Fault analysis method and device for medical power supply system, and storage medium

Abstract

The invention discloses a fault analysis method and device for a medical power supply system and a storage medium, and belongs to the technical field of medical power supplies, and the method comprises the steps: injecting a non-invasive preset excitation signal into the medical power supply system in a normal working state; synchronously acquiring a response signal of the medical power supply system, and preprocessing the response signal; generating a system dynamic response characteristic based on the excitation signal and the processed response signal; performing difference analysis on the dynamic response characteristics and pre-stored reference characteristics to obtain characteristic parameters representing system performance degradation; and matching the characteristic parameters with a pre-established fault database, identifying the type of the fault element, and outputting a quantitative index of performance degradation of the fault element. Through active excitation and dynamic response analysis, the fault can be early warned with high sensitivity, the specific element type is accurately traced, and the performance degradation degree is quantified, so that the maintenance efficiency and the system reliability are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical power supplies, and in particular relates to a fault analysis method, equipment and storage medium for a medical power supply system. Background Art

[0002] Medical power systems are the core power supply for life-support equipment (such as ventilators and monitors) and diagnostic equipment (such as CT and MRI). Their reliability is directly related to patient safety and the accuracy of medical diagnoses. Therefore, real-time and accurate fault prediction and health management (PHM) of medical power systems is extremely important.

[0003] However, existing medical power system fault monitoring technologies (such as threshold alarm method, regular inspection and offline testing method, and online monitoring method based on impedance spectroscopy) are unable to accurately locate and quantitatively evaluate early weak nonlinear faults in internal components without interrupting the normal operation of the equipment, resulting in delayed fault detection and low maintenance efficiency, and failing to meet the core technical requirements of high reliability of medical equipment. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a fault analysis method, device, and storage medium for medical power supply systems. Through active excitation and dynamic response analysis, faults can be detected early with high sensitivity, accurately traced to specific component types, and quantified in terms of their performance degradation, thereby significantly improving maintenance efficiency and system reliability.

[0005] A first aspect of an embodiment of the present invention provides a fault analysis method for a medical power supply system, comprising: Injecting a non-invasive preset excitation signal into a medical power system in normal working condition; Synchronously collecting a response signal of the medical power system and preprocessing the response signal; generating a system dynamic response characteristic based on the excitation signal and the processed response signal; Performing a difference analysis between the dynamic response characteristics and pre-stored benchmark characteristics to obtain characteristic parameters characterizing system performance degradation; The characteristic parameters are matched with a pre-established fault database to identify the type of faulty component and output a quantitative index of its performance degradation.

[0006] In one embodiment, the preset excitation signal is an electrical pulse sequence.

[0007] In one embodiment, the electrical pulse sequence is a set of stepped pulses with increasing amplitude or a pulse width modulated pulse train.

[0008] In one embodiment, the system dynamic response characteristic is a dynamic voltage and current trajectory.

[0009] In one embodiment, performing a difference analysis between the dynamic response characteristics and pre-stored reference characteristics to obtain characteristic parameters characterizing system performance degradation includes: Performing a differential operation on the dynamic voltage and current trajectory and a pre-stored reference voltage and current trajectory to obtain a differential trajectory curve; The characteristic parameters are extracted from the differential trajectory curve, where the characteristic parameters include one or more of the following: trajectory enclosed area, differential peak value, and trajectory curvature change rate.

[0010] In one embodiment, before matching the characteristic parameters with a pre-established fault database, identifying the type of the faulty component, and outputting a quantitative indicator of its performance degradation, the method further includes: Injecting the preset excitation signal into a normal medical power system of the same model, collecting its response signal and generating corresponding benchmark characteristics; Injecting the same preset excitation signal into a medical power system of the same model and implanted with a known faulty component, collecting its response signal and generating corresponding dynamic response characteristics, and extracting characteristic parameters after performing the difference analysis; The characteristic parameters extracted from the medical power supply system implanted with known faulty components are associated with the corresponding faulty component types to form a set of fault characteristic vectors to construct the fault database.

[0011] In one embodiment, matching the characteristic parameters with a pre-established fault database, identifying the type of the faulty component, and outputting a quantitative indicator of its performance degradation includes: Matching the characteristic parameters with the set of fault feature vectors, wherein the matching process adopts an unsupervised clustering algorithm or a supervised classification algorithm to identify the type of the faulty component; According to the matching result, the quantitative index of performance degradation corresponding to the fault component type is queried in the fault database, where the quantitative index of performance degradation is the deviation percentage of the parameter value of the component relative to its nominal value.

[0012] A second aspect of an embodiment of the present application provides a fault analysis device for a medical power supply system, comprising: A first injection module is used to inject a non-invasive preset excitation signal into the medical power system in a normal working state; An acquisition module, configured to synchronously acquire a response signal from the medical power system and pre-process the response signal; A generating module, configured to generate a system dynamic response characteristic based on the excitation signal and the processed response signal; An analysis module, configured to perform a difference analysis between the dynamic response characteristics and pre-stored reference characteristics to obtain characteristic parameters characterizing system performance degradation; The identification module is used to match the characteristic parameters with a pre-established fault database, identify the type of faulty component and output a quantitative index of its performance degradation.

[0013] In one embodiment, the preset excitation signal is an electrical pulse sequence.

[0014] In one embodiment, the electrical pulse sequence is a set of stepped pulses with increasing amplitude or a pulse width modulated pulse train.

[0015] In one embodiment, the system dynamic response characteristic is a dynamic voltage and current trajectory.

[0016] In one embodiment, the analysis module includes: an operation unit, configured to perform a differential operation on the dynamic voltage and current trajectory and a pre-stored reference voltage and current trajectory to obtain a differential trajectory curve; An extraction unit is used to extract the characteristic parameters from the differential trajectory curve, where the characteristic parameters include one or more of the following: trajectory enclosed area, differential peak value, and trajectory curvature change rate.

[0017] In one embodiment, the apparatus further comprises: A second injection module is used to inject the preset excitation signal into a normal medical power system of the same model, collect its response signal and generate a corresponding benchmark characteristic; A third injection module is used to inject the same preset excitation signal into a medical power system of the same model and implanted with a known faulty component, collect its response signal and generate corresponding dynamic response characteristics, and extract characteristic parameters after performing the difference analysis; A construction module is used to associate the characteristic parameters extracted from the medical power supply system implanted with known faulty components with the corresponding faulty component types to form a set of fault characteristic vectors to construct the fault database.

[0018] In one embodiment, the identification module includes: a matching unit, configured to match the characteristic parameter with the set of fault characteristic vectors, wherein the matching process adopts an unsupervised clustering algorithm or a supervised classification algorithm to identify the type of the faulty component; A query unit is used to query the fault database for a quantitative index of performance degradation corresponding to the fault component type based on the matching result, where the quantitative index of performance degradation is a deviation percentage of a parameter value of a component relative to its nominal value.

[0019] A third aspect of an embodiment of the present application provides a fault analysis device for a medical power supply system, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the method described in the first aspect above is implemented.

[0020] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect above is implemented.

[0021] The beneficial effects of the embodiments of the present application include: injecting a non-invasive, preset excitation signal into a medical power system in normal working condition; synchronously acquiring and preprocessing the response signal of the medical power system; generating the system's dynamic response characteristics based on the excitation signal and the processed response signal; performing differential analysis between the dynamic response characteristics and pre-stored baseline characteristics to obtain characteristic parameters representing system performance degradation; matching the characteristic parameters with a pre-established fault database to identify the type of faulty component and output a quantitative indicator of its performance degradation. Through active excitation and dynamic response analysis, faults can be detected early and with high sensitivity, accurately traced to specific component types, and their degree of performance degradation can be quantified, significantly improving maintenance efficiency and system reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0023] Figure 1 A flowchart of a fault analysis method for a medical power system provided in one embodiment of the present application; Figure 2 for Figure 1 Schematic diagram of the specific implementation process of S140; Figure 3 A schematic diagram of a fault analysis device for a medical power system provided in one embodiment of the present application; Figure 4 A schematic diagram of a fault analysis device for a medical power system provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0026] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0027] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0028] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0029] In the description of the embodiments of the present application, the term "multi-frame" refers to two or more (including two).

[0030] In the description of the embodiments of the present application, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present application.

[0031] See also Figure 1 , Figure 1This is a flowchart of a fault analysis method for a medical power system provided in one embodiment of the present application. The fault analysis method for a medical power system is performed by a fault analysis device for a medical power system. The fault analysis device for a medical power system includes, but is not limited to, a terminal or a server.

[0032] Depend on Figure 1 It can be seen that the fault analysis method for a medical power system provided in the embodiment of the present application includes the following steps S110 to S150. The details are as follows: S110: Injecting a non-invasive preset excitation signal into the medical power system in a normal working state.

[0033] Normal operating state refers to the operating state in which the medical power system is connected to the input power and supplies power to the load (such as medical equipment) within its rated output voltage and current range.

[0034] Non-intrusive means that the amplitude of the excitation signal is much smaller than the rated operating current of the medical power system (for example, not exceeding 1% to 5% of the rated current), and its energy is not enough to cause the output voltage of the medical power system to fluctuate beyond its voltage regulation accuracy range, thereby ensuring that it does not interfere with its normal operation and the quality of the output power.

[0035] A preset excitation signal refers to a test signal whose waveform, amplitude, duration, and other parameters are pre-set. In one embodiment, the preset excitation signal is an electrical pulse sequence. Preferably, the electrical pulse sequence can be a series of step pulses with increasing amplitudes, or a pulse width modulated (PWM) pulse train.

[0036] S120: Synchronously collect response signals from the medical power supply system and pre-process the response signals.

[0037] Synchronous acquisition refers to collecting the response of key measurement points in the medical power system (such as the output terminal and both ends of the power device) within the same time window as the injection of the excitation signal. The response signal is usually a voltage signal.

[0038] In this embodiment, preprocessing includes, but is not limited to, noise reduction (such as using a filter to remove power frequency interference and high-frequency noise), alignment (ensuring that the stimulus and response signals accurately correspond on the time axis), and normalization operations to provide high-quality data for subsequent analysis.

[0039] S130: Generate system dynamic response characteristics based on the excitation signal and the processed response signal.

[0040] It should be noted that this step aims to quantitatively characterize the dynamic behavior of the medical power system by analyzing the relationship between the excitation and response signals. Specifically, the excitation signal refers to the excitation current, and the response signal usually refers to the response voltage.

[0041] Exemplarily, based on the excitation signal and the processed response signal, the system dynamic response characteristics are generated, including: first, ensuring that the excitation current signal and the response voltage signal are synchronized on the time axis; for periodic excitation, one or more stable period signal segments can be intercepted for analysis to eliminate transient interference and improve the signal-to-noise ratio.

[0042] The response voltage and excitation current of the same time series are mapped onto a two-dimensional plane (with the excitation current on the horizontal axis and the response voltage on the vertical axis), forming a closed trajectory curve. This trajectory curve is a characteristic graphical representation of the system's dynamic response. Its shape and structure reflect the operating mode and nonlinear characteristics of the medical power system. Specifically, the area enclosed by the trajectory curve within a cycle is proportional to the system's average energy loss. An increase in area generally indicates decreased efficiency or component aging. The slope (rate of change of trajectory curvature) at a point on the trajectory intuitively reflects the system's dynamic impedance at that operating point.

[0043] Furthermore, the spectrum of the excitation and response signals can be obtained by performing a fast Fourier transform on them. By calculating the transfer function of the spectrum, the system's amplitude-frequency and phase-frequency characteristics can be obtained. This characteristic accurately characterizes the system's gain (impedance amplitude) and phase delay at different frequencies, making it suitable for analyzing the frequency-domain dynamic performance of filters, feedback loops, and other systems. Alternatively, key parameters characterizing dynamic performance can be directly extracted from the response voltage signal, such as rise time (characterizing the system's response speed), overshoot rate (characterizing the system's damping and stability), settling time (characterizing how quickly the system reaches steady state), and harmonic distortion (characterizing the system's degree of nonlinearity).

[0044] It should be noted that the above implementation process is only an example and does not constitute a limitation on the specific implementation method. One or more methods can be selected and combined for analysis based on the specific diagnostic objectives and system characteristics.

[0045] S140: Performing a difference analysis between the dynamic response characteristics and the pre-stored reference characteristics to obtain characteristic parameters that characterize system performance degradation.

[0046] In this embodiment, the dynamic voltage and current trajectory of the system dynamic response characteristics is taken as an example to illustrate the process of performing difference analysis between the dynamic response characteristics and the pre-stored reference characteristics to obtain characteristic parameters that characterize the system performance degradation. Figure 2 As shown, Figure 2 for Figure 1 Schematic diagram of the specific implementation process of S140. Figure 2 It can be seen that, in this embodiment, S140 includes: S141: performing a differential operation on the dynamic voltage and current trajectory and the pre-stored reference voltage and current trajectory to obtain a differential trajectory curve.

[0047] Specifically, the currently measured dynamic voltage and current traces are numerically subtracted from the pre-stored reference voltage and current traces at the same operating point to produce a differential trace curve. Ideally, if the medical power system is completely healthy, the differential curve should be a straight line close to zero. Any deviation will be clearly visible on this differential curve as a bump, depression, or deformation. This differential curve amplifies the changes in system characteristics, making fault characteristics more obvious.

[0048] S142: Extracting characteristic parameters from the differential trajectory curve, the characteristic parameters including one or more of the following: trajectory enclosed area, differential peak value, and trajectory curvature change rate.

[0049] Specific numerical indicators that can quantify performance degradation are extracted from the differential curve as characteristic parameters. These characteristic parameters include the area enclosed by the trajectory, the differential peak, and the rate of change of the trajectory curvature. Among them, the larger the area enclosed by the differential curve, the greater the difference from the healthy state, which usually means the increase in energy loss, and is a comprehensive degradation indicator. The differential peak is the absolute value of the maximum positive deviation and / or the maximum negative deviation in the differential curve. The higher the peak, the more serious the performance deviation at a specific operating point, which may correspond to a local failure of a component. The rate of change of trajectory curvature is used to describe the rate of change of the curvature of the differential curve. This parameter is very sensitive to specific failure modes. For example, an increase in the ESR (equivalent series resistance) of a capacitor or a change in the saturation characteristics of an inductor may cause a specific change in the trajectory curvature.

[0050] Specifically, the characteristic parameter may be a set of characteristic parameters (eg, area=XX, peak value=YY), which quantitatively describe the degree of system performance degradation.

[0051] S150: Match the characteristic parameters with a pre-established fault database, identify the type of the faulty component and output a quantitative index of its performance degradation.

[0052] The fault database is a knowledge base or matching model pre-built through experiments, simulations, or historical fault data. It establishes a mapping relationship between the characteristic parameters (patterns) of various fault modes and specific fault types. For example, a record in the database indicates that when the area enclosed by the trace increases by 20% and the differential peak primarily occurs in a certain area, there is a 90% probability that the capacitance of filter capacitor C1 has decreased by 30%.

[0053] Specifically, the characteristic parameters are matched with a pre-established fault database to identify the type of faulty component and output a quantitative index of its performance degradation, including: matching the characteristic parameters with a set of fault feature vectors, using an unsupervised clustering algorithm or a supervised classification algorithm in the matching process to identify the type of faulty component; based on the matching results, querying the fault database for a quantitative index of performance degradation corresponding to the type of faulty component, where the quantitative index of performance degradation is the percentage deviation of the component's parameter value relative to its nominal value.

[0054] In this embodiment, the obtained characteristic parameter set is compared with records in the fault database. Depending on the complexity of the medical power supply system, various methods such as simple threshold judgment, pattern recognition, or machine learning algorithms (such as classifiers and neural networks) can be used to calculate the similarity or probability between the current data and various fault modes, thereby finding the most matching fault mode. Based on this, the type of faulty component can be identified, such as "increased on-resistance of the main power MOSFET," "increased ESR of the output filter capacitor," or "imbalanced feedback loop compensation network," achieving accurate fault location and identification.

[0055] After identifying the type of faulty component, the system outputs quantitative indicators of its performance degradation, such as "the capacitance of capacitor C1 has degraded to 70% of its nominal value" or "the on-resistance of the MOSFET has doubled." These indicators provide a direct basis for predictive maintenance, allowing assessment of the component's remaining margin of failure.

[0056] The output of this step is a structured diagnostic conclusion, which not only contains qualitative fault location information, but more importantly, provides quantitative performance degradation indicators, providing a direct and accurate data basis for the formulation of predictive maintenance strategies.

[0057] It should be noted that before matching the characteristic parameters with the pre-established fault database, identifying the type of faulty component and outputting the quantitative index of its performance degradation, it also includes: injecting a preset excitation signal into a normal medical power system of the same model, collecting its response signal and generating the corresponding benchmark characteristics; The same preset excitation signal is injected into the medical power supply system of the same model and with a known fault component, and its response signal is collected and the corresponding dynamic response characteristics are generated. After difference analysis, the characteristic parameters are extracted; The characteristic parameters extracted from the medical power supply system implanted with known faulty components are associated with the corresponding faulty component types to form a set of fault feature vectors to construct a fault database.

[0058] Through the above analysis, it can be seen that the embodiment of the present application provides a fault analysis method for a medical power system, including: injecting a non-invasive preset excitation signal into a medical power system in normal working condition; synchronously collecting the response signal of the medical power system and pre-processing the response signal; generating the system dynamic response characteristics based on the excitation signal and the processed response signal; performing a differential analysis between the dynamic response characteristics and the pre-stored baseline characteristics to obtain characteristic parameters that characterize the system performance degradation; matching the characteristic parameters with a pre-established fault database to identify the type of faulty component and output a quantitative indicator of its performance degradation. Through active excitation and dynamic response analysis, it is possible to provide a highly sensitive early warning of faults, accurately trace them to specific component types, and quantify the degree of their performance degradation, thereby significantly improving maintenance efficiency and system reliability.

[0059] See Figure 3 , Figure 3 Schematic diagram of a fault analysis device for a medical power system provided in one embodiment of the present application. The fault analysis device for a medical power system includes modules or units for performing Figure 1 or Figure 2 Each step in the corresponding embodiment. Please refer to Figure 1 or Figure 2 For the convenience of explanation, only the parts related to this embodiment are shown. Figure 3 , a fault analysis device 300 for a medical power system, comprising: The first injection module 310 is configured to inject a non-invasive preset excitation signal into the medical power system in a normal working state.

[0060] An acquisition module 320 is configured to synchronously acquire a response signal from the medical power system and pre-process the response signal; A generating module 330, configured to generate a system dynamic response characteristic based on the excitation signal and the processed response signal; An analysis module 340 is configured to perform a difference analysis between the dynamic response characteristics and pre-stored reference characteristics to obtain characteristic parameters representing system performance degradation; The identification module 350 is used to match the characteristic parameters with a pre-established fault database, identify the type of the faulty component and output a quantitative indicator of its performance degradation.

[0061] In one embodiment, the preset excitation signal is an electrical pulse sequence.

[0062] In one embodiment, the electrical pulse sequence is a set of stepped pulses with increasing amplitude or a pulse width modulated pulse train.

[0063] In one embodiment, the system dynamic response characteristic is a dynamic voltage and current trajectory.

[0064] In one embodiment, the analysis module 340 includes: an operation unit, configured to perform a differential operation on the dynamic voltage and current trajectory and a pre-stored reference voltage and current trajectory to obtain a differential trajectory curve; An extraction unit is used to extract the characteristic parameters from the differential trajectory curve, where the characteristic parameters include one or more of the following: trajectory enclosed area, differential peak value, and trajectory curvature change rate.

[0065] In one embodiment, the apparatus 300 further includes: A second injection module is used to inject the preset excitation signal into a normal medical power system of the same model, collect its response signal and generate a corresponding benchmark characteristic; A third injection module is used to inject the same preset excitation signal into a medical power system of the same model and implanted with a known faulty component, collect its response signal and generate corresponding dynamic response characteristics, and extract characteristic parameters after performing the difference analysis; A construction module is used to associate the characteristic parameters extracted from the medical power supply system implanted with known faulty components with the corresponding faulty component types to form a set of fault characteristic vectors to construct the fault database.

[0066] In one embodiment, the identification module 350 includes: a matching unit, configured to match the characteristic parameter with the set of fault characteristic vectors, wherein the matching process adopts an unsupervised clustering algorithm or a supervised classification algorithm to identify the type of the faulty component; A query unit is used to query the fault database for a quantitative index of performance degradation corresponding to the fault component type based on the matching result, where the quantitative index of performance degradation is a deviation percentage of a parameter value of a component relative to its nominal value.

[0067] See Figure 4 , Figure 4 A schematic diagram of a fault analysis device for a medical power system provided in one embodiment of the present application. Figure 4 It can be seen that the fault analysis device 400 for a medical power system includes: a processor 410, a memory 420, and a computer program 430 stored in the memory 420 and executable on the processor 410; when the processor 410 executes the computer program 430, the steps in the above-mentioned embodiments of the fault analysis method for a medical power system are implemented, such as Figure 1 Alternatively, when the processor 410 executes the computer program 430, the functions of the modules / units in the above-mentioned device embodiments are realized, for example Figure 3 Functions of modules 310 to 350 are shown.

[0068] Exemplarily, computer program 430 may be divided into one or more modules / units, one or more of which are stored in memory 420 and executed by processor 410 to implement the present application. One or more modules / units may be a series of computer program instruction segments capable of performing specific functions, and these instruction segments are used to describe the execution process of computer program 430 in a fault analysis device for a medical power system. For example, computer program 430 may be divided into a first injection module, an acquisition module, a generation module, an analysis module, and an identification module.

[0069] The fault analysis device for medical power supply system provided in this embodiment may include, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that Figure 4 The present invention is merely an example of a fault analysis device for a medical power system and does not constitute a limitation on the fault analysis device for a medical power system. The present invention may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the fault analysis device for a medical power system may also include input and output devices, network access devices, buses, etc.

[0070] The processor 410 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0071] Memory 420 can be an internal storage unit of the medical power system fault analysis device, such as a hard drive or memory. Memory 420 can also be an external storage device, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, or a flash memory card. Furthermore, the medical power system fault analysis device can include both an internal storage unit and an external storage device. Memory 420 is used to store computer programs and other programs and data required by the medical power system fault analysis device. Memory 420 can also be used to temporarily store data that has been output or is about to be output.

[0072] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0073] An embodiment of the present application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the computer program.

[0074] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned various method embodiments can be implemented.

[0075] An embodiment of the present application provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned various method embodiments when executing the computer program product.

[0076] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, removable hard drives, magnetic disks, or optical disks. In some jurisdictions, based on legislation and patent practice, computer-readable media cannot be electric carrier signals or telecommunication signals.

[0077] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0078] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0079] In the embodiments provided in this application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0080] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0081] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A fault analysis method for a medical power supply system, characterized in that: The method comprises: Injecting a non-invasive preset excitation signal into a medical power system in normal working condition; Synchronously collecting a response signal of the medical power system and preprocessing the response signal; generating a system dynamic response characteristic based on the excitation signal and the processed response signal; Performing a difference analysis between the dynamic response characteristics and pre-stored benchmark characteristics to obtain characteristic parameters characterizing system performance degradation; The characteristic parameters are matched with a pre-established fault database to identify the type of faulty component and output a quantitative index of its performance degradation.

2. The fault analysis method for a medical power supply system according to claim 1, wherein: The preset excitation signal is an electric pulse sequence.

3. The fault analysis method for a medical power supply system according to claim 2, wherein: The electric pulse sequence is a group of step pulses with increasing amplitude or a pulse train of pulse width modulation.

4. The fault analysis method for a medical power supply system according to claim 1, wherein: The system dynamic response characteristic is a dynamic voltage and current trajectory.

5. The fault analysis method for a medical power supply system according to claim 4, wherein: The difference analysis between the dynamic response characteristics and the pre-stored reference characteristics is performed to obtain characteristic parameters characterizing system performance degradation, including: Performing a differential operation on the dynamic voltage and current trajectory and a pre-stored reference voltage and current trajectory to obtain a differential trajectory curve; The characteristic parameters are extracted from the differential trajectory curve, where the characteristic parameters include one or more of the following: trajectory enclosed area, differential peak value, and trajectory curvature change rate.

6. The fault analysis method for a medical power supply system according to claim 1, wherein: Before matching the characteristic parameters with a pre-established fault database, identifying the type of the faulty component, and outputting a quantitative index of its performance degradation, the method further includes: Injecting the preset excitation signal into a normal medical power system of the same model, collecting its response signal and generating corresponding benchmark characteristics; Injecting the same preset excitation signal into a medical power system of the same model and implanted with a known faulty component, collecting its response signal and generating corresponding dynamic response characteristics, and extracting characteristic parameters after performing the difference analysis; The characteristic parameters extracted from the medical power supply system implanted with known faulty components are associated with the corresponding faulty component types to form a set of fault characteristic vectors to construct the fault database.

7. The fault analysis method for a medical power supply system according to claim 6, wherein: The matching of the characteristic parameters with a pre-established fault database, identifying the type of the faulty component and outputting a quantitative index of its performance degradation includes: Matching the characteristic parameters with the set of fault feature vectors, wherein the matching process adopts an unsupervised clustering algorithm or a supervised classification algorithm to identify the type of the faulty component; According to the matching result, the quantitative index of performance degradation corresponding to the fault component type is queried in the fault database, where the quantitative index of performance degradation is the deviation percentage of the parameter value of the component relative to its nominal value.

8. A fault analysis device for a medical power supply system, characterized in that: include: A first injection module is used to inject a non-invasive preset excitation signal into the medical power system in a normal working state; An acquisition module, configured to synchronously acquire a response signal from the medical power system and pre-process the response signal; A generating module, configured to generate a system dynamic response characteristic based on the excitation signal and the processed response signal; An analysis module, configured to perform a difference analysis between the dynamic response characteristics and pre-stored reference characteristics to obtain characteristic parameters characterizing system performance degradation; The identification module is used to match the characteristic parameters with a pre-established fault database, identify the type of faulty component and output a quantitative index of its performance degradation.

9. A fault analysis device for a medical power supply system, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor; When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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