A medical equipment intelligent inspection and state early warning system

Abstract

The application discloses a kind of medical equipment intelligent inspection and state early warning system, it is related to medical informatization technical field.The system includes multidimensional signal sensing unit, edge computing processing unit, digital twin mapping unit, dynamic baseline evolution unit, space-time correlation early warning engine, intelligent inspection path scheduling unit and closed-loop execution evaluation unit;System extracts features by sensing multi-source signals, combines digital twin model with dynamic baseline automatically corrected with working conditions, uses neural network to realize fault trend prediction and graded early warning, and optimizes inspection scheduling path according to early warning signal.The application solves the problems of existing inspection artificial dependence, monitoring lag and other problems, realizes the fine monitoring and preventive maintenance of the whole life cycle of medical equipment, effectively reduces the probability of equipment unplanned downtime, and guarantees the continuity and safety of medical business.

Description

Technical Field

[0001] This invention relates to the field of medical information technology, and more specifically, to an intelligent inspection and status early warning system for medical equipment. Background Technology

[0002] In the construction of modern smart hospitals, efficient operation and maintenance of medical equipment assets and full life-cycle security assurance have become key supports for ensuring medical quality and patient safety. As the physical carriers of medical services, medical equipment is becoming increasingly sophisticated and valuable, encompassing multiple core aspects such as diagnosis, treatment, emergency care, and monitoring. To maintain the continuity of medical processes and reduce operational risks, medical institutions urgently need unified and efficient supervision of large-scale equipment clusters distributed across different departments, making the field of equipment operation and maintenance management increasingly important.

[0003] Among these, intelligent inspection and status early warning of medical equipment, as a fundamental component of the equipment operation and maintenance system, aims to achieve real-time dynamic tracking of equipment operating parameters through digital means. The core of this technology lies in utilizing the IoT sensing layer to acquire key indicators such as equipment operating power consumption, environmental parameters, and workload, and then aggregating this status information to a monitoring platform in real time via a data transmission network. Its basic goal is to build a transparent view of equipment asset operation, thereby replacing traditional manual inspection methods, providing hospitals with a scientific basis for operation and maintenance decisions, and ensuring the smooth operation of medical work.

[0004] However, existing technologies still face significant challenges in practical applications. Traditional inspection models rely excessively on regular manual checks and post-fault reporting, leading to high labor costs and severe time lags in monitoring feedback, leaving maintenance response mechanisms in a reactive state for extended periods. Most existing monitoring systems are limited to simple fixed-threshold alarm logic, lacking the ability to deeply analyze and predict abnormal trends in complex equipment operating data, making truly proactive maintenance difficult. These shortcomings directly result in frequent unplanned downtime, causing bottlenecks in the turnover of precision medical resources and reduced diagnostic and treatment efficiency, and posing significant safety hazards in critical clinical scenarios. Therefore, an optimized intelligent inspection and status early warning system for medical equipment is urgently needed. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent inspection and status early warning system for medical equipment, addressing the technical problems of existing medical equipment inspection methods, such as high reliance on manual labor, delayed monitoring feedback, lack of foresight in fixed threshold alarm logic, and difficulty in achieving efficient turnover of precision medical resources. This invention aims to achieve refined monitoring and early fault warning throughout the entire lifecycle of medical equipment by constructing a system architecture based on multi-dimensional perception, dynamic baseline evolution, and spatiotemporal correlation prediction, ensuring the continuity and safety of medical services.

[0006] To achieve the above objectives, the technical solution provided by the present invention is as follows: A medical device intelligent inspection and status early warning system includes: The multidimensional signal sensing unit is used to acquire multi-source heterogeneous raw signals in real time, including operating current waveform, equipment surface temperature rise, mechanical vibration spectrum, ambient humidity and electromagnetic interference intensity, through a group of physical sensors preset in key parts of the medical device. The unit then uses a high-frequency sampling module to convert the analog signals into digital sequences.

[0007] The edge computing processing unit is used to perform localized preprocessing on the digital sequence output by the multidimensional signal sensing unit. The localized preprocessing includes windowing and truncation using the Hanning window function, and converting the time-domain signal into a frequency-domain feature vector using the fast Fourier transform algorithm to extract the characteristic peaks, total harmonic distortion rate, and energy distribution entropy that characterize the device's operating status. At the same time, the non-stationary signal is denoised at multiple scales using the wavelet packet decomposition algorithm to generate a standardized and simplified feature dataset.

[0008] The digital twin mapping unit is used to construct a virtual model that corresponds one-to-one with the physical medical device, and injects the simplified feature dataset output by the edge computing processing unit into the virtual model in real time. The virtual model has a built-in structural mechanics model and thermodynamic simulation model of the device. By comparing the real-time observed features with the simulation theoretical values, the current functional attenuation coefficient and structural stress load distribution of the device are calculated.

[0009] The dynamic baseline evolution unit is used to construct a dynamic reference baseline for equipment operating parameters based on historical operating trajectory data and using sliding time window technology. The dynamic reference baseline is not a fixed value, but a confidence interval that is automatically corrected as the equipment load changes, the working mode switches, and the natural aging trend of components changes. By performing mean drift detection and variance contribution rate analysis on similar operating conditions in the past 30 cycles, the dynamic alarm boundary at the current moment is determined.

[0010] The spatiotemporal correlation early warning engine receives the functional decay coefficient from the digital twin mapping unit and the dynamic alarm boundary from the dynamic baseline evolution unit. It uses a long short-term memory neural network model to perform time series modeling of the evolution trend of feature parameters and identify implicit degradation patterns. Furthermore, it analyzes the interactive influence of environmental factors on the device status by combining the spatial topology of the device's geographical location. When the probability that the predicted value deviates from the dynamic alarm boundary within a preset time step in the future exceeds 85%, a graded early warning signal is triggered.

[0011] The intelligent inspection path scheduling unit is used to respond to graded early warning signals. It combines the real-time location, skill qualification level of inspection personnel and the priority weight of the equipment to be inspected to construct a multi-objective optimization scheduling model. It uses a heuristic search algorithm to solve for the optimal inspection path and achieve efficient allocation of maintenance resources.

[0012] The closed-loop execution evaluation unit is used to record the actual operation feedback during the inspection and maintenance process, and compare the equipment performance indicators after maintenance with the status parameters before and after the warning to evaluate the accuracy of the warning and the effectiveness of the maintenance. The generated evaluation results are used to adjust the correction weights in the dynamic baseline evolution unit.

[0013] As one embodiment of the present invention, the physical sensor group in the multidimensional signal sensing unit includes a non-invasive Hall current sensor, an infrared array thermal imager, and a triaxial accelerometer; the non-invasive Hall current sensor is mounted on the outside of the power supply cable of the medical device to capture minute current fluctuations in the frequency range of 0.1 Hz to 10000 Hz in order to identify abnormal load fluctuations in the internal motor or circuit board.

[0014] As one embodiment of the present invention, the edge computing processing unit also performs data quality alignment operations; it calculates the time delay between different sensor signals through a cross-correlation function and performs sub-millisecond timestamp synchronization in the local cache to ensure that current, temperature and vibration characteristics have strict logical correlation under the same physical event dimension.

[0015] As one embodiment of the present invention, the virtual model in the digital twin mapping unit also includes a component fatigue accumulation algorithm; the component fatigue accumulation algorithm is based on the Pammgren-Miner linear damage theory and, combined with real-time stress load distribution, dynamically calculates the predicted residual life of key components such as high-voltage generators, vacuum pumps, or scanning slip rings.

[0016] As one embodiment of the present invention, the dynamic baseline evolution unit adopts an outlier removal mechanism based on density clustering when constructing the dynamic reference baseline; at the end of each sampling period, the Euclidean distance between the newly acquired feature point and the existing cluster center is calculated; if the distance exceeds a preset range of 3 times the standard deviation, it is marked as an isolated point and does not participate in the baseline update, so as to prevent instantaneous random noise from interfering with the stability of the baseline.

[0017] As one embodiment of the present invention, the spatiotemporal correlation early warning engine executes a multi-level early warning mechanism including a blue alert signal, a yellow warning signal, an orange warning signal, and a red alarm signal; wherein, the blue alert signal corresponds to the predicted value touching the edge of the dynamic baseline; the yellow warning signal corresponds to the parameter showing a continuous unidirectional offset trend; the orange warning signal corresponds to the predicted failure time being less than 48 hours; and the red alarm signal corresponds to the key performance indicator exceeding the safety threshold.

[0018] As one embodiment of the present invention, the intelligent inspection path scheduling unit introduces a departmental business busyness factor when constructing a multi-objective optimization scheduling model. The departmental business busyness factor is obtained by accessing the hospital information system. When the target equipment is in a high-frequency surgery or emergency state, the inspection priority is automatically increased, and technical experts with senior professional titles are given priority in the scheduling path.

[0019] As one embodiment of the present invention, the closed-loop execution evaluation unit adopts a parameter adaptive adjustment strategy based on reinforcement learning; if a false alarm or false alarm event occurs, the system automatically increases the penalty term of the loss function, prompting the dynamic baseline evolution unit to recalculate the sensitivity weights of the feature parameters.

[0020] As one embodiment of the present invention, the system runs in a distributed architecture, with sensing and edge computing distributed on departmental terminals, while digital twins and early warning engines are deployed on the central server cloud, realizing real-time transmission and interaction of massive amounts of data through a low-latency dedicated wireless network.

[0021] As one embodiment of the present invention, the dynamic baseline evolution unit also has a working condition identification function; by analyzing the envelope shape of the current characteristics, the system can automatically identify whether the medical device is in standby, self-test, full-load operation or energy-saving mode, and match an independent feature weight template for each mode to ensure the accuracy of the baseline under different business conditions.

[0022] As one embodiment of the present invention, the spatiotemporal correlation early warning engine also integrates a multi-source information fusion algorithm based on evidence theory; when the current sensor indicates an abnormal load but the vibration sensor does not show a significant shift, the engine reduces the alarm weight of a single dimension by calculating the basic probability assignment function, thereby filtering out false fault diagnoses caused by power grid fluctuations.

[0023] As one embodiment of the present invention, the path instructions generated by the intelligent inspection path scheduling unit are pushed to the inspection personnel in real time through a wearable device. The instructions include a 3D internal structure diagram of the equipment, virtual enhanced guidance of the fault prediction location, and a recommended spare parts list, thereby reducing the time consumption for on-site diagnosis through digital interaction.

[0024] As one embodiment of the present invention, the system also has the capability of multi-device collaborative monitoring; by calculating the performance median of a group of devices of the same model in the same area, the deviation between the performance of individual devices and the group can be identified, thereby discovering common batch quality defects or systemic environmental risks.

[0025] Furthermore, in the multi-dimensional signal sensing unit, in order to ensure the purity of the signal acquisition from high-precision medical equipment, the system is equipped with an isolated data acquisition front end with a common-mode rejection ratio of not less than 120 dB and an input impedance greater than 10 megohms. For interventional surgical equipment, the sensor group adopts non-contact magnetic coupling sensing technology to ensure accurate reading of physical quantities without damaging the medical-grade packaging of the equipment.

[0026] Furthermore, within the edge computing processing unit, the feature extraction process follows the principle of dimensionality reduction mapping; the original 128-dimensional sensor features are compressed into a 16-dimensional principal feature vector with high interpretability through principal component analysis algorithm, reducing the pressure on subsequent network transmission bandwidth; at the same time, a lightweight state machine monitoring logic is deployed on the edge side, which can independently execute the issuance of emergency shutdown signals in the event of network communication interruption, ensuring the safety of the equipment under extreme operating conditions.

[0027] Furthermore, in the digital twin mapping unit, the virtual model has a real-time parameter feedback function; by verifying the simulation results with the actual operating parameters twice, the system can automatically adjust the material parameter coefficients of the simulation model in reverse, so that the virtual model can maintain a very high physical consistency throughout the entire life cycle of the equipment.

[0028] Furthermore, in the dynamic baseline evolution unit, the baseline update frequency is dynamically allocated according to the importance of the equipment; for life support equipment, such as ventilators and extracorporeal membrane oxygenation machines, the baseline update frequency is set to once every 1 minute; for ordinary examination equipment, the baseline update frequency is set to once every 24 hours; this non-uniform computing power allocation mechanism effectively balances the monitoring intensity and computing resource consumption of the system.

[0029] Furthermore, in the spatiotemporal correlation early warning engine, an attention mechanism is introduced into the time series modeling process; this mechanism can automatically identify key feature fluctuation points before the occurrence of a fault, such as the instantaneous increase of specific high-frequency harmonics in the current spectrum, and assign them higher early warning weights; this deep feature mining capability enables the system to advance the early warning time window for latent faults such as power module aging and bearing micro-wear by at least 72 hours.

[0030] Furthermore, in the intelligent inspection path scheduling unit, the process of solving the optimal inspection path takes into account the physical space constraints within the hospital; by importing the hospital building information model, the system calculates the shortest physical path through different floors and areas with different cleanliness levels, and automatically avoids corridors in the midst of surgical procedures, ensuring that the inspection behavior minimizes the interference with the daily medical order.

[0031] Furthermore, in the closed-loop execution evaluation unit, the system constructs a maintenance knowledge graph; each successful maintenance case is abstracted into a logical node, and by associating fault phenomena, predictive features and handling measures, it provides auxiliary decision support for subsequent similar early warnings; the knowledge graph is continuously improved as the number of inspections accumulates, realizing the transformation of operation and maintenance experience from personal skills to system assets.

[0032] Furthermore, this system also integrates energy efficiency analysis functions; through refined statistics on the power consumption of medical equipment, it analyzes the abnormal standby power consumption of the equipment during non-working periods and uses it as a side dimension for equipment status assessment, helping to determine whether the equipment has secondary status hazards such as dust accumulation in the heat dissipation system or leakage in the internal circuit.

[0033] Furthermore, the system's user interface adopts a multi-level view architecture; the strategic layer view displays the health index distribution map of all equipment assets in the hospital, the tactical layer view provides the performance evolution curve of a single device, and the operation layer view pushes specific maintenance tasks in real time; through this multi-level permission and view design, it is ensured that operation and maintenance managers with different functions can obtain the core data related to their decision-making.

[0034] Furthermore, for the access of large-scale device clusters, the system adopts load balancing technology based on microservice architecture; when the number of access devices expands from 100 to more than 10,000, the backend processing unit can achieve seamless horizontal scaling, ensuring that the execution time of the early warning logic is always kept within the real-time response range of less than 1 second.

[0035] Furthermore, the system adopts a hybrid storage mode of time-series database and graph database in the data storage stage; the time-series database is responsible for storing high-frequency state stream data to ensure the accuracy of data traceability; the graph database is responsible for the logical connection and topological dependency relationship between storage devices, providing underlying data structure support for system-level root cause analysis of faults.

[0036] Compared with the prior art, the beneficial effects of the present invention are: (1) By introducing multi-dimensional signal perception and edge computing processing, the problem of single and lagging information acquisition in traditional inspection is fundamentally solved. By high-frequency sampling and localized feature extraction of multi-source signals such as current waveform, surface temperature rise, and mechanical vibration, the system can capture microscopic operating features that are invisible to the naked eye, significantly advancing the time node of fault detection from post-fault reporting to the latent degradation stage, significantly reducing the unplanned downtime rate of medical equipment, and ensuring the continuity of key clinical scenarios such as critical care.

[0037] (2) An innovative dynamic baseline evolution mechanism was constructed, effectively overcoming the high false alarm and high false negative defects of traditional fixed threshold alarm logic. Through sliding time windows and mean drift detection, the system can automatically adapt to parameter drift caused by load switching, changes in operating conditions, and natural aging of medical equipment, ensuring that the warning boundary always maintains a dynamic fit with the actual operating state of the equipment. This adaptive capability greatly improves the accuracy of the warning, reduces the effort of maintenance personnel in dealing with invalid alarms, and allows medical resource allocation to be more focused on truly high-risk assets.

[0038] (3) By utilizing digital twin mapping and a spatiotemporal correlation early warning engine, a qualitative leap from monitoring to prediction has been achieved. By injecting real-time data of physical entities into a highly simulated virtual model, the system can not only perceive the current state but also predict future decay trends through time series modeling and attention mechanisms. This forward-looking maintenance view provides hospital management with scientific maintenance window suggestions, realizing the transformation from passive maintenance to proactive preventive maintenance, effectively extending the service life of precision medical equipment, and improving the return on investment throughout the asset's life cycle.

[0039] (4) A complete operation and maintenance management closed loop is constructed through intelligent inspection path scheduling and closed-loop execution evaluation. The system optimizes the scheduling of complex inspection tasks with personnel qualifications, geographical location, and business workload, ensuring the optimality of inspection paths and the efficiency of maintenance response. At the same time, through continuous closed-loop feedback adjustment, the system has the ability to self-evolve and accumulate knowledge, enabling the early warning model to be continuously optimized in practical applications, and ultimately building a transparent, efficient, and self-healing modern intelligent management ecosystem for medical equipment.

[0040] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, embodiments of the present invention are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the overall technical architecture of the intelligent inspection and status early warning system for medical equipment proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the spatiotemporal correlation early warning engine in this invention; Figure 3 This is a logical flowchart of the intelligent inspection path scheduling in this invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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 some embodiments of the present invention, but not all embodiments.

[0044] Example 1 Please refer to the attached document. Figure 1 This embodiment provides an intelligent inspection and status early warning system for medical equipment. Its overall architecture is deployed in a distributed network environment within a medical institution. It aims to achieve full lifecycle health management of medical equipment through precise data acquisition, efficient edge processing, and deep digital twin simulation. At the physical level, the system encompasses monitoring terminals widely distributed across various departments, while at the logical level, it receives powerful computing support from a central cloud server. The core operating mechanism relies on the collaborative interaction of multiple functional units to ensure a complete closed loop from physical signal acquisition to the issuance of operational decisions.

[0045] The multi-dimensional signal sensing unit, as the front end of the system, is responsible for directly observing the physical state of the medical equipment. This unit acquires multi-source heterogeneous raw signals in real time, including operating current waveforms, surface temperature rise, mechanical vibration spectrum, ambient humidity, and electromagnetic interference intensity, through a set of physical sensors pre-installed at key parts of the medical equipment. In this embodiment, the configuration of the physical sensor set is highly targeted. A non-invasive Hall current sensor is mounted on the outside of the medical equipment's power supply cable. This design does not require alteration to the original circuit structure of the equipment. By capturing minute current fluctuations in the frequency range of 0.1 Hz to 10000 Hz, it can sensitively identify abnormal load fluctuations in internal motors or circuit boards. For example, in the power supply circuit of the RF amplifier in a large MRI machine, this sensor can detect sub-ampere level current distortion, thereby predicting early performance degradation of power devices. Simultaneously, an infrared array thermal imager is deployed on the equipment's heat dissipation window or opposite the high-voltage power module, acquiring a two-dimensional thermal distribution map in a non-contact manner to capture subtle changes in the surface temperature rise of the equipment. The triaxial accelerometer is attached to the slip ring support of rotating components such as computed tomography equipment to monitor the mechanical vibration spectrum and identify abnormal simple harmonic vibrations caused by bearing wear or dynamic imbalance.

[0046] The multi-dimensional signal sensing unit integrates a high-frequency sampling module. This module features an isolated data acquisition front-end with a common-mode rejection ratio (CMRR) of no less than 120 dB and an input impedance greater than 10 megohms. This high-impedance, high-rejection-ratio design ensures that weak physiological electrical signals or device status signals acquired in complex medical electromagnetic environments are not annihilated by background noise. For scenarios with stringent packaging requirements, such as interventional surgical equipment, the sensor group employs non-contact magnetic coupling sensing technology to ensure accurate reading of physical quantities without damaging the medical-grade packaging of the device. The high-frequency sampling module converts the captured analog signals into digital sequences via a 24-bit analog-to-digital converter after anti-aliasing filtering. To ensure real-time data transmission, the sampling frequency is set to 50 kHz, enabling the system to capture high-frequency harmonic components and providing rich data dimensions for subsequent fault diagnosis.

[0047] The digital sequence output by the multidimensional signal sensing unit is transmitted to the edge computing processing unit. This unit, deployed on an embedded computing platform near the department, performs localized preprocessing of the raw data. The first step in localized preprocessing is data quality alignment. Due to differences in response speed and data link latency among different sensors, the edge computing processing unit calculates the time delay between signals from different sensors using a cross-correlation function and performs sub-millisecond timestamp synchronization in its local cache. This mechanism ensures that current, temperature, and vibration characteristics have strict logical correlation within the same physical event dimension. Subsequently, the system uses a Hanning window function to window and truncate the synchronized signal to reduce spectral leakage. Using a fast Fourier transform algorithm, the time-domain signal is efficiently converted into a frequency-domain feature vector. Through analysis of the frequency-domain features, the system extracts characteristic peak values, total harmonic distortion rate, and energy distribution entropy representing the equipment's operating status. For non-stationary signals, such as pressure fluctuation signals from a ventilator during different respiratory cycles, the edge computing processing unit uses a wavelet packet decomposition algorithm for multi-scale denoising. The algorithm decomposes the signal into subspaces of different frequency bands and reconstructs a clean characteristic waveform by thresholding the high-frequency noise coefficient.

[0048] To reduce bandwidth pressure on subsequent network transmission and improve early warning efficiency, the edge computing processing unit performs feature extraction and dimensionality reduction mapping operations. Using principal component analysis (PCA) algorithms, the original 128-dimensional sensor features are compressed into 16-dimensional, highly interpretable principal feature vectors. These feature vectors cover core indicators such as the device's main torque energy, temperature rise rate slope, and vibration order distribution. Furthermore, a lightweight state machine monitoring logic is deployed at the edge. When critical indicators such as a sudden current exceeding the safety threshold and network communication is interrupted, the edge computing processing unit can independently issue an emergency shutdown signal, directly cutting off the power to the malfunctioning module via the local control bus, ensuring the device's safety under extreme operating conditions.

[0049] The processed, streamlined feature dataset is then injected into the digital twin mapping unit. This unit constructs a virtual model corresponding one-to-one with the physical medical device within a central server. This virtual model is not a simple 3D geometric representation, but rather incorporates the device's structural mechanics and thermodynamic simulation models. By inputting real-time observed features into the simulation environment, the system can simulate the physical state of points within the device that cannot be directly measured. For example, in the digital twin mapping unit, using real-time current and vibration data combined with finite element analysis, the stress load distribution of key structural components within the device can be calculated. Simultaneously, the thermodynamic simulation model uses surface temperature rise data to infer the junction temperature of high-power components inside, calculating the device's current functional degradation coefficient.

[0050] The virtual model in the digital twin mapping unit also includes a component fatigue accumulation algorithm. This algorithm is based on the Pamgren-Miner linear damage theory and dynamically calculates the predicted remaining life of key components.

[0051] In the above formula, D represents the fatigue damage accumulation factor, and k represents the total number of stress level grades. Representative equipment in the The actual number of operating cycles under the stress level. Representative equipment in the The algorithm calculates the maximum number of operating cycles required to prevent fatigue failure under a given stress level. When the accumulation factor D approaches 1, the system determines that the component has entered a high-risk failure period. Using this algorithm, the digital twin mapping unit can accurately assess the residual life of core components such as high-voltage generators, vacuum pumps, or scanning slip rings. Furthermore, the virtual model has a real-time parameter feedback function. By verifying the simulation results twice with actual operating parameters, the system can automatically adjust the material parameter coefficients of the simulation model, such as the coefficient of friction or thermal conductivity, ensuring that the virtual model maintains extremely high physical consistency throughout the entire service life of the equipment.

[0052] The dynamic baseline evolution unit receives historical trajectory data and real-time features output by the edge computing processing unit, and constructs a dynamic reference baseline for equipment operating parameters using sliding time window technology. This dynamic reference baseline is not a fixed value, but a confidence interval that is automatically corrected according to changes in equipment load, switching of operating modes, and the natural aging trend of components. By performing mean drift detection and variance contribution rate analysis on similar operating conditions over the past 30 cycles, the system can determine the dynamic alarm boundary at the current moment. The dynamic baseline evolution unit has a powerful operating condition identification function. By analyzing the envelope shape of current characteristics, the system can automatically identify whether the medical equipment is in standby, self-test, full-load operation, or energy-saving mode, and match an independent feature weight template for each mode.

[0053] When constructing the dynamic reference baseline, the dynamic baseline evolution unit employs an outlier removal mechanism based on density clustering. At the end of each sampling period, the Euclidean distance between newly acquired feature points and existing cluster centers is calculated. If the distance exceeds a preset range of three standard deviations, it is marked as an isolated point and not included in the baseline update. This mechanism effectively prevents random noise, such as instantaneous power grid fluctuations or human error, from interfering with the stability of the baseline. Furthermore, the baseline update frequency is dynamically allocated according to the importance of the equipment. For life support equipment such as ventilators and extracorporeal membrane oxygenation (ECMO) machines, the baseline update frequency is set to once per minute to achieve second-level state drift capture. For equipment such as ordinary ultrasound examinations or electrocardiographs, the baseline update frequency is set to once every 24 hours. This non-uniform computing power allocation mechanism greatly optimizes the system's computational resource consumption while ensuring monitoring intensity.

[0054] The spatiotemporal correlation early warning engine, as the system's decision-making core, receives the functional attenuation coefficient from the digital twin mapping unit and the dynamic alarm boundary from the dynamic baseline evolution unit. Please refer to the appendix. Figure 2 This engine uses a long short-term memory (LSTM) neural network model to perform time-series modeling of the evolution trends of feature parameters, aiming to identify implicit degradation patterns. The LSM neural network can remember state information over a long period, filtering out unimportant short-term fluctuations through a gating mechanism, focusing on low-frequency components reflecting long-term aging. During the modeling process, the system introduces an attention mechanism that can automatically identify key feature fluctuation points before a fault occurs, such as the instantaneous surge of specific high-frequency harmonics in the current spectrum.

[0055] The spatiotemporal correlation early warning engine further integrates the spatial topology of the equipment's location to analyze the interactive impact of environmental factors on equipment status. For example, when multiple devices in a certain area simultaneously experience abnormal temperature rises, the engine will combine spatial data to determine whether the excessive ambient temperature is caused by a malfunction in the central air conditioning system. The engine's multi-level early warning mechanism includes blue alert signals, yellow warning signals, orange warning signals, and red alarm signals. A blue alert signal corresponds to the predicted value reaching the edge of the dynamic baseline, indicating a potential deviation in equipment status. A yellow warning signal corresponds to a continuous unidirectional deviation trend in parameters, indicating a potential risk of degradation. An orange warning signal corresponds to a predicted failure time of less than 48 hours, requiring immediate scheduling and inspection. A red alarm signal corresponds to a critical performance indicator exceeding the safety threshold, recommending immediate shutdown and maintenance. When the probability of the predicted value deviating from the dynamic alarm boundary within a preset time step exceeds 85%, the system will trigger the corresponding early warning signal according to the above levels.

[0056] To improve the reliability of early warnings, the spatiotemporal correlation early warning engine also integrates a multi-source information fusion algorithm based on evidence theory. This algorithm performs conflict detection and fusion processing on diagnostic evidence provided by different sensor sources. For example, when a current sensor indicates an abnormal load but a vibration sensor does not show a significant shift, the engine reduces the alarm weight of a single dimension by calculating a basic probability assignment function. Only when evidence from multiple independent dimensions points to the same fault mode will the system raise the warning level. This method effectively filters out false fault diagnoses caused by single sensor failures or local interference, keeping the false alarm rate at an extremely low level.

[0057] The intelligent inspection path scheduling unit is used in response to the tiered early warning signals issued by the spatiotemporal correlation early warning engine. Please refer to the appendix. Figure 3 This unit combines the real-time location, skill level, and priority weight of the equipment to be inspected by the inspection personnel to construct a multi-objective optimization scheduling model. When constructing the model, the system incorporates a departmental workload factor. This factor is obtained in real-time through access to the hospital information system; when a target device is undergoing high-frequency surgery or emergency procedures, its inspection priority is automatically elevated to the highest level.

[0058] Using a heuristic search algorithm, the intelligent inspection path scheduling unit can solve for the optimal inspection path.

[0059] In the above path scheduling optimization formula, F represents the comprehensive cost function of the inspection path. p represents the candidate path scheme. m represents the total number of devices to be inspected covered by the path. This represents the time cost required to reach the j-th device along path p, including the physical distance conversion across floors and corridors. This represents the priority weight of the j-th device, which is influenced by a combination of the device's life support attributes and the department's workload factor. This represents the degree of qualification matching among the personnel performing the inspection task. (Greek letters) and These are the adjustment weighting coefficients for time cost and task matching, respectively. The system minimizes the function F to assign the most suitable technician to the equipment site that most urgently needs maintenance in the shortest possible time.

[0060] The solution process fully considers the physical space constraints within the hospital. By importing the hospital's building information model, the system calculates the shortest physical path through different floors and areas with different cleanliness levels, automatically avoiding corridors in the midst of surgical procedures. The generated path instructions are pushed to inspection personnel in real time via wearable devices. These instructions not only include geographic navigation but also integrate 3D internal structural diagrams of equipment, virtual enhanced guidance for predicted fault locations, and a recommended spare parts list. This digital interaction method significantly reduces the time spent on on-site diagnosis.

[0061] The closed-loop execution evaluation unit, serving as a self-evolutionary link in the system, records actual operational feedback during inspection and maintenance. After completing maintenance, inspection personnel input the replaced components, the true cause of the fault, and the equipment's recovery status via a mobile terminal. The system compares the equipment performance indicators after maintenance with the status parameters before and after the warning to evaluate the accuracy of the warning and the effectiveness of the maintenance. The generated evaluation results are used to adjust the correction weights in the dynamic baseline evolution unit. For example, if a warning is confirmed as a false alarm, the system will automatically increase the penalty term in the loss function, prompting the dynamic baseline evolution unit to recalculate the sensitivity weights of feature parameters, reducing the contribution rate of such features in future warnings.

[0062] The closed-loop execution evaluation unit internally constructs a maintenance knowledge graph. This graph abstracts each successful maintenance case into a logical node, forming searchable knowledge entries by associating fault phenomena, predictive characteristics, and handling measures. This knowledge graph is continuously improved with the accumulation of inspections, realizing the transformation of operational experience from individual skills to system assets. Furthermore, the closed-loop execution evaluation unit employs a reinforcement learning-based parameter adaptive adjustment strategy. Through continuous trial and error and reward mechanisms, the system can autonomously optimize warning thresholds and path scheduling strategies, ensuring that the overall system operating efficiency continuously improves during long-term service.

[0063] The system provided in this embodiment also integrates energy efficiency analysis functionality. Through refined statistics of the power consumption of medical devices, the system can analyze abnormal standby power consumption during non-working periods. Abnormal power consumption characteristics often indicate overloaded cooling fans due to dust accumulation in the heat dissipation system, or minor current leakage in internal circuit modules. These energy consumption anomalies are used as a side dimension for device status assessment, assisting in determining the sub-health state of the device. For large-scale device cluster access, the system employs load balancing technology based on a microservice architecture. When the number of connected devices expands from 100 to over 10,000, the backend processing unit can achieve seamless horizontal scaling, ensuring that the execution time of the warning logic remains within a real-time response range of less than one second.

[0064] The system's user interface adopts a multi-level view architecture to meet the needs of managers at different levels. The strategic layer view, aimed at hospital directors and equipment department heads, displays a health index distribution map of the entire hospital's equipment assets, visually presenting the overall operational risks of equipment in each department through a heatmap. The tactical layer view, provided to department head nurses or equipment administrators, offers performance evolution curves and warning histories for individual devices. The operational layer view, directly aimed at clinical engineers, pushes specific maintenance tasks in real time. In terms of data storage, the system employs a hybrid storage mode combining time-series and graph databases. The time-series database records high-frequency state flow data, such as current and vibration sequences, ensuring data traceability. The graph database stores logical connections between devices, physical location topology, and knowledge graph nodes, providing underlying data structure support for system-level root cause analysis.

[0065] In summary, the intelligent medical equipment inspection and status early warning system constructed in this embodiment, through the deep integration of the perception layer, computing layer, simulation layer, and execution layer, realizes a transformation in medical equipment management from a passive response to a proactive early warning model. This comprehensive technological innovation not only improves the operational safety and reliability of medical equipment but also provides a solid data foundation for the refined operation and maintenance of medical institutions.

[0066] Example 2 Based on Example 1, this embodiment optimizes the system specifically for highly mobile medical devices, such as mobile digital X-ray machines and portable ultrasound diagnostic instruments. These devices are characterized by variable working environments, unstable power supplies, and frequent movement between different departments, thus placing higher demands on the robustness of the sensing unit and spatial correlation early warning capabilities.

[0067] In the multi-dimensional signal sensing unit, wireless passive sensing technology is introduced into the physical sensor array to address the non-fixed power supply characteristics of mobile devices. Energy is harvested using radio frequency identification (RFID) technology, enabling the sensors to monitor structural vibrations and ambient temperature and humidity of the mobile device without battery power. Considering the frequent mechanical collision risks of mobile devices, the sampling frequency of the triaxial accelerometer is increased to 100 kHz, and impact detection logic is added. When an instantaneous impact exceeding five gravitational accelerations is detected, the system automatically triggers a device integrity self-check process, assessing the potential impact of structural deformation on image quality through a digital twin mapping unit.

[0068] The edge computing processing unit incorporates a location information fusion module when processing mobile device data. By combining signal strength fingerprint positioning from the hospital's wireless LAN with Bluetooth Low Energy beacons, the system can accurately acquire the spatiotemporal trajectory of mobile devices. During localized preprocessing, the edge computing processing unit compensates for electromagnetic environment fluctuations caused by location changes. For example, when a device moves from a regular ward to an interventional operating room with strong magnetic shielding, the system automatically switches the electromagnetic interference compensation coefficient to ensure that the extracted operational features are not affected by sudden changes in the external environment. Simultaneously, to address the limited bandwidth of mobile devices, the edge computing processing unit employs a more aggressive lossless compression algorithm to further reduce the size of the feature dataset, ensuring continuous data uploads even in areas with weak signals.

[0069] The digital twin mapping unit establishes a dynamic spatial dependency model for each mobile device. This model includes not only the device's own physical characteristics but also the physical interaction attributes between the device and its surrounding environment. For example, when a mobile digital X-ray machine is located at different charging station positions, the virtual model automatically loads the corresponding heat dissipation boundary conditions. The component fatigue accumulation algorithm focuses on the battery cycle life of the mobile device and the joint wear of the mechanical support arm. By recording the distance of each movement and the number of movements of the support arm, the system can accurately predict the maintenance cycle of the mobile mechanism, preventing equipment tipping or positioning failure due to mechanical fatigue.

[0070] The Dynamic Baseline Evolution Unit introduces a context-aware multi-baseline parallel mechanism tailored to the operating characteristics of mobile devices. The system automatically switches to a matching reference baseline based on the department the device is currently in and the type of task being performed. For example, when a portable ultrasound machine is used for routine physical examinations, its current baseline is in low-power mode. However, when used for intraoperative guidance, the system automatically switches to a high-performance working baseline. This context-sensitive baseline evolution capability significantly improves the specificity of early warnings. Simultaneously, the Dynamic Baseline Evolution Unit incorporates logic to compensate for environmental vibrations. By analyzing reference signals from environmental sensors, it subtracts background vibrations caused by uneven ground or transportation processes from the original vibration of the device, thereby accurately identifying subtle abnormal noises from the internal motors of the equipment.

[0071] The spatiotemporal correlation early warning engine enhances spatial risk assessment when handling mobile device warnings. The engine analyzes the contribution of environmental factors in a specific area to the failure rate of this type of equipment. If persistent bumpiness in a corridor is found to cause imaging panel failure in mobile devices, the engine will issue environmental improvement suggestions to the intelligent inspection path scheduling unit. The early warning mechanism includes a mobile path risk warning; when a failure risk is predicted in the device's walking mechanism and it is currently undergoing long-distance transport, the warning level will automatically escalate, reminding operators to immediately stop movement and await professional maintenance. Furthermore, the multi-source information fusion algorithm incorporates joint judgment of battery health status and charging behavior, effectively preventing unexpected shutdowns of mobile devices during critical clinical tasks due to battery depletion or overheating.

[0072] The intelligent inspection route scheduling unit employs a dynamic convergence scheduling algorithm to address the mobility of mobile devices. This algorithm no longer fixes inspection locations but tracks the dynamic relative distance between inspection personnel and the mobile devices to be maintained in real time. Using a heuristic search algorithm, the system identifies the most likely intersection point between the inspection personnel and the mobile devices within the next 10 minutes and assigns the inspection task to maintenance personnel near that intersection point. This significantly reduces the time inspection personnel spend blindly searching within the hospital, improving the maintenance efficiency of mobile assets. Furthermore, the scheduling model considers the mobile devices' scheduled appointments to ensure that inspection activities do not conflict with pre-arranged clinical examinations.

[0073] The closed-loop execution evaluation unit adds an environmental adaptability dimension to the assessment of mobile device maintenance effectiveness. The system records the operational stability of the maintained equipment in different departmental environments. If a maintenance plan is found to fail rapidly in a specific high-humidity environment, the system automatically associates environmental factors through a knowledge graph and recommends more environmentally tolerant spare parts in subsequent equipment warnings for similar environments. Reinforcement learning strategies are used here to optimize the preventive maintenance cycle of mobile devices, customizing personalized health management plans for each mobile asset by balancing maintenance costs with the clinical risks of unexpected failures.

[0074] This embodiment also introduces collaborative monitoring capabilities for large-scale mobile device clusters. By calculating the median performance of the same batch of mobile devices under different usage frequencies, the system can identify issues such as shortened equipment lifespan due to differences in operator habits. For example, if the failure rate of the mobile device support arm in a certain department is significantly higher than the hospital average, the system will automatically generate recommendations for standardized operation training. This advancement from individual monitoring to group behavior analysis provides hospital management with deeper asset governance tools. Furthermore, the system also possesses multi-device collaborative prediction capabilities. When environmental parameters in a certain area experience a collective shift, it can proactively issue protective alerts to all mobile devices in that area, achieving a leap from individual early warning to systemic defense.

[0075] Example 3 This embodiment, building upon Embodiment 1, further expands the system's support for high-precision therapeutic equipment, such as radiotherapy linear accelerators and surgical robots. These devices have extremely stringent requirements for operational accuracy and safety redundancy; even the slightest deviation can directly affect treatment outcomes and even patient safety. Therefore, this embodiment has achieved extreme enhancements in sensing accuracy, simulation depth, and deterministic early warning capabilities.

[0076] The multi-dimensional signal sensing unit is equipped with an ultra-high-precision sensor array for therapeutic devices. In the microwave power system of the linear accelerator, high-precision directional couplers and detector modules are used to acquire the waveform envelope and phase shift of microwave pulses in real time. For the robotic arm of the surgical robot, dual-redundant absolute photoelectric encoders are installed at each joint, with a sampling frequency of 200 kHz to capture sub-micron level positioning fluctuations. The isolated data acquisition front end of the sensing unit has been further enhanced, with its input impedance increased to 100 megohms to ensure zero-load effect for measurements of sensitive control circuits. For the radioactive environment of the treatment room, the sensor array employs a radiation-resistant packaging design to ensure that signal transmission does not experience zero-point drift under long-term radiation exposure.

[0077] In such devices, the edge computing processing unit undertakes some of the near real-time closed-loop verification tasks. In addition to conventional frequency domain analysis, the edge side also executes model-reference-based real-time comparison logic. The system pre-stores the transfer function model of normal device operation at the edge side and instantaneously compares real-time control commands with sensor feedback results. If the residual between the two exceeds a set precision threshold, the edge computing processing unit will trigger a safety interlock within 100 milliseconds to prevent excessive X-ray exposure due to misoperation or hardware failure. To handle massive amounts of high-frequency sampling data, the edge side employs a hardware acceleration architecture based on a field-programmable gate array (FPGA) to achieve parallel processing of Fourier transform and wavelet analysis, ensuring that feature extraction latency remains at the microsecond level.

[0078] The digital twin mapping unit constructs a high-fidelity multiphysics coupling model for the treatment equipment. This model not only includes the mechanical structure and thermodynamic distribution but also integrates electromagnetic field simulation and beam dynamics simulation. In the digital twin model of the surgical robot, the system calculates the dynamic load of each joint and the clamping force distribution of the end effector in real time. The component fatigue accumulation algorithm is refined to the tooth surface contact fatigue of micro-transmission gears. Through high-precision stress mapping, the system can discover the evolution trend of micro-cracks hidden inside precision mechanics. In addition, the digital twin mapping unit also has a treatment plan pre-simulation function, which assesses the actual impact of the current physical state of the equipment on dose distribution or surgical accuracy by injecting the performance parameters of the actual equipment into the treatment planning system.

[0079] The dynamic baseline evolution unit employs an ultra-narrow confidence interval baseline strategy for such devices. Since the operating parameters of treatment equipment are typically strictly limited to extremely small tolerances, the baseline evolution unit constructs a dynamic sensitivity model based on standard deviation regression through in-depth mining of historical fine-calibration data. The system not only eliminates random noise but also extracts a pure baseline reflecting the intrinsic performance of the equipment by decoupling external variables such as power grid quality and cooling water temperature. The baseline update frequency is increased to the millisecond level during treatment execution. By monitoring inter-pulse stability, the system can detect minute fluctuations in output energy, such as those caused by thyristor aging, which are often precursors to catastrophic failures.

[0080] The spatiotemporal correlation early warning engine incorporates a variational autoencoder architecture from deep learning to identify anomalous manifolds in the operational data of treatment equipment. By mapping high-dimensional sensor features to a low-dimensional latent space, the engine can sensitively detect minute deviations in equipment status from the normal operating manifold. An attention mechanism is used to focus on feature points along the critical treatment path, such as the flatness and symmetry of the X-ray beam. A multi-level early warning mechanism adds a preventative shutdown recommendation level, triggering an intervention process when the system assesses that the equipment accuracy has decreased to 80% of the clinically acceptable tolerance. A multi-source information fusion algorithm combines the expected and measured outputs of the treatment planning system, using Bayesian network inference to precisely pinpoint whether the fault source originates from micro-leakage in the vacuum system or component aging on the control board.

[0081] The intelligent inspection path scheduling unit employs expert-level scheduling logic for high-precision treatment equipment. Maintenance of such equipment typically requires senior engineers with specific manufacturer certifications. The scheduling model considers optimal paths while incorporating the engineer's qualification validity period, past repair success rate, and fatigue level into the optimization objectives. For equipment performing treatment tasks, the scheduling unit generates an immediate maintenance preparation checklist to guide maintenance personnel in conducting rapid checks during treatment breaks. Furthermore, path planning includes allocation logic for precision testing instruments, ensuring that necessary calibration tools such as oscilloscopes and ionization chambers are readily available when engineers arrive on-site.

[0082] The closed-loop evaluation unit focuses on verifying the physical accuracy of such equipment after maintenance. The system automatically compares key clinical indicators such as isocenter accuracy and dose calibration factors before and after maintenance, ensuring that each maintenance restores the equipment to its design specifications. The maintenance knowledge graph integrates the manufacturer's technical manuals and a global failure case database, providing evidence-based reasoning support for complex fault diagnosis. Reinforcement learning strategies are used here to optimize spare parts inventory strategies, predicting the failure probability of high-value, easily damaged parts to ensure high equipment availability while reducing the hospital's inventory capital tied up.

[0083] This embodiment also places special emphasis on the system's security design. Due to the involvement of radioactive and invasive operations, all warning and scheduling commands undergo multiple layers of encryption and integrity verification. An insurmountable hardware watchdog is implemented in the system's underlying logic to ensure that even in the event of a catastrophic software system failure, the equipment can still enter a predefined controlled and safe state. This extreme reliability design makes the system not only an auxiliary maintenance tool but also a fundamental guardian of the safe clinical operation of high-precision medical equipment. Through meticulous monitoring of energy efficiency, the system can also detect hidden blockages in the cooling circulation system, thereby preventing huge losses such as superconducting magnet quenching due to poor heat dissipation, comprehensively protecting the value and safety of the medical institution's core assets.

[0084] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A medical equipment intelligent inspection and status early warning system, characterized in that, include: The multidimensional signal sensing unit is used to acquire multi-source heterogeneous raw signals in real time, including operating current waveform, equipment surface temperature rise, mechanical vibration spectrum, ambient humidity and electromagnetic interference intensity, through a group of physical sensors preset in key parts of the medical device, and convert them into digital sequences using a high-frequency sampling module. The edge computing processing unit is used to perform localized preprocessing of digital sequences, including windowing and truncation using the Hanning window function, and converting the time-domain signal into a frequency-domain feature vector using the fast Fourier transform algorithm. It extracts the characteristic peaks, total harmonic distortion rate and energy distribution entropy that characterize the device's operating status. At the same time, it performs multi-scale denoising on non-stationary signals using the wavelet packet decomposition algorithm to generate a standardized and simplified feature dataset. The digital twin mapping unit is used to construct a virtual model that corresponds one-to-one with the physical medical equipment, and to inject a simplified feature dataset into the virtual model in real time. The virtual model has a built-in structural mechanics model and thermodynamic simulation model of the equipment, which is used to calculate the current functional attenuation coefficient and structural stress load distribution of the equipment by comparing real-time observed features with simulation theoretical values. The dynamic baseline evolution unit is used to construct a dynamic reference baseline that is automatically corrected according to the equipment's operating conditions based on historical operating trajectory data and using sliding time window technology. By performing mean drift detection and variance contribution rate analysis on similar operating conditions within a preset period, the dynamic alarm boundary at the current moment is determined. The spatiotemporal correlation early warning engine is used to receive the functional attenuation coefficient and dynamic alarm boundary. It performs time series modeling through a long short-term memory neural network model to identify hidden degradation patterns. It also analyzes the impact of environmental factors in conjunction with the spatial topology relationship of the device's geographical location. When the probability of the predicted value deviating from the dynamic alarm boundary exceeds 85%, a graded early warning signal is triggered. The intelligent inspection path scheduling unit is used to respond to graded early warning signals. It combines the real-time location of inspection personnel, skill qualification level and priority weight of equipment to be inspected to build a multi-objective optimization scheduling model and uses a heuristic search algorithm to solve for the optimal inspection path. The closed-loop execution evaluation unit is used to record the actual operation feedback during the inspection and maintenance process, and compare the equipment performance indicators after maintenance with the status parameters before and after the warning to evaluate the accuracy of the warning and the effectiveness of the maintenance. The generated evaluation results are used to adjust the correction weights in the dynamic baseline evolution unit.

2. The intelligent inspection and status early warning system for medical equipment according to claim 1, characterized in that, The multidimensional signal sensing unit also performs the following operations: acquiring signals through a configured isolated data acquisition front end, wherein the common-mode rejection ratio of the isolated data acquisition front end is not less than 120 dB and the input impedance is greater than 10 megohms; for interventional surgical devices, reading physical quantities using non-contact magnetic coupling sensing technology in the physical sensor group; the physical sensor group includes a non-invasive Hall current sensor mounted on the outside of the power supply cable of the medical device, an infrared array thermal imager deployed opposite the heat dissipation module of the device, and a triaxial accelerometer closely attached to the support frame of the rotating component.

3. The intelligent inspection and status early warning system for medical equipment according to claim 1, characterized in that, The edge computing processing unit is also used to: calculate the time delay between different sensor signals through a cross-correlation function and perform sub-millisecond timestamp synchronization in the local cache; perform data quality alignment operations to ensure that current features, temperature features, and vibration features have logical correlation under the same physical event dimension; compress the original 128-dimensional sensor features into a 16-dimensional principal feature vector using a principal component analysis algorithm; and deploy lightweight state machine monitoring logic on the edge side to issue an emergency shutdown signal in response to events where key indicators exceed safety thresholds in the event of network communication interruption.

4. The intelligent inspection and status early warning system for medical equipment according to claim 1, characterized in that, The virtual model in the digital twin mapping unit also performs the following process: using the built-in component fatigue accumulation algorithm, combined with real-time stress load distribution, to dynamically calculate the predicted value of the remaining residual life of key components; the component fatigue accumulation algorithm is based on the Pamgren-Miner linear damage theory, and accumulates and sums the ratio of the actual number of operating cycles to the corresponding limit number of operating cycles under each stress level to generate a fatigue damage accumulation factor. When the fatigue damage accumulation factor approaches 1, the component is determined to have entered the failure period.

5. The intelligent inspection and status early warning system for medical equipment according to claim 4, characterized in that, The digital twin mapping unit is also used to: perform two verifications between the simulation results and the actual operating parameters using the real-time parameter feedback function, and adjust the simulation material parameter coefficients in the virtual model in reverse according to the verification residuals; the simulation material parameter coefficients include the friction coefficient and the thermal conductivity coefficient, so as to ensure that the virtual model maintains physical consistency with the physical entity throughout the entire service life of the equipment.

6. The intelligent inspection and status early warning system for medical equipment according to claim 1, characterized in that, When constructing a dynamic reference baseline, the dynamic baseline evolution unit also performs the following process: at the end of each sampling period, it calculates the Euclidean distance between the newly acquired feature point and the existing cluster center; if the Euclidean distance exceeds a preset range of 3 times the standard deviation, the corresponding feature point is marked as an isolated point that does not participate in the baseline update; at the same time, the dynamic baseline evolution unit allocates the baseline update frequency according to the importance of the equipment, wherein the baseline update frequency for life support equipment is set to once every 1 minute, and the baseline update frequency for ordinary inspection equipment is set to once every 24 hours.

7. The intelligent inspection and status early warning system for medical equipment according to claim 1, characterized in that, The spatiotemporal correlation early warning engine is also used to: introduce an attention mechanism during time series modeling to identify key feature points in the characteristic fluctuations before a fault occurs, and increase the early warning weight of the key feature points; the spatiotemporal correlation early warning engine also integrates a multi-source information fusion algorithm based on evidence theory, which performs conflict detection by calculating the basic probability assignment function. When the current sensor indicates an abnormal load and the vibration sensor does not show a significant offset, the alarm weight of a single dimension is reduced to filter out false faults caused by power grid fluctuations.

8. The intelligent inspection and status early warning system for medical equipment according to claim 1, characterized in that, When constructing a multi-objective optimization scheduling model, the intelligent inspection path scheduling unit incorporates the departmental business busyness factor obtained by accessing the hospital information system. When the target equipment is in surgical or emergency status, the inspection priority is increased and senior technical personnel are given priority in allocation. The intelligent inspection path scheduling unit also calculates the shortest physical path to avoid the surgical process corridor area by importing the hospital building information model, and pushes path instructions containing a 3D internal structure diagram of the equipment and virtual enhanced guidance of the fault prediction location to the inspection personnel through wearable devices.

9. The intelligent inspection and status early warning system for medical equipment according to claim 1, characterized in that, The closed-loop execution evaluation unit is also used to: adopt a parameter adaptive adjustment strategy based on reinforcement learning, increase the penalty term of the loss function in response to false alarms or missed alarms, and prompt the dynamic baseline evolution unit to recalculate the sensitivity weights of the feature parameters; at the same time, the closed-loop execution evaluation unit constructs a maintenance knowledge graph, abstracts maintenance cases into logical nodes, and associates fault phenomena, predicted features and handling measures to provide auxiliary decision support for subsequent early warning.

10. The intelligent inspection and status early warning system for medical equipment according to claim 1, characterized in that, The system also integrates the following functions: performing energy efficiency analysis, identifying abnormal standby power consumption of medical equipment during non-working periods through statistical analysis of the equipment's operating power consumption, in order to determine whether the equipment has dust accumulation in the heat dissipation system or potential leakage in the internal circuit; at the same time, the system has the ability to monitor multiple devices collaboratively, and by calculating the performance median of a group of devices of the same model in the same area, identifying the deviation between the performance of individual devices and the group, in order to discover batch quality defects or systemic environmental risks.

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