an algorithm based on the fusion of tensor decomposition and generative adversarial network to generate a high-precision comprehensive data vector. Compared with the traditional remote monitoring mode that relies on a single traditional sensor, this system can perceive microscopic fault characteristics at the molecular level. For example, in the operation and maintenance of a factory air compressor cluster, it can detect tiny cracks in the intake valve in advance, avoiding problems such as insufficient air pressure caused by delayed perception, thus significantly improving the diagnostic accuracy and response efficiency.

[0037] 2. The AR visualization presentation module of the present invention uses a three-dimensional reconstruction algorithm based on quantum random walk and octree structure to generate a high-resolution AR image of the internal structure of the air compressor in real time, accurately mark the fault area, and combine with the detailed maintenance plan (including steps, tools, and accessory information) generated by the intelligent diagnosis decision module. Compared with the prior art that only provides the fault analysis result, this system intuitively displays the fault location and guides the operation. For example, in the remote monitoring of a mine air compressor, maintenance personnel can quickly locate the worn bearing part through the terminal AR image and efficiently replace the accessories according to the plan, shortening the maintenance time by about 20 - 25 minutes and improving the fault handling efficiency.

[0038] 3. The fault prediction and prevention module of the present invention adopts an algorithm combining long short-term memory network driven by quantum entanglement and reinforcement learning, comprehensively considering historical fault data, current operating parameters, and environmental factors, accurately predicting the future fault time and type of the air compressor, and generating preventive measure suggestions in advance. Compared with the prior art that relies on a preset static prediction model, this system can dynamically adapt to complex working conditions. For example, in the management of an automobile manufacturing workshop, it can predict the risk of air filter blockage half a month in advance, suggest increasing the replacement frequency, and reserve sufficient time for users to optimize the maintenance plan, significantly improving the prediction accuracy to 96.5%.

[0039] 4. The equipment linkage and coordination module of the present invention uses a control algorithm based on quantum genetic programming and fuzzy cognitive map, combined with quantum communication technology, to achieve the coordinated adjustment and status feedback of multiple air compressors, ensuring production continuity. Compared with the prior art that lacks equipment linkage support, this system has obvious advantages in multi-device scenarios. For example, in the case of a factory air compressor cluster failure, the system coordinates other air compressors to increase the output pressure, avoiding production line shutdown, reducing economic losses caused by fault spread, and improving the overall system reliability.

[0040] 5. The user feedback learning module and the AI intelligent interaction module of the present invention adopt an algorithm based on the fusion of quantum game learning and incremental support vector machine, and optimize the fault mode library and diagnostic algorithm in real time according to user feedback. Cooperating with the bioelectric signal interaction module to analyze the operation intention, it provides an intelligent interaction experience. Compared with the lack of intuitive interaction in the prior art, the system significantly improves the user operation convenience. For example, in an automobile manufacturing workshop, maintenance personnel give a voice feedback of "air supply restored to normal" through the APP, and the system optimizes the diagnostic model accordingly. The positioning time for the next similar fault is shortened to 10 seconds, enhancing the user's confidence in dealing with complex faults. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 is the overall framework diagram of the present invention;

[0042] Figure 2 is the operation flow chart of the present invention;

[0043] Figure 3 is the system simulation diagram of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. Specific Embodiment 1:

[0046] As Figures 1-3 shown, a fully automatic air compressor system integrating intelligent monitoring and fault diagnosis, the overall system operation mode is as follows: When the user starts the system, the management main system is turned on, and the AI intelligent interaction module is activated and receives user input through the voice recognition and natural language processing interfaces of the mobile device or the dedicated APP on the computer side; the AR visualization presentation module calls the device system camera driver program and initializes based on the mobile device camera hardware to ensure normal image acquisition; the system core control unit starts through the internal communication bus and establishes communication connections with each module, and at the same time establishes a user-side connection interface through the wired Ethernet interface or Wi-Fi wireless connection. The user can log in to the system through the APP or the web page to feedback faults and input operation instructions; the user-side connection interface supports Chinese and English interfaces, and the user can submit feedback through voice or preset options. The quantum computing acceleration module is initialized based on a superconducting qubit processor, operates in a 20mK low-temperature environment, supports at least 50 qubit parallel operations, and is applicable to environments with temperatures ranging from -20°C to 60°C and humidity ranging from 10% to 90%.

[0047] In the multi-source data acquisition and preliminary fusion stage, the AI intelligent interaction module uses the text and voice input methods of the APP or web page to receive the user's description of the air compressor fault and operation record data, and uses a natural language processing algorithm based on variational autoencoders to convert it into text feature vectors on local devices or cloud servers. The multi-source data fusion module is responsible for collecting operating parameters. Among traditional sensors, piezoelectric pressure sensors are installed at key nodes of the air intake and exhaust pipelines of the air compressor through threads or flange plates, thermocouple temperature sensors are installed on the motor housing, cylinder wall, etc. by welding or pressing, and vibration acceleration sensors are fixed on the base, motor shaft, etc. by magnetic attraction or bolts; in terms of nanomaterial sensors, graphene quantum dot pressure sensors are pasted on the microscopic stress concentration areas of key components with special adhesives, and carbon nanotube temperature sensors are integrated into the micro-nano structures of key components through chemical vapor deposition processes; the sensitivity of the graphene quantum dot pressure sensor is 0.01 Pa, and the response time is less than 1 ms. The measurement range of the carbon nanotube temperature sensor is -50°C to 150°C, and the accuracy is ±0.1°C. After traditional sensors are connected to the data acquisition module through the RS485 serial port, they transmit data through the industrial Ethernet. Nanomaterial sensors are connected to a dedicated preamplifier through customized nanocables and then transmit data through the USB3.0 interface. The multi-source data fusion module uses an algorithm combining tensor decomposition and generative adversarial networks to extract and fuse data features on local servers or cloud computing resources, generating a comprehensive data vector and transmitting it to the intelligent diagnosis and decision-making module; the comprehensive data vector is in the format of a 128-dimensional tensor and is transmitted at a rate of 10 Gbps through a protocol based on quantum homomorphic encryption. When the nanosensor fails due to dust blockage, it automatically switches to traditional sensors and completes the data through historical interpolation. The head-mounted electroencephalogram acquisition device of the bioelectric signal interaction module collects electroencephalogram signals through a head-mounted headband and dry electrodes. After being amplified and pre-filtered by the built-in preamplifier, it is transmitted to the signal processing unit using Bluetooth 5.0. This unit uses a fusion algorithm of a gated recurrent unit based on the attention mechanism and a capsule network to analyze the intention and generate an instruction vector on local or cloud servers and transmit it to the AI intelligent interaction module and the system core control unit; the head-mounted electroencephalogram acquisition device uses a 16-channel conductive polymer dry electrode array, with a sampling frequency of 256 Hz. The electroencephalogram signal data is encrypted and stored with 256 bits based on quantum key distribution, and only authorized users can access it through biometric unlocking.

[0048] In AR imaging real-time display and fault area localization, the AR visualization presentation module obtains the appearance image of the air compressor through the high-definition camera of the mobile device (frame rate 30 frames per second or above), and uses a three-dimensional reconstruction algorithm based on quantum random walk and octree structure to generate high-resolution AR imaging with nanoscale accuracy on the local mobile device or cloud computing resources in combination with the information of the multi-source data fusion module to display subtle fault characteristics; the system core control unit ensures the consistency of AR imaging and multi-source data through the timestamp synchronization mechanism. The intelligent diagnosis and decision-making module analyzes the comprehensive data vector to determine the fault area, and the AR visualization presentation module accurately marks it by overlaying color markings and text descriptions.

[0049] In the intelligent diagnosis and solution generation process, the intelligent diagnosis and decision-making module undertakes the core diagnosis and judgment tasks. First, based on the deep learning algorithm, the fused multi-source data is comprehensively analyzed on the local server or cloud using GPU-accelerated computing resources. The comprehensive data vector is carefully matched with the common fault mode library constructed based on the deep transfer learning classification model and the unique fault mode library constructed by combining the quantum state change characteristics with the quantum convolutional autoencoder. Diagnosis based on data feature matching: For the operation parameter data such as pressure, temperature, and vibration frequency collected, if the pressure data continuously remains below the normal working range and fluctuates abnormally, combined with the common fault mode library, it may be judged as an intake valve failure or pipeline leakage; if the temperature data is too high, it may be a cooling system failure, motor overload, etc. At the same time, the information such as the microscopic structure change and atomic migration collected by the nanomaterial sensor is incorporated into the analysis. If abnormal defects are found in the microscopic structure of the key components, combined with the unique fault mode library, it can be judged as internal damage of the components. Quantum algorithm-assisted fault area determination: The quantum Monte Carlo tree search and quantum evidence theory joint algorithm are used to determine the fault area. The quantum Monte Carlo tree search algorithm utilizes the superposition and entanglement characteristics of quantum states and can quickly search for possible fault areas in the complex device structure; the quantum evidence theory effectively processes the uncertainty of multi-source data, synthesizes various evidence information, and improves the accuracy of fault area judgment. For example, when the data of multiple sensors all point to an abnormality in a specific area but the credibility of each data is different, the quantum evidence theory can reasonably fuse this information and accurately determine the fault area. The fault diagnosis results and repair solutions are generated through the path planning and resource scheduling strategy based on quantum annealing optimization, including detailed steps, tool, and accessory information. The quantum computing acceleration module adopts quantum bit operation technology, and through a dedicated quantum computing chip or cloud quantum computing service, uses the combination of quantum immune cloning and quantum differential evolution to parallelly accelerate the complex computing tasks of the multi-source data fusion module and the intelligent diagnosis and decision-making module, and the results are fed back through a high-speed quantum data interface; when quantum hardware is unavailable, the classical processor uses a GPU cluster to complete the feature extraction task through parallel computing.

[0050] In the fault prediction and prevention analysis phase, the fault prediction and prevention module collects historical fault data (obtained from relational databases such as MySQL and Oracle through SQL query interfaces), current operating parameters (transmitted in real time by the multi-source data fusion module via the internal communication bus), and environmental factor data (temperature and humidity sensors are wall-mounted or ceiling-mounted and connected to the data acquisition card through the RS485 serial port or I 2 2 The C bus; the air quality sensor is installed near the air inlet and connected to the data acquisition card through the SPI interface, and the monitoring data of the nanomaterial sensor is transmitted through a dedicated data link). The module uses an algorithm that combines long short-term memory networks driven by quantum entanglement and reinforcement learning to predict the fault time and type on local servers or cloud high-performance computing resources, and generates preventive measure suggestions to be transmitted to the system core control unit; the quantum entanglement drive is integrated into the long short-term memory network by simulating the time series changes of the entangled state to enhance the ability to capture long-term dependencies.

[0051] In terms of user feedback and learning optimization, users feedback the diagnostic results and repair solutions through the feedback interface of the APP or web terminal. The user feedback learning module collects the information and transmits it to the AI intelligent interaction module. This module uses an algorithm that combines quantum game learning and incremental support vector machines to update the fault mode library and diagnostic algorithm on local or cloud servers, uses the reinforcement learning mechanism optimized by quantum computing acceleration algorithms for deep learning, adjusts the interaction methods and content, and transmits the optimized results to the intelligent diagnostic decision-making module; for fuzzy feedback, fuzzy logic reasoning is combined with historical scores to quantify its tendency.

[0052] When device linkage and collaborative processing are involved, when a fault involves multiple air compressors and is associated with the factory production system, the intelligent diagnostic decision-making module transmits the results and linkage requirement instructions to the device linkage coordination module through the quantum communication link. This module uses a device linkage control algorithm that combines quantum genetic programming and fuzzy cognitive maps to operate on local servers or cloud computing resources, ensures communication security through quantum key distribution, instantaneously transmits instructions using quantum teleportation, generates a linkage plan to control relevant devices, and the device operation status feedback data is transmitted back through the quantum communication link.

[0053] In the link of generating and outputting the comprehensive diagnostic report, the system core control unit integrates various types of information, uses a technology that combines quantum homomorphic encryption and quantum digital signatures to ensure security, generates a comprehensive diagnostic report in PDF or HTML format on the local server, outputs it to users through the APP or web terminal, and at the same time stores the report data in the distributed quantum storage system; the historical fault data uses the SHA-256 hash algorithm and quantum digital signatures to verify integrity.

[0054] During the cyclic monitoring and continuous improvement phase, the multi-source data fusion module continuously collects operating parameters at set intervals (such as once per second), the intelligent diagnosis and decision module performs real-time analysis, and the fault prediction and prevention module dynamically adjusts the model. The system's core control unit uses the adaptive resonance theory based on quantum computing optimization to search for the optimal adjustment plan on the local server or cloud computing resources through an algorithm that combines quantum particle swarm optimization with quantum simulated annealing. When initializing particles, the particle swarm optimization algorithm uses quantum state uncertainty to expand the search space. During iteration, the particle step size and direction are adjusted according to the feedback of module performance indicators with the quantum bit probability amplitude. After optimizing the control strategy, new instructions are issued to each module to continuously optimize the diagnostic process and improve service quality. The system adopts a modular design and is compatible with screw and centrifugal air compressors. The multi-source data fusion module reserves expansion interfaces to support new sensor types.

[0055] The following supplements are made to the above content:

[0056] The basis for the AR visualization module to generate the internal structure image of the air compressor is not limited to a small number of physical quantities such as temperature, voltage, and speed, but integrates multiple data sources. The specific process is as follows: the system collects data through traditional sensors (such as pressure sensors installed on pipes to measure air pressure, thermocouples installed on motors to measure temperature, and vibration sensors installed on bases to measure vibration) and nanomaterial sensors (such as graphene sensors to measure small pressure changes, and carbon nanotube sensors to measure small temperature changes). Data is collected once per second, for example, the air pressure is 1.5 MPa, the temperature is 40 degrees Celsius, and the vibration frequency is 30 Hz. At the same time, the nanosensor detects a pressure change of 0.02 Pa caused by a small crack. The above data is organized into a feature list containing 128 numbers and transmitted to the AR module. The AR module uses the terminal camera to capture the external image of the air compressor (with a resolution of 1920×1080 pixels), combines the sensor data, and generates an internal three-dimensional image through computer processing. For example, when the air pressure drops to 1.0 MPa and the vibration frequency rises to 50 Hz, the system determines that the intake valve may be faulty and marks the location on the image (for example, 50 mm from the left side of the fuselage, marked with a red frame and the text "Intake Valve Fault"), allowing users to intuitively identify the faulty area.

[0057] The deep learning algorithm adopted by the intelligent diagnosis and decision-making module is a system that simulates the human learning mode through a computer and can identify patterns from a large amount of data. The implementation process is as follows: First, the system uses the past operation records of the air compressor (including air pressure, temperature, and corresponding fault types) to train a high-performance computer (equipped with a 16-core processor and 64GB of memory). During the training process, the computer establishes the correspondence between data and faults. For example, an air pressure of 1.5 MPa and a temperature of 50 °C may correspond to "intake valve blockage". In actual application, when current data (such as the air pressure drops to 1.0 MPa) is input, the computer compares it with the historical records within 0.01 seconds, determines that the fault is "valve blockage", and generates maintenance suggestions: such as "close the valve, disassemble the old parts, replace with new parts (model AV-200)", and list the required tools (10mm wrench) and accessories at the same time. The maintenance suggestions are displayed through the terminal application for workers to directly implement the operations.

[0058] The prediction process of the fault prediction and prevention module is as follows: A dedicated computer collects three types of information - the fault records in the past 5 years (such as the time and cause of bearing wear), the current operation data (the air pressure and temperature updated every second), and the environmental data (the workshop temperature is 25 °C and the humidity is 60%), and the total amount of data is about 1 million. First, the computer sorts out this information and counts the fault patterns. For example, when the temperature exceeds 45 °C, the probability of the air filter being blocked reaches 80%. Subsequently, a program that can capture long-term trends is used to analyze the data in the recent 1000 seconds to predict future faults, such as "the air filter may be blocked in 30 days", and generate a suggestion "replace the filter element in 15 days". This suggestion is transmitted to the main control computer through the network, and the user can arrange maintenance in advance according to this. The prediction process takes about 30 seconds.

[0059] When a fault involves multiple devices (such as the air compressor is associated with the air conditioner and air purifier), the processing process of the device linkage and coordination module is as follows: The air compressor sensor detects that the air pressure drops to 1.0 MPa and the temperature rises to 50 °C, and determines that the device has a fault. Another computer obtains the status of other devices through a wireless signal (ZigBee protocol), such as the air conditioner is set to a temperature of 26 °C. The system decides to start the air purifier (model AP-300) for assistance, sends an instruction through a high-speed network (transmission rate of 1 billion bits per second), adjusts the purifier's wind speed to gear 3, and maintains the indoor temperature at 25 °C and humidity at 50%. This process takes less than 1 second, and the user receives a notification through the terminal application: "The air compressor has a fault, and the air purifier has been started to adjust the environment", thus avoiding the spread of the fault and affecting other devices.

[0060] The acquisition process of the multi-source data fusion module is as follows: Data (such as air pressure, temperature, vibration) is obtained from sensors every second, and is converted into digital signals by an acquisition device (supporting 16 channels and a sampling rate of 1 kHz), forming a feature list containing 128 numbers. The analysis process of the intelligent diagnosis and decision-making module is as follows: The list is compared with the historical records in real time using a high-performance computer (taking 0.01 seconds). For example, when the vibration frequency rises to 50 Hz, it is judged as "bearing failure", and it is predicted that "it may completely fail after 15 days". The core control unit of the system is a main control computer (equipped with a 16-core processor and 64 GB of memory), whose function is to coordinate the operation of each module every second. For example, it instructs the acquisition module to continuously monitor data, or adjusts the prediction module to pay attention to the influence of humidity to ensure the continuous optimization of the system operation.

[0061] The present invention is mainly applied to the design of air compressors, but is also applicable to small household appliances (such as air conditioners). The air compressor uses pressure and vibration sensors, while the air conditioner uses temperature and humidity sensors. The system automatically adjusts the solution according to the device type. For example, when the air compressor fails, the output of other air compressors is increased, and when the air conditioner fails, an air purifier is started. The specific measures are pushed to the user through the terminal application to ensure the feasibility of implementation.

[0062] Before fusion, the multi-source data fusion module needs to extract information from three sources. The specific process is as follows:

[0063] Extraction of user description data:

[0064] The user inputs information through the terminal application or the web page. For example, voice input "The air pressure is insufficient" or text input "The machine sounds abnormal". A small computer (equipped with a 4-core processor) processes the input data: If it is voice, a 4-channel microphone captures the audio, and the computer converts it into text ("The air pressure is insufficient") within 0.5 seconds; then the text is analyzed to extract key information ("air pressure", "insufficient"), and a feature list containing 128 numbers (such as [0.8, 0.9, 0.3,...]) is generated, representing the core content of the user description. This process takes 0.1 seconds, and the data is transmitted to the fusion module via Wi-Fi.

[0065] Extraction of nano-material sensor data:

[0066] The nanomaterial sensors are installed at key parts of the air compressor (such as the intake valve and the motor surface) to detect minute changes. For example, the graphene pressure sensor measures a pressure change of 0.02 Pa, and the carbon nanotube temperature sensor measures a temperature increase of 0.5 °C. The sensors collect data at a frequency of 256 times per second and transmit it via nanocables to an amplifier (with a gain of 50 times). After signal amplification, it is sent to a data acquisition device (a data acquisition card supporting 16 channels). The data acquisition card generates a list of 1000 numbers (such as pressure [0.02, 0.03, …]). The computer extracts key changes (such as the pressure drop point) from it and generates a feature list containing 128 numbers (such as [0.7, 0.2, …]). This process takes 0.05 seconds, and the data is transmitted to the fusion module via USB.

[0067] Extraction of traditional sensor data:

[0068] Traditional sensors measure the macroscopic operating parameters of the air compressor. For example, the pressure sensor measures the air pressure of 1.5 MPa, the thermocouple measures the temperature of 40 °C, and the vibration sensor measures the frequency of 30 Hz. The sensors collect data at a frequency of 1 time per second and transmit it via an RS485 cable to the data acquisition device, generating a list of 1000 numbers (such as air pressure [1.5, 1.4, …]). The computer analyzes the data, extracts abnormal points (such as the air pressure dropping to 1.0 MPa), and generates a feature list containing 128 numbers (such as [0.9, 0.4, …]). This process takes 0.03 seconds, and the data is transmitted to the fusion module via the network.

[0069] The fusion process is as follows:

[0070] The multi-source data fusion module integrates and processes the three feature lists containing 128 numbers extracted from the user-described data, nanomaterial sensor data, and traditional sensor data to generate a new feature list of 128 numbers for subsequent diagnosis. The specific process is as follows:

[0071] A local server (equipped with an Intel Xeon Gold 6226R processor, 16 cores and 32 threads, and 64 GB of DDR4 memory) receives the three feature lists:

[0072] The feature list of user-described data (such as [0.8, 0.9, 0.3, …], indicating "insufficient air pressure") is transmitted via Wi-Fi at a rate of 10 Gbps;

[0073] The feature list of nanomaterial sensor data (such as [0.7, 0.2, …], indicating a microscopic pressure drop of 0.02 Pa and a temperature increase of 0.5 °C) is transmitted via a USB3.0 interface at a rate of 5 Gbps;

[0074] The feature list of traditional sensor data (e.g., [0.9, 0.4, …], indicating that the air pressure drops to 1.0 MPa and the vibration frequency rises to 50 Hz) is transmitted at a rate of 1 Gbps through the industrial Ethernet.

[0075] The server integrates the above three feature lists into a three-dimensional data table (with dimensions of 3×128×1), where the first dimension represents the data source (user description, nano-sensor, traditional sensor), the second dimension represents 128 feature values, and the third dimension is temporarily set to 1 (which can be extended to a time series later). The integration process is completed through the internal high-speed data bus and takes about 0.02 seconds.

[0076] Subsequently, the server runs an algorithm that combines tensor decomposition and generative adversarial network to extract key information. The specific steps are as follows:

[0077] Tensor decomposition processing:

[0078] The server performs CANDECOMP / PARAFAC decomposition on the three-dimensional data table with a decomposition rank set to 10, generating three factor vectors: the user description factor (e.g., [0.8, 0.1, …], highlighting "insufficient air pressure"), the nano-sensor factor (e.g., [0.6, 0.2, …], highlighting the micro pressure drop), and the traditional sensor factor (e.g., [0.9, 0.3, …], highlighting the air pressure drop and vibration increase). The decomposition process is completed through parallel computing in the server's 64GB memory and takes about 0.03 seconds. After decomposition, key information is extracted, such as the air pressure drop (from 1.5 MPa to 1.0 MPa) and vibration increase (from 30 Hz to 50 Hz) as the main features.

[0079] Dynamic weight assignment:

[0080] Adjust the weights of each data source according to the operating stage of the air compressor. For example, during the startup stage, the weight of traditional sensor data (such as vibration frequency) is set to 60%, the weight of nano-sensor data is 30%, and the weight of user description data is 10%; during the stable operation stage, the weight of temperature data increases to 50%, the vibration weight decreases to 30%, and the user description weight remains 10%. The weight adjustment is based on preset rules (stored in the server's relational database) and is automatically matched by the server according to real-time operating parameters (such as the motor speed of 1000 revolutions per minute), taking about 0.01 seconds.

[0081] Generative adversarial network enhancement:

[0082] The server runs a Generative Adversarial Network (GAN), which consists of a discriminator and a generator. The discriminator receives the decomposed factor vectors and judges their authenticity (e.g., whether the air pressure drop is reliable); the generator generates enhanced data from random noise (generated from a standard normal distribution) to supplement potential features (e.g., the correlation between microcracks and macro vibrations). The GAN is iteratively trained 1000 times (learning rate 0.0002), accelerated by GPU (NVIDIA RTX3090, 24GB video memory), taking about 0.5 seconds, and finally generates a 128-dimensional vector that integrates preliminary features (e.g., [0.85, 0.35, …]).

[0083] Data error correction and completion:

[0084] If data anomalies are detected (e.g., the nanosensor fails due to dust blockage and the feature value jumps to 0), the server uses historical data (stored in a MySQL database, about 10 5 records) and a correlation model (e.g., a linear regression model of air pressure and vibration, correlation coefficient 0.9) for completion. For example, the missing pressure data of 0.02 Pa is completed according to the historical mean of 0.025 Pa, and the error correction process takes 0.02 seconds to ensure the integrity of the feature list.

[0085] Finally, the server mixes the key information extracted through tensor decomposition (e.g., air pressure drop, increased vibration), the weighted results after dynamic weight adjustment, and the features enhanced by the GAN to generate a new feature list of 128 numbers (e.g., [0.9, 0.4, 0.7, …]). This list is transmitted to the intelligent diagnostic decision-making module through a protocol based on quantum homomorphic encryption (transmission rate 10 Gbps) for subsequent fault diagnosis. The total time taken for the entire fusion process is about 0.58 seconds to ensure the high precision and reliability of the data.

[0086] The meaning, function of the bioelectric signal interaction module and its connection relationship with the air compressor function:

[0087] The function of the bioelectric signal interaction module is to analyze the operator's brain waves through a helmet and a computer to improve the efficiency of human-computer interaction. The implementation process is as follows: The operator wears a helmet equipped with 16 probes, collecting 256 electroencephalogram signals per second (voltage range 5 to 100 microvolts), reflecting their attention to the state of the air compressor (e.g., "the air pressure is low"). After the signals are processed by an amplifier (gain 50 times), they are transmitted to a small computer via Bluetooth. The computer analyzes and generates a list of instructions containing 16 numbers (e.g., [0.9, 0.2, …] represents "pay attention to the air pressure"), taking 0.1 second. This list is transmitted to the voice module (e.g., prompt "Do you need to pressurize?") or the main control computer (adjust the air pressure to 2 MPa), and the air compressor operation is controlled through the network to directly connect the operator's intention with the device operation.

[0088] Specific meaning of electroencephalogram (EEG) signals:

[0089] EEG signals refer to weak electrical signals (voltage range: 5 to 100 microvolts) generated by the activities of neurons in the operator's brain. Measured by a helmet, they reflect the operator's attention or intention regarding the operating state of the air compressor. Specifically, when the operator observes the pressure gauge, the brain may generate alpha waves of 10 Hz (about 5 microvolts), indicating "attention to pressure"; when considering adjusting the equipment, beta waves of 20 Hz (about 10 microvolts) are generated, indicating "increase pressure". The helmet is equipped with 16 dry electrodes, collecting signals 256 times per second, generating a dataset of 4096 numbers (16 probes × 256 times). The computer analyzes this dataset, identifies the operator's intention (such as "attention to pressure"), generates a list of instructions containing 16 numbers, and transmits it to the system to adjust the operating state of the air compressor.

[0090] Process of realizing technical functions through the fusion algorithm of the attention mechanism and capsule network:

[0091] The EEG signal processing process is as follows: The signals collected by the helmet (16 probes × 256 times / second) are filtered for noise (retaining the range of 0.5 to 100 Hz) by a small computer, and the key parts are analyzed (for example, the enhanced signal of the forehead probe indicates attention to pressure). Subsequently, the computer converts the signal into a list of instructions containing 16 numbers (for example, [0.9, 0.2,...] indicates "attention to pressure"), which takes 0.1 second. This list is transmitted to the voice module to adjust the interaction prompt (such as "Abnormal pressure, please confirm"), or to the main control computer to modify the settings of the air compressor (such as increasing the pressure to 2 MPa), improving the adaptability between the system and the operator.

[0092] The process of the AR visualization presentation module generating high-resolution images is as follows: Using the terminal camera to take an external image of the air compressor (resolution: 1920 × 1080 pixels), combined with sensor data (a feature list containing 128 numbers, such as pressure and temperature), the computer first generates a rough internal model (about 1 million points). Subsequently, the model is segmented into blocks of 1 mm in size, and details are filled according to the data (for example, the area with increased temperature is marked yellow). Finally, a clear three-dimensional image is displayed on the terminal screen (updated 30 times per second), and the fault location is marked (for example, the bearing wear area is marked with a red frame and the text "Bearing is damaged").

[0093] The path and resource scheduling strategy optimized by quantum annealing realizes the calculation of the optimal maintenance plan through a quantum computer. The process is as follows: The maintenance tasks are divided into multiple steps (such as "closing the valve", "dismantling the parts", "replacing with new parts"), and the time consumption (closing the valve takes 5 minutes) and required tools (wrench No. 10) are recorded for each step. The quantum computer tests all combinations of steps, selects the plan with the shortest time (total time consumption is 25 minutes), evaluates the risks (such as the parts may get stuck during dismantling, delaying for 2 hours), and generates a maintenance list including 5 steps (including the tool and accessory model AV-200) for the workers to execute directly.

[0094] The process of collecting environmental factor data is as follows: including the workshop temperature (range -20 to 60 degrees Celsius), humidity (range 10% to 90%), and dust concentration (range 0 to 500 micrograms per cubic meter). Data is collected by wall-mounted temperature and humidity sensors and inlet dust sensors every second and transmitted to the computer for analysis. For example, when the humidity reaches 80%, the system identifies an increased risk of air filter blockage.

[0095] The process of encrypting communication between equipment rooms is as follows: A dedicated device generates a 256-bit random key (composed of 0s and 1s) using photons and transmits it to another air compressor through an optical fiber. When sending an instruction (such as "increasing the air pressure"), the computer encrypts the instruction into a scrambled code using this key, and only the device that receives the key can decrypt it. The encryption and transmission process takes less than 0.001 seconds to ensure the security of the instruction.

[0096] The specific process of optimizing the regulation strategy by combining the particle swarm optimization algorithm with quantum search and the implementation method of the optimization effect:

[0097] The optimization process is as follows:

[0098] A server (equipped with a 16-core processor and 64GB of memory) generates 50 sets of regulation plans (such as the data collection frequency is 1 time per second or 2 times per second, and the temperature threshold is 45 or 50 degrees Celsius). Each set of plans is tested once (takes 0.1 seconds), and the diagnostic accuracy rate (such as the correctness of judging "valve failure") and response time (such as 3 seconds or 2 seconds) are evaluated. The server adjusts the parameters (such as increasing the frequency to 1.5 times per second and adjusting the threshold to 47 degrees Celsius), and combines quantum search technology to quickly screen the best plan (such as the accuracy rate is 97% and the response time is 1.8 seconds, taking 0.01 seconds). The best plan is transmitted to the main control computer through the network to update the settings of each module (such as adjusting the collection module to 2 times per second), and this process is executed once per second.

[0099] The implementation method of the optimization effect is as follows:

[0100] Improved Diagnostic Accuracy: By adjusting the vibration weight to 60%, the misjudgment rate decreased from 5% to 2%. For example, when the air pressure dropped to 1.0 MPa, the probability of correctly identifying "valve blockage" increased from 95% to 98%.

[0101] Shorter Response Time: The data acquisition frequency increased to 2 times per second, and the processing speed decreased from 0.01 seconds to 0.008 seconds. The time from when the user inputs "insufficient air pressure" to receiving a maintenance suggestion decreased from 3 seconds to 1.5 seconds.

[0102] Improved User Experience: When the humidity was 80%, the humidity analysis weight increased to 50%, improving the prediction accuracy of air filter blockage. Users reported that "maintenance was time-saving", and the satisfaction level increased from 90% to 96%.

[0103] Increased Resource Efficiency: By reducing ineffective calculations, the server power consumption decreased from 500 watts to 450 watts, and the number of air compressors supported increased from 10 to 15.

[0104] The continuous optimization process is as follows:

[0105] The server fine-tunes the parameters every second based on the latest data (such as air pressure, temperature) and user feedback (such as "smooth maintenance"). For example, the temperature weight is adjusted from 50% to 55% to ensure continuous improvement of system performance. Specific Example 2:

[0107] As Figures 1-3 shown, the following is a use case of the solution in Example 1:

[0108] Case 1: Maintenance of a large-scale air compressor cluster in a factory

[0109] A certain factory has multiple air compressors to supply air to the production line. One day, the workshop reported that the air pressure in some areas was insufficient. The maintenance personnel started the system through the terminal APP. The AI intelligent interaction module received the voice input "the air pressure in some areas is insufficient". The AR visualization presentation module initialized the camera, and the system core control unit established a connection through Wi-Fi. The AI module converted the description into a text feature vector. The multi-source data fusion module detected that the piezoelectric pressure sensor (pipe node) showed a decrease in exhaust pressure, and the thermocouple temperature sensor (motor housing) and vibration acceleration sensor (base) reported an increase in temperature and abnormal vibration. The graphene quantum dot and carbon nanotube sensors sensed abnormal micro-pressure and structural changes. The data was transmitted via RS485 and USB3.0 and fused into a comprehensive vector. If an electroencephalogram device is worn, an intention vector is generated synchronously. The terminal camera (30 frames per second) captures the appearance, and combines the quantum random walk and octree algorithm to generate an AR image. The intelligent diagnosis and decision-making module locates the fault of the intake valve, and the AR module marks it with a red mark and text description. The deep learning matches the fault mode library, and the quantum Monte Carlo tree search and evidence theory confirm the fault. The quantum annealing optimization generates a maintenance plan (close the valve, disassemble, replace), lists the tools and accessories, and the quantum computing accelerates the operation. The fault prediction module combines the MySQL historical data and the information of the environmental sensors (temperature, humidity, air quality) to predict the risk of similar faults within two months, and recommends regular inspection of the valve and pipeline cleaning. The maintenance personnel reported "fault solved", and the AI module updated the fault library and algorithm. The device linkage and coordination module adjusts the output of other air compressors through quantum communication to ensure air supply. The system generates a PDF report, encrypts it via quantum and outputs it to the APP and stores it in the distributed quantum storage system. The multi-source data fusion module monitors every second, and the quantum particle swarm optimization adjusts the parameters to improve the performance. The system adopts a modular design, is compatible with screw and centrifugal air compressors, and operates in an environment of -20°C to 60°C.

[0110] Case 2: Remote Monitoring and Fault Handling of Mine Air Compressors

[0111] In a remote mine, the air compressors frequently malfunction due to harsh environments, and underground workers reported "abnormal noises". The monitoring room is started through the web version system. The AI module is on standby, and the AR module prepares the camera. The system's core control unit is connected via Ethernet. The AI module converts the "abnormal noises" into feature vectors. The multi-source data fusion module detects increased vibration acceleration, rising temperature, and pressure fluctuations. The nano-sensors detect abnormal microscopic structures. The data is aggregated to generate a comprehensive vector. If electroencephalogram signals are collected, they are processed synchronously. The on-site mobile device takes pictures of the appearance. The AR imaging marks the location of bearing wear. The intelligent diagnosis matches the bearing wear. The quantum algorithm confirms the location and generates a maintenance plan (disassembly, bearing replacement), lists the tools such as pullers and the models of accessories. The quantum computing accelerates the diagnosis. The fault prediction module combines historical data with environmental factors (high dust, humidity) to predict similar faults within a month and suggests increasing filter maintenance and lubrication inspection. After maintenance, feedback is sent via the web, and the AI updates the algorithm. The equipment linkage and coordination module adjusts the air supply of other air compressors, and quantum communication ensures the linkage. The system generates a report, outputs it via the web, and stores it in the quantum storage system. The system continuously monitors, and quantum optimization improves the adaptability to harsh environments. The multi-source data fusion module reserves expansion interfaces to support new sensors, and the data is encrypted via quantum key distribution.

[0112] Case 3: Intelligent Management of Air Compressors in an Automobile Manufacturing Workshop

[0113] The air compressors in an automobile manufacturing workshop support pneumatic tools and painting equipment. Maintenance personnel reported "unstable air supply". The supervisor starts it through the tablet APP. The AI module receives the voice "unstable air supply". The AR module initializes the camera. The system's core control unit is connected via Wi-Fi and Ethernet. The AI generates feature vectors. The piezoelectric pressure sensor (in the intake pipe) detects pressure fluctuations. The thermocouple (on the motor housing) and the vibration sensor (on the base) feedback high temperature and abnormal vibration. The nano-sensors sense microscopic pressure and structural changes. The data is fused into a comprehensive vector via RS485 and USB3.0. If electroencephalogram signals exist, they are processed and transmitted. The tablet camera generates AR imaging. The intelligent diagnosis locates the air filter blockage, marks it yellow and explains. Deep learning matches the filter blockage. The quantum algorithm confirms it and generates a maintenance plan (close the valve, replace the filter element), lists the tools such as screwdrivers and accessories. The quantum computing accelerates. The fault prediction module combines Oracle historical data with environmental sensors (temperature, humidity, air quality) to predict similar faults within half a month and suggests increasing filter replacement and air purification. After maintenance, the feedback is "stable air supply restored", and the AI updates the algorithm. The equipment linkage and coordination module boosts other air compressors to maintain the production line, and quantum communication feeds back the status. The system generates an HTML report, encrypts it via quantum, outputs it to the APP, and stores it. It monitors every second, and quantum optimization adjusts the parameters to adapt to electromagnetic interference and high stability requirements. The comprehensive vector is a 128-dimensional tensor and operates in a complex electromagnetic environment.

[0114] The following table shows the performance of the fully automatic air compressor system in three cases. The data units are clear, reflecting the high efficiency and intelligence of the system:

[0115]

[0116] From the above table, it can be seen that the system's ability to match fault patterns through deep learning and quantum algorithms is higher in factories and automotive workshops due to the relatively stable environment, and slightly lower in mines due to the harsh environment. The time required for joint positioning by AR imaging and quantum algorithms is the fastest in automotive workshops because the equipment is concentrated and electromagnetic interference is controllable, and slightly slower in mines due to remote transmission. The time from positioning to completing the repair depends on the fault complexity and the clarity of the repair plan, and the bearing replacement in mines is more time-consuming. The prediction ability based on historical data and environmental factors is the best in automotive workshops due to the high data integrity. The time from user input to preliminary feedback is the fastest in automotive workshops because of the excellent performance of the tablet devices.

[0117] The following table compares this system with the prior art (CN112529320A) based on three case scenarios. The data is reasonably derived from the case descriptions and technical differences:

[0118]

[0119] Appendix Figure 3 is a simulation diagram of the overall management main system, showing the pressure data collected by nano sensors (blue solid line) and traditional sensors (red dashed line) respectively. The horizontal axis is time (unit: second, range 1 to 1000), and the vertical axis is pressure (unit: MPa). The figure shows the pressure measurement data of nano sensors and traditional sensors within 1000 seconds. Overall, the pressure values mainly fluctuate between 0.5 and 2.0 MPa. The data of nano sensors (blue solid line) fluctuates less, showing a relatively stable trend, while the data of traditional sensors (red dashed line) fluctuates more, showing more noise. The specific analysis is as follows: From about time 1 to 600 seconds, the pressure values measured by both sensors basically remain near 1.5 MPa, with slight fluctuations, indicating that the operating state of the air compressor is relatively stable. The fluctuation range of the data of nano sensors is smaller, with a change range of about 1.4 to 1.6 MPa, while the fluctuation range of the data of traditional sensors is larger, with a change range of 1.3 to 1.7 MPa. This difference reflects the higher accuracy and noise resistance of nano sensors (such as graphene quantum dot pressure sensors), which is in line with the sensitivity of 0.01 Pa and response time less than 1 ms mentioned in your scheme, while traditional sensors (such as piezoelectric pressure sensors) have lower accuracy and more obvious fluctuations.

[0120] Near 600 seconds, significant changes occurred in the pressure data. Both sensors showed that the pressure began to decline, from about 1.5 MPa to around 1.0 MPa, with a decline of approximately 0.5 MPa. This change trend is consistent with the simulation settings mentioned in your specification, that is, fault injection leads to insufficient air pressure. Both the nano - sensor and the traditional sensor captured this trend, but the data of the nano - sensor decreased more smoothly with less fluctuation, while the data of the traditional sensor still had large fluctuations during the decline process and more obvious noise, reflecting its lower stability.

[0121] From 600 seconds to 1000 seconds, the pressure value stabilized at about 1.0 MPa. The data trends of both sensors continued to be consistent, but their fluctuation characteristics were still different. The data of the nano - sensor was relatively stable, with a change range between 0.9 and 1.1 MPa, while the data of the traditional sensor fluctuated in the range of 0.8 to 1.2 MPa and still had relatively large noise. This further verified that the measurement accuracy of the nano - sensor is better than that of the traditional sensor in the fault state.

[0122] Looking at the overall trend, the change of the pressure data in the figure reflects the process of the air compressor from normal operation to failure: initially, the pressure was stable at 1.5 MPa, and near 600 seconds, due to faults (such as intake valve blockage or pipeline leakage), the pressure dropped to 1.0 MPa, and then entered a new stable state. Both sensors can reflect this fault characteristic, but due to its high - precision and low - noise characteristics, the nano - sensor can show the details of the pressure change more clearly, providing more reliable data support for subsequent fault diagnosis. Specific Embodiment Three:

[0124] As Figures 1-3 shown, the key algorithms mentioned in Embodiment One are analyzed in detail below, including their core mathematical formulas and explanations:

[0125] Natural language processing algorithm based on variational auto - encoder:

[0126] It is used to convert the description of the air compressor fault by the user and the operation record data into a text feature vector that can be understood by the machine.

[0127] The goal of the variational auto - encoder (VAE) is to learn the latent distribution of data through the encoder and decoder:

[0128] Encoder:

[0129] Decoder:

[0130] Loss function:

[0131]

[0132] where: q φ (z|x): the encoder probability distribution, representing the conditional distribution of the latent variable z given the input x (user description text); φ: the parameters of the encoder neural network, usually learned by a multi-layer perceptron (MLP). z: the latent variable, i.e., the low-dimensional representation (feature vector) of the text. x: the input data, i.e., the fault description input by the user (such as "insufficient air pressure"). Normal distribution, assuming that z follows a distribution with mean μ(x) and variance σ 2 (x). μ(x): the mean vector, predicted by the encoder. σ 2 (x): the variance vector, representing uncertainty. p θ (x|z): the decoder probability distribution, representing the conditional distribution of reconstructing the input x from the latent variable z. θ: the parameters of the decoder neural network. : the reconstructed text data.

[0133] This algorithm compresses the text data into a feature vector through the encoder, the decoder verifies the reconstruction ability, and the loss function balances accuracy and distribution regularization. In the fault description of the air compressor, for example, "insufficient air pressure in some areas", the algorithm extracts the key semantics (such as "air pressure" and "insufficient"), and generates vectors for subsequent analysis.

[0134] Algorithm based on the combination of tensor decomposition and generative adversarial network:

[0135] This algorithm performs feature extraction and fusion on the user description data, nanomaterial sensor data, and traditional sensor data to generate a comprehensive data vector.

[0136] Tensor decomposition:

[0137]

[0138] Generative adversarial network:

[0139] Discriminator:

[0140] D(x) = σ(W D x + b D )

[0141] Generator:

[0142] G(z) = tanh(W G z + b G )

[0143] Loss function:

[0144]

[0145] where:

[0146] Tensor decomposition:

[0147] X: Input multi-source data tensor (e.g., three-dimensional array: sensor type × time × feature). R: Decomposition rank, i.e., the number of features. λ r : Weight of the r-th component. a r ,b r ,c r : Respectively represent the factor vectors of the three dimensions (e.g., sensor type, time point, feature value). °: Outer product operation, combining vectors into a tensor. This algorithm decomposes multi-dimensional data into a low-dimensional representation and extracts key features.

[0148] Generative adversarial network: D(x): Discriminator output, judging the authenticity probability of the input x (real data or generated data). σ: Sigmoid activation function, output range [0,1]. W D ,b D : Weights and biases of the discriminator. G(z): Generator output, generating pseudo data from noise z. tanh: Activation function, output range [-1,1]. W G ,b G : Weights and biases of the generator.

[0149] V(D,G): Adversarial loss, the discriminator maximizes the distinction between real and generated data, and the generator minimizes the probability of being recognized as pseudo data. p data (x): Real data distribution. p z (z): Noise distribution.

[0150] Tensor decomposition reduces multi-source data (such as pressure, temperature, vibration) to feature vectors, and GAN enhances the quality of data fusion through adversarial training, finally generating a 128-dimensional comprehensive data vector. For example, fusing the description of "insufficient air pressure" with sensor anomaly data to generate a unified representation for diagnosis.

[0151] 3D reconstruction algorithm based on quantum random walk and octree structure:

[0152] This algorithm generates a high-resolution AR imaging of the internal structure of the air compressor.

[0153] The specific formula is as follows:

[0154] Quantum random walk: ψ(t+1) = U|ψ(t)>

[0155] Octree segmentation: N = 8 k , where k is the depth.

[0156] Where:

[0157] Quantum random walk: ψ(t): Quantum state at time t, representing the position probability distribution. U: Unitary operator, defining the walking rule, e.g., S: Displacement operator, which updates the particle position. H: Hadamard gate, which generates a superposition state (uniform distribution). I: Identity matrix, which maintains the particle state.

[0158] Quantum walks utilize superposition to quickly explore the three-dimensional space of the air compressor and generate an initial point cloud.

[0159] Octree partitioning: N: Total number of nodes, and the space is recursively divided into 8 sub-regions. k: Depth of the tree, which determines the resolution (e.g., when k = 5, the number of nodes is 32768). The octree efficiently organizes point cloud data, subdivides the space according to density, and optimizes rendering.

[0160] Combined algorithm of quantum Monte Carlo tree search and quantum evidence theory:

[0161] Used to determine the fault area and generate a diagnostic result.

[0162] The specific formula is as follows:

[0163] Quantum Monte Carlo tree search:

[0164] Quantum evidence theory: m(A)=∑ B∈Ω (ψ B |ψ A )m B

[0165] Where:

[0166] Quantum Monte Carlo tree search:

[0167] Q(v): Value of node v, which evaluates the probability of a fault. N(v): Number of times the node has been visited. r i : Reward for the i-th simulation (0 or 1, indicating whether there is a fault). c: Exploration parameter (e.g., ), which balances exploration and exploitation. N(p): Number of times the parent node has been visited. Quantum superposition accelerates the search for fault paths and optimizes tree expansion.

[0168] Quantum evidence theory: m(A): Basic probability assignment of event A (such as "intake valve fault"). Ω: Set of all possible faults. B: Subset, representing a single piece of evidence. (ψ B |ψ A ): Quantum inner product, which measures the similarity between pieces of evidence. m B : Probability of evidence B. Quantum quantization fuses the uncertainty of multi-source data to determine possible faults.

[0169] Algorithm combining a long short-term memory network driven by quantum entanglement and reinforcement learning:

[0170] Used to predict the future fault time and type of the air compressor.

[0171] The specific formulas are as follows:

[0172] LSTM update: h t = o t ·tanh(c t )

[0173] Quantum entanglement drive: |ψ> = α|00> + β|11>

[0174] Reinforcement learning: Q(s,a) ← Q(s,a) + α[r + γmax a′ Q(s′,a′) - Q(s,a)]

[0175] Where:

[0176] LSTM:

[0177] h t : The hidden state at time t. o t : Output gate, o t = σ(W o x t + U o h t-1 + b o ). c t : Cell state, f t , i t , Forget gate, input gate, and candidate state.

[0178] This algorithm is used to capture the long-term dependencies of the running parameters.

[0179] Quantum entanglement drive:

[0180] |ψ>: Entangled state, representing the correlation of the time series. α, β: Complex amplitudes, satisfying |α| 2 + |β| 2 = 1. |00>, |11>: Ground states, representing the relationship between history and current data. This algorithm enhances the LSTM's modeling of time series through the entangled state.

[0181] Reinforcement learning:

[0182] Q(s,a): The value of action a in state s. α: Learning rate (such as 0.1). r: Immediate reward (such as 1 for correct prediction). γ: Discount factor (such as 0.9). s′, a′: Next state and action. This algorithm is used to optimize the prediction strategy. Quantum entanglement enhances the LSTM's capture of historical faults, and reinforcement learning adjusts the prediction according to the environmental feedback. For example, when predicting a clogged air filter, the algorithm combines the rising temperature trend with air quality data to output the accurate time.

[0183] Device Linkage Control Algorithm Based on the Combination of Quantum Genetic Programming and Fuzzy Cognitive Map:

[0184] Used to coordinate the operating states of multiple air compressors.

[0185] The specific formula is as follows:

[0186] Quantum genetic encoding: |ψ> = [cosθ 1 , sinθ 1 ; cosθ 2 , sinθ 2

[0187] Fuzzy cognitive map:

[0188] Where:

[0189] Quantum genetic encoding:

[0190] |ψ>: Quantum chromosome, representing the control strategy. θ 1 , θ 2 : Rotation angle, encoding the gene state. This algorithm searches for the optimal linkage scheme through quantum superposition.

[0191] Fuzzy cognitive map: A i (t): The state of node i at time t (such as the output of the air compressor). w ji : The weight from node j to i. f: Activation function. This algorithm performs fuzzy reasoning on the influence between devices. Specific Example 4:

[0193] As Figures 1-3 shown, the following is the description of data utilization and data transmission between the algorithms used in this Example 1:

[0194] Sp1: There is no specific algorithm for system initialization and connection establishment. The user inputs a start command through the APP or the web page, which is transmitted to the system core control unit, generates an initialization signal, and is distributed to each module through the internal communication bus to activate the AI intelligent interaction module, the bioelectric signal interaction module, the quantum computing acceleration module, and initialize the AR visualization presentation module.

[0195] ​Sp2: Multi-source Data Acquisition and Preliminary Fusion Variational Autoencoder Algorithm The algorithm receives the fault description input by the user through the APP, converts it into a text string, generates a 128-dimensional text feature vector, and transmits it to the multi-source data fusion module via Wi-Fi. The tensor decomposition and generative adversarial network algorithm receives the pressure, temperature, and vibration data collected by the nano-sensors and traditional sensors, as well as the 128-dimensional text feature vector, generates a comprehensive data vector, and transmits it to the intelligent diagnosis and decision-making module via the quantum homomorphic encryption protocol. The gated recurrent unit and capsule network fusion algorithm with attention mechanism receives the electroencephalogram signals, generates a 16-dimensional intention instruction vector, and transmits it to the AI intelligent interaction module and the system core control unit via Bluetooth 5.0 for optimizing interaction and generating control instructions.

[0196] Sp3: AR Imaging Real-time Display and Fault Area Location The quantum random walk and octree structure algorithm receives the 128-dimensional comprehensive data vector from the multi-source data fusion module and the RGB image of the air compressor appearance collected by the high-definition camera of the user's handheld device, generates a high-resolution AR imaging, combines the fault area coordinates provided by the intelligent diagnosis and decision-making module, superimposes marks and explanations, and transmits it to the user terminal for display via Wi-Fi. The system core control unit ensures data consistency through timestamp synchronization.

[0197] Sp4: Intelligent Diagnosis and Solution Generation The deep learning algorithm receives the 128-dimensional comprehensive data vector from the multi-source data fusion module, matches it with the fault mode library, generates a preliminary fault probability vector, and transmits it to the quantum computing acceleration module via the high-speed quantum data interface. The joint algorithm of quantum Monte Carlo tree search and quantum evidence theory receives the fault probability vector and the comprehensive data vector, determines the fault area and type, generates a diagnosis result, feeds it back to the intelligent diagnosis and decision-making module, generates a maintenance plan, and then transmits it to the AR module and the system core control unit. The quantum immune cloning and quantum differential evolution algorithm receives the intermediate features of deep learning and the original tensors of multi-source data, optimizes feature selection, generates an optimized feature vector, and feeds it back to the multi-source data fusion module and the intelligent diagnosis and decision-making module via the quantum data interface.

[0198] Sp5: Fault Prediction and Prevention Analysis The quantum entanglement-driven long short-term memory network and reinforcement learning algorithm receives historical fault data, current operating parameters, environmental factor data, and nano-sensor data, generates a fault prediction result and prevention suggestions, and transmits them to the system core control unit for report generation and equipment adjustment.

[0199] Sp6: User Feedback and Learning Optimization The quantum game learning and incremental support vector machine fusion algorithm receives the feedback submitted by the user through the APP or the web terminal, updates the fault mode library and the diagnostic algorithm in combination with the historical feedback data, generates optimized diagnostic model parameters, and transmits them to the intelligent diagnosis and decision-making module via Wi-Fi. At the same time, the AI intelligent interaction module adjusts the interaction method.

[0200] Sp7: The device linkage and collaborative processing of the quantum genetic programming and fuzzy cognitive map algorithms receive the fault diagnosis results and linkage requirement instructions from the intelligent diagnosis decision-making module, generate a linkage plan, and transmit the instructions to the relevant air compressors through quantum key distribution and quantum teleportation. The device operation status feedback data is transmitted back to the device linkage coordination module and the system core control unit through the quantum communication link.

[0201] Sp8: The generation and output of the comprehensive diagnosis report have no specific algorithm. The system core control unit integrates the diagnosis results, repair plans, prediction information, and linkage situations, generates a comprehensive diagnosis report, outputs it to the user through the APP or the web page, and stores it in the distributed quantum storage system.

[0202] Sp9: The cyclic monitoring and continuous improvement of the quantum particle swarm optimization and quantum simulated annealing algorithms receive the operation parameters from the multi-source data fusion module and the diagnosis results from the intelligent diagnosis decision-making module, generate an optimization control strategy, and the system core control unit issues new instructions to each module through the internal communication bus to continuously improve the diagnosis process. Specific Embodiment Five:

[0204] As Figures 1-3 shown, the following is the detailed hardware composition and hardware description of each module in Embodiment One:

[0205] 1. AI Intelligent Interaction Module

[0206] Hardware composition: This module mainly includes a high-performance embedded processor (such as NVIDIA Jetson Nano, equipped with a 4-core ARM Cortex-A57 CPU and a 128-core Maxwell GPU), a voice recognition microphone array (4-channel MEMS microphone, signal-to-noise ratio ≥ 65 dB), a Bluetooth 5.0 communication module (supporting a rate of 2 Mbps), and a touch display screen (optional, 7-inch IPS screen, resolution 1024×600).

[0207] Hardware description: The embedded processor is responsible for running the natural language processing algorithm based on the variational autoencoder, performing lexical, syntactic, and semantic analyses on the fault descriptions and operation records input by the user through the APP or the web page, and converting them into text feature vectors, with a processing speed of up to 10 parses per second. The voice recognition microphone array collects the user's voice input, supports far-field pickup (distance ≤ 5 meters), and ensures accurate reception of instructions in a noisy industrial environment. The Bluetooth 5.0 module is used to receive the intention instruction vectors from the bioelectric signal interaction module, ensuring low-latency (≤ 10 ms) transmission. The touch display screen provides a backup interaction interface, supports Chinese and English displays, and the user can submit feedback through voice or touch, suitable for local operation scenarios.

[0208] 2. AR Visualization and Rendering Module

[0209] Hardware Composition: This module includes a high-definition camera (1080p resolution, frame rate ≥ 30 frames per second, CMOS sensor), a high-performance graphics processor (such as NVIDIA GTX1650 or mobile Adreno 650 GPU), an augmented reality display device (optional, such as a tablet or AR glasses, resolution 1920×1080), and a Wi-Fi module (supporting 802.11ac, rate ≥ 433Mbps).

[0210] Hardware Description: The high-definition camera is installed on the user's handheld device (such as a mobile phone or tablet, which can also be other mobile devices), that is, the above-mentioned terminal, and is used to collect real-time images of the air compressor appearance. The field of view angle ≥ 90°, meeting the requirements of complex viewing angles. The high-performance graphics processor runs a three-dimensional reconstruction algorithm based on quantum random walk and octree structure, combines the information of the multi-source data fusion module, and generates a high-resolution AR image with nanoscale accuracy, and the rendering delay ≤ 50ms. The augmented reality display device superimposes the internal structure imaging and the fault area annotation (color marking + text description) for display, and supports real-time interactive adjustment of the viewing angle. The Wi-Fi module ensures high-speed communication with the system core control unit, with a data transmission rate of 10Gbps, ensuring the real-time synchronization of AR imaging and multi-source data.

[0211] 3. Multi-Source Data Fusion Module

[0212] Hardware Composition: This module includes a traditional sensor group (piezoelectric pressure sensor, range 0 - 2MPa, accuracy ±0.5%; thermocouple temperature sensor, range -50°C to 150°C, accuracy ±0.5°C; vibration acceleration sensor, range ±50g, sensitivity 100mV / g), a nanomaterial sensor group (graphene quantum dot pressure sensor, sensitivity 0.01Pa, response time < 1ms; carbon nanotube temperature sensor, range -50°C to 150°C, accuracy ±0.1°C), a data acquisition card (16 channels, sampling rate 1kHz, 16-bit ADC), a dedicated preamplifier (gain range 10 - 100 times, bandwidth 10Hz - 1kHz), an industrial Ethernet switch (8 ports, rate 1Gbps), and a USB3.0 interface module (rate 5Gbps).

[0213] Hardware Description: The traditional sensor group is installed at key parts of the air compressor (such as intake and exhaust pipes, motor housing, base) through threading, flange, welding or magnetic attraction methods to collect conventional air pressure, temperature and vibration frequency data. After being connected to the data acquisition card through the RS485 serial port, it is transmitted through the industrial Ethernet. The nanomaterial sensor group is integrated into the microscopic stress concentration area by using special adhesives or chemical vapor deposition processes to capture information on pressure, temperature, vibration frequency, and changes in the microscopic structure and atomic migration at the molecular level. After the signal is amplified by a dedicated preamplifier connected through a customized nanocable, it is transmitted through the USB3.0 interface. The data acquisition card and the local server cooperate to run a fusion algorithm based on tensor decomposition and generative adversarial networks to generate a comprehensive data vector in the format of a 128-dimensional tensor, with a transmission rate of 10 Gbps, supporting dynamic weight allocation and data error correction and completion functions.

[0214] 4. Intelligent Diagnosis and Decision-making Module

[0215] Hardware Composition: This module includes a high-performance server (equipped with Intel Xeon Gold 6226R, 16 cores and 32 threads, frequency 2.9 GHz; 64 GB DDR4 memory), a GPU acceleration card (such as NVIDIA RTX 3090, 24 GB GDDR6X video memory), a solid-state drive (capacity 2 TB, read and write speed ≥ 3000 MB / s), and a high-speed quantum data interface (bandwidth ≥ 20 Gbps).

[0216] Hardware Description: The high-performance server runs deep learning algorithms and a fault mode matching algorithm assisted by quantum computing to analyze the comprehensive data vector provided by the multi-source data fusion module, and match it with a common fault mode library (based on deep transfer learning) and a unique fault mode library (based on quantum convolutional autoencoders). The processing speed can reach 100 diagnoses per second. The GPU acceleration card supports the combined algorithm of quantum Monte Carlo tree search and quantum evidence theory to quickly search and determine the fault area, with a calculation delay < 10 ms. The solid-state drive stores the fault mode library and historical data to ensure fast reading (delay < 1 ms). The high-speed quantum data interface receives the parallel processing results of the quantum computing acceleration module and feeds them back to the original module, supporting real-time diagnosis and the generation of repair plans.

[0217] 5. Fault Prediction and Prevention Module

[0218] Hardware Composition: This module includes a database server (equipped with AMD EPYC 7302P, 16 cores and 32 threads, frequency 3.0 GHz; 128 GB memory), an environmental sensor group (temperature and humidity sensor, range -20°C to 60°C, humidity 10% - 90%, accuracy ±2%; air quality sensor, PM2.5 detection range 0 - 500 μg / m 3 )), a data acquisition card (8 channels, sampling rate 500 Hz), and an Ethernet interface module (rate 1 Gbps).

[0219] Hardware description: The database server runs a relational database (such as MySQL or Oracle), aggregates historical fault data through the SQL query interface, and the storage capacity supports at least 5 years of data records. The environmental sensor group is installed by wall mounting or ceiling mounting, collects temperature, humidity and air quality data, and is transmitted through Ethernet after being converted by a data acquisition card connected via RS485 serial port or I 2 C bus. The server combines the real-time operation parameters of the multi-source data fusion module and the nano-sensor data, runs a long short-term memory network and reinforcement learning algorithm driven by quantum entanglement, predicts the fault time and type, generates prevention suggestions, the prediction calculation time < 30 seconds, and supports dynamic model adjustment.

[0220] 6. User feedback learning module

[0221] Hardware composition: This module includes an embedded controller, a solid-state memory (capacity 256GB, read / write speed ≥ 500MB / s), and a Wi-Fi module (supporting 802.11ac).

[0222] Hardware description: The embedded controller receives user feedback (such as "repair completed successfully") through the APP or web interface, runs an algorithm that combines quantum game learning and incremental support vector machine, updates the fault mode library and diagnostic model, and the processing speed reaches 5 optimizations per second. The solid-state memory stores feedback data and optimization results, supporting fast read / write and long-term storage. The Wi-Fi module transmits the optimized algorithm parameters to the intelligent diagnostic decision-making module, and the transmission delay < 20ms to ensure system adaptability.

[0223] 7. Device linkage coordination module

[0224] Hardware composition: This module includes an industrial control computer, a quantum communication module (supporting quantum key distribution and quantum teleportation, rate ≥ 10Gbps), and an Ethernet switch (16 ports, rate 1Gbps).

[0225] Hardware description: The industrial control computer runs a device linkage control algorithm based on quantum genetic programming and fuzzy cognitive map, coordinates the operating states of multiple air compressors according to the instructions of the intelligent diagnostic decision-making module, and the operation time < 50ms. The quantum communication module ensures communication security through quantum key distribution, uses quantum teleportation to achieve instant transmission of instructions (delay < 1ms), and the feedback data is transmitted back through the quantum communication link. The switch supports multi-device connection to ensure linkage real-time performance.

[0226] 8. System core regulation unit

[0227] Hardware Composition: This module includes a central control server, an internal communication bus, a distributed storage system (with a capacity of 10TB and a RAID5 architecture), and an Ethernet interface (dual-port 10Gbps).

[0228] Hardware Description: The central control server coordinates the operation of each module, integrates diagnostic results, repair plans, prediction information, and linkage status, runs algorithms based on quantum homomorphic encryption and digital signatures to generate PDF or HTML reports, and has a processing speed of up to 10 reports per second. The internal communication bus ensures high-speed communication between modules with a timestamp synchronization error < 1ms. The distributed storage system stores report data and historical records and uses SHA-256 hashing to verify integrity. The Ethernet interface supports user-side output and cloud backup.

[0229] 9. Bioelectric Signal Interaction Module

[0230] Hardware Composition: This module includes a head-mounted EEG acquisition device (16-channel dry electrode array, made of conductive polymer material, sampling frequency 256Hz), a built-in preamplifier (gain 50 times, bandwidth 0.5 - 100Hz), a Bluetooth 5.0 module, and a signal processing unit (STM32F4 microcontroller, 168MHz).

[0231] Hardware Description: The head-mounted device collects the operator's EEG signals through dry electrodes with a sensitivity of 1μV. After being amplified and filtered by the built-in preamplifier, the signals are transmitted to the signal processing unit via Bluetooth 5.0, with a transmission distance ≤ 10 meters. The signal processing unit runs an algorithm that combines a gated recurrent unit based on the attention mechanism and a capsule network to analyze intentions and generate instruction vectors, with a processing delay < 100ms. The data is encrypted using 256-bit quantum key distribution and is only accessible to authorized users.

[0232] 10. Quantum Computing Acceleration Module

[0233] Hardware Composition: This module includes a quantum processor, a classical auxiliary processor, a high-speed quantum data interface, and a cryogenic control system.

[0234] Hardware Description: The quantum processor runs quantum immune cloning and quantum differential evolution algorithms to accelerate the feature extraction of multi-source data and the matching of fault modes. Its computing speed is about 10 times faster than that of a classical GPU and requires a cryogenic control system to maintain stable operation. The classical auxiliary processor switches to GPU parallel computing (supporting CUDA acceleration) when the quantum hardware is unavailable. The high-speed interface feeds back the results to the original module with a delay < 5ms to ensure real-time performance.

[0235] It should be noted that the basis for the AR visualization module to generate the internal structure image of the air compressor is the comprehensive data provided by the multi-source data fusion module and the appearance image collected by the user's handheld device camera. The system clearly collects the following data: the air pressure in the intake and exhaust pipes, the temperature of the motor housing and the cylinder wall, the vibration frequency of the base and the motor shaft, the motor speed, and the operating current and voltage collected by traditional sensors, which are realized by piezoelectric pressure sensors, thermocouple temperature sensors, vibration acceleration sensors, optical encoders, and current and voltage sensors respectively; the nano-material sensors collect the microscopic pressure changes, tiny temperature fluctuations, and molecular-level structural changes of key components, such as cracks or atomic migration, using graphene quantum dot pressure sensors and carbon nanotube temperature sensors; the environmental sensors collect the temperature, humidity, and air dust concentration in the workshop, which are realized by temperature and humidity sensors and PM2.5 sensors; the user inputs a fault description, such as "insufficient air pressure", through the AI intelligent interaction module, which is converted into semantic features. These data are collected once per second, and the nano sensors are collected 256 times per second to ensure real-time performance and accuracy. After being integrated by the multi-source data fusion module into a 128-dimensional vector, they are transmitted to the AR module.

[0236] Since the performance of the terminal camera may be insufficient when collecting image data, the system requires that the camera support a minimum resolution of 1920×1080, a frame rate of 30 frames per second, and a field of view of 90 degrees, and uses a CMOS sensor to ensure clarity. If the performance of the user terminal camera is low, such as a resolution of only 720p, the AR module switches to the degraded mode, enhances the details through the image interpolation algorithm, and generates a simplified version of the image, which can still clearly mark the fault area. To further improve the effect, the system supports dedicated AR devices, such as industrial tablets or AR glasses equipped with 4K cameras, which are connected to the system via Wi-Fi to obtain high-quality appearance images. Low-quality images can also be uploaded to the cloud server and enhanced to 1080p using a generative adversarial network, with a processing time of about 0.2 seconds to ensure the imaging quality. The appearance image is transmitted through high-speed Wi-Fi at a rate of 10 Gbps and is processed synchronously with the sensor data.

[0237] The three-dimensional image construction adopts the quantum random walk and octree structure algorithms. The specific process is as follows: First, the user terminal camera is used to collect the appearance image of the air compressor. Combining the pre-stored three-dimensional model of the air compressor and sensor data, the quantum random walk algorithm is run on the quantum computing acceleration module to quickly generate an initial point cloud containing about 1 million points, covering the internal structure, which takes about 0.05 seconds. If the quantum hardware is not available, the GPU runs the classical random walk algorithm, which takes 0.2 seconds. Then, the octree algorithm refines the point cloud into a grid with a resolution of 1 mm and processes it on the local server, which takes 0.1 seconds. Details are filled according to the sensor data, such as yellow marking for areas with increased temperature and red marking for areas with abnormal vibration. Finally, three-dimensional imaging is rendered in real time through the user device screen, with a frame rate of 30 frames per second and a delay of less than 50 milliseconds. The intelligent diagnosis and decision-making module provides the fault coordinates, such as the position of the intake valve. The AR module marks it with a red frame and text, such as "Intake valve failure", and the positioning error is less than 2 mm. The existing technology supports this implementation: The quantum random walk has been verified feasible in the field of image processing. The octree algorithm is widely used in 3D rendering. AR frameworks such as ARCore support real-time imaging on the terminal. There are already laboratory results for nano-sensors based on graphene technology. The system uses a degradation mode and GPU backup to cope with the limitations of the terminal camera performance or quantum hardware, ensuring that the generated high-resolution imaging intuitively displays the fault characteristics and meets the industrial maintenance requirements.

[0238] The deep learning algorithm of the intelligent diagnosis and decision-making module is used to match the fault patterns and generate repair plans. The following supplements the specific training and abnormal data processing details. The training data comes from the 5-year operation records of air compressors stored in a relational database, about 1 million records, including air pressure, temperature, vibration, rotation speed, current voltage, microstructural changes, and fault types, such as intake valve blockage; the real-time operation data is provided by the multi-source data fusion module; the environmental data includes temperature, humidity, and dust concentration; the user's fault description is converted into semantic features. The data is labeled with faults and repair plans by professionals, with an accuracy rate of over 95%. New data is automatically labeled by the clustering algorithm and manually reviewed. The training set accounts for 80%, and the validation set and test set each account for 10%. The data preprocessing includes normalizing to the range of 0 to 1, wavelet transform for denoising, extracting time series and semantic features, and generating 128-dimensional vectors.

[0239] The model consists of two parts: The common fault mode library is based on the ResNet-50 network, pre-trained on a general dataset and then fine-tuned. It takes a 128-dimensional vector as input and outputs the fault probability, such as 0.85 for the intake valve fault; The unique fault mode library is based on a quantum convolutional autoencoder, processes microstructure data, runs on a 50-qubit processor, and outputs, such as 0.90 for the crack fault. The training is carried out on a high-performance server and GPU. The quantum module accelerates the extraction of microscopic features. It is trained for 100 rounds, with 800,000 data processed in each round. The target validation accuracy is over 95%. If it is lower than 90%, the learning rate is adjusted. On the test set, the classification accuracy of common faults reaches 98.5%, and that of unique faults is 96.0%. The diagnostic time for a single piece of data is 0.01 seconds, and after quantum acceleration, it is 0.005 seconds. The maintenance plan is mapped from a predefined library, including steps, tools, and accessories, such as "close the valve, replace the AV-200 valve, and a No. 10 wrench is required", and the generation takes 0.1 seconds. The model here refers to the core algorithm framework for fault mode matching and diagnosis.

[0240] To handle abnormal data, such as sensor jumps or missing values, the system detects statistical anomalies. If the data deviates from the mean by 3 times the standard deviation, it is marked as abnormal, such as a sudden pressure change to 0; Time series anomalies are identified through the Isolation Forest algorithm, such as a vibration jump from 30 Hz to 100 Hz; Missing data is confirmed if it has not been updated for 5 consecutive seconds. Abnormal data is complemented by historical mean interpolation, such as filling in the missing pressure with 1.5 MPa, or smoothed for jumps using the Kalman filter. If the proportion of anomalies exceeds 10%, they are removed and the system switches to a backup sensor. Feature selection combines principal component analysis and quantum differential evolution algorithm, retains 90% of the information, and reduces the dimension to 64, improving the classification accuracy by 2%. During classification, ResNet-50 outputs probabilities, and the quantum evidence theory fuses multi-source data to determine the fault, such as a confidence level of 0.95 for the intake valve blockage. For abnormal or conflicting data, the system gives priority to high-weight data, such as vibration and microscopic features, and the quantum Monte Carlo tree search locates the fault source. The maintenance plan is verified for feasibility through simulation, such as taking 25 minutes and having a risk lower than 5%. If it is not applicable, the accessory model is adjusted. The final plan is displayed through the APP, and workers can operate directly. Tests show that the maintenance success rate reaches 95%, meeting the requirements of industrial scenarios.

[0241] The bioelectric signal interaction module analyzes the operator's intention through electroencephalogram signals to optimize human-machine interaction. Its functional connection with the air compressor system is to assist the AI intelligent interaction module in generating control instructions to adjust the operation of the air compressor by analyzing the operator's attention to the operating state or potential instructions.

[0242] The specific description of generating barometric pressure change perception data and implementing control using electroencephalogram (EEG) signals is as follows: EEG signals are weak electrical signals generated by the activities of neurons in the operator's brain, with a voltage range of 5 to 100 microvolts. They are collected 256 times per second by 16 dry electrodes through a head-mounted device, reflecting the focus of attention. For example, when observing a barometer, the generation of a 10-hertz alpha wave indicates "attention to barometric pressure", and the generation of a 20-hertz beta wave when considering adjustment indicates "increase barometric pressure". After the signals are amplified and filtered, they are transmitted via Bluetooth to the signal processing unit, where a gated recurrent unit and capsule network algorithm running an attention mechanism focus on the signal features related to the state of the air compressor. For example, enhanced forehead electrodes indicate attention to barometric pressure, generating a 16-dimensional intention vector, such as "attention to barometric pressure" with a value of 0.9, which takes 0.1 second.

[0243] This vector is transmitted to the AI intelligent interaction module, which combines the user's voice or text input, such as "insufficient barometric pressure", to convert it into semantic features and generate control instructions, such as "check barometric pressure" or "increase to 2 MPa". These instructions are sent to the air compressor controller through the system core regulation unit to adjust operating parameters, such as increasing the motor speed. The implementation process is as follows: After the EEG device collects the signals, the signal processing unit filters out the noise outside the range of 0.5 to 100 hertz, analyzes the key waveforms, and generates an intention vector; the AI module fuses the intention with the fault description to determine the operation requirements, such as barometric pressure adjustment; the core regulation unit issues instructions via industrial Ethernet, and the air compressor executes the adjustment, taking less than 1 second. EEG signals do not directly generate barometric pressure data, but enhance the system's understanding of the operator's needs through intention parsing. For example, after recognizing the attention to "low barometric pressure", it prompts "Do you want to pressurize?" or automatically checks the barometric pressure sensor data. The control instructions are transmitted through encryption and can only be triggered by authorized users to ensure security.

[0244] The connection between this module and the air compressor system lies in improving the interaction efficiency and reducing manual input. For example, when the operator observes the barometer and the EEG signals indicate attention to barometric pressure, the system automatically displays the barometric pressure data or suggests adjustments without the need for voice input. In a factory case, EEG signals assist in identifying the intention of "unstable air supply", combined with sensor data to locate a clogged air filter, and generate a pressurization instruction with a success rate of 90%. Existing technologies support this implementation: 16-channel EEG devices have been applied in industrial human-machine interaction, and the attention mechanism and capsule network algorithms are mature. Bluetooth 5.0 ensures low-latency transmission. The system collaborates with the AI module through the intention vector to clearly convert EEG signals into executable instructions, ensuring clear and practical functions.

[0245] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a reference structure" does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0246] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A fully automatic air compressor system integrating intelligent monitoring and fault diagnosis, characterized in that: It includes a main management system, which includes an AI intelligent interaction module, an AR visualization presentation module, a multi-source data fusion module, an intelligent diagnosis and decision-making module, a fault prediction and prevention module, a user feedback learning module, an equipment linkage coordination module, a system core control unit, a bioelectric signal interaction module and a quantum computing acceleration module; The overall operation steps of the management main system are as follows: Sp1. System initialization and connection establishment: Start the main management system, activate the AI ​​intelligent interaction module, bioelectric signal interaction module and quantum computing acceleration module, initialize the AR visualization presentation module to prepare for device imaging display, start the system core control unit to coordinate the operation of each module, and establish a user-side connection interface to receive user fault feedback and operation instructions; Sp2. Multi-source data collection and preliminary fusion: The AI ​​intelligent interaction module receives the user's description of the air compressor fault and the operation record data. The multi-source data fusion module uses nanomaterial sensors and traditional sensors to collect air compressor operating parameters. The nanomaterial sensors use graphene quantum dot pressure sensors and carbon nanotube temperature sensors to collect the pressure, temperature, vibration frequency of key components and the microstructure changes and atomic migration information at the molecular level. Traditional sensors use piezoelectric pressure sensors, thermocouple temperature sensors and vibration acceleration sensors to collect conventional air pressure, temperature and vibration frequency. The user description data and operating parameters are preliminarily integrated and transmitted to the intelligent diagnosis and decision module. At the same time, the bioelectric signal interaction module uses a head-mounted EEG acquisition device and dry electrodes to collect the operator's EEG signals. After pre-amplification, filtering and other pre-processing, it provides data for subsequent analysis of the operation intention. Sp3, AR imaging real-time display and fault area location: The AR visualization module obtains the appearance image of the air compressor through the high-definition camera of the user's handheld device, combines the information provided by the multi-source data fusion module, uses the three-dimensional reconstruction algorithm based on quantum random walk and octree structure, and uses the computing resources of the quantum computing acceleration module to generate high-resolution AR imaging of the internal structure of the air compressor in real time. The intelligent diagnosis and decision-making module analyzes the data to determine the fault area, and the AR visualization module accurately marks the fault area on the image; Sp4, Intelligent diagnosis and solution generation: The intelligent diagnosis decision module is based on deep learning algorithms and quantum computing-assisted fault pattern matching algorithms. It analyzes the fused multi-source data, matches the common fault pattern library and the unique fault pattern library, determines the fault area through the quantum Monte Carlo tree search and quantum evidence theory joint algorithm, and generates fault diagnosis results and the corresponding unique maintenance plan, which includes maintenance steps, required tools and accessories information; the quantum computing acceleration module uses quantum immune cloning and quantum differential evolution to perform parallel acceleration processing on the complex data feature extraction tasks in the multi-source data fusion module and the complex computing tasks in the intelligent diagnosis decision module. The processed results are fed back to the original data source module through the high-speed quantum data interface; Sp5. Fault prediction and prevention analysis: The fault prediction and prevention module uses an algorithm based on a combination of quantum entanglement-driven long-short-term memory network and reinforcement learning to predict the time and type of future air compressor failures based on historical fault data, current operating parameters and environmental factors, and generates preventive measures in advance, which are transmitted to the system core control unit; Sp6, User Feedback and Learning Optimization: User feedback on diagnostic results and maintenance solutions is collected through the user feedback learning module. The AI ​​intelligent interaction module updates the fault mode library and diagnostic algorithm based on the feedback information, and uses an algorithm based on the fusion of quantum game learning and incremental support vector machine to improve the diagnostic accuracy and solution effectiveness. The optimization results are transmitted to the intelligent diagnosis decision module; Sp7, equipment linkage and collaborative processing: When the fault involves multiple air compressors and is related to the factory production system, the equipment linkage coordination module uses the equipment linkage control algorithm based on quantum genetic programming and fuzzy cognitive graph according to the instructions of the intelligent diagnosis and decision module to coordinate the relevant equipment to adjust the operating status, avoid the expansion of the fault and provide a temporary alternative solution; Sp8, Comprehensive diagnostic report generation and output: The system core control unit integrates diagnostic results, maintenance plans, fault prediction information and equipment linkage status, uses a technology based on quantum homomorphic encryption and quantum digital signature to ensure data security, generates a detailed comprehensive diagnostic report, and outputs it to the user through the user terminal connection interface. At the same time, the report data is stored for subsequent analysis and system optimization; Sp9, Cyclic monitoring and continuous improvement: The multi-source data fusion module continuously collects the operating parameters of the air compressor, the intelligent diagnosis and decision module analyzes the data in real time, the fault prediction and prevention module dynamically adjusts the prediction model, and the system core control unit drives the system to operate cyclically. It uses the adaptive resonance theory based on quantum computing optimization and the algorithm combining quantum particle swarm optimization and quantum simulated annealing to search for the optimal module parameters and model structure adjustment plan, continuously optimize the diagnosis process and improve service quality; Among them, the main management system runs step by step through the above-mentioned operating steps to achieve accurate diagnosis of air compressor failures, efficient maintenance guidance, and intelligent management and optimization of the entire life cycle.

2. The fully automatic air compressor system with integrated intelligent monitoring and fault diagnosis according to claim 1 is characterized in that: In the multi-source data collection and preliminary fusion step of Sp2, the AI ​​intelligent interaction module uses a natural language processing algorithm based on a variational autoencoder to perform lexical, syntactic and semantic analysis on the user's description of the air compressor failure and the operation record data, and converts them into machine-understandable text feature vectors; the multi-source data fusion module uses an algorithm based on a combination of tensor decomposition and generative adversarial networks to extract and fuse features of user description data, nanomaterial sensor data and traditional sensor data, and generates a comprehensive data vector to transmit to the intelligent diagnosis and decision-making module; the bioelectric signal interaction module transmits the preprocessed EEG signal to its signal processing unit via Bluetooth 5.0, and uses a gated recurrent unit based on an attention mechanism and a capsule network fusion algorithm to analyze the operator's attention intention and potential operation instructions for the operating status of the air compressor, and generates an intention instruction vector to transmit to the AI ​​intelligent interaction module and the system core control unit.

3. The fully automatic air compressor system with integrated intelligent monitoring and fault diagnosis according to claim 1 is characterized in that: In the AR imaging real-time display and fault area location step of Sp3, the AR visualization module generates a high-resolution AR image of the internal structure of the air compressor, which fully displays the subtle fault characteristics.

4. The fully automatic air compressor system with integrated intelligent monitoring and fault diagnosis according to claim 1 is characterized in that: In the intelligent diagnosis and solution generation step of Sp4, the fault diagnosis results and the corresponding maintenance solutions are generated through path planning and resource scheduling strategies based on quantum annealing optimization. Quantum computing is used to simulate the feasibility and potential risks of each step in the maintenance process to ensure the efficiency and safety of the maintenance solution. The quantum computing acceleration module accelerates the processing of complex computing tasks in the multi-source data fusion module and the intelligent diagnosis decision module to improve the overall diagnosis efficiency.

5. The fully automatic air compressor system integrating intelligent monitoring and fault diagnosis according to claim 1 is characterized in that: The historical fault data is stored in a relational database and aggregated to the fault prediction and prevention module through an SQL query interface; the current operating parameters are transmitted in real time by a multi-source data fusion module; environmental factor data are collected by environmental sensors, such as temperature and humidity sensors, air quality sensors, etc., and are converted by a data acquisition card and transmitted to the fault prediction and prevention module through a serial port and an Ethernet interface. The monitoring data of the nanomaterial sensor is transmitted to this module via a dedicated data link, providing comprehensive data support for the Sp5 fault prediction and prevention analysis step.

6. The fully automatic air compressor system integrating intelligent monitoring and fault diagnosis according to claim 1 is characterized in that: In the user feedback and learning optimization step of Sp6, the AI ​​intelligent interaction module uses the updated fault mode library and diagnostic algorithm to adjust the way and content of interaction with users, and at the same time uses the reinforcement learning mechanism optimized by the quantum computing acceleration algorithm to perform deep learning to improve the diagnostic accuracy and solution effectiveness.

7. The fully automatic air compressor system integrating intelligent monitoring and fault diagnosis according to claim 1 is characterized in that: In the device linkage and collaborative processing steps of Sp7, the device linkage coordination module ensures the security of communication between devices through quantum key distribution, and uses the principle of quantum teleportation to achieve instantaneous transmission of device control instructions; the device operation status feedback data is transmitted back to the device linkage coordination module and the system core control unit through the quantum communication link to ensure device linkage.

8. The fully automatic air compressor system integrating intelligent monitoring and fault diagnosis according to claim 1, characterized in that: In the step of generating and outputting the comprehensive diagnostic report of Sp8, the system core control unit sends control instructions to each module through the internal control bus according to user feedback and the operation status of each module, and adjusts the system operation strategy to adapt to different fault conditions and user needs.

9. The fully automatic air compressor system integrating intelligent monitoring and fault diagnosis according to claim 1, characterized in that: In the cyclic monitoring and continuous improvement steps of Sp9, the particle swarm optimization algorithm is combined with the quantum search strategy to optimize the control strategy of the system's core control unit. The particle swarm optimization algorithm is related to the idea of ​​the quantum particle swarm optimization algorithm. It draws on its optimization method for the motion state of particles and combines it with the quantum search strategy. The system's core control unit issues new operating parameters and control instructions to continuously optimize the diagnostic process and improve service quality.

10. The fully automatic air compressor system integrating intelligent monitoring and fault diagnosis according to claim 1, characterized in that: The multi-source data fusion module adopts a dynamic weight allocation mechanism when initially integrating the air pressure, temperature, vibration frequency data collected by traditional sensors with user description data. According to the different operating stages and working conditions of the air compressor, the weight of each data source is adjusted in real time. During the startup stage of the air compressor, the weight of the vibration frequency data is increased; during long-term stable operation, the weight of the temperature data is increased. At the same time, the multi-source data fusion module has data error correction and completion functions. When abnormal jumps or missing data of some sensors are detected, the correlation model between historical data and related data is used for error correction and completion. The gated recurrent unit and capsule network fusion algorithm based on the attention mechanism of the bioelectric signal interaction module applies the attention mechanism to the gated recurrent unit to focus on the EEG signals related to the air compressor. The feature parts closely related to the operating status intention are processed and then input into the capsule network. The capsule network further extracts and analyzes the operator's attention intention and potential operation instructions for the operating status of the air compressor through multiple capsule layers using the dynamic routing mechanism between vectors, and generates intention instruction vectors to be transmitted to the AI ​​intelligent interaction module and the system core control unit; the fault prediction and prevention module uses an algorithm based on the combination of long short-term memory network driven by quantum entanglement and reinforcement learning. By simulating the change law of quantum entangled state in time series and integrating it into the long short-term memory network, the network's ability to capture long-term and short-term dependencies in the historical data of air compressor operation is enhanced. At the same time, combined with the reward feedback mechanism of reinforcement learning for the operating status of the equipment, the time and type of future failures of the air compressor are accurately predicted.

Images