Medical equipment maintenance inspection method

Abstract

The invention relates to the technical field of artificial intelligence, and discloses a medical equipment maintenance inspection method, which comprises the following steps: S1, acquiring operation state data, environmental parameters and historical maintenance records of medical equipment in real time through a multi-source heterogeneous sensor network, and constructing equipment full life cycle digital twin bodies; s2, performing abnormal feature extraction on the equipment operation state data based on a deep convolutional neural network, and generating a fault risk probability matrix; a multi-source heterogeneous sensor network and an equipment full-life-cycle digital twinborn are constructed, an operation state, environmental parameters and historical maintenance data are fused, and environmental interference and real equipment signals are separated in combination with a dynamic filtering algorithm; the system can identify and compensate data acquisition deviation in real time, and the accuracy of fault feature extraction is ensured; the key operation nodes are solidified by adopting a block chain evidence storage technology, so that the maintenance process is verifiable and cannot be tampered, diagnosis errors caused by environmental interference are eliminated, and the accuracy and reliability of medical equipment state evaluation are improved.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, specifically to a method for the maintenance and inspection of medical equipment. Background Technology

[0002] Medical equipment refers to instruments, equipment, appliances, materials or other items used alone or in combination on the human body, and also includes the necessary software. Medical equipment is the most basic element of medical, scientific research, teaching, institutional and clinical work, including both professional medical equipment and home medical equipment.

[0003] In the existing medical equipment maintenance and inspection system, although regular inspection and preventive maintenance strategies have been adopted, there are still bottlenecks in the implementation process: when maintenance personnel collect equipment operating status data through multi-source sensors, factors such as electromagnetic interference, mechanical vibration transmission, and temperature and humidity fluctuations in the equipment operating environment will be uncontrollably coupled into the monitoring signal, forming characteristic pollution noise. Such noise will cause baseline drift, current waveform distortion, and thermal imaging temperature field distortion in the collected vibration spectrum. The existing inspection system lacks the ability to identify the degree of pollution in the signal acquisition process in real time.

[0004] Therefore, a method for the maintenance and inspection of medical equipment is proposed to solve the above problems. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a method for the maintenance and inspection of medical equipment, thus solving the problems mentioned in the background section.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution: a medical equipment maintenance and inspection method, comprising the following steps:

[0009] S1. Real-time acquisition of medical equipment's operating status data, environmental parameters, and historical maintenance records through a multi-source heterogeneous sensor network to construct a digital twin of the equipment's entire lifecycle;

[0010] S2. Extract abnormal features from equipment operating status data based on deep convolutional neural networks to generate a fault risk probability matrix;

[0011] S3. Dynamically generate a graded inspection task list based on the fault risk probability matrix and match the optimal maintenance resource path;

[0012] S4. Visualize and display internal fault location information and maintenance operation instructions through augmented reality interactive terminals;

[0013] S5. Use IoT gateways to achieve real-time verification and quality traceability of key actions in the maintenance process;

[0014] S6. Execute the multi-dimensional performance verification protocol to test the safety, accuracy and stability of the repaired equipment;

[0015] S7. Establish a dynamic knowledge graph model and iteratively optimize the fault diagnosis rule base based on maintenance results;

[0016] S8. When a high-risk fault mode is detected, the cross-institutional expert collaborative consultation mechanism is automatically triggered.

[0017] S9. Based on blockchain technology, maintain data throughout the entire process of evidence storage and generate an unalterable audit trail;

[0018] S10. Predict the remaining lifespan of equipment components through an adaptive learning engine and dynamically adjust the preventive maintenance cycle.

[0019] Preferably, step S1 includes:

[0020] S11. Deploy triaxial vibration sensors, infrared thermal imagers, and high-frequency current clamps at key nodes of the equipment to synchronously collect mechanical vibration spectra, temperature field distribution, and current fingerprint characteristics at a sampling rate of 200Hz.

[0021] S12. Integrate the operation logs and alarm event streams of the equipment control system through the OPC-UA protocol;

[0022] S13. Construct a spatiotemporal correlation database, integrating real-time monitoring data with historical maintenance work orders, spare parts replacement records, and calibration reports.

[0023] Preferably, step S2 includes:

[0024] S21. Multi-scale convolution kernels are used to extract the time-frequency domain features of vibration signals, and the energy of abnormal frequency bands is weighted through an attention mechanism.

[0025] S22. Establish a thermal gradient change model to identify local overheating areas that exceed the safety threshold;

[0026] S23. Decompose the current waveform into fundamental and harmonic components, and quantify the power supply stability index based on the energy entropy algorithm.

[0027] S24. Output a four-dimensional probability matrix containing fault codes, risk levels, and predicted failure times, where the fault risk probability is calculated using the following formula:

[0028] P risk =w v ·F v +w t ·ΔT max +wc ·I THD ;

[0029] Where w v F is the weighting coefficient for vibration characteristics. v For vibration characteristic values, w t ΔT is the weighting factor for temperature deviation. max The maximum temperature deviation value, w c I is the weighting factor for current harmonics. THD It is the index of total harmonic distortion of current.

[0030] Preferably, step S3 includes:

[0031] S31. Inspection tasks are classified into three categories based on risk level: red emergency, yellow warning, and blue observation.

[0032] S32. Optimize maintenance routes based on genetic algorithms, taking into account geographical location, skill qualifications, and spare parts inventory constraints.

[0033] S33. Automatically assign a dual-person review mechanism and emergency response plan for high-risk tasks.

[0034] Preferably, the implementation of the augmented reality interactive terminal in step S4 includes:

[0035] S41. Construct a three-dimensional spatial coordinate system inside the equipment using SLAM technology;

[0036] S42. Overlay and display the disassembly sequence animation and torque parameter prompts on the surface of the faulty component;

[0037] S43. A gesture recognition engine is used to capture the operation trajectory of maintenance personnel in real time and compare it with the standard operating procedure.

[0038] Preferably, step S5 includes:

[0039] S51. Install pressure sensors and gyroscopes on maintenance tools to monitor the tightening angle and force curve in real time;

[0040] S52. Verify the model compatibility and certification information of the replacement parts through the near-field communication chip;

[0041] S53. Generate an encrypted quality packet containing an operation timestamp, biometric signature, and ambient temperature and humidity.

[0042] Preferably, the multidimensional performance verification protocol in step S6 includes:

[0043] S61. Use the phantom module to perform accuracy calibration test and verify the spatial resolution deviation of the imaging equipment.

[0044] S62. Inject simulated physiological signals to test the response delay and waveform fidelity of life support devices;

[0045] S63. The output stability margin of the treatment equipment is tested using a load pressure tester.

[0046] Preferably, the cross-institutional expert collaborative consultation mechanism in step S8 includes:

[0047] S81. Establish a fault characteristic sharing alliance chain to synchronize abnormal data of high-risk equipment in real time;

[0048] S82. Initiate a consultation request while protecting device privacy information through zero-knowledge proof technology;

[0049] S83, an augmented reality annotation system integrating a multi-party video conferencing platform enables remote guidance.

[0050] Preferably, step S9 includes:

[0051] S91. Generate a Merkle tree structure from key data of the maintenance process and write it into a distributed ledger. The leaf nodes of the Merkle tree contain tool sensor readings, AR operation trajectory check codes and biometric hash values. A fast data traceability is achieved through a lightweight node verification mechanism.

[0052] S92. Create a timestamp certificate and digital fingerprint for each maintenance record, and use an elliptic curve-based zero-knowledge proof algorithm to generate verifiable claims to ensure that the auditor can verify the integrity of the record without obtaining the original data.

[0053] S93. Data verification requests to the regulatory agency's audit interface are automatically triggered through smart contracts. When an anomaly is detected in the maintenance records of high-risk equipment, the smart contract is synchronized across chains to the medical device regulatory consortium chain and the real-time video audit channel is activated.

[0054] Preferably, step S10 includes:

[0055] S101. Calculate the wear coefficient of key components based on the Weibull accelerated life model, where the remaining life is predicted using the following formula:

[0056]

[0057] Where L0 is the initial design life, S is the actual stress level, S0 is the reference stress level, and β is the shape parameter;

[0058] S102. Dynamically correct the remaining life prediction curve by combining real-time operating intensity parameters;

[0059] S103. When the predicted lifespan is lower than the safety threshold, automatically generate a preventive maintenance work order and lock the spare parts inventory.

[0060] S104. Iteratively update and maintain the strategy parameter library using the Bayesian optimization algorithm.

[0061] (III) Beneficial Effects

[0062] Compared with the prior art, the present invention provides a method for maintenance and inspection of medical equipment, which has the following beneficial effects:

[0063] 1. In this invention, during predictive maintenance and inspection of medical equipment, a multi-source heterogeneous sensor network and a digital twin of the equipment's entire lifecycle are constructed to fuse operating status, environmental parameters, and historical maintenance data in real time. A dynamic filtering algorithm is used to separate environmental interference from actual equipment signals. When electromagnetic interference and mechanical vibration noise occur during equipment operation monitoring, the system can identify and compensate for data acquisition deviations in real time, ensuring the accuracy of fault feature extraction. Blockchain-based evidence storage technology is used to solidify key operational nodes, making the maintenance process verifiable and tamper-proof, eliminating diagnostic errors caused by environmental interference, and improving the accuracy and reliability of medical equipment status assessment.

[0064] 2. In this invention, when scheduling medical equipment maintenance tasks, a graded inspection list is dynamically generated based on the fault risk probability matrix output by a deep convolutional neural network, and the maintenance path and resource matching strategy are optimized by combining a genetic algorithm. When equipment experiences different levels of fault risk, the system can identify the risk characteristics and maintenance resource suitability in real time, and automatically assign a dual-person review mechanism and emergency response plan to high-risk tasks, thereby avoiding response delays and resource misconfiguration caused by traditional manual task assignment. The preventive maintenance cycle is dynamically adjusted through the Westerbower life prediction model, so that the maintenance strategy keeps pace with the actual equipment degradation state, ensuring early prevention of high-risk faults and maximizing resource utilization efficiency.

[0065] 3. In this invention, during the verification of medical equipment maintenance operations, the internal fault location information and maintenance operation instructions of the equipment are visualized through an augmented reality interactive terminal. IoT tools are used to capture the movement trajectories of maintenance personnel and compare them with standard operating procedures. When performing critical operations such as torque calibration and component replacement, the system can verify the compliance of operating parameters in real time and generate encrypted quality packages to ensure the standardization and traceability of maintenance actions. Based on a multi-dimensional performance verification protocol, the system simulates clinical use scenarios to test equipment performance and iteratively optimizes diagnostic rules using a dynamic knowledge graph. This ensures that the maintenance quality assessment results truly reflect clinical usage needs, eliminate the risk of equipment rework, and extend the safe operation cycle. Attached Figure Description

[0066] Figure 1 This is a flowchart of the medical equipment maintenance and inspection method of the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] Specific embodiment: Medical equipment maintenance and inspection method, including the following steps:

[0069] S1. Real-time acquisition of medical equipment's operating status data, environmental parameters, and historical maintenance records through a multi-source heterogeneous sensor network to construct a digital twin of the equipment's entire lifecycle;

[0070] S2. Extract abnormal features from equipment operating status data based on deep convolutional neural networks to generate a fault risk probability matrix;

[0071] S3. Dynamically generate a graded inspection task list based on the fault risk probability matrix and match the optimal maintenance resource path;

[0072] S4. Visualize and display internal fault location information and maintenance operation instructions through augmented reality interactive terminals;

[0073] S5. Use IoT gateways to achieve real-time verification and quality traceability of key actions in the maintenance process;

[0074] S6. Execute the multi-dimensional performance verification protocol to test the safety, accuracy and stability of the repaired equipment;

[0075] S7. Establish a dynamic knowledge graph model and iteratively optimize the fault diagnosis rule base based on maintenance results;

[0076] S8. When a high-risk fault mode is detected, the cross-institutional expert collaborative consultation mechanism is automatically triggered.

[0077] S9. Based on blockchain technology, maintain data throughout the entire process of evidence storage and generate an unalterable audit trail;

[0078] S10. Predict the remaining lifespan of equipment components through an adaptive learning engine and dynamically adjust the preventive maintenance cycle.

[0079] Step S1 includes:

[0080] S11. Deploy triaxial vibration sensors, infrared thermal imagers, and high-frequency current clamps at key nodes of the equipment to synchronously collect mechanical vibration spectra, temperature field distribution, and current fingerprint characteristics at a sampling rate of 200Hz.

[0081] S12. Integrate the operation logs and alarm event streams of the equipment control system through the OPC-UA protocol;

[0082] S13. Construct a spatiotemporal correlation database, integrating real-time monitoring data with historical maintenance work orders, spare parts replacement records, and calibration reports.

[0083] Step S2 includes:

[0084] S21. Multi-scale convolution kernels are used to extract the time-frequency domain features of vibration signals, and the energy of abnormal frequency bands is weighted through an attention mechanism.

[0085] S22. Establish a thermal gradient change model to identify local overheating areas that exceed the safety threshold;

[0086] S23. Decompose the current waveform into fundamental and harmonic components, and quantify the power supply stability index based on the energy entropy algorithm:

[0087]

[0088] Where H e Let P(f) be the entropy value of the current energy. i () represents the frequency f i The power percentage of the component;

[0089] S24. Output a four-dimensional probability matrix containing fault codes, risk levels, and predicted failure times, where the fault risk probability is calculated using the following formula:

[0090] P risk =w v ·F v +w t ·ΔT max +w c ·I THD ;

[0091] Where w v F is the weighting coefficient for vibration characteristics. v For vibration characteristic values, w t ΔT is the weighting factor for temperature deviation. max The maximum temperature deviation value, w c I is the weighting factor for current harmonics. THD It is the index of total harmonic distortion of current.

[0092] Step S3 includes:

[0093] S31. Inspection tasks are classified into three categories based on risk level: red emergency, yellow warning, and blue observation.

[0094] S32. Optimize maintenance routes based on genetic algorithms, taking into account geographical location, skill qualifications, and spare parts inventory constraints.

[0095] The fitness function is defined as:

[0096]

[0097] Where F cost For path-wide fitness, T travel To estimate the trip time, R match S represents skill matching degree, ranging from 0 to 1. risk The task risk is weighted by α, β, and γ, which are dynamic weight coefficients that correspond to the optimization priorities of time, skills, and risk, respectively, and α+β+γ=1;

[0098] S33. Automatically assign a dual-person review mechanism and emergency response plan for high-risk tasks.

[0099] The implementation of the augmented reality interactive terminal in step S4 includes:

[0100] S41. Construct a three-dimensional spatial coordinate system inside the equipment using SLAM technology;

[0101] S42. Overlay and display the disassembly sequence animation and torque parameter prompts on the surface of the faulty component;

[0102] S43. A gesture recognition engine is used to capture the operation trajectory of maintenance personnel in real time and compare it with the standard operating procedure. The similarity calculation is as follows:

[0103]

[0104] in The real-time gesture space coordinate vector. Let be the standard motion space coordinate vector, T be the total operation time step, λ be the time decay factor, and Δt be the deviation between real-time operation and standard time.

[0105] Step S5 includes:

[0106] S51. Install pressure sensors and gyroscopes on maintenance tools to monitor the tightening angle and force curve in real time;

[0107] S52. Verify the model compatibility and certification information of the replacement parts through the near-field communication chip;

[0108] S53. Generate an encrypted quality packet containing an operation timestamp, biometric signature, and ambient temperature and humidity.

[0109] The multidimensional performance verification protocol in step S6 includes:

[0110] S61. Use the phantom module to perform accuracy calibration test and verify the spatial resolution deviation of the imaging equipment.

[0111] S62. Inject simulated physiological signals to test the response delay and waveform fidelity of life support devices;

[0112] S63. Verify the output stability margin of the treatment equipment using a load pressure tester:

[0113]

[0114] Where P max P is the maximum safe load of the equipment. oper σ represents the actual operating power. noise This represents the standard deviation of power noise.

[0115] The cross-institutional expert collaborative consultation mechanism in step S8 includes:

[0116] S81. Establish a fault characteristic sharing alliance chain to synchronize abnormal data of high-risk equipment in real time;

[0117] S82. Initiate a consultation request while protecting device privacy information through zero-knowledge proof technology;

[0118] S83, an augmented reality annotation system integrating a multi-party video conferencing platform enables remote guidance.

[0119] Step S9 includes:

[0120] S91. Generate a Merkle tree structure from key data of the maintenance process and write it into a distributed ledger. The leaf nodes of the Merkle tree contain tool sensor readings, AR operation trajectory check codes and biometric hash values. A fast data traceability is achieved through a lightweight node verification mechanism.

[0121] S92. Create a timestamp certificate and digital fingerprint for each maintenance record, and use an elliptic curve-based zero-knowledge proof algorithm to generate verifiable claims to ensure that the auditor can verify the integrity of the record without obtaining the original data.

[0122] S93. Data verification requests to the regulatory agency's audit interface are automatically triggered through smart contracts. When an anomaly is detected in the maintenance records of high-risk equipment, the smart contract is synchronized across chains to the medical device regulatory consortium chain and the real-time video audit channel is activated.

[0123] Step S10 includes:

[0124] S101. Calculate the wear coefficient of key components based on the Weibull accelerated life model, where the remaining life is predicted using the following formula:

[0125]

[0126] Where L0 is the initial design life, S is the actual stress level, S0 is the reference stress level, and β is the shape parameter;

[0127] S102. Dynamically correct the remaining life prediction curve by combining real-time operating intensity parameters;

[0128] S103. When the predicted lifespan is lower than the safety threshold, automatically generate a preventive maintenance work order and lock the spare parts inventory.

[0129] S104. Iteratively update and maintain the strategy parameter library using the Bayesian optimization algorithm:

[0130]

[0131] Where P(θ|D) is the confidence level of parameter θ given data D, P(D|θ) is the probability of data D occurring under parameter θ, and P(θ) is the initial probability hypothesis of θ.

[0132] The steps of this method are as follows;

[0133] Step 1: Multi-source sensing and digital twin construction

[0134] By integrating a heterogeneous sensor network consisting of a triaxial vibration sensor, an infrared thermal imager, and a high-frequency current clamp, the system acquires real-time data on the mechanical vibration spectrum, temperature field distribution, and current waveform characteristics of medical equipment at a sampling rate of 200Hz. It simultaneously merges equipment control system logs and historical maintenance records to construct a digital twin covering the entire lifecycle. When environmental electromagnetic interference and mechanically conducted noise contaminate the original signal, the system automatically triggers a spatiotemporal correlation filtering algorithm to remove spurious spectral peaks and temperature drift data, generating a high-confidence baseline of the equipment's operating status.

[0135] Step 2: Intelligent Diagnosis and Risk Classification

[0136] Multimodal sensor data is analyzed using deep convolutional neural networks: First, multi-scale convolutional kernels are used to extract time-frequency domain features of vibration signals, and an attention mechanism is used to focus on energy in abnormal frequency bands. Second, a thermal gradient change model is established to identify the deviation between local overheating areas and safety thresholds. Finally, the current waveform is decomposed into fundamental and harmonic components to quantify the power supply stability degradation trend. The above features are fused to generate a four-dimensional fault risk probability matrix, labeling fault codes, risk levels (red, yellow, blue), and predicted failure times, providing a quantitative basis for subsequent decision-making.

[0137] Step 3: Dynamic Scheduling and Resource Optimization

[0138] Based on the fault risk matrix, a tiered inspection task list is dynamically generated, and maintenance resource paths are optimized using a genetic algorithm. The optimal dispatch plan is calculated by comprehensively considering the engineer's geographical location, skill and qualification matching, and spare parts inventory constraints. When a high-risk (red) task is identified, the system forcibly activates a dual-person review mechanism and emergency response plan, while simultaneously pushing internal fault location information to the equipment via augmented reality terminals. After maintenance personnel arrive on-site, the AR interactive system constructs a three-dimensional spatial coordinate system for the equipment in real time, overlaying disassembly animations and torque parameter guidance onto the surface of the faulty component.

[0139] Step 4: Human-Machine Collaboration and Process Verification

[0140] During maintenance, IoT tools monitor the tightening angle and force curve in real time, capturing the operation trajectory through a gesture recognition engine and comparing it with standard operating procedures. When an operational deviation is detected, the AR system immediately triggers a correction prompt. After maintenance is completed, a prosthetic module is used to perform accuracy calibration tests, simulated physiological signal injection tests, and load stress tests to verify whether the equipment performance meets the requirements of clinical use scenarios.

[0141] Step 5: Knowledge Evolution and Strategy Iteration

[0142] Key maintenance data is used to generate an immutable audit trail via blockchain. A dynamic knowledge graph model is built based on historical maintenance data to identify implicit correlations between failure modes and maintenance strategies, iteratively optimizing the diagnostic rule base. Simultaneously, by combining real-time equipment operating intensity parameters, an adaptive learning engine predicts the remaining lifespan of key components and dynamically adjusts preventative maintenance cycles. When high-risk failure modes occur across organizations, the system automatically triggers an expert collaborative consultation mechanism based on zero-knowledge proofs, achieving closed-loop sharing of global maintenance experience.

[0143] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0144] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for maintenance and inspection of medical equipment, characterized in that: Includes the following steps: S1. Real-time acquisition of medical equipment's operating status data, environmental parameters, and historical maintenance records through a multi-source heterogeneous sensor network to construct a digital twin of the equipment's entire lifecycle; S2. Extract abnormal features from equipment operating status data based on deep convolutional neural networks to generate a fault risk probability matrix; S3. Dynamically generate a graded inspection task list based on the fault risk probability matrix and match the optimal maintenance resource path; S4. Visualize and display internal fault location information and maintenance operation instructions through augmented reality interactive terminals; S5. Use IoT gateways to achieve real-time verification and quality traceability of key actions in the maintenance process; S6. Execute the multi-dimensional performance verification protocol to test the safety, accuracy and stability of the repaired equipment; S7. Establish a dynamic knowledge graph model and iteratively optimize the fault diagnosis rule base based on maintenance results; S8. When a high-risk fault mode is detected, the cross-institutional expert collaborative consultation mechanism is automatically triggered. S9. Based on blockchain technology, maintain data throughout the entire process of evidence storage and generate an unalterable audit trail; S10. Predict the remaining lifespan of equipment components through an adaptive learning engine and dynamically adjust the preventive maintenance cycle.

2. The medical equipment maintenance and inspection method according to claim 1, characterized in that: Step S1 includes: S11. Deploy triaxial vibration sensors, infrared thermal imagers, and high-frequency current clamps at key nodes of the equipment to synchronously collect mechanical vibration spectra, temperature field distribution, and current fingerprint characteristics at a sampling rate of 200Hz. S12. Integrate the operation logs and alarm event streams of the equipment control system through the OPC-UA protocol; S13. Construct a spatiotemporal correlation database, integrating real-time monitoring data with historical maintenance work orders, spare parts replacement records, and calibration reports.

3. The medical equipment maintenance and inspection method according to claim 1, characterized in that: Step S2 includes: S21. Multi-scale convolution kernels are used to extract the time-frequency domain features of vibration signals, and the energy of abnormal frequency bands is weighted through an attention mechanism. S22. Establish a thermal gradient change model to identify local overheating areas that exceed the safety threshold; S23. Decompose the current waveform into fundamental and harmonic components, and quantify the power supply stability index based on the energy entropy algorithm. S24. Output a four-dimensional probability matrix containing fault codes, risk levels, and predicted failure times, where the fault risk probability is calculated using the following formula: P risk =w v ·F v +w t ·ΔT max +w c ·I THD ; Where w v F is the weighting coefficient for vibration characteristics. v For vibration characteristic values, w t ΔT is the weighting factor for temperature deviation. max The maximum temperature deviation value, w c I is the weighting factor for current harmonics. THD It is the index of total harmonic distortion of current.

4. The medical equipment maintenance and inspection method according to claim 1, characterized in that: Step S3 includes: S31. Inspection tasks are classified into three categories based on risk level: red emergency, yellow warning, and blue observation. S32. Optimize maintenance routes based on genetic algorithms, taking into account geographical location, skill qualifications, and spare parts inventory constraints. S33. Automatically assign a dual-person review mechanism and emergency response plan for high-risk tasks.

5. The medical equipment maintenance and inspection method according to claim 1, characterized in that: The implementation of the augmented reality interactive terminal in step S4 includes: S41. Construct a three-dimensional spatial coordinate system inside the equipment using SLAM technology; S42. Overlay and display the disassembly sequence animation and torque parameter prompts on the surface of the faulty component; S43. A gesture recognition engine is used to capture the operation trajectory of maintenance personnel in real time and compare it with the standard operating procedure.

6. The medical equipment maintenance and inspection method according to claim 1, characterized in that: Step S5 includes: S51. Install pressure sensors and gyroscopes on maintenance tools to monitor the tightening angle and force curve in real time; S52. Verify the model compatibility and certification information of the replacement parts through the near-field communication chip; S53. Generate an encrypted quality packet containing an operation timestamp, biometric signature, and ambient temperature and humidity.

7. The medical equipment maintenance and inspection method according to claim 1, characterized in that: The multi-dimensional performance verification protocol in step S6 includes: S61. Use the phantom module to perform accuracy calibration test and verify the spatial resolution deviation of the imaging equipment. S62. Inject simulated physiological signals to test the response delay and waveform fidelity of life support devices; S63. The output stability margin of the treatment equipment is tested using a load pressure tester.

8. The medical equipment maintenance and inspection method according to claim 1, characterized in that: The cross-institutional expert collaborative consultation mechanism in step S8 includes: S81. Establish a fault characteristic sharing alliance chain to synchronize abnormal data of high-risk equipment in real time; S82. Initiate a consultation request while protecting device privacy information through zero-knowledge proof technology; S83, an augmented reality annotation system integrating a multi-party video conferencing platform enables remote guidance.

9. The medical equipment maintenance and inspection method according to claim 1, characterized in that: Step S9 includes: S91. Generate a Merkle tree structure from key data of the maintenance process and write it into a distributed ledger. The leaf nodes of the Merkle tree contain tool sensor readings, AR operation trajectory check codes and biometric hash values. A fast data traceability is achieved through a lightweight node verification mechanism. S92. Create a timestamp certificate and digital fingerprint for each maintenance record, and use an elliptic curve-based zero-knowledge proof algorithm to generate verifiable claims to ensure that the auditor can verify the integrity of the record without obtaining the original data. S93. Data verification requests to the regulatory agency's audit interface are automatically triggered through smart contracts. When an anomaly is detected in the maintenance records of high-risk equipment, the smart contract is synchronized across chains to the medical device regulatory consortium chain and the real-time video audit channel is activated.

10. The medical equipment maintenance and inspection method according to claim 1, characterized in that: Step S10 includes: S101. Calculate the wear coefficient of key components based on the Weibull accelerated life model, where the remaining life is predicted using the following formula: Where L0 is the initial design life, S is the actual stress level, S0 is the reference stress level, and β is the shape parameter; S102. Dynamically correct the remaining life prediction curve by combining real-time operating intensity parameters; S103. When the predicted lifespan is lower than the safety threshold, automatically generate a preventive maintenance work order and lock the spare parts inventory. S104. Iteratively update and maintain the strategy parameter library using the Bayesian optimization algorithm.

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