Anomaly monitoring method and apparatus for a medical device

Abstract

The present disclosure relates to an abnormality monitoring method and device of a medical device, comprising: acquiring a plurality of historical operation data generated by the medical device; processing and associatively displaying the plurality of historical operation data according to a unified time dimension to form an operation state association result of the medical device; and determining an abnormal state of the medical device based on the operation state association result. The method is beneficial to discovering abnormal conditions in the operation process of the device and improving the stability and reliability of the operation of the medical device.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, and in particular to a method and apparatus for abnormal monitoring of medical devices. Background Technology

[0002] Medical devices generate a large amount of data related to their operation. In existing technologies, when equipment malfunctions or fails, technicians typically rely on analyzing and investigating this data to determine the cause. However, this reactive approach suffers from low efficiency and limited analytical dimensions due to the sheer volume and complexity of the data. This makes it difficult to detect some potential anomalies in a timely manner, impacting the timeliness of anomaly warnings and the stability of equipment operation. Therefore, improving the accuracy and timeliness of anomaly identification and prediction in medical devices has become a pressing technical problem in this field.

[0003] The methods described in this section are not necessarily methods that had been previously conceived or adopted. Unless otherwise specified, no method described in this section should be assumed to be prior art simply because it is included in this section. Similarly, unless otherwise specified, the issues mentioned in this section should not be considered to be accepted in any prior art. Summary of the Invention

[0004] In view of the above, according to the first aspect of this disclosure, a method for monitoring the anomalies of medical devices is proposed, comprising: acquiring multiple historical operating data generated by the medical devices; processing and displaying the multiple historical operating data in association according to a unified time dimension to form an operating status association result of the medical devices; and determining the abnormal state of the medical devices based on the operating status association result.

[0005] According to a second aspect of this disclosure, an apparatus for detecting anomalies in medical devices is provided, comprising: a first module configured to acquire various historical operating data generated by the medical device; a second module configured to process and correlate the various historical operating data according to a unified time dimension to form an operating status correlation result of the medical device; and a third module configured to determine an abnormal state of the medical device based on the operating status correlation result.

[0006] According to a third aspect of this disclosure, a computing device is provided, comprising: at least one processor; and at least one memory communicatively connected to the at least one processor, the at least one memory storing instructions that, when executed individually or jointly by the at least one processor, cause the computing device to perform the method described in the first aspect of this disclosure.

[0007] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided storing instructions that, when executed individually or jointly by one or more processors of a computing device, cause the computing device to perform the method described in the first aspect of this disclosure.

[0008] According to a fifth aspect of this disclosure, a computer program product is provided, including instructions that, when executed individually or jointly by one or more processors of a computing device, cause the computing device to perform the method described in the first aspect of this disclosure.

[0009] According to one or more embodiments of this disclosure, by acquiring various historical operating data generated by medical devices and processing and displaying the various historical operating data according to a unified time dimension, the resulting operating status association results of medical devices can establish time-related relationships and conduct unified analysis of originally scattered operating information. This helps to comprehensively utilize the correlation features between multiple operating information to reflect the overall operating status of the device, improve the accuracy and timeliness of abnormal status identification, facilitate the early detection of abnormal situations during device operation, and improve the stability and reliability of medical device operation.

[0010] It should be understood that the content described in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. These and other aspects of this disclosure will become clear from the embodiments described below and will be elucidated with reference to the embodiments described below. Attached Figure Description

[0011] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings, so that those skilled in the art can more clearly understand the above and other features and advantages of the present invention, in which: Figure 1 A flowchart illustrating an anomaly monitoring method for a medical device according to an exemplary embodiment of the present disclosure is provided. Figure 2 For illustration purposes, according to an exemplary embodiment, Figure 1 A flowchart of a portion of the example process in the method; Figure 3 For illustration purposes, according to an exemplary embodiment, Figure 2 A flowchart of a portion of the example process in the method; Figure 4 The figure illustrates an exemplary embodiment. Figure 1 A flowchart of a portion of the example process in the method; Figure 5 A flowchart illustrating some of the steps of an anomaly monitoring method for a medical device according to an exemplary embodiment; Figure 6This is a schematic block diagram illustrating an apparatus for anomaly detection in a medical device according to an exemplary embodiment; and Figure 7 This is a schematic block diagram illustrating a computing device according to an exemplary embodiment of the present disclosure. Detailed Implementation

[0012] To provide a clearer understanding of the technical features, objectives, and effects of the present invention, specific embodiments of the invention will now be described with reference to the accompanying drawings, in which the same reference numerals denote the same parts.

[0013] In this document, “illustrative” means “serving as an example, illustration or description”, and any illustration or implementation described herein as “illustrative” should not be construed as a more preferred or advantageous technical solution.

[0014] To keep the drawings concise, only the parts relevant to the invention are shown schematically in each figure, and they do not represent the actual structure of the product. Furthermore, for ease of understanding, in some figures, components with the same structure or function are shown only schematically, or only one is labeled.

[0015] In this article, "one" can mean not only "only one" but also "more than one". In this article, "first", "second", etc., are used only to distinguish one from another, not to indicate their importance, order, or mutual dependence.

[0016] Medical devices generate a large amount of data related to their operation. In existing technologies, when equipment malfunctions or fails, technicians typically rely on analyzing and investigating this data to determine the cause. However, this reactive approach suffers from low efficiency and limited analytical dimensions due to the sheer volume and complexity of the data. This makes it difficult to detect some potential anomalies in a timely manner, impacting the timeliness of anomaly warnings and the stability of equipment operation. Therefore, improving the accuracy and timeliness of anomaly identification and prediction in medical devices has become a pressing technical problem in this field.

[0017] In view of this, this disclosure proposes an anomaly monitoring method for medical devices. By acquiring various historical operating data generated by the medical device and processing and displaying these historical operating data according to a unified time dimension, the resulting operational status correlation results of the medical device enable the establishment of time-related relationships between originally scattered operational information and conduct unified analysis. This helps to comprehensively utilize the correlation features between multiple operational information to reflect the overall operating status of the device, improve the accuracy and timeliness of anomaly identification, facilitate the early detection of anomalies during device operation, and improve the stability and reliability of medical device operation.

[0018] Exemplary embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0019] In some implementations, reference Figure 1 The abnormal monitoring method 100 for medical devices includes steps 110 to 130.

[0020] Step 110: Obtain various historical operating data generated by the medical device; Step 120: Process and correlate the various historical operational data according to a unified time dimension to form an operational status correlation result for the medical device; and Step 130: Based on the operational status correlation results, determine the abnormal status of the medical device.

[0021] In the above embodiments, by acquiring various historical operating data generated by medical devices and processing and displaying these various historical operating data according to a unified time dimension, the resulting operating status association results of medical devices can establish time-related relationships and conduct unified analysis of originally scattered operating information. This helps to comprehensively utilize the correlation features between multiple operating information to reflect the overall operating status of the equipment, improve the accuracy and timeliness of abnormal status identification, facilitate the early detection of abnormal situations during equipment operation, and improve the stability and reliability of medical device operation.

[0022] Within the scope of this disclosure, medical devices can be various types of medical devices capable of generating operational data and recording operational status information, such as CT scanners, DR devices, ultrasound devices, radiotherapy devices, blood analysis devices, biochemical analysis devices, and MRI devices. Historical operational data of a medical device refers to data generated and stored during the device's operation (e.g., log data recorded and stored by the device's log system). In some examples, historical operational data can be all historical data generated during device operation, or data within a certain preset time period. Depending on the configuration of the log system of different medical devices, historical operational data can be either continuously collected data or data recorded based on events. Furthermore, since different data may have different sampling frequencies, recording periods, and time formats, the unified time dimension in this embodiment can refer to mapping different historical operational data to the same time reference system for time alignment processing.

[0023] In some embodiments, the operational status correlation results may include information represented by visualizations of various types of data. In this embodiment, the operational status correlation results refer to the results that reflect the correlation between different operational statuses after processing various historical operational data generated by the medical device according to a unified time dimension. The operational status correlation results can intuitively reflect the changes in each historical operational data point itself, and also reflect the correspondence, trends, and correlations between different historical operational data points in the time dimension, thereby characterizing the overall operational status of the medical device.

[0024] In some implementations, the various historical operational data include workflow status data, environmental status data, and component operational status data obtained from the medical device's log system. The workflow status data characterizes the operational stage of the medical device, the environmental status data characterizes the operating environment of the medical device, and the operational status data characterizes the operational status of the internal components of the medical device. By acquiring these different types of data, the operation of the medical device can be described from multiple dimensions, including device operating conditions, external operating environment, and internal operational status, forming more complete operational status information. Based on this, the correlation between data from different dimensions can be combined to perform anomaly analysis on the medical device, avoiding misjudgments caused by relying on only a single data point and improving the accuracy of anomaly identification and prediction.

[0025] In some implementations, reference Figure 2 Step 110 involves acquiring various historical operational data generated by the medical device, including: Step 111: Obtain multiple historical log files from the medical device; Step 112: Filter the multiple historical log files based on a preset identifier associated with the various historical operational data to obtain a target log file containing the preset identifier; and Step 113: Parse the target log file to obtain the various historical running data.

[0026] Because medical device log systems typically contain numerous log types and massive amounts of data, with only a portion truly relevant to anomaly prediction, it's necessary to first filter the logs using preset identifiers before parsing the filtered target log files. In the above implementation, preset identifiers associated with target operational data are used to filter the multiple historical log files, identifying the target log file containing the target information from a large pool of log files. This target log file is then parsed to obtain various types of historical operational data. Since medical devices generate a large number of log files during operation, many containing irrelevant information, filtering using preset identifiers reduces the number of log files to be processed, decreases data processing volume, improves the efficiency of target data location and extraction, and reduces interference from irrelevant data in subsequent anomaly analysis. This provides a more accurate and effective data foundation for medical device anomaly prediction.

[0027] In some examples, the preset identifier can establish a correspondence with the target runtime data and be used to identify information related to the target runtime data from a large amount of log data. In some embodiments, the preset identifier can be a field name, node name, data label, keyword, or other data marker that can characterize the target runtime data.

[0028] In some implementations, the plurality of historical log files include structured format files. Because data in structured format files is organized and stored according to preset rules, and there are clear data structure relationships between data items, it facilitates the location and extraction of target data, improving the standardization and accuracy of the historical runtime data acquisition process. In some examples, historical log files may also include compressed structured format files, such as XML files, JSON files, or other structured files in a ZIP archive. Decompression can be performed before parsing, and then the target runtime data can be extracted according to the corresponding data structure.

[0029] refer to Figure 3 In some implementations, step 113 involves parsing the target log file to obtain the various historical runtime data, including: Step 1131: Read the target log file segment by segment according to the predetermined data reading order; Step 1132, filtering data in the read current data segment based on the preset identifier, to obtain target historical operation data matching the preset identifier; Step 1133, in response to obtaining the target historical operation data, releasing the data cache corresponding to the current data segment; and Step 1134, performing unit conversion operation and / or abnormal data filtering operation on the target historical operation data.

[0030] Medical devices usually generate a large number of historical log files during operation, and the data volume of a single log file is relatively large. If an overall loading method is adopted for parsing, a large amount of log data needs to be loaded into the memory at the same time, which not only easily causes excessive occupation of memory resources, but also may lead to decreased parsing efficiency and even affect the system operation stability as the log scale increases. In the foregoing embodiment,分段 reading, real-time filtering and synchronous cache release processing are performed on the target log file, which avoids a large amount of memory occupation caused by overall loading of the log file, and realizes efficient parsing of large-scale log data. Since only data matching the preset identifier is processed, irrelevant data participating in analysis is reduced, the log parsing efficiency is improved, and an efficient data acquisition basis is provided for subsequent operation state analysis and abnormality prediction.

[0031] In some examples, the predetermined data reading order may include reading in the chronological order of log records or reading in fixed-length data blocks. In addition, it can be understood that since log data may come from different systems or different configuration environments, there may be a situation where Chinese unit representation and English unit representation are mixed. Through the unit conversion operation, unified expression of data can be achieved, and the comparability between different data can be improved. For example, in some examples, the Chinese unit "毫" or the English unit "m" can be automatically identified and converted into a standard unit.

[0032] In some examples, abnormal data in the medical device log system may be caused by sensor measurement abnormality, communication error, data recording error or instantaneous abnormal state of the device. Therefore, through the abnormal data filtering operation, data that significantly deviates from the normal range or does not conform to physical laws can be identified and eliminated, which can avoid the interference of abnormal values on subsequent analysis and improve data reliability.

[0033] Reference Figure 4 , in some embodiments, step 120, processing the plurality of types of historical operation data according to a unified time dimension and displaying them in association, so as to form an operation state association result of the medical device, includes: step 121, synchronously displaying the workflow state data, the environment state data and the component operation state data on a unified timeline; and Step 122: Based on the correspondence between the work stage information represented by the workflow status data and the environmental status data and / or component operation status data within the corresponding time period, obtain the operation status association result of the medical device.

[0034] In the above implementation, by synchronously displaying workflow status data, environmental status data, and component operating status data on a unified timeline, historical operating data from different sources can be mapped to the same time dimension, thereby achieving time alignment and synchronous presentation of multiple operating states. When different operating data are synchronously displayed on the same timeline, the corresponding changes between various types of operating data can be directly observed. For example, during a certain working stage, an increase or decrease in environmental status data may correspond to a synchronous or inverse change in component operating status data, thus reflecting the linkage or coupling relationship between different operating parameters. Since different types of data may have differences in recording time granularity, timestamp sources, data recording frequency, and log generation cycle during the operation of medical equipment, a unified timeline can map data from different sources to the same time base for expression.

[0035] In some implementations, method 100 further includes: determining the abnormal state of the medical device based on the matching between the operational state association result and the expected operational state association result; and / or determining the condensation risk coefficient of the medical device based on the working stage information and the environmental state data within the corresponding time period.

[0036] Medical devices may operate in different phases, such as startup and self-test, normal diagnostic and treatment operation, or standby / low load. Because different types of operational data typically exhibit relatively stable logical relationships under specific operating conditions—for example, different operating parameters may show synchronous, proportional, or inverse changes—the correspondence between various types of operational data possesses a certain degree of stability and predictability under normal operating conditions. Therefore, based on the matching between the operational status correlation results and the expected operational status correlation results, it can be determined whether the current operational status of the medical device deviates from the normal operating mode. When the synchronous or linkage relationships between different types of operational data weaken, disappear, or deviate abnormally during actual operation, it can be used to determine if the device is in an abnormal state.

[0037] Furthermore, the condensation risk coefficient can be determined comprehensively based on workflow status information and environmental status data within the corresponding time period. In some embodiments, a dew point algorithm can be used to calculate environmental risk parameters based on environmental status data, and further determine the condensation risk coefficient. The environmental status data may include parameters related to condensation phenomena, such as ambient temperature and humidity. Within the scope of this disclosure, the dew point refers to the temperature to which air must be cooled to condense water vapor in ambient air into dew or frost. In some examples, any suitable dew point algorithm can be used to calculate the probability of condensation occurring in the current environment based on environmental status data, providing basic data for condensation risk assessment.

[0038] Understandably, the condensation risk coefficient can be used to characterize the likelihood of condensation occurring in medical equipment under its current operating conditions and environment. Since the heat load and temperature distribution of equipment vary under different operating conditions, the condensation risk under the same environmental conditions will also change. Therefore, by introducing operating condition factors to correct the environmental parameter assessment results, the condensation risk coefficient can reflect the actual condensation tendency of the equipment under specific operating conditions, thereby achieving a dynamic and comprehensive assessment of condensation risk.

[0039] In some implementations, reference Figure 5 Method 100 also includes: Step 140: Establish the operating benchmark of the medical device based on the various historical operating data; Step 150: Obtain multiple current log files of the medical device to obtain the current operating data of the medical device; and Step 160: Based on the deviation between the current operating data and the operating benchmark, confirm the abnormal state of the medical device and generate intervention instructions and / or prompt information corresponding to the abnormal state.

[0040] In the above embodiments, by comparing and analyzing the current operating data with the operating baseline (e.g., taking the difference), the deviation of the current operating state from the normal operating mode can be determined, which helps to realize an anomaly identification and response mechanism based on historical operating baselines and improves the stability and reliability of anomaly judgment. In this disclosure, the operating baseline and the current operating data can be obtained by parsing and processing the operating logs recorded in the medical device log system. The operating baseline can be a reference result used to characterize the normal operating state of the device based on historical operating data from historical log files, while the current operating data corresponds to the operating status information obtained by parsing the current log file. In some examples, intervention instructions and prompts can be generated based on deviation analysis results and risk assessment results. It can be understood that intervention instructions can be used to guide the user of the medical device to perform corresponding inspection, maintenance, or treatment operations, and prompts are used to provide feedback to the user on the current operating status, risk status, or abnormal trend of the device. For example, the intervention instructions and / or prompts can be presented through the user interface in the form of text, graphics, markers, pop-ups, or highlights. By converting the log analysis results into perceptible and executable information output, the efficiency of anomaly detection and equipment maintenance can be improved, and early warning and early intervention for potential abnormal states can be achieved.

[0041] The method described in this disclosure can establish a correspondence between different operating states by displaying multiple operating data in a unified time dimension, and analyze the operating status and risk status of the equipment based on the correspondence. In addition, this disclosure verifies the deviation of the current operating status by combining the operating benchmark established by historical operating data, thereby realizing the identification and early warning of abnormal status of medical equipment, and improving the accuracy and reliability of abnormal judgment and risk assessment.

[0042] In some implementations, the operating baseline includes at least one of an environmental state data threshold, a workflow execution frequency threshold corresponding to workflow state data, and a condensation risk coefficient threshold. In some examples, the environmental state data threshold is used to characterize the normal fluctuation range of parameters related to the device's operating environment, reflecting whether the environment in which the device is located meets the requirements for stable operation. In some examples, the workflow execution frequency threshold is used to characterize the execution patterns of various workflows of the medical device under normal operating conditions, such as the execution frequency, duration, and state switching of different workflows. For example, if the device frequently switches between power on and off within a short period, remains in self-test mode for extended periods, or repeatedly executes the same workflow, it indicates that the device's operating behavior may deviate from the normal operating mode. Therefore, setting an appropriate workflow execution frequency threshold serves as a reference for judging abnormal states.

[0043] According to a second aspect of this disclosure, a method such as Figure 6 The device 800 shown for detecting abnormalities in medical devices includes: The first module 810 is configured to acquire various historical operating data generated by the medical device; The second module 820 is configured to process and correlate the various historical operational data according to a unified time dimension to form an operational status correlation result for the medical device; and The third module 830 is configured to determine the abnormal state of the medical device based on the associated results of the operating state.

[0044] It should be noted that the functions of the modules discussed herein can be divided into multiple modules, and / or at least some functions of multiple modules can be combined into a single module. The specific actions performed by a particular module discussed herein include the specific module itself performing the action, or alternatively, the specific module calling or otherwise accessing another component or module that performs the action (or performs the action in conjunction with the specific module). Therefore, a specific module performing an action can include the specific module performing the action itself and / or another module that performs the action, called or otherwise accessed by the specific module.

[0045] It should also be understood that this article can describe various technologies in the general context of software and hardware components or program modules. The above regarding... Figure 6 The described modules can be implemented in hardware or in hardware in combination with software and / or firmware. For example, these modules can be implemented as computer program code / instructions configured to execute in one or more processors and stored in a computer-readable storage medium. Alternatively, these modules can be implemented as hardware logic / circuit. Hardware logic / circuit may include integrated circuit chips (which include processors (e.g., Central Processing Unit (CPU), microcontrollers, microprocessors, digital signal processors (DSPs), etc.), memory, one or more communication interfaces, and / or one or more components in other circuitry), and may optionally execute received program code and / or include embedded firmware to perform functions.

[0046] This application provides a computing device 900, such as... Figure 7 As shown. Figure 7 An example configuration of a computing device 900 that can be used to implement the multi-channel video processing method 100 described herein is shown. For example, the multi-channel video processing apparatus 800 described above may be implemented wholly or at least partially by the computing device 900 or a similar device or system.

[0047] The computing device 900 may include at least one processor 905 capable of communicating with each other, such as via a bus 904 or other suitable connection, a memory 907, multiple communication interfaces 902, a display device 901, other input / output (I / O) devices 903, and one or more mass storage devices 906. Instructions are stored on the memory 907, which, when executed by the processor 905, cause the processor 905 to perform a multi-channel video processing method as described in the above embodiments.

[0048] The computing device 900 can be a variety of different types of devices. Examples of the computing device 900 include, but are not limited to: desktop computers, server computers, laptop or netbook computers, mobile devices (e.g., tablets, cellular or other wireless phones (e.g., smartphones), notebook computers, mobile stations), wearable devices (e.g., glasses, watches), entertainment devices (e.g., entertainment appliances, set-top boxes communicatively coupled to a display device, game consoles), televisions or other display devices, automotive computers, and so on.

[0049] Processor 905 may be a single processing unit or multiple processing units, and all processing units may include single or multiple computing units or multiple cores. Processor 905 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuits, and / or any device that manipulates signals based on operating instructions. Among other capabilities, processor 905 may be configured to fetch and execute computer-readable instructions stored in memory 907, mass storage device 906, or other computer-readable media, such as program code of operating system 908, program code of application program 909, program code of other program 910, etc.

[0050] Memory 907 and mass storage device 906 are examples of computer-readable storage media for storing instructions executed by processor 905 to perform the various functions described above. For example, memory 907 can generally include both volatile and non-volatile memory (e.g., RAM, ROM, etc.). Furthermore, mass storage device 906 can generally include hard disk drives, solid-state drives, removable media, including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CDs, DVDs), storage arrays, network-attached storage, storage area networks, etc. Both memory 907 and mass storage device 906 can be collectively referred to herein as memory or computer-readable storage media, and can be non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code, which can be executed by processor 905 as a specific machine configured to perform the operations and functions described in the examples herein.

[0051] Multiple programs can be stored on mass storage device 906. These programs include operating system 908, one or more application programs 909, other programs 910, and program data 911, and they can be loaded into memory 907 for execution. Examples of such application programs or program modules may include, for example, computer program logic (e.g., computer program code or instructions) for implementing components / functions such as multi-channel video processing apparatus 800 (including first module 810, second module 820, and third module 830), multi-channel video processing method 100 (including any suitable steps of multi-channel video processing method 100), and / or other embodiments described herein.

[0052] Although Figure 7 The data is illustrated as being stored in memory 907 of computing device 900, but operating system 908, application program 909, other programs 910 and program data 911 or portions thereof may be implemented using any form of computer-readable medium accessible by computing device 900.

[0053] One or more communication interfaces 902 are used for exchanging data with other devices, such as via a network, direct connection, etc. Such communication interfaces can be one or more of the following: any type of network interface (e.g., a network interface card (NIC)), wired or wireless (such as IEEE 802.11 Wireless LAN (WLAN)) wireless interface, Wi-MAX interface, Ethernet interface, Universal Serial Bus (USB) interface, cellular network interface, Bluetooth™ interface, Near Field Communication (NFC) interface, etc. Communication interface 902 can facilitate communication across various network and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite, etc.), the Internet, etc. Communication interface 902 can also provide communication with external storage devices (not shown), such as storage arrays, network-attached storage, storage area networks, etc.

[0054] In some examples, a display device 901, such as a monitor, may be included for displaying information and images to the user. Other I / O devices 903 may be devices that receive various inputs from the user and provide various outputs to the user, and may include touch input devices, gesture input devices, cameras, keyboards, remote controls, mice, printers, audio input / output devices, and so on.

[0055] The technologies described herein can be supported by these various configurations of computing device 900, and are not limited to specific examples of the technologies described herein. For example, the functionality can also be implemented wholly or partially on a “cloud” using a distributed system. A cloud includes and / or represents a platform for resources. The platform abstracts the underlying functionality of the cloud’s hardware (e.g., servers) and software resources. Resources may include applications and / or data that can be used when performing computational processing on servers remote from computing device 900. Resources may also include services provided via the Internet and / or via subscriber networks such as cellular or Wi-Fi networks. The platform can abstract resources and functionality to connect computing device 900 to other computing devices. Therefore, the implementation of the functionality described herein can be distributed throughout the cloud. For example, the functionality can be implemented partly on computing device 900 and partly through a platform that abstracts the functionality of the cloud.

[0056] This application also provides a computer-readable storage medium storing instructions that, when executed individually or jointly by one or more processors of a computing device, cause the computing device to perform the methods described in any of the above embodiments.

[0057] Computer-readable storage media include volatile and non-volatile, removable and non-removable media implemented by any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer-readable storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, Digital Universal Disc (DVD) or other optical storage devices, magnetic cassettes, magnetic tapes, disk storage devices or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by computer equipment.

[0058] This application also provides a computer program product including instructions that, when executed individually or jointly by one or more processors of a computing device, cause the computing device to perform the methods as described in any of the above embodiments.

[0059] The above description is merely an embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention. Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this disclosure. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as technology evolves, many elements described herein can be replaced by equivalent elements that appear after this disclosure.

Claims

1. A method for monitoring abnormalities in a medical device, comprising: Acquire various historical operating data generated by the medical device; The various historical operational data are processed and correlated based on a unified time dimension to form a correlation result of the operational status of the medical equipment; and Based on the operational status correlation results, the abnormal status of the medical device is determined.

2. The method according to claim 1, wherein, The various historical operational data include workflow status data, environmental status data, and component operational status data obtained from the log system of the medical device. The workflow status data is used to characterize the working stage information of the medical device, the environmental status data is used to characterize the operating environment information of the medical device, and the operating status data is used to characterize the operating status information of the internal components of the medical device.

3. The method according to claim 2, wherein, Acquire various historical operational data generated by the medical device, including: Obtain multiple historical log files from the medical device; The multiple historical log files are filtered based on preset identifiers associated with the various historical operational data to obtain target log files containing the preset identifiers; and The target log file is parsed to obtain the various historical runtime data.

4. The method according to claim 3, wherein, The multiple historical log files include structured format files.

5. The method according to claim 3, wherein, The target log file is parsed to obtain the various historical runtime data, including: The target log file is read segment by segment according to the predetermined data reading order; Based on the preset identifier, the data in the current data segment is filtered to obtain the target historical running data that matches the preset identifier; In response to obtaining the target's historical running data, release the data cache corresponding to the current data segment; and Perform unit conversion operations and / or abnormal data filtering operations on the target historical operation data.

6. The method according to any one of claims 3 to 5, wherein, The various historical operational data are processed and correlated based on a unified time dimension to form an operational status correlation result for the medical equipment, including: The workflow status data, environmental status data, and component operation status data are displayed synchronously on a unified timeline; and Based on the correspondence between the work stage information represented by the workflow status data and the environmental status data and / or component operation status data within the corresponding time period, the operation status association result of the medical device is obtained.

7. The method according to claim 6, wherein, The method further includes: Based on the matching between the operational status correlation results and the expected operational status correlation results, the abnormal state of the medical equipment is determined; and / or Based on the information about the working stage and the environmental status data within the corresponding time period, the condensation risk coefficient of the medical device is determined.

8. The method according to claim 7, wherein, The method further includes: The operating benchmark of the medical equipment is established based on the various historical operating data. Obtain multiple current log files of the medical device to obtain the current operating data of the medical device; and Based on the deviation between the current operating data and the operating baseline, the abnormal state of the medical device is confirmed and intervention instructions and / or prompts corresponding to the abnormal state are generated.

9. The method according to claim 8, wherein, The operating baseline includes at least one of the following: environmental status data threshold, workflow execution frequency threshold corresponding to the workflow status data, and condensation risk coefficient threshold.

10. An apparatus for detecting anomalies in medical devices, comprising: The first module is configured to acquire various historical operational data generated by the medical device; The second module is configured to process and display the various historical operating data according to a unified time dimension, so as to form the operating status association result of the medical device. as well as The third module is configured to determine the abnormal state of the medical device based on the associated results of the operating status.

Images