Performance evaluation method and device of medical equipment, equipment and storage medium

Abstract

The invention provides a medical equipment performance evaluation method and device, equipment and a storage medium. The medical equipment performance evaluation method is applied to a server, and comprises the following steps: obtaining performance influence data transmitted by edge equipment deployed at a medical equipment end, the performance influence data comprises at least one of an abnormal image frame in the image data collected by the medical equipment, a first type of information content in the log data and a second type of information content in the sensor data; processing the performance influence data through a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical equipment; the equipment operation parameters of the medical equipment are obtained, and the performance indexes of the medical equipment are determined based on at least one of the component parameters of the key components and the equipment operation parameters, so that the integration rate of the multi-modal data is improved, the performance evaluation of the medical equipment is realized, and the accuracy of the performance evaluation of the medical equipment is improved. And data support is provided for subsequent maintenance of the medical equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment, and in particular to a performance evaluation method, apparatus, device and storage medium for medical equipment. Background Art

[0002] Medical imaging equipment plays an important role in the diagnosis and treatment of diseases. In order to ensure the performance of medical imaging equipment, it is necessary to conduct performance evaluation of medical imaging equipment.

[0003] In the prior art, the performance of medical imaging equipment is usually evaluated based on the image data collected by the medical imaging equipment. The data used for performance evaluation is single, and there are problems such as low data integration rate and low accuracy of medical imaging equipment performance evaluation. Summary of the Invention

[0004] The present invention provides a performance evaluation method, apparatus, device and storage medium for medical equipment to improve the data integration rate and the accuracy of medical equipment performance evaluation.

[0005] According to one aspect of the present invention, a performance evaluation method for medical equipment is provided, which is applied to a server and includes:

[0006] Obtaining performance impact data transmitted by an edge device deployed on the medical device side, where the performance impact data includes at least one of abnormal image frames in image data collected by the medical device, information content of a first type in log data, and information content of a second type in sensor data;

[0007] Using pre-deployed parameter evaluation models, performance impact data is processed to obtain component parameters of key components in medical devices.

[0008] Obtaining device operating parameters of the medical device, and determining a performance indicator of the medical device based on at least one of component parameters of key components and the device operating parameters.

[0009] According to another aspect of the present invention, a performance evaluation device for medical equipment is provided, which is applied to a server and includes:

[0010] A performance impact data acquisition module is configured to: acquire performance impact data transmitted by an edge device deployed on the medical device side, the performance impact data including at least one of abnormal image frames in image data collected by the medical device, information content of a first type in log data, and information content of a second type in sensor data;

[0011] A component parameter determination module for key components, configured to: process performance impact data using a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device;

[0012] The performance indicator determination module is used to: obtain equipment operating parameters of the medical equipment, and determine the performance indicator of the medical equipment based on at least one of the component parameters of the key components and the equipment operating parameters.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by the at least one processor so that the at least one processor can perform the performance evaluation method of a medical device according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for enabling a processor to implement the performance evaluation method for a medical device according to any embodiment of the present invention when the computer instructions are executed.

[0018] The technical solution of the embodiment of the present invention obtains performance impact data transmitted by the edge device deployed on the medical device side, the performance impact data includes abnormal image frames in the image data collected by the medical device, at least one of the first type of information content in the log data and the second type of information content in the sensor data, and performs subsequent analysis and processing based on the transmitted performance impact data; processes the performance impact data through a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device, providing data support for subsequent analysis; obtains device operating parameters of the medical device, determines the performance indicators of the medical device based on the component parameters of the key components and at least one of the device operating parameters, realizes performance evaluation of the medical device, solves the problems of low data integration rate and low accuracy of medical device performance evaluation, improves the data integration rate and accuracy of medical device performance evaluation, and provides accurate data support for subsequent maintenance.

[0019] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] 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 description of the embodiments. 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 creative work.

[0021] Figure 1 This is a schematic structural diagram of a server, medical equipment, and edge device provided by an embodiment of the present invention;

[0022] Figure 2 This is a flow chart of a performance evaluation method for a medical device provided in Example 1 of the present invention;

[0023] Figure 3 This is a flow chart of a performance evaluation method for medical equipment provided in Example 2 of the present invention;

[0024] Figure 4 This is a flow chart of a performance evaluation method for medical equipment provided in Example 3 of the present invention;

[0025] Figure 5 This is a schematic structural diagram of a performance evaluation device for medical equipment provided by a fourth embodiment of the present invention;

[0026] Figure 6 This is a structural diagram of an electronic device provided in Example 5 of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] The present invention provides a performance evaluation method for medical devices, which is applied to a server and is suitable for medical scenarios. In this method, an edge device is deployed for each medical device. The medical device generates performance impact data during operation, and the performance impact data is stored in the medical device. When the performance of the medical device is evaluated, the performance impact data stored in the medical device is transmitted to the edge device. The edge device analyzes the performance impact data to achieve performance evaluation of the medical device. For example, see Figure 1 , Figure 1 The diagram is a schematic diagram of the structure of a server, medical device, and edge device provided by an embodiment of the present invention. In particular, each medical device deploys an edge device, and each medical device is connected to the server through communication.

[0030] Example 1

[0031] Figure 2 This is a flow chart of a performance evaluation method for medical equipment provided by the first embodiment of the present invention. This embodiment is applicable to the situation of performing performance evaluation on medical equipment. The method is applied to a server and can be executed by a performance evaluation device for medical equipment. The performance evaluation device for medical equipment can be implemented in the form of hardware and / or software and can be configured in a server. Figure 2 As shown, the method is applied to the server and includes:

[0032] S110. Obtain performance impact data transmitted by the edge device deployed on the medical device side, where the performance impact data includes at least one of abnormal image frames in the image data collected by the medical device, a first type of information content in the log data, and a second type of information content in the sensor data.

[0033] Medical devices are hardware devices used to perform medical functions. Medical devices include, but are not limited to, medical imaging devices. For example, medical imaging devices may be computed tomography (CT) devices and magnetic resonance imaging (MRI) devices. Edge devices are hardware devices deployed for each medical device that provide data caching, data processing, and data transmission services for the medical device. Edge devices and medical devices can be connected via communication. Performance impact data is data reflecting the performance of the medical device collected by the medical device and transmitted via the edge device. Performance impact data includes at least one of abnormal image frames in the image data collected by the medical device, a first type of information content in log data, and a second type of information content in sensor data. Image data is image data collected by the medical device, including, but not limited to, CT images and MRI images. Abnormal image frames are images containing abnormalities in the image data. For example, abnormal image frames include, but are not limited to, MRI images containing artifacts and CT images containing bed motion errors. Log data is serial information recorded by the medical device during operation. For example, log data may include the device name, operating time information, and operating parameters of the medical device. The first type of information content includes fault codes corresponding to medical device failures. Sensor data is information collected by sensors during the operation of a medical device. This sensor data includes, but is not limited to, the device's temperature. Different types of medical devices correspond to different sensor data. The second type of information includes the temperature of each component and the number of uses during operation.

[0034] Specifically, the edge device is connected to the medical device for communication, and stores and analyzes the image data, log data, and sensor data generated by the medical device. The performance impact data is determined from the above image data, log data, and sensor data. The edge device caches the above performance impact data and transmits it to the server to realize the performance evaluation of the medical device.

[0035] In some embodiments of the present invention, an image detection model and a text parsing model are integrated into an edge device deployed on the medical device side, wherein the image detection model is used to identify abnormal image frames in the image data collected by the medical device; the text parsing model identifies the first type of information content in the log data and / or the second type of information content in the sensor data.

[0036] Among them, the image detection model is a model for identifying abnormal image frames in image data collected by medical equipment. For example, the image detection model can perform artifact detection on abnormal image frames in MRI images. The image detection model includes but is not limited to a neural network model and a transformer model. Optionally, the image detection model can be a lightweight convolutional neural network (CNN) model. The lightweight CNN model includes but is not limited to a MobileNetV3 model. The text parsing model can be used to identify the first type of information content in log data, and can also be used to identify the second type of information content in sensor data, and can also identify the first type of information content in log data and the second type of information content in sensor data. The text parsing model includes but is not limited to a neural network model and a transformer model. Optionally, the text parsing model can be a bidirectional long short-term memory network (Bidirectional Long Short-Term Memory Network, BiLSTM) model.

[0037] Specifically, edge devices are deployed on the medical device side, and the edge devices are integrated with image detection models and text parsing models. The machine learning models integrated in the edge devices analyze the data generated by the medical devices to obtain performance impact data, namely, at least one of the first type of information content in abnormal image frames and log data and the second type of information content in sensor data. This enables the parsing of sensor data and provides data support for subsequent analysis and processing. Furthermore, the performance impact data is local data within the data generated by the medical device. The performance impact data obtained through edge device analysis is then transmitted for performance evaluation of the medical device, reducing the amount of data transmitted and improving processing efficiency. At the same time, it reduces the interference of other data on the performance evaluation process and improves the accuracy of the performance evaluation.

[0038] In an embodiment of the present invention, the performance impact data includes sensitive information of the patient. In order to protect the privacy of the patient and prevent information leakage, the sensitive information in the performance impact data can be encrypted.

[0039] Optionally, the performance impact data includes encrypted sensitive information and unencrypted non-sensitive information. Specifically, the information type of sensitive information is pre-set, the sensitive information in the performance impact data is identified, the identified sensitive information is encrypted, and non-sensitive information is not encrypted, thereby implementing layered encryption of data. While encrypting sensitive information, unnecessary encryption processes are avoided, reducing the amount of computation caused by unnecessary encryption.

[0040] Sensitive information includes, but is not limited to, the patient's name, age, and diagnosis. Optionally, the sensitive information may be encrypted using Advanced Encryption Standard (AES)-256.

[0041] Optionally, the performance impact data is transmitted via a time-series data pipeline between an edge device deployed on the medical device side and a server.

[0042] Among them, the time series data pipeline is used to transmit performance impact data. The performance impact data is transmitted from the edge device deployed on the medical device side to the server through the time series data pipeline, realizing the reliability of data transmission between the edge device and the server.

[0043] In the process of storing the performance impact data, in order to prevent leakage of the performance impact data, the performance impact data corresponding to the medical device may be protected.

[0044] Optionally, S110 further includes: adding random noise to the performance impact data corresponding to each medical device, and storing the performance impact data after the noise is added.

[0045] The random noise includes but is not limited to Laplace noise.

[0046] Specifically, by adding Laplace noise to the performance impact data corresponding to each medical device, the performance impact data after adding Laplace noise is stored in a pre-set database, thereby achieving storage of the performance impact data and reducing leakage of the performance impact data.

[0047] S120. Process the performance impact data using a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device.

[0048] When a key component in a medical device fails, abnormal image frames will be present in the image data in the performance impact data. The log data will include the fault code of the fault, which characterizes the failure type of the key component. The pre-deployed parameter evaluation model learns the association between the performance impact data and the key components in the medical device. Furthermore, the parameter evaluation model can be used to obtain the component parameters of the key components in the medical device based on the performance impact data analysis.

[0049] Among them, the parameter evaluation model is used to analyze the performance impact data corresponding to the medical equipment. The parameter evaluation model includes but is not limited to a neural network model and a transformer model. Optionally, the parameter evaluation model can be a long short-term memory network-gated recurrent unit (LSTM-GRU) model. Different types of medical equipment correspond to different key components. For example, the key components corresponding to CT equipment include X-ray tubes, and the key components corresponding to MRI equipment include superconducting magnets. Component parameters are information that characterizes the operating status of key components of medical equipment. Component parameters can be used to evaluate whether key components are working properly and whether there are potential faults. Component parameters include but are not limited to the number of exposures of the X-ray tube of the CT equipment.

[0050] Specifically, the performance impact data of each medical device is input into the pre-deployed LSTM-GRU model for processing to obtain the component parameters of the key components in each medical device, providing data support for subsequent analysis.

[0051] Optionally, the parameter evaluation model is obtained by dynamic optimization based on incremental information; wherein, the parameter evaluation model is preliminarily constructed based on a historical sample data set.

[0052] Among them, the incremental information is the performance impact data transmitted by the edge devices deployed on each medical device side within a preset time. The historical sample data set is the performance impact data corresponding to each medical device during its historical operation. As the medical equipment is used, the status of the medical equipment is changing. The device status of the medical equipment is constantly changing. For example, the medical equipment may have new faults, or the component parameters of the key components in the medical equipment are constantly changing. In order to improve the applicability of the parameter evaluation model to medical equipment and to realize the performance evaluation of the constantly changing medical equipment, the dynamic relationship between the abnormal image frames in the image data in the incremental information, the first type of information content in the log data, the second type of information content in the sensor data and the component parameters of the key components is learned to achieve dynamic optimization of the parameter evaluation model.

[0053] Specifically, an untrained parameter evaluation model is trained using a historical sample data set to obtain a trained parameter evaluation model. The trained parameter evaluation model is dynamically optimized based on incremental information, and the parameters of the parameter evaluation model are adjusted to improve the accuracy of the parameter evaluation model.

[0054] Optionally, S120 also includes: during the use of the parameter evaluation model, obtaining performance impact data transmitted by edge devices deployed on each medical device side, forming incremental information, and dynamically optimizing the parameters of the parameter evaluation model based on the incremental information.

[0055] Specifically, during the use of the parameter evaluation model, the performance impact data is transmitted from the edge device to the server through the time series data pipeline, and the transmitted performance impact data is used as incremental information. The parameter evaluation model is dynamically optimized based on the incremental information, and the parameters of the parameter evaluation model are adjusted to improve the accuracy of the parameter evaluation model.

[0056] S130: Acquire device operating parameters of the medical device, and determine a performance indicator of the medical device based on at least one of the component parameters of the key components and the device operating parameters.

[0057] Among them, the equipment operating parameters are data that characterize the operating status of the medical device during operation. The equipment operating parameters of each medical device are stored in the equipment management platform, and the identification code of the medical device is matched with the identification code of each medical device in the equipment management platform. When the match is successful, the equipment operating parameters corresponding to the identification code of the medical device in the equipment management platform are obtained to achieve the acquisition of the equipment operating parameters of the medical device. Optionally, the equipment operating parameters include equipment failure information, and the equipment failure information includes failure time information. The failure time information is the time corresponding to the occurrence of a failure in the medical device. Performance indicators are data that characterize the safety and operating efficiency of medical equipment. Performance indicators can be determined based on the component parameters of key components, or based on the equipment operating parameters, or based on the component parameters of key components and equipment operating parameters.

[0058] Optionally, the performance indicators of the medical equipment include at least one of a safety risk index and an equipment comprehensive efficiency index.

[0059] The safety risk index is data used to quantitatively assess the safety risks of medical equipment. The safety risk index can be determined based on the equipment's operating parameters, the parameters of key components, or both the parameters of key components and the equipment's operating parameters. The overall equipment efficiency index is data that comprehensively reflects the operating efficiency and maintenance of medical equipment. The overall equipment efficiency index can be determined based on the operating parameters, the parameters of key components, or both the parameters of key components and the equipment's operating parameters.

[0060] For example, component parameters of key components and equipment operating parameters are input into a pre-trained performance indicator calculation model to obtain a safety risk index and an overall equipment efficiency index. For example, mathematical models corresponding to the safety risk index and the overall equipment efficiency index are pre-constructed. Taking the safety risk index as an example, the mathematical model is a mapping relationship between the safety risk index and at least one of the component parameters of the key components and the equipment operating parameters. The index value of the safety risk index can be obtained using the data value of at least one of the component parameters of the key components and the equipment operating parameters and the mathematical model.

[0061] The technical solution of this embodiment obtains performance impact data transmitted by the edge device deployed on the medical device side, the performance impact data including abnormal image frames in the image data collected by the medical device, at least one of the first type of information content in the log data and the second type of information content in the sensor data, and performs subsequent analysis and processing based on the transmitted performance impact data; processes the performance impact data through a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device, providing data support for subsequent analysis; obtains equipment operating parameters of the medical device, and determines the performance indicators of the medical device based on the component parameters of the key components and at least one of the equipment operating parameters, thereby realizing performance evaluation of the medical device, improving the data integration rate and the accuracy of the medical equipment performance evaluation, and providing accurate data support for subsequent maintenance.

[0062] Example 2

[0063] Figure 3 This is a flowchart of a performance evaluation method for medical equipment provided by Example 2 of the present invention. This embodiment is a refinement of the above embodiment. On the basis of the above embodiment, it provides a detailed description of obtaining the equipment operating parameters of the medical equipment and determining the performance indicators of the medical equipment based on at least one of the component parameters of the key components and the equipment operating parameters. The specific implementation method can be found in the technical solution of this embodiment. Among them, the technical terms that are the same as or corresponding to the above embodiment are not repeated here. Figure 3 As shown, the method includes:

[0064] S210. Obtain performance impact data transmitted by the edge device deployed on the medical device side, where the performance impact data includes at least one of abnormal image frames in the image data collected by the medical device, the first type of information content in the log data, and the second type of information content in the sensor data.

[0065] S220. Process the performance impact data using a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device; the component parameters of the key components include the remaining life of the key components and the life decay rate of the key components.

[0066] The remaining lifespan is the remaining usage time of a key component of a medical device during its current operation, from the start of the performance evaluation to the moment the key component fails. The lifespan decay rate is the rate at which the lifespan of a key component of a medical device decays per unit time.

[0067] Specifically, the performance impact data is input into a pre-deployed parameter evaluation model. Analysis and processing of the performance impact data can obtain the remaining life of key components in the medical device and the life decay rate of key components, and subsequent analysis and processing are performed based on the remaining life and life decay rate of key components.

[0068] Optionally, S220 further includes: generating maintenance reminder information of the key components according to the remaining life of the key components.

[0069] Among them, maintenance reminder information is data used to prompt maintenance personnel to perform maintenance on key components. Maintenance reminder information can be determined based on the remaining life of the key components. For example, a remaining life-maintenance reminder information comparison table is pre-set, and the remaining life of the key components of the medical device is matched in the pre-set remaining life-maintenance reminder information comparison table. The maintenance reminder information corresponding to the matched remaining life is used as the maintenance reminder information for the key components of the medical device. Maintenance reminder information includes but is not limited to text and voice formats, and can be selected according to needs and is not limited here.

[0070] Specifically, maintenance reminder information for key components is generated based on the remaining life of key components, providing data support for maintenance personnel to maintain medical equipment.

[0071] S230: Acquire device operating parameters of the medical device, where the device operating parameters include device fault information, and the device fault information includes fault time information.

[0072] S240. Determine the number of failures within the preset time window based on the device failure information of the medical device within the preset time window, and determine the failure rate of the medical device within the preset time window based on the number of failures within the preset time window and the average number of historical failures.

[0073] The failure rate represents the frequency with which a medical device fails within a preset time window and can be expressed as a probability value. The failure rate can be determined based on the number of failures the medical device has experienced within the preset time window and the average number of failures in its history. For example, the ratio between the number of failures the medical device has experienced within the preset time window and the average number of failures in its history can be calculated and used as the failure rate of the medical device within the preset time window.

[0074] Specifically, based on the failure time information in the equipment failure information of the medical equipment within the preset time window, the number of failures of the medical equipment within the preset time window is counted, the historical average failure number of the medical equipment is obtained through the equipment management platform, and the ratio between the number of failures of the medical equipment within the preset time window and the historical average failure number is calculated. The ratio is used as the failure rate of the medical equipment within the preset time window, thereby achieving accurate calculation of the failure rate of the medical equipment within the preset time window and providing accurate data support for subsequent analysis and processing.

[0075] S250. Determine a safety risk index based on the life decay rate of key components and the failure rate of the medical device within a preset time window.

[0076] The safety risk index can be calculated based on the life decay rate of key components and the failure rate of the medical device within a preset time window. For example, a weighted sum of the life decay rate and the failure rate can be calculated and used as the safety risk index. Another example is the sum of the life decay rate and the failure rate, and the sum of the life decay rate and the failure rate can be used as the safety risk index.

[0077] Specifically, by calculating the weighted sum of the life decay rate of key components and the failure rate of medical equipment within a preset time window, and using this weighted sum as a safety risk index, the performance evaluation of medical equipment is achieved, the data integration rate and the accuracy of medical equipment performance evaluation are improved, and accurate data support is provided for maintenance personnel to maintain medical equipment.

[0078] For example, the calculation formula of the security risk index is as follows:

[0079] Safety risk index = a*failure rate + b*life decay rate;

[0080] Where a and b are weights, for example, a = 0.6 and b = 0.4. By calculating the weighted sum of the lifespan decay rate of key components and the failure rate of medical equipment within a preset time window, the safety risk index is accurately determined.

[0081] The technical solution of this embodiment is to obtain the performance impact data transmitted by the edge device deployed on the medical device side, the performance impact data includes at least one of abnormal image frames in the image data collected by the medical device, the first type of information content in the log data and the second type of information content in the sensor data, and perform subsequent analysis and processing based on the transmitted performance impact data; process the performance impact data through a pre-deployed parameter evaluation model to obtain the component parameters of key components in the medical device; the component parameters of key components include the remaining life of key components and the life decay rate of key components, which provide data support for subsequent analysis; obtain the equipment operating parameters of the medical device, which include equipment failure information, equipment Fault information includes fault time information, which provides data support for subsequent analysis; based on the equipment fault information of the medical equipment within the preset time window, the number of faults within the preset time window is determined, and the failure rate of the medical equipment within the preset time window is determined based on the number of failures within the preset time window and the average number of historical failures, thereby achieving accurate calculation of the failure rate of the medical equipment within the preset time window and providing accurate data support for subsequent analysis and processing; based on the life decay rate of key components and the failure rate of the medical equipment within the preset time window, the safety risk index is determined, thereby improving the data integration rate and the accuracy of the medical equipment performance evaluation, and providing accurate data support for maintenance personnel to maintain the medical equipment.

[0082] Example 3

[0083] Figure 4 This is a flowchart of a performance evaluation method for medical equipment provided by Example 3 of the present invention. This embodiment is a refinement of the above embodiment. On the basis of the above embodiment, it provides a detailed description of obtaining the equipment operating parameters of the medical equipment and determining the performance indicators of the medical equipment based on at least one of the component parameters of the key components and the equipment operating parameters. The specific implementation method can be found in the technical solution of this embodiment. Among them, the technical terms that are the same as or corresponding to the above embodiment are not repeated here. Figure 4 As shown, the method includes:

[0084] S310. Obtain performance impact data transmitted by the edge device deployed on the medical device side, where the performance impact data includes at least one of abnormal image frames in the image data collected by the medical device, the first type of information content in the log data, and the second type of information content in the sensor data.

[0085] S320: Process the performance impact data using a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device.

[0086] S330. Obtain equipment operating parameters of the medical equipment, where the equipment operating parameters include actual usage time, actual output, actual response time, and equipment failure information of the medical equipment.

[0087] The actual usage duration is the actual usage time of the medical device per unit time. For example, for CT equipment, the actual usage duration is the actual weekly usage time of the CT equipment. The actual output is the number of times the medical device is used per unit time. For example, for CT equipment, the actual output is the number of scans performed per week. The actual response time is the average maintenance time of the medical device during historical maintenance. Equipment failure information includes failure time information. Subsequent analysis and processing are performed based on the equipment operating parameters obtained from the equipment management platform.

[0088] S340. Determine a usage duration evaluation item based on the actual usage duration of the medical device and the planned usage duration of the medical device.

[0089] Among them, the planned usage time is the theoretical usage time of the medical device in unit time. The planned usage time of the medical device can be set in units of weeks. The equipment management platform stores the planned usage time of each medical device, matches the identification code of the medical device with the identification code of each medical device in the equipment management platform, and when the match is successful, obtains the planned usage time corresponding to the successfully matched identification code in the equipment management platform to achieve the acquisition of the planned usage time. The usage time evaluation item is data used to evaluate the usage status of the medical device. The usage time evaluation item can be determined based on the actual usage time of the medical device and the planned usage time of the medical device. For example, the ratio between the actual usage time of the medical device and the planned usage time of the medical device is calculated, and the above ratio is used as the usage time evaluation item.

[0090] Specifically, by calculating the ratio between the actual usage time of the medical equipment and the planned usage time of the medical equipment, and using this ratio as the usage time evaluation item, the usage time evaluation item can be accurately determined, providing accurate data support for subsequent analysis and processing.

[0091] S350. Determine output evaluation items based on the actual output of the medical equipment and the theoretical output of the medical equipment.

[0092] Among them, the theoretical output is the output capacity of the medical equipment under ideal operating conditions. Different types of medical equipment correspond to different theoretical outputs. The equipment management platform stores the theoretical output of each medical device, matches the identification code of the medical device with the identification code of each medical device in the equipment management platform, and when the match is successful, obtains the theoretical output corresponding to the successfully matched identification code in the equipment management platform to achieve the acquisition of the theoretical output. The output evaluation item is data used to evaluate the output capacity of the medical equipment. The output evaluation item can be determined based on the actual output of the medical equipment and the theoretical output of the medical equipment. For example, the ratio between the actual output of the medical equipment and the theoretical output of the medical equipment is calculated, and the ratio is used as the output evaluation item.

[0093] Specifically, by calculating the ratio between the actual output of medical equipment and the theoretical output of medical equipment, and using this ratio as the output evaluation item, the output evaluation item can be accurately determined, providing accurate data support for subsequent analysis and processing.

[0094] S360. Determine a response time evaluation item based on the actual response time of the medical device and the target maintenance response time of the medical device.

[0095] Among them, the target maintenance response time is the theoretical maintenance time of the medical equipment per unit time. The equipment management platform stores the target maintenance response time of each medical device, matches the identification code of the medical device with the identification code of each medical device in the equipment management platform, and when the match is successful, obtains the target maintenance response time corresponding to the successfully matched identification code in the equipment management platform to achieve the acquisition of the target maintenance response time. The response time evaluation item is data used to evaluate the maintenance efficiency of medical equipment. The response time evaluation item can be determined based on the actual response time of the medical equipment and the target maintenance response time of the medical equipment. For example, calculate the ratio between the target maintenance response time of the medical equipment and the actual response time of the medical equipment, and use the ratio as the response time evaluation item.

[0096] Specifically, by calculating the ratio between the target maintenance response time of medical equipment and the actual response time of medical equipment, and using this ratio as the response time evaluation item, the response time evaluation item can be accurately determined, providing accurate data support for subsequent analysis and processing.

[0097] S370. Determine a failure evaluation item based on the number of failures of the medical device and the actual usage time of the medical device.

[0098] The failure evaluation item is used to assess the occurrence of failures during the actual use of the medical device. The failure evaluation item can be determined based on the number of failures and the actual use time of the medical device. For example, the ratio between the number of failures and the actual use time of the medical device can be calculated and used as the failure evaluation item.

[0099] Implemented fault assessment items

[0100] Specifically, by calculating the ratio between the number of failures of medical equipment and the actual usage time of the medical equipment, and using this ratio as a failure evaluation item, the failure evaluation item can be accurately determined, providing accurate data support for subsequent analysis and processing.

[0101] S380: Fusing at least one of the usage time evaluation item, the output evaluation item, the response time evaluation item, and the fault evaluation item to obtain a comprehensive equipment efficiency index.

[0102] Among them, the comprehensive equipment efficiency index is data that comprehensively reflects the operating efficiency and maintenance of medical equipment. The comprehensive equipment efficiency index can be determined based on one of the usage time evaluation items, output evaluation items, response time evaluation items, and fault evaluation items. It can also be determined based on two of the usage time evaluation items, output evaluation items, response time evaluation items, and fault evaluation items. It can also be determined based on three of the usage time evaluation items, output evaluation items, response time evaluation items, and fault evaluation items. It can also be determined based on the usage time evaluation items, output evaluation items, response time evaluation items, and fault evaluation items. It can be set according to needs and is not restricted here. For example, the usage time evaluation items, output evaluation items, response time evaluation items, and fault evaluation items can be summed to obtain the comprehensive equipment efficiency index.

[0103] Specifically, the usage time evaluation items, output evaluation items, response time evaluation items and fault evaluation items can be directly summed, and the usage time evaluation items, output evaluation items, response time evaluation items and fault evaluation items can also be weighted and summed to obtain the equipment comprehensive efficiency index, thereby achieving accurate determination of the equipment comprehensive efficiency index, that is, realizing the performance evaluation of medical equipment, improving the data integration rate and the accuracy of medical equipment performance evaluation, and providing accurate data support for maintenance personnel to maintain medical equipment.

[0104] Optionally, S380 also includes: determining the fusion weights corresponding to the usage time evaluation item, output evaluation item, response time evaluation item and fault evaluation item respectively according to the medical scenario to which the medical equipment belongs; fusing the usage time evaluation item, output evaluation item, response time evaluation item and fault evaluation item based on the fusion weight to obtain the equipment comprehensive efficiency index; wherein, each medical scenario corresponds to a set of fusion weights.

[0105] Medical scenarios include, but are not limited to, emergency and imaging scenarios. The fusion weight represents the importance of the usage time, output, response time, and failure evaluation items in the overall device efficiency index. The fusion weight can be determined based on the medical scenario to which the medical device belongs. Different medical scenarios have different fusion weights for the usage time, output, response time, and failure evaluation items.

[0106] Specifically, according to the medical scenario to which the medical equipment belongs, the fusion weight corresponding to the usage time evaluation item, the fusion weight corresponding to the output evaluation item, the fusion weight corresponding to the response time evaluation item and the fusion weight corresponding to the fault evaluation item are determined respectively. The weighted sum of each evaluation item is calculated and used as the comprehensive efficiency index of the equipment. This achieves accurate determination of the comprehensive efficiency index of the equipment, realizes the performance evaluation of the medical equipment, improves the data integration rate and the accuracy of the performance evaluation of the medical equipment, and provides accurate data support for maintenance personnel to maintain the medical equipment.

[0107] In some embodiments of the present invention, performance evaluation of medical devices can be performed based on different time dimensions. For example, performance evaluation of medical devices can be performed on a weekly, monthly, or quarterly basis. This can be set as needed and is not limited here.

[0108] For example, the calculation formula of the overall equipment efficiency index is as follows:

[0109]

[0110] Among them, T 实际使用 Indicates the actual usage time of medical equipment, T 计划使用 Indicates the planned usage time of the medical equipment, represents the duration evaluation item, α represents the fusion weight corresponding to the duration evaluation item, Q 实际产出 Indicates the actual output of medical equipment, Q 计划产出 represents the theoretical output of medical equipment, represents the output evaluation item, β represents the fusion weight corresponding to the output evaluation item, N 故障 Indicates the number of failures of medical equipment, T 总运行 Indicates the total actual usage time of medical equipment, represents the fault evaluation item, γ represents the fusion weight corresponding to the fault evaluation item, T 目标响应 Indicates the actual response time of the medical device, T 实际响应 Indicates the target maintenance response time for medical equipment. represents the response time evaluation item, and δ represents the fusion weight corresponding to the response time evaluation item.

[0111] In some embodiments, the overall equipment efficiency index may also include a positive rate item, which may be determined based on the ratio of the number of positive tests to the total number of tests. Accordingly, the overall equipment efficiency index is derived by fusing the corresponding fusion weights for the usage time evaluation item, the output evaluation item, the response time evaluation item, the fault evaluation item, and the positive rate item. The fusion weights may differ for different medical scenarios, which may be different medical departments.

[0112] The technical solution of this embodiment is to obtain performance impact data transmitted by edge devices deployed on the medical device side, the performance impact data including abnormal image frames in the image data collected by the medical device, at least one of the first type of information content in the log data and the second type of information content in the sensor data, and perform subsequent analysis and processing based on the transmitted performance impact data; process the performance impact data through a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device, providing data support for subsequent analysis; obtain equipment operating parameters of the medical device, the equipment operating parameters including the actual usage time, actual output, actual response time and equipment failure information of the medical device, providing data support for subsequent analysis; determine usage time evaluation items based on the actual usage time of the medical device and the planned usage time of the medical device, thereby achieving accurate determination of usage time evaluation items; and calculate the actual output and actual response time of the medical device based on the actual output and actual response time of the medical device. The theoretical output of medical equipment determines the output evaluation items, which realizes the accurate determination of the output evaluation items; the response time evaluation items are determined based on the actual response time of medical equipment and the target maintenance response time of medical equipment, which realizes the accurate determination of the response time evaluation items; the fault evaluation items are determined based on the number of faults of medical equipment and the actual usage time of medical equipment, which realizes the accurate determination of the fault evaluation items, and the accurate determination of the usage time evaluation items, output evaluation items, response time evaluation items and fault evaluation items provides accurate data support for subsequent analysis and processing; the comprehensive efficiency index of the equipment is obtained by fusing at least one of the usage time evaluation items, output evaluation items, response time evaluation items and fault evaluation items, which realizes the performance evaluation of medical equipment, improves the data integration rate and the accuracy of the performance evaluation of medical equipment, and provides accurate data support for maintenance personnel to maintain medical equipment.

[0113] Example 4

[0114] Figure 5 This is a schematic diagram of the structure of a performance evaluation device for medical equipment provided by the fourth embodiment of the present invention. Figure 5 As shown, the device includes:

[0115] The performance impact data acquisition module 410 is configured to: acquire performance impact data transmitted by an edge device deployed on the medical device side, where the performance impact data includes at least one of abnormal image frames in image data collected by the medical device, information content of a first type in log data, and information content of a second type in sensor data;

[0116] The component parameter determination module 420 of the key component is used to: process the performance impact data using a pre-deployed parameter evaluation model to obtain the component parameters of the key components in the medical device;

[0117] The performance indicator determination module 430 is configured to obtain device operating parameters of the medical device and determine a performance indicator of the medical device based on at least one of the component parameters of the key components and the device operating parameters.

[0118] The technical solution of this embodiment obtains performance impact data transmitted by the edge device deployed on the medical device side, the performance impact data including abnormal image frames in the image data collected by the medical device, at least one of the first type of information content in the log data and the second type of information content in the sensor data, and performs subsequent analysis and processing based on the transmitted performance impact data; processes the performance impact data through a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device, providing data support for subsequent analysis; obtains equipment operating parameters of the medical device, and determines the performance indicators of the medical device based on the component parameters of the key components and at least one of the equipment operating parameters, thereby realizing performance evaluation of the medical device, improving the data integration rate and the accuracy of the medical equipment performance evaluation, and providing accurate data support for subsequent maintenance.

[0119] Based on the above embodiment, optionally, an image detection model and a text parsing model are integrated in the edge device deployed on the medical device side, wherein the image detection model is used to identify abnormal image frames in the image data collected by the medical device; the text parsing model identifies the first type of information content in the log data and / or the second type of information content in the sensor data.

[0120] Optionally, the performance impact data includes encrypted sensitive information and unencrypted non-sensitive information; the performance impact data is transmitted via a time series data pipeline between the edge device deployed on the medical device side and the server.

[0121] Optionally, the performance impact data acquisition module 410 is further configured to add random noise to the performance impact data corresponding to each medical device, and store the performance impact data after the noise is added.

[0122] Optionally, the component parameter determination module 420 of the key component is also used to: during the use of the parameter evaluation model, obtain the performance impact data transmitted by the edge devices deployed on each medical device end, form incremental information, and dynamically optimize the parameters of the parameter evaluation model based on the incremental information.

[0123] Optionally, the performance indicators of the medical device include a safety risk index.

[0124] Optionally, the component parameters of the key component include the remaining life of the key component and the life decay rate of the key component.

[0125] Optionally, the device operating parameters include device fault information, and the device fault information includes fault time information.

[0126] Optionally, the performance indicator determination module 430 is also used to: determine the number of failures within the preset time window based on the equipment failure information of the medical device within the preset time window, determine the failure rate of the medical device within the preset time window based on the number of failures within the preset time window and the average number of historical failures; determine the safety risk index based on the life decay rate of key components and the failure rate of the medical device within the preset time window.

[0127] Optionally, the performance indicator determination module 430 is further configured to generate maintenance reminder information for the key components according to the remaining life of the key components.

[0128] Optionally, the performance indicators of the medical equipment include the comprehensive efficiency indicators of the equipment, and the equipment operating parameters include the actual usage time, actual output, actual response time and equipment failure information of the medical equipment.

[0129] Optionally, the performance indicator determination module 430 is also used to: determine a usage time evaluation item based on the actual usage time of the medical equipment and the planned usage time of the medical equipment; determine an output evaluation item based on the actual output of the medical equipment and the theoretical output of the medical equipment; determine a response time evaluation item based on the actual response time of the medical equipment and the target maintenance response time of the medical equipment; determine a failure evaluation item based on the number of failures of the medical equipment and the actual usage time of the medical equipment; and fuse at least one of the usage time evaluation item, the output evaluation item, the response time evaluation item and the failure evaluation item to obtain a comprehensive equipment efficiency index.

[0130] Optionally, the performance indicator determination module 430 is also used to: determine the fusion weights corresponding to the usage time evaluation item, output evaluation item, response time evaluation item and fault evaluation item respectively according to the medical scenario to which the medical equipment belongs; and fuse the usage time evaluation item, output evaluation item, response time evaluation item and fault evaluation item based on the fusion weight to obtain the equipment comprehensive efficiency index; wherein each medical scenario corresponds to a set of fusion weights.

[0131] Optionally, the parameter evaluation model is obtained by dynamic optimization based on incremental information; wherein, the parameter evaluation model is preliminarily constructed based on a historical sample data set.

[0132] The performance evaluation device for medical equipment provided in the embodiment of the present invention can execute the performance evaluation method for medical equipment provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0133] Example 5

[0134] Figure 61 is a structural diagram of an electronic device provided in Example 5 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0135] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the random access memory (RAM) 13. The processor 11, the read-only memory (ROM) 12, and the random access memory (RAM) 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0136] Various components in the electronic device 10 are connected to an input / output (I / O) interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0137] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the performance evaluation method for a medical device.

[0138] In some embodiments, the performance evaluation method of a medical device can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the read-only memory (ROM) 12 and / or the communication unit 19. When the computer program is loaded into the random access memory (RAM) 13 and executed by the processor 11, one or more steps of the performance evaluation method of the medical device described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the performance evaluation method of the medical device in any other appropriate manner (for example, by means of firmware).

[0139] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0140] Computer programs for implementing the medical device performance evaluation methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0141] Example 6

[0142] Embodiment 6 of the present invention further provides a computer-readable storage medium storing computer instructions, the computer instructions being used to cause a processor to execute a performance evaluation method for a medical device, the method comprising:

[0143] Acquire performance impact data transmitted by an edge device deployed on the medical device side, where the performance impact data includes at least one of abnormal image frames in image data collected by the medical device, a first type of information content in log data, and a second type of information content in sensor data; process the performance impact data through a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device; obtain device operating parameters of the medical device, and determine performance indicators of the medical device based on at least one of the component parameters of the key components and the device operating parameters.

[0144] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0145] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0146] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0147] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0148] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0149] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A performance evaluation method for medical equipment, characterized in that: Applicable to servers, including: Obtaining performance impact data transmitted by an edge device deployed on the medical device side, the performance impact data comprising at least one of abnormal image frames in image data collected by the medical device, a first type of information content in log data, and a second type of information content in sensor data; Processing the performance impact data using a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device; Acquire device operating parameters of the medical device, and determine a performance indicator of the medical device based on at least one of the component parameters of the key component and the device operating parameters.

2. The method according to claim 1, characterized in that The edge device deployed on the medical device side integrates an image detection model and a text parsing model, wherein the image detection model is used to identify abnormal image frames in the image data collected by the medical device; the text parsing model identifies the first type of information content in the log data and / or the second type of information content in the sensor data.

3. The method according to claim 1, characterized in that The performance impact data includes encrypted sensitive information and unencrypted non-sensitive information; the performance impact data is transmitted via a time series data pipeline between the edge device deployed on the medical device side and the server; And / or, the method further includes: adding random noise to the performance impact data corresponding to each of the medical devices, and storing the performance impact data after the noise is added.

4. The method according to claim 1, wherein The performance indicators of the medical device include a safety risk index, and the component parameters of the key components include the remaining life of the key components and the life decay rate of the key components; the equipment operation parameters include equipment failure information, and the equipment failure information includes failure time information; Determining a performance indicator of the medical device based on at least one of the component parameters of the key component and the device operating parameter includes: Determining the number of failures within the preset time window based on device failure information of the medical device within the preset time window, and determining the failure rate of the medical device within the preset time window based on the number of failures within the preset time window and the average number of failures in historical failures; The safety risk index is determined based on the life decay rate of the key component and the failure rate of the medical device within a preset time window.

5. The method according to claim 4, characterized in that The method further comprises: Maintenance reminder information of the key component is generated according to the remaining life of the key component.

6. The method according to claim 1, characterized in that The performance indicators of the medical equipment include the comprehensive efficiency indicators of the equipment, and the equipment operation parameters include the actual usage time, actual output, actual response time and equipment failure information of the medical equipment; Determining a performance indicator of the medical device based on the device operating parameters includes: determining a usage duration evaluation item based on the actual usage duration of the medical device and the planned usage duration of the medical device; Determining an output evaluation item based on an actual output of the medical device and a theoretically possible output of the medical device; determining a response time evaluation item based on an actual response time of the medical device and a target maintenance response time of the medical device; determining a failure evaluation item based on the number of failures of the medical device and the actual usage time of the medical device; The equipment comprehensive efficiency index is obtained by fusing at least one of the usage time evaluation item, the output evaluation item, the response time evaluation item, and the fault evaluation item.

7. The method according to claim 6, characterized in that The method further comprises: Determining, according to the medical scenario to which the medical device belongs, fusion weights corresponding to the usage time evaluation item, the output evaluation item, the response time evaluation item, and the fault evaluation item, respectively; The equipment comprehensive efficiency index is obtained by fusing the usage time evaluation item, the output evaluation item, the response time evaluation item, and the fault evaluation item based on the fusion weight; Each of the medical scenarios corresponds to a set of fusion weights.

8. The method according to claim 1, characterized in that The parameter evaluation model is obtained by dynamic optimization based on incremental information; Wherein, the parameter evaluation model is preliminarily constructed based on the historical sample data set; The method also includes: during the use of the parameter evaluation model, obtaining performance impact data transmitted by edge devices deployed at each medical device end to form incremental information, and dynamically optimizing the parameters of the parameter evaluation model based on the incremental information.

9. A performance evaluation device for medical equipment, characterized in that: include: A performance impact data acquisition module is configured to: acquire performance impact data transmitted by an edge device deployed on the medical device side, wherein the performance impact data includes at least one of abnormal image frames in image data collected by the medical device, information content of a first type in log data, and information content of a second type in sensor data; a component parameter determination module for key components, configured to: process the performance impact data using a pre-deployed parameter evaluation model to obtain component parameters of key components in the medical device; The performance indicator determination module is used to: obtain the device operating parameters of the medical device, and determine the performance indicator of the medical device based on the component parameters of the key components and at least one of the device operating parameters.

10. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the performance evaluation method of the medical device according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the performance evaluation method of a medical device according to any one of claims 1 to 8 when executed.

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