Medical equipment fault prediction and full life cycle benefit optimization method and system, electronic equipment and storage medium
By integrating multi-dimensional data and using multi-algorithm collaborative prediction models, the problems of low fault prediction accuracy and lack of full life-cycle benefits in medical equipment management have been solved. This has enabled personalized maintenance scheduling and intelligent management of equipment operation, reducing operation and maintenance costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- JIANGSU KANGYITONG TECH CO LTD
- Filing Date
- 2025-12-30
- Publication Date
- 2026-05-08
AI Technical Summary
Existing medical equipment management solutions suffer from low accuracy in fault prediction, rigid maintenance models, and a lack of life-cycle benefit control. In particular, they fail to adequately consider equipment load differences, parameter coupling relationships, and environmental impacts, resulting in high false alarm and false alarm rates, increased costs, and a lack of scientific basis for management.
By adopting multi-dimensional data collection and integration, combining environmental data and full life cycle management data, a multi-algorithm collaborative prediction model is used to predict faults. A dynamic correction mechanism and a greedy algorithm are used to generate personalized maintenance scheduling schemes, and a full life cycle benefit evaluation model is constructed to achieve intelligent control of equipment operation.
It significantly improves the accuracy of fault prediction, avoids excessive or insufficient maintenance, reduces operation and maintenance costs, and realizes scientific management and efficiency optimization of the entire equipment life cycle.
Smart Images
Figure CN122000009A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of medical equipment management and digital operation and maintenance technology, and in particular to a method, system, electronic device and storage medium for predicting medical equipment failures and optimizing its benefits throughout its entire life cycle. Background Technology
[0002] In the process of digital transformation in the healthcare industry, high-end medical equipment such as CT scanners, MRI scanners, and ultrasound diagnostic instruments are the core support for clinical diagnosis and treatment. Currently, the mainstream management solutions for medical equipment are mainly divided into two categories: one is periodic preventive maintenance, which involves checking and replacing parts at fixed time intervals; the other is passive response based on basic data monitoring, which triggers alarms when parameters such as voltage and current exceed limits.
[0003] However, existing technologies have significant shortcomings: 1. The rigid "one-size-fits-all" maintenance model: Regular maintenance cannot adapt to the actual load differences of different equipment. High-frequency equipment is prone to failure within the maintenance cycle, while low-frequency equipment faces over-maintenance, increasing costs.
[0004] 2. The early warning mechanism is simple and has a high false alarm and false alarm rate: It relies on a single parameter threshold for judgment, without considering the coupling relationship between parameters and the influence of the environment, making it difficult to distinguish between instantaneous fluctuations and fault precursors.
[0005] 3. Lack of overall life cycle planning: Existing solutions mostly focus on the maintenance process and fail to integrate data from the entire process of procurement, operation and maintenance, treatment benefits and disposal, resulting in a lack of scientific basis for equipment management decisions and a common phenomenon of "emphasizing procurement and neglecting management".
[0006] 4. Poor adaptability of single algorithms: Medical equipment operation data has nonlinear and non-stationary characteristics. Single threshold judgment or traditional machine learning model prediction accuracy is insufficient, making it difficult to identify hidden faults in advance. Summary of the Invention
[0007] This invention proposes a method, system, electronic device, and storage medium for predicting medical equipment failures and optimizing its life-cycle benefits, in order to solve the problems of low accuracy in failure prediction, rigid maintenance modes, and lack of life-cycle benefit management in the prior art.
[0008] The first aspect of this invention discloses a method for predicting medical device failures and optimizing its life-cycle benefits, the method comprising: Step S1: Multi-dimensional data collection and integration: Real-time acquisition of equipment operation status data of medical equipment through data acquisition interface, acquisition of environmental data of the environment in which the equipment is located through environmental sensors, and acquisition of full life cycle management data from hospital management system; Step S2: Data Preprocessing and Feature Engineering: The collected data is cleaned and standardized, and a fault feature set is constructed by combining domain knowledge and statistical analysis; the fault feature set includes at least the environmental temperature and humidity synergy coefficient. Step S3: Multi-algorithm collaborative fault prediction: Input the fault feature set into the pre-trained multi-algorithm collaborative prediction model and output the fault prediction result; the multi-algorithm collaborative prediction model is based on the weighted fusion of the results of multiple basic prediction models and has a dynamic correction mechanism based on prediction bias. Step S4: Personalized maintenance scheduling optimization: Based on the fault risk level determined by the fault prediction results, combined with the treatment plan constraints and the status of operation and maintenance resources, a personalized maintenance scheduling plan is generated; Step S5: Dynamic evaluation and optimization of full life cycle benefits: Based on full-process data, a multi-dimensional benefit evaluation model is constructed, benefit indicators are calculated, and equipment operation control instructions are generated and fed back to medical equipment according to the benefit evaluation results to achieve closed-loop technical control.
[0009] Preferably, in step S2, the formula for calculating the environmental temperature and humidity synergy coefficient is:
[0010] Where C is the environmental temperature and humidity synergy coefficient; T is the current environmental temperature, and T0 is the optimal operating environment temperature of the equipment; H is the current relative humidity, and H0 is the optimal operating environment relative humidity of the equipment; α and β are weighting coefficients calibrated for different types of medical equipment; and exp() is an exponential function.
[0011] Preferably, in step S3, the multi-algorithm collaborative prediction model includes an LSTM time-series prediction model, a random forest fault classification model, and a gradient boosting tree risk assessment model; the weighted fusion adopts dynamic weight adjustment, and the weight update formula is:
[0012] Among them, W i,t Let be the weight of the i-th base model in period t; A is the step size coefficient; cci,t−1 represents the actual prediction accuracy of the i-th base model in the previous period; Accbase is the preset accuracy threshold; the updated weights need to be normalized.
[0013] Preferably, the dynamic correction mechanism includes: Calculate the deviation between the predicted failure frequency and the actual failure frequency; When the deviation value exceeds the preset threshold, the basic model that needs to be adjusted is located based on the source of the deviation. If the deviation originates from the LSTM model, adjust the number of hidden layer neurons or the Dropout probability; if the deviation originates from the random forest model, adjust the number of decision trees or the depth of the trees; if the deviation originates from the gradient boosting tree model, adjust the learning rate or the number of trees. The adjustment adopts a gradual strategy, with each adjustment step being a preset proportion of the current parameter value, until the verification deviation meets the requirements.
[0014] Preferably, in step S4, the generation of the personalized maintenance scheduling scheme adopts a greedy algorithm, with the objective function being to minimize the overall operation and maintenance cost:
[0015] Where Z is the comprehensive operation and maintenance cost, Closs is the equipment downtime loss, Clabor is the maintenance manpower cost, Cpart is the spare parts allocation cost, and k1, k2, k3 are weighting coefficients; The constraints must include at least the following: maintenance work time does not overlap with core diagnosis and treatment periods, the skill matching degree of maintenance personnel meets the requirements, and key spare parts are available in stock; For key components of medical devices with thermal damage accumulation characteristics, a physical-data dual-drive model is constructed.
[0016] Preferably, a physical constraint term is added to the loss function of the LSTM model:
[0017] Among them, Q pred Q represents the remaining heat capacity predicted by the model. rated P(t) is the rated heat capacity, P(t) is the real-time heat power, and η is the heat dissipation efficiency; maintenance timing is arranged based on the predicted remaining heat capacity.
[0018] Preferably, the method further includes an adaptive threshold adjustment step based on environmental perception: Set the dynamic threshold V(H,T,N) for the fault alarm:
[0019] Among them, V base The baseline threshold is defined as N, where N is the average daily load of the equipment, N0 is the standard average daily load, and ka, kb, and kc are influence coefficients. When environmental parameters deviate from the optimal value or the load increases, the alarm threshold is automatically tightened.
[0020] Preferably, in step S5, the generating device operation control command includes: When the full life cycle benefit assessment results meet the end-of-life cycle determination criteria, a downgrade operation instruction is generated, which locks the maximum operating power of the equipment or limits the functional modules through the equipment control interface. When the cost of parts replacement exceeds a threshold, the feature weights and maintenance cycle parameters of the fault prediction model are automatically adjusted.
[0021] A second aspect of this invention discloses a medical device failure prediction and life-cycle benefit optimization system, the system comprising: The data acquisition module is used to collect equipment operating status data, environmental data, and full lifecycle management data; The data preprocessing module is used to clean, standardize, and perform feature engineering on the data. The fault prediction module is used for fault prediction based on a multi-algorithm collaborative model. The maintenance scheduling module is used to generate personalized maintenance scheduling plans; The full life cycle benefit assessment module is used to evaluate benefit indicators and generate control instructions; The terminal display module is used to display the evaluation results and control command status.
[0022] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for predicting medical device failures and optimizing life-cycle benefits according to any one of the first aspects of this disclosure.
[0023] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for predicting medical device failures and optimizing life-cycle benefits according to any one of the first aspects of this disclosure.
[0024] The beneficial effects of this invention are as follows: 1. By integrating multi-dimensional data, especially by introducing environmental collaborative data, the comprehensiveness of data analysis has been significantly improved.
[0025] 2. The multi-algorithm collaboration and dynamic correction mechanism solves the problem of poor adaptability of a single algorithm to nonlinear data, and the accuracy of fault prediction is greatly improved.
[0026] 3. Personalized scheduling based on greedy algorithms avoids over-maintenance and under-maintenance, reducing operation and maintenance costs.
[0027] 4. A closed-loop technology system has been established, from benefit assessment to equipment control, enabling intelligent and scientific management of the entire equipment lifecycle. Attached Figure Description
[0028] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0029] Figure 1 A flowchart illustrating a method for predicting medical device failures and optimizing life-cycle benefits according to an embodiment of the present invention; Figure 2 This is a structural diagram of a medical device fault prediction and life-cycle benefit optimization system according to an embodiment of the present invention; Figure 3 This is a structural diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation
[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.
[0031] The first aspect of this invention discloses a method for predicting medical device failures and optimizing its life-cycle benefits. Figure 1 This is a flowchart of a method for predicting medical device failures and optimizing life-cycle benefits according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes: Step S1: Multi-dimensional data collection and integration: Real-time acquisition of equipment operation status data of medical equipment through data acquisition interface, acquisition of environmental data of the environment in which the equipment is located through environmental sensors, and acquisition of full life cycle management data from hospital management system; In some specific embodiments, the X-ray tube temperature, number of exposures, room temperature and humidity, historical maintenance records, and treatment revenue data of the CT scanner can be collected.
[0032] Step S2: Data Preprocessing and Feature Engineering: The collected data is cleaned and standardized, and a fault feature set is constructed by combining domain knowledge and statistical analysis; the fault feature set includes at least the environmental temperature and humidity synergy coefficient. Furthermore, the focus is on calculating the environmental temperature and humidity synergy coefficient C. Considering the accelerating effect of high temperature and high humidity on circuit aging, the formula is defined as: Among them, T0=23℃, H0=50%RH. This feature can effectively capture the impact of the environment on the latent faults of equipment.
[0033] Step S3: Multi-algorithm collaborative fault prediction: Input the fault feature set into the pre-trained multi-algorithm collaborative prediction model and output the fault prediction result; the multi-algorithm collaborative prediction model is based on the weighted fusion of the results of multiple basic prediction models and has a dynamic correction mechanism based on prediction bias. In step S3, the multi-algorithm collaborative prediction model includes an LSTM time-series prediction model, a random forest fault classification model, and a gradient boosting tree risk assessment model; the weighted fusion adopts dynamic weight adjustment, and the weight update formula is:
[0034] Among them, W i,t Let be the weight of the i-th base model in period t; A is the step size coefficient; cci,t−1 represents the actual prediction accuracy of the i-th base model in the previous period; Accbase is the preset accuracy threshold; the updated weights need to be normalized.
[0035] Furthermore, a physical constraint term is added to the loss function of the LSTM model:
[0036] Among them, Q pred Q represents the remaining heat capacity predicted by the model. rated P(t) is the rated heat capacity, P(t) is the real-time heat power, and η is the heat dissipation efficiency; maintenance timing is arranged based on the predicted remaining heat capacity.
[0037] Specifically, the dynamic correction mechanism includes: Calculate the deviation between the predicted failure frequency and the actual failure frequency; When the deviation value exceeds the preset threshold, the basic model that needs to be adjusted is located based on the source of the deviation. If the deviation originates from the LSTM model, adjust the number of hidden layer neurons or the Dropout probability; if the deviation originates from the random forest model, adjust the number of decision trees or the depth of the trees; if the deviation originates from the gradient boosting tree model, adjust the learning rate or the number of trees. The adjustment adopts a gradual strategy, with each adjustment step being a preset proportion of the current parameter value, until the verification deviation meets the requirements.
[0038] In some specific embodiments, multiple algorithms work together for prediction, specifically employing a three-level prediction mechanism: 1. Basic Model: LSTM predicts time series trends, Random Forest is used for fault classification, and XGBoost is used to evaluate risk probability.
[0039] 2. Model Fusion: A weighted voting method is used. The weights (WW) are updated every 7 days, using the following formula:
[0040] If the recent accuracy of a model is higher than 0.85, the weights are increased; otherwise, they are decreased.
[0041] 3. Dynamic correction: If the predicted fault frequency deviates from the actual frequency by more than 10%, parameter adjustment is triggered. For example, if the LSTM predicts too few faults, the number of hidden layer neurons is automatically increased (step size 10%) and retraining is performed.
[0042] The above dynamic correction mechanism ensures that the model parameters always match the actual operating state of the equipment, maintaining high prediction accuracy.
[0043] Step S4: Personalized maintenance scheduling optimization: Based on the fault risk level determined by the fault prediction results, combined with the treatment plan constraints and the status of operation and maintenance resources, a personalized maintenance scheduling plan is generated; In step S4, the generation of the personalized maintenance scheduling scheme adopts a greedy algorithm, with the objective function being to minimize the overall operation and maintenance cost:
[0044] Where Z is the comprehensive operation and maintenance cost, Closs is the equipment downtime loss, Clabor is the maintenance manpower cost, Cpart is the spare parts allocation cost, and k1, k2, k3 are weighting coefficients; The constraints must include at least the following: maintenance work time does not overlap with core diagnosis and treatment periods, the skill matching degree of maintenance personnel meets the requirements, and key spare parts are available in stock.
[0045] In step S4, a physical-data dual-drive model is constructed for key components of medical devices that have thermal damage accumulation characteristics; In some specific embodiments, personalized maintenance scheduling is as follows: 1. Risk classification: High risk (≥70%) triggers emergency maintenance within 24 hours; medium risk (30%-70%) is scheduled for maintenance based on available time slots in the clinic.
[0046] 2. Resource Scheduling: A greedy algorithm is used to minimize the objective function. The algorithm automatically finds the optimal maintenance window that satisfies both "off-peak treatment periods" and "spare parts are available".
[0047] 3. Physics-Data Dual-Driven Approach: For CT tubes, a physical constraint term, Lossphysics, is added to the LSTM loss function to ensure that the predicted heat capacity consumption conforms to the physical heat dissipation law and avoids overfitting caused by pure data-driven approaches.
[0048] 4. Adaptive threshold: The alarm threshold is dynamically adjusted based on environmental data. For example, the voltage fluctuation alarm threshold is automatically reduced in a high humidity environment to prevent missed alarms.
[0049] Step S5: Dynamic evaluation and optimization of full life cycle benefits: Based on full-process data, a multi-dimensional benefit evaluation model is constructed, benefit indicators are calculated, and equipment operation control instructions are generated and fed back to medical equipment according to the benefit evaluation results to achieve closed-loop technical control.
[0050] In step S5, the generating device operation control commands include: When the full life cycle benefit assessment results meet the end-of-life cycle determination criteria, a downgrade operation instruction is generated, which locks the maximum operating power of the equipment or limits the functional modules through the equipment control interface. When the cost of parts replacement exceeds a threshold, the feature weights and maintenance cycle parameters of the fault prediction model are automatically adjusted.
[0051] Furthermore, the method also includes an environment-aware adaptive threshold adjustment step: Set the dynamic threshold V(H,T,N) for the fault alarm:
[0052] Among them, V base The baseline threshold is defined as N, where N is the average daily load of the equipment, N0 is the standard average daily load, and ka, kb, and kc are influence coefficients. When environmental parameters deviate from the optimal value or the load increases, the alarm threshold is automatically tightened.
[0053] Specifically, the benefit assessment and closed-loop control are as follows: 1. Benefit Calculation: Calculate the total cost of ownership (TCO) (including procurement, operation and maintenance, downtime losses, and scrap costs) and return on investment (ROI).
[0054] 2. Closed-loop technology control: 1) Degraded operation strategy: When the system detects that the equipment has entered the end of its life cycle (such as a continuous decline in ROI and a high incidence of failures), the system issues a command through the Modbus / IoT interface to forcibly lock the maximum tube current of the CT machine to 150mA (originally 300mA) and limit high-load scanning in order to extend the remaining life.
[0055] 2) Strategy Reverse Optimization: If the cost of replacing parts is too high, the system automatically increases the weight of the "parts aging" related features in the fault prediction model and shortens the detection cycle of the corresponding parts.
[0056] Through the above steps, the present invention achieves intelligent management of the entire process from data perception to decision control.
[0057] In summary, the medical equipment failure prediction and life-cycle benefit optimization method provided by this invention can: 1. To solve the problem of the "one-size-fits-all" approach in the existing regular maintenance model, and to realize personalized maintenance scheduling based on the actual operating status of the equipment, so as to avoid over-maintenance or under-maintenance and reduce operation and maintenance costs; 2. To address the issues of simple early warning mechanisms and high false alarm / missed alarm rates in existing monitoring systems, improve the accuracy of fault prediction through multi-dimensional data fusion and intelligent algorithm analysis, identify hidden faults in advance, and reduce unplanned downtime; 3. To address the lack of comprehensive lifecycle management in existing solutions, integrate data from the entire process of equipment procurement, operation, maintenance, and disposal, and construct a benefit evaluation model to provide a scientific basis for equipment management decisions; 4. To address the problem of poor adaptability of existing single algorithms to nonlinear operating data of medical equipment, improve the robustness of fault prediction through multi-algorithm fusion models and adapt to the operating characteristics of different types of medical equipment.
[0058] The second aspect of this invention discloses a medical equipment failure prediction and full life cycle benefit optimization system. Figure 2 This is a structural diagram of a medical device fault prediction and life-cycle benefit optimization system according to an embodiment of the present invention; as shown below. Figure 2 As shown, the system 100 of the present invention includes a data acquisition module 101, a data preprocessing module 102, a fault prediction module 103, a maintenance scheduling module 104, a full life cycle benefit assessment module 105, and a terminal display module 106 connected via a communication link.
[0059] The data acquisition module 101 is used to collect equipment operating status data, environmental data, and full life cycle management data. Specifically, it includes an equipment operating status acquisition unit (collecting temperature, voltage, load rate, etc. via DICOM / HL7 protocol), an environmental data acquisition unit (collecting computer room temperature, humidity, dust, etc.), and a full life cycle management data acquisition unit (interfacing with HIS / LIS system to obtain asset and medical data).
[0060] The data preprocessing module 102 is used to clean, standardize, and perform feature engineering on the data. This module is specifically responsible for data cleaning (removing outliers and interpolating), Z-Score standardization, and feature engineering.
[0061] The fault prediction module 103 is used for fault prediction based on a multi-algorithm collaborative model. This module has built-in algorithm models such as LSTM, random forest, and XGBoost, which are responsible for predicting the probability and type of faults.
[0062] The maintenance scheduling module 104 is used to generate personalized maintenance scheduling schemes; this module outputs a maintenance plan based on the prediction results and resource status.
[0063] The life cycle benefit assessment module 105 is used to assess benefit indicators and generate control instructions; this module calculates TCO and ROI and generates equipment control instructions.
[0064] The terminal display module 106 is used to display the evaluation results and control command status.
[0065] This invention constructs a complete technical system and framework encompassing "data acquisition, preprocessing, intelligent prediction, maintenance scheduling, and benefit optimization," combining multi-dimensional data fusion and multi-algorithm collaborative analysis to achieve accurate prediction of medical equipment failures and dynamic optimization of benefits throughout their entire lifecycle.
[0066] A third aspect of this invention discloses an electronic device. The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the method for predicting medical device failures and optimizing life-cycle benefits according to any one of the first aspects of this invention.
[0067] Figure 3 This is a structural diagram of an electronic device according to an embodiment of the present invention, such as... Figure 3 As shown, the electronic device includes a processor, memory, communication interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, Near Field Communication (NFC), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.
[0068] Those skilled in the art will understand that Figure 3 The structure shown is merely a structural diagram of the part related to the technical solution of this disclosure and does not constitute a limitation on the electronic device to which the solution of this application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.
[0069] A fourth aspect of this invention discloses a computer-readable storage medium. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for predicting medical device failures and optimizing life-cycle benefits according to any one of the first aspects of this invention.
[0070] Please note that the technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. The above embodiments only illustrate several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.
[0071] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.
Claims
1. A method for predicting medical equipment failures and optimizing its life-cycle benefits, characterized in that, The method includes: Step S1: Multi-dimensional data collection and integration: Real-time acquisition of equipment operation status data of medical equipment through data acquisition interface, acquisition of environmental data of the environment in which the equipment is located through environmental sensors, and acquisition of full life cycle management data from hospital management system; Step S2: Data Preprocessing and Feature Engineering: The collected data is cleaned and standardized, and a fault feature set is constructed by combining domain knowledge and statistical analysis; the fault feature set includes at least the environmental temperature and humidity synergy coefficient. Step S3: Multi-algorithm collaborative fault prediction: Input the fault feature set into the pre-trained multi-algorithm collaborative prediction model and output the fault prediction result; the multi-algorithm collaborative prediction model is based on the weighted fusion of the results of multiple basic prediction models and has a dynamic correction mechanism based on prediction bias. Step S4: Personalized maintenance scheduling optimization: Based on the fault risk level determined by the fault prediction results, combined with the treatment plan constraints and the status of operation and maintenance resources, a personalized maintenance scheduling plan is generated; Step S5: Dynamic evaluation and optimization of full life cycle benefits: Based on full-process data, a multi-dimensional benefit evaluation model is constructed, benefit indicators are calculated, and equipment operation control instructions are generated and fed back to medical equipment according to the benefit evaluation results to achieve closed-loop technical control.
2. The method according to claim 1, characterized in that, In step S2, the formula for calculating the environmental temperature and humidity synergy coefficient is: Where C is the environmental temperature and humidity synergy coefficient; T is the current environmental temperature, and T0 is the optimal operating environment temperature of the equipment; H is the current relative humidity, and H0 is the optimal operating environment relative humidity of the equipment; α and β are weighting coefficients calibrated for different types of medical equipment; and exp() is an exponential function.
3. The method according to claim 1, characterized in that, In step S3, the multi-algorithm collaborative prediction model includes an LSTM time-series prediction model, a random forest fault classification model, and a gradient boosting tree risk assessment model; the weighted fusion adopts dynamic weight adjustment, and the weight update formula is: Among them, W i,t Let be the weight of the i-th base model in period t; A is the step size coefficient; cci,t−1 represents the actual prediction accuracy of the i-th base model in the previous period; Accbase is the preset accuracy threshold; the updated weights need to be normalized. Add a physical constraint term to the loss function of the LSTM model: Among them, Q pred Q represents the remaining heat capacity predicted by the model. rated P(t) is the rated heat capacity, P(t) is the real-time heat power, and η is the heat dissipation efficiency; maintenance timing is arranged based on the predicted remaining heat capacity.
4. The method according to claim 3, characterized in that, The dynamic correction mechanism includes: Calculate the deviation between the predicted failure frequency and the actual failure frequency; When the deviation value exceeds the preset threshold, the basic model that needs to be adjusted is located based on the source of the deviation. If the deviation originates from the LSTM model, adjust the number of hidden layer neurons or the Dropout probability; if the deviation originates from the random forest model, adjust the number of decision trees or the depth of the trees; if the deviation originates from the gradient boosting tree model, adjust the learning rate or the number of trees. The adjustment adopts a gradual strategy, with each adjustment step being a preset proportion of the current parameter value, until the verification deviation meets the requirements.
5. The method according to claim 1, characterized in that, In step S4, the generation of the personalized maintenance scheduling scheme adopts a greedy algorithm, with the objective function being to minimize the overall operation and maintenance cost: Where Z is the comprehensive operation and maintenance cost, Closs is the equipment downtime loss, Clabor is the maintenance manpower cost, Cpart is the spare parts allocation cost, and k1, k2, k3 are weighting coefficients; The constraints must include at least the following: maintenance work time does not overlap with core diagnosis and treatment periods, the skill matching degree of maintenance personnel meets the requirements, and key spare parts are available in stock; For key components of medical devices with thermal damage accumulation characteristics, a physical-data dual-drive model is constructed.
6. The method according to claim 1, characterized in that, The method further includes an environment-aware adaptive threshold adjustment step: Set the dynamic threshold V(H,T,N) for the fault alarm: Among them, V base The baseline threshold is defined as N, where N is the average daily load of the equipment, N0 is the standard average daily load, and ka, kb, and kc are influence coefficients. When environmental parameters deviate from the optimal value or the load increases, the alarm threshold is automatically tightened.
7. The method according to claim 1, characterized in that, In step S5, the generating device operation control commands include: When the full life cycle benefit assessment results meet the end-of-life cycle determination criteria, a downgrade operation instruction is generated, which locks the maximum operating power of the equipment or limits the functional modules through the equipment control interface. When the cost of parts replacement exceeds a threshold, the feature weights and maintenance cycle parameters of the fault prediction model are automatically adjusted.
8. A medical equipment failure prediction and life-cycle benefit optimization system, used to implement the method described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect equipment operating status data, environmental data, and full lifecycle management data; The data preprocessing module is used to clean, standardize, and perform feature engineering on the data. The fault prediction module is used for fault prediction based on a multi-algorithm collaborative model. The maintenance scheduling module is used to generate personalized maintenance scheduling plans; The full life cycle benefit assessment module is used to evaluate benefit indicators and generate control instructions; The terminal display module is used to display the evaluation results and control command status.
9. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the steps in the method for predicting medical device failures and optimizing life-cycle benefits according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for predicting medical device failures and optimizing life-cycle benefits according to any one of claims 1 to 7.