Medical equipment intelligent operation and maintenance management method and related device

Abstract

The invention relates to the technical field of medical equipment management, and provides a medical equipment intelligent operation and maintenance management method and a related device. The method comprises the following steps: acquiring an equipment parameter sequence, a user operation event sequence and an environment monitoring data sequence of medical equipment; for each operation event in the operation event sequence, extracting a corresponding window period equipment parameter from the equipment parameter sequence according to the occurrence time of the operation event, extracting a corresponding window period environment parameter from the environment monitoring data sequence, and generating a composite event record set; matching the composite event record set with a preset rule base, and generating an operation-related exception record list and an equipment fault exception record list; and generating a user prompt instruction according to the operation-related exception record list, generating a maintenance instruction according to the equipment fault exception record list, and updating the operation and maintenance state table based on the operation-related exception record list and the equipment fault exception record list. According to the method, root cause analysis of the abnormal state of the medical equipment is realized, and the operation and maintenance management efficiency can be improved.

Description

Technical Field

[0001] This application relates to the field of medical device management technology, and in particular to a method and related apparatus for intelligent operation and maintenance management of medical devices. Background Technology

[0002] The operation of modern medical institutions relies on the stability and reliability of various automated diagnostic and treatment devices. The performance of these devices directly affects the accuracy of diagnostic and treatment results and patient safety. Therefore, the industry widely adopts automated operation and maintenance technologies to continuously monitor critical medical equipment. This involves collecting the equipment's own operational data and environmental parameters and comparing them with preset standards to determine whether the equipment is in a controllable state.

[0003] However, the underlying causes of equipment anomalies identified by current mainstream operation and maintenance technologies are often unclear. System alarms can only indicate that parameters have exceeded limits, but cannot distinguish whether the anomaly is caused by natural wear and tear or failure of internal components, or by improper calibration, maintenance, or preparation work performed by operators. Such alarms with unclear causes often lead to the misallocation of maintenance resources. On the one hand, this may lead to over-repair of normal equipment; on the other hand, it may lead to the neglect of timely correction of operational oversights, thereby restricting the efficiency and depth of operation and maintenance management and harboring potential risks to the quality of frontline medical care. Summary of the Invention

[0004] This application provides a method and related apparatus for intelligent operation and maintenance management of medical equipment, in order to solve the problems mentioned in the background art.

[0005] Firstly, this application provides a method for intelligent operation and maintenance management of medical devices, including: Acquire the sequence of equipment parameters, user operation events, and environmental monitoring data from medical devices; For each operation event in the operation event sequence, corresponding window period equipment parameters are extracted from the equipment parameter sequence based on the occurrence time of the operation event, and corresponding window period environmental parameters are extracted from the environmental monitoring data sequence to generate a composite event record set. The composite event record set is matched with a preset rule base to generate an operation-related anomaly record list and a device fault anomaly record list. User prompt instructions are generated based on the operation-related anomaly record list, maintenance instructions are generated based on the device fault anomaly record list, and the operation and maintenance status table is updated based on the operation-related anomaly record list and the device fault anomaly record list.

[0006] Secondly, this application provides an intelligent operation and maintenance management system for medical devices, comprising: The acquisition module is used to acquire the equipment parameter sequence of medical devices, the user operation event sequence, and the environmental monitoring data sequence. The extraction module is used to extract corresponding window period equipment parameters from the equipment parameter sequence and corresponding window period environmental parameters from the environmental monitoring data sequence for each operation event in the operation event sequence, based on the occurrence time of the operation event, to generate a composite event record set; the matching module is used to match the composite event record set with a preset rule base to generate an operation-related anomaly record list and an equipment fault anomaly record list; the generation module is used to generate user prompt instructions based on the operation-related anomaly record list, generate maintenance instructions based on the equipment fault anomaly record list, and update the operation and maintenance status table based on the operation-related anomaly record list and the equipment fault anomaly record list.

[0007] Thirdly, this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements the intelligent operation and maintenance management method for medical devices as described in any of the preceding claims.

[0008] Fourthly, this application provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the intelligent operation and maintenance management method for medical devices as described in any of the preceding claims.

[0009] This application provides a method and related apparatus for intelligent operation and maintenance management of medical equipment. The method includes: firstly, by associating user operations with equipment parameters and environmental parameters over time, forming a composite event record containing complete contextual information, thus providing a data foundation for distinguishing the root causes of anomalies, and decomposing previously vague equipment anomaly alarms into a list of specific problems with clear targets; secondly, by automatically matching and classifying the composite event record with predefined rules, generating two independent lists of anomaly records: operation-related and equipment fault-related, thereby achieving automatic separation and attribution of anomalies caused by human factors and equipment factors, and thus providing clear action guidelines for solving problems with different root causes; thirdly, by automatically generating guiding instructions based on the classification list and updating the status overview, a closed-loop management process from problem identification and cause analysis to handling suggestions and status monitoring is completed, thereby enabling intelligent operation and maintenance management to shift from passively responding to alarms to proactive prevention and precise maintenance. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating the intelligent operation and maintenance management method for medical devices provided in this application embodiment; Figure 2 A schematic block diagram of the structure of the intelligent operation and maintenance management system for medical devices provided in the embodiments of this application; Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0014] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0015] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0016] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features described herein can be combined with each other.

[0017] Please see Figure 1 , Figure 1 This is a flowchart illustrating the intelligent operation and maintenance management method for medical devices provided in the embodiments of this application, as shown below. Figure 1 As shown, the method includes steps S1 to S4.

[0018] Step S1: Obtain the equipment parameter sequence, user operation event sequence, and environmental monitoring data sequence of the medical equipment.

[0019] Specifically, the system continuously and automatically reads various readings indicating the medical device's operational status, such as temperature, pressure, and voltage. These readings are arranged chronologically to form a sequence of device parameters. Simultaneously, it automatically records every action performed by the operator on the device, such as pressing a button, changing reagents, or executing calibration procedures, and arranges these actions chronologically to form a sequence of user operation events. Furthermore, it automatically acquires environmental information, such as room temperature and humidity, from various sensors installed near the device. This environmental data is also arranged chronologically to form a sequence of environmental monitoring data.

[0020] Step S2: For each operation event in the operation event sequence, extract the corresponding window period equipment parameters from the equipment parameter sequence based on the occurrence time of the operation event, and extract the corresponding window period environmental parameters from the environmental monitoring data sequence to generate a composite event record set. Specifically, for each recorded operation event, a time range is defined both forward and backward from its occurrence time, forming an analysis window. Then, from the equipment parameter sequence, find all equipment status readings whose time points fall within this window range; this set of readings is the window period equipment parameter corresponding to the operation. Similarly, from the environmental monitoring data sequence, find the environmental data closest to the operation occurrence time, as the window period environmental parameter. Next, package the operation event itself, its corresponding window period equipment parameter, and the window period environmental parameter together to form a complete descriptive unit, called a composite event record. Repeat this process for all operation events; all the resulting composite event records constitute the composite event record set.

[0021] Step S3: Match the composite event record set with a pre-defined rule base to generate a list of operation-related anomaly records and a list of equipment failure anomaly records. Specifically, there is a pre-defined rule base where each rule describes the conditions that an anomaly should meet, such as "after performing a 'calibration' operation, the fluctuation of the equipment's 'measurement stability' index exceeds a certain limit in the following hour." Each record in the composite event record set is compared one by one with all the rules in the rule base to check whether the operation, equipment parameter changes, and environmental parameters in the record meet all the conditions set by a certain rule. If they are met, it is considered that the rule has been triggered, and according to the definition of the rule, a cause label is assigned to the composite event record, such as "non-standard operation" or "equipment component aging." All records labeled with "non-standard operation" are grouped together to form a list of operation-related anomaly records; all records labeled with equipment problem labels such as "equipment component aging" are grouped together to form a list of equipment failure anomaly records.

[0022] Step S4: Generate user prompt instructions based on the operation-related anomaly record list, generate maintenance instructions based on the device fault anomaly record list, and update the operation and maintenance status table based on the operation-related anomaly record list and the device fault anomaly record list.

[0023] Specifically, for each record in the operation-related anomaly log list, an automatic message is generated based on the specific operation and the type of problem triggered. This message directly guides operators on how to correct errors or standardize subsequent operations, such as "Next time you perform calibration, please be sure to wait for the machine to warm up completely." For each record in the equipment malfunction anomaly log list, a repair or maintenance suggestion work order is automatically generated based on the type of equipment problem and the anomaly data recorded, such as "It is recommended to check and replace the hydraulic pressure sensor." Simultaneously, the number of records and the main problem types in both lists are counted, and this summary information is entered or updated into a table reflecting the overall intelligent operation and maintenance status of all medical equipment.

[0024] The method provided in this embodiment, on the one hand, forms a composite event record containing complete contextual information by associating user operations with device parameters and environmental parameters in the time dimension, thereby providing a data foundation for distinguishing the root causes of anomalies and decomposing the originally vague device anomaly alarms into a list of specific problems with clear targets. On the other hand, by automatically matching and classifying the composite event record with predefined rules, two independent anomaly record lists are generated: operation-related and device fault-related. This achieves automatic separation and attribution of anomalies caused by human factors and equipment factors, thereby providing a clear basis for action to solve problems with different root causes. Furthermore, by automatically generating guiding instructions based on the classification list and updating the status overview, a closed-loop management process from problem identification and cause analysis to handling suggestions and status monitoring is completed, thereby enabling intelligent operation and maintenance management to shift from passively responding to alarms to proactive prevention and precise maintenance.

[0025] In some embodiments, acquiring the device parameter sequence of the medical device, the user operation event sequence, and the environmental monitoring data sequence includes: S11. Collect the raw monitoring data stream generated during the operation of the medical device at a preset sampling frequency or in response to a specific working event of the medical device; add a timestamp to each data point in the raw monitoring data stream, and convert each data point into a standardized device parameter value with a clear physical unit according to a predefined device parameter mapping relationship, forming the device parameter sequence.

[0026] Specifically, the system automatically collects raw electrical signals or values ​​generated by internal sensors at set time intervals, or at specific moments such as when the device completes a test or begins a calibration. For each piece of raw data collected, the precise time of acquisition is recorded. Then, referring to a pre-defined lookup table, these raw, meaningless values ​​are converted into concrete physical quantities with standard units of measurement that are understandable to humans; for example, converting "value 1024" into "temperature 37.0 degrees Celsius". All the converted and time-stamped data, arranged in chronological order, constitute the device parameter sequence.

[0027] S12. Monitor the human-computer interaction interface of the medical device and the information system interface associated with the medical device in real time, and capture user operation logs; parse the operator identifier, operation timestamp, operation type code and operation object identifier from the user operation logs, encapsulate them into operation event objects in a standard format, and store them in the user operation event sequence according to the order of the operation timestamps.

[0028] Specifically, continuous monitoring is conducted on key presses and touch commands displayed on the medical device's operating screen, as well as data packets exchanged between the device and the hospital's laboratory information system, to capture and record log entries of personnel operations. From each operation log, analysis is performed to extract who performed the operation, the specific time the operation occurred, the type of operation performed (e.g., "power on," "calibrate," "clean"), and which component of the device or batch of reagents the operation targeted. These extracted information items are combined into a standardized "operation event" data packet. All such data packets are arranged and stored in chronological order of their operation time, forming a user operation event sequence.

[0029] S13. Read environmental monitoring data from environmental sensors deployed around the medical device in real time, and add a collection timestamp to each set of environmental monitoring data to form the environmental monitoring data sequence.

[0030] Specifically, the system continuously acquires current environmental readings from sensors such as thermometers and hygrometers installed at the location of the medical equipment. Each set of environmental data (e.g., "Temperature: 23.5℃, Humidity: 45%) is time-stamped with the acquisition time. These time-stamped environmental data are arranged in chronological order of acquisition to form an environmental monitoring data sequence.

[0031] The method provided in this embodiment, on the one hand, generates unified and interpretable time-series data of equipment performance by time-stamping and standardizing the original equipment monitoring data, providing a benchmark for subsequent accurate correlation analysis; on the other hand, it organizes user behavior data in a structured and time-series manner by automatically listening to and parsing operation logs from multiple sources, enabling the computer system to identify and process human operation factors; and furthermore, it establishes a time-varying environmental background information database by continuously collecting and labeling environmental data sequences, providing a reference for judging whether equipment abnormalities are caused by environmental interference.

[0032] In some embodiments, for each operation event in the operation event sequence, extracting corresponding window period device parameters from the device parameter sequence based on the occurrence time of the operation event, and extracting corresponding window period environmental parameters from the environmental monitoring data sequence to generate a composite event record set includes: Step S21: For each operation event in the user operation event sequence, based on the operation timestamp of the operation event, determine an analysis time window covering the period before and after the operation.

[0033] Specifically, for each recorded user action, starting from the moment the action occurred, a period of time is traced back (e.g., 30 minutes) and then extended forward (e.g., 2 hours). This entire time period is defined as the analysis time window for this action.

[0034] Step S22: Extract all equipment parameter data points whose timestamps fall within the analysis time window from the equipment parameter sequence, forming a subset of equipment parameters within the window period. Specifically, within the entire large dataset of equipment parameter sequences, find all equipment status readings whose time points fall within the aforementioned analysis time window. Collecting these found readings together constitutes a subset of equipment parameter data within the window period specifically associated with this operation.

[0035] Step S23: Based on the equipment parameter data prior to the operation timestamp within the window period equipment parameter subset, calculate one or more statistical characteristic values ​​to serve as the equipment parameter baseline before the operation event. Specifically, from the window period equipment parameter subset obtained in step S22, only those equipment readings occurring before the operation time are selected. Using this pre-operation data, calculate their average level (mean) or fluctuation range (standard deviation), etc. These statistics represent the normal state level of the relevant equipment parameters before the execution of this operation and are referred to as the equipment parameter baseline.

[0036] Step S24: Compare each device parameter data point after the operation timestamp in the window period device parameter subset with the corresponding device parameter baseline to calculate the parameter deviation of each data point. Specifically, for each device reading occurring after the operation time in the window period device parameter subset, its value is compared with the baseline (such as the average value) of the same type of parameter established in step S23. The specific comparison method is to calculate the difference between the reading value and the baseline value; this difference value is called the parameter deviation of the data point. A positive deviation indicates that the reading is higher than the baseline, and a negative deviation indicates that it is lower than the baseline; its absolute value indicates the degree of deviation.

[0037] Step S25: Extract the environmental parameter data whose timestamp is closest to the operation timestamp from the environmental monitoring data sequence, and use it as the associated environmental state of the operation event. Specifically, in the entire environmental monitoring data sequence, find the set of environmental readings (such as room temperature and humidity at that time) that are closest to the moment the operation occurred. This set of data represents the environmental conditions of the equipment when the operation occurred and is recorded as the associated environmental state of the operation event.

[0038] Step S26: Encapsulate the operation event, the window period device parameter subset, the parameter deviation of each data point, the device parameter baseline, and the associated environmental state to form a composite event record. All composite event records corresponding to the operation events constitute the composite event record set.

[0039] Specifically, the information of the operation event itself, all the original equipment readings within the window period, the calculated parameter deviation of each reading, the baseline of equipment parameters before the operation, and the environmental conditions at the time of the operation are all packaged together to form a complete "composite event record". The above processes S21 to S26 are executed for each operation event to generate the corresponding composite event record. The collection of all these records is the composite event record set.

[0040] The method provided in this embodiment, on the one hand, ensures the precise temporal correspondence between the operation behavior and the changes in the equipment state before and after by dynamically defining the analysis window centered on the operation time and extracting the corresponding data, thus creating conditions for causal analysis; on the other hand, by calculating the baseline of the equipment parameters before the operation and comparing it one by one with the data points after the operation to obtain the deviation, the abstract changes in the equipment state are transformed into specific and quantifiable numerical indicators, making the degree of equipment response to the operation clear and measurable; furthermore, by integrating the operation event, the quantified equipment response (raw data and deviation), the baseline state and the environmental information at that time into a single record, a knowledge unit containing all elements of "human-machine-environment" and with inherent correlation is constructed, providing a structured and information-complete input for subsequent rule-based intelligent judgment.

[0041] In some implementations, comparing each device parameter data point after the operation timestamp in the subset of device parameters during the window period with the corresponding device parameter baseline to calculate the parameter deviation of each data point includes: Step S241: For each data point in the window period device parameter subset whose timestamp is after the operation timestamp, determine the device parameter type to which the data point belongs. Specifically, for each device reading collected after the operation, first identify which device parameter it represents, such as "reaction cup temperature" or "light source intensity".

[0042] Step S242: Obtain the central trend value in the equipment parameter baseline calculated for the equipment parameter type. Specifically, locate the equipment parameter baseline calculated in the previous steps for this specific type of equipment parameter (e.g., "reaction cup temperature"), and extract a representative value of its central trend from that baseline, typically the average of all pre-operation readings.

[0043] Step S243: Subtract the central trend value from the value of the data point to obtain the initial difference for the data point. Specifically, subtract the central trend value (average value) taken from the baseline from the specific value of the reading after this operation to obtain a preliminary difference.

[0044] Step S244: Calculate the initial difference with a preset normalized reference value to obtain a standardized deviation metric value, which serves as the parameter deviation of the data point; wherein, the central trend value is the average or median of the equipment parameter baseline, and the normalized reference value is the standard deviation of the equipment parameter baseline, the absolute value of the central trend value, or a fixed physical unit constant.

[0045] Specifically, this initial difference is calculated against a preset reference value. This reference value can be the fluctuation range (standard deviation) of the baseline data itself, the magnitude of the central trend value, or a fixed constant. Through this calculation, the initial difference is transformed into a standardized, more comparable metric, which is the final parameter deviation.

[0046] The method provided in this embodiment, on the one hand, ensures that the comparison is between quantities with the same physical meaning by first identifying the parameter type and then obtaining the corresponding baseline center value, thus avoiding logical errors in cross-parameter type comparisons; on the other hand, by first calculating the initial difference and then performing a standardization operation with the normalized reference value, the influence of different parameters due to different dimensions and inherent fluctuation ranges is eliminated, making the deviations from parameters from different devices comparable, and providing the possibility for setting a unified anomaly judgment threshold or rule in the future.

[0047] In some embodiments, matching the composite event record set with a preset rule base to generate an operation-related anomaly record list and a device malfunction anomaly record list includes: Step S31: Each composite event record in the composite event record set is logically matched against each rule entry in the preset rule base. Each rule entry contains a combination of matching conditions for operational characteristics, device response parameter deviation, and associated environmental state within the composite event record. Specifically, each record in the composite event record set is compared sequentially with each rule in the preset rule base. Each rule contains a set of conditions that simultaneously set multiple aspects of the record: for example, requiring the operation to be of a specific type, requiring the deviation of a certain device parameter to continuously exceed a certain range after the operation, and requiring the environmental parameters at the time to be within the normal range.

[0048] Step S32: When a composite event record satisfies all the matching conditions of a rule entry, the composite event record is assigned the root cause classification identifier corresponding to that rule entry. Specifically, if the data in a composite event record fully meets all the conditions set by a rule, then it is determined that the record has triggered that rule. Subsequently, according to the definition of this rule, a classification label indicating the root cause of the problem is assigned to the composite event record, such as "improper calibration operation" or "degraded performance of photoelectric sensor".

[0049] Step S33: Aggregate all composite event records that are assigned root cause classification labels pointing to operational problems to generate the operation-related anomaly record list. Specifically, among all the tagged composite event records, select all records whose labels point to operator behavior problems (such as "non-standard operation" or "process omission"), and put them together to form a special list, which is the operation-related anomaly record list.

[0050] Step S34: Aggregate all composite event records that are assigned root cause classification identifiers pointing to device failures to generate the device failure exception record list.

[0051] Specifically, similarly, among all the tagged records, all records whose tags point to hardware or software problems of the device itself (such as "component aging", "sensor failure", "software error") are selected and grouped together to form another special list, which is the device fault and abnormal record list.

[0052] The method provided in this embodiment, on the one hand, achieves automated identification of complex anomaly patterns by logically matching each record with rules containing multi-dimensional conditions, thus making the implicit relationships in the data explicit; on the other hand, by assigning clear root cause classification labels to successfully matched records, the identified anomalies are qualitatively characterized, and the most likely cause is labeled for each anomaly; furthermore, by aggregating records into two independent lists, operation-related and equipment failure, based on the root cause labels, the two different types of problems are completely separated logically and organizationally, laying a clear and unambiguous foundation for taking drastically different subsequent handling measures.

[0053] In some embodiments, the step of aggregating all composite event records assigned root cause classification identifiers pointing to device failures to generate the device failure anomaly record list includes: Step S341: Traverse all compound event records that have been assigned root cause classification labels.

[0054] Specifically, each composite event record that has been labeled with a root cause classification is examined in turn.

[0055] Step S342: Obtain all root cause classification identifiers defined as pointing to equipment failures from the preset rule base, forming a set of equipment failure root cause identifiers. Specifically, find all those classification label codes that are predefined as representing equipment failures (rather than operational problems) from the entire rule base, collect these codes together to form a "set of equipment failure cause labels".

[0056] Step S343: Compare the root cause classification identifier contained in the currently traversed composite event record with the set of device fault root cause identifiers. Specifically, for the composite event record currently being checked, read out the root cause classification label that was attached to it.

[0057] Step S344: If the root cause classification identifier of the current composite event record exists in the set of device failure root cause identifiers, then add the composite event record to a temporary set. Specifically, compare this label with the "set of device failure cause labels". If the label is found to exist in the set, it means that this record is determined to be a device failure. Then, make a copy of this complete record and add it to a temporary collection container.

[0058] Step S345: After completing the traversal of all composite event records, output the temporary set as the list of equipment fault and abnormal records.

[0059] Specifically, after all tagged records have been checked and processed, the temporary collection container holds all records identified as equipment malfunctions. The contents of this container are then formally output as the equipment malfunction / abnormality record list.

[0060] The method provided in this embodiment, on the one hand, achieves centralized management and efficient reuse of the filtering criteria by pre-extracting a set of device fault identifiers from the rule base and using it for traversal comparison, thereby avoiding repeated parsing of rules when processing each record; on the other hand, it uses simple set membership relationship judgment as the filtering logic, thereby accurately separating all device fault-related entries from the mixed record pool in a deterministic and efficient manner, ensuring the accuracy and consistency of the list generation.

[0061] In some embodiments, the step of generating user prompt instructions based on the operation-related anomaly record list, generating maintenance instructions based on the device fault anomaly record list, and updating the operation and maintenance status table based on the operation-related anomaly record list and the device fault anomaly record list includes: Step S41: For each record in the operation-related anomaly record list, generate a user prompt message containing specific operation instructions based on the root cause classification identifier and operator identifier contained in the record. Specifically, for each record in the operation-related anomaly record list, automatically create a prompt message based on the specific problem cause (e.g., "reagent expired") and the operator who performed the action. This message contains specific operation instructions for the problem and the operator, such as "Operator Zhang San: Please check the expiration date of reagent A before the next use."

[0062] Step S42: For each record in the equipment fault anomaly record list, generate a maintenance work order containing a fault description and maintenance suggestions based on the root cause classification identifier and abnormal equipment parameter information contained in the record. Specifically, for each record in the equipment fault anomaly record list, automatically create a repair request form based on the marked equipment fault type (e.g., "low hydraulic pressure") and the detailed equipment data of the anomaly in the record. The work order will clearly describe the fault phenomenon, possible causes, and suggested repairs or replacement parts.

[0063] Step S43: Statistically analyze the operation-related anomaly record list and the equipment fault anomaly record list respectively to obtain the number of operation-related anomalies and the number of equipment fault anomalies; specifically, calculate the number of records in each of the operation-related anomaly record list and the equipment fault anomaly record list to obtain two quantity values.

[0064] Step S44: Update the anomaly statistics field associated with the corresponding medical device in the operation and maintenance status table according to the number of operation-related anomalies and the number of equipment failure anomalies.

[0065] Specifically, the two values ​​calculated above are entered into a table that provides an overview of the intelligent operation and maintenance status of all devices, and the data columns for "Number of Operation-Related Anomalies" and "Number of Device Failure Anomalies" in the corresponding device row are updated.

[0066] The method provided in this embodiment, on the one hand, generates differentiated instructions and work orders based on the specific content of the anomaly records, thereby transforming the analysis conclusions into executable and clearly directed action guidelines to drive problem resolution; on the other hand, it abstracts scattered anomaly events into measurable management indicators by performing quantity statistics on the classified anomaly list; and furthermore, it constructs a dynamic and visualized panoramic view of equipment health and operational compliance by updating the statistical indicators to a centralized operation and maintenance status table, supporting data-driven continuous management decisions.

[0067] Please see Figure 2 , Figure 2 A schematic block diagram of the structure of the intelligent operation and maintenance management system for medical devices provided in the embodiments of this application, such as... Figure 2 As shown in the embodiments of this application, the intelligent operation and maintenance management system for medical devices includes: The acquisition module 110 is used to acquire the equipment parameter sequence of the medical device, the user operation event sequence, and the environmental monitoring data sequence.

[0068] Extraction module 120 is used to extract corresponding window period equipment parameters from the equipment parameter sequence and corresponding window period environmental parameters from the environmental monitoring data sequence for each operation event in the operation event sequence, based on the occurrence time of the operation event, to generate a composite event record set. Matching module 130 is used to match the composite event record set with a preset rule base to generate an operation-related anomaly record list and a device fault anomaly record list. Generation module 140 is used to generate user prompt instructions based on the operation-related anomaly record list, generate maintenance instructions based on the device fault anomaly record list, and update the operation and maintenance status table based on the operation-related anomaly record list and the device fault anomaly record list.

[0069] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the system and its modules described above can be referred to the corresponding processes in the aforementioned embodiments of the intelligent operation and maintenance management method for medical equipment, and will not be repeated here.

[0070] The intelligent operation and maintenance management system 100 for medical devices provided in the above embodiments can be implemented as a computer program, which can be used in, for example... Figure 3 The terminal device 200 shown is running on it.

[0071] Please see Figure 3 , Figure 3 The following is a schematic block diagram of the structure of a terminal device 200 provided in an embodiment of this application. The terminal device 200 includes a processor 201 and a memory 202, which are connected through a system bus 203. The memory 202 may include a non-volatile storage medium and internal memory.

[0072] The non-volatile storage medium can store a computer program. The computer program includes program instructions, which, when executed by the processor 201, cause the processor 201 to perform any of the aforementioned intelligent operation and maintenance management methods for medical devices.

[0073] The processor 201 provides computing and control capabilities to support the operation of the entire terminal device 200.

[0074] The internal memory provides an environment for the execution of computer programs in non-volatile storage media. When the computer program is executed by the processor 201, the processor 201 can execute any of the above-mentioned intelligent operation and maintenance management methods for medical devices.

[0075] Those skilled in the art will understand that Figure 3The structure shown in the figure is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the terminal device 200 involved in the present application. The specific terminal device 200 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0076] It should be understood that processor 201 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, the general-purpose processor can be a microprocessor or any conventional processor.

[0077] In some embodiments, the processor 201 is configured to run a computer program stored in memory to perform the following steps: Acquire the sequence of equipment parameters, user operation events, and environmental monitoring data from medical devices; For each operation event in the operation event sequence, corresponding window period equipment parameters are extracted from the equipment parameter sequence based on the occurrence time of the operation event, and corresponding window period environmental parameters are extracted from the environmental monitoring data sequence to generate a composite event record set. The composite event record set is matched with a preset rule base to generate an operation-related anomaly record list and a device fault anomaly record list. User prompt instructions are generated based on the operation-related anomaly record list, maintenance instructions are generated based on the device fault anomaly record list, and the operation and maintenance status table is updated based on the operation-related anomaly record list and the device fault anomaly record list.

[0078] It should be noted that, for the sake of convenience and brevity, the specific working process of the terminal device 200 described above can be referred to the corresponding process of the aforementioned intelligent operation and maintenance management method for medical devices, and will not be repeated here.

[0079] This application also provides a computer-readable storage medium storing a computer program that, when executed by one or more processors, enables the one or more processors to implement the intelligent operation and maintenance management method for medical devices provided in this application.

[0080] The computer-readable storage medium can be an internal storage unit of the terminal device 200 in the aforementioned embodiments, such as a hard disk or memory of the terminal device 200. The computer-readable storage medium can also be an external storage device of the terminal device 200, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., provided with the terminal device 200.

[0081] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for intelligent operation and maintenance management of medical equipment, characterized in that, include: Acquire the sequence of equipment parameters, user operation events, and environmental monitoring data from medical devices; For each operation event in the operation event sequence, corresponding window period equipment parameters are extracted from the equipment parameter sequence based on the occurrence time of the operation event, and corresponding window period environmental parameters are extracted from the environmental monitoring data sequence to generate a composite event record set. The composite event record set is matched with a preset rule base to generate an operation-related anomaly record list and a device fault anomaly record list. User prompt instructions are generated based on the operation-related anomaly record list, maintenance instructions are generated based on the device fault anomaly record list, and the operation and maintenance status table is updated based on the operation-related anomaly record list and the device fault anomaly record list.

2. The intelligent operation and maintenance management method for medical equipment according to claim 1, characterized in that, The acquisition of the medical device parameter sequence, user operation event sequence, and environmental monitoring data sequence includes: The system collects raw monitoring data streams generated during the operation of the medical device at a preset sampling frequency or in response to specific working events of the medical device. It adds a timestamp to each data point in the raw monitoring data stream and converts each data point into a standardized device parameter value with a defined physical unit based on a predefined device parameter mapping relationship, forming the device parameter sequence. It monitors the human-machine interface of the medical device and the information system interface associated with the medical device in real time, capturing user operation logs. It parses the operator identifier, operation timestamp, operation type code, and operation object identifier from the user operation logs, encapsulates them into standard format operation event objects, and stores them in the user operation event sequence according to the order of the operation timestamps. It reads environmental monitoring data from environmental sensors deployed around the medical device in real time, adds a collection timestamp to each set of environmental monitoring data, forming the environmental monitoring data sequence.

3. The intelligent operation and maintenance management method for medical equipment according to claim 1, characterized in that, For each operation event in the operation event sequence, the corresponding window period equipment parameters are extracted from the equipment parameter sequence based on the occurrence time of the operation event, and the corresponding window period environmental parameters are extracted from the environmental monitoring data sequence to generate a composite event record set, including: For each operation event in the user operation event sequence, an analysis time window covering the period before and after the operation is determined based on the operation timestamp of the operation event. All device parameter data points whose timestamps fall within the analysis time window are extracted from the device parameter sequence, forming a window period device parameter subset. One or more statistical characteristic values ​​are calculated based on the device parameter data before the operation timestamp in the window period device parameter subset, serving as the device parameter baseline before the operation event. Each device parameter data point after the operation timestamp in the window period device parameter subset is compared with the corresponding device parameter baseline to calculate the parameter deviation of each data point. Environmental parameter data whose timestamp is closest to the operation timestamp is extracted from the environmental monitoring data sequence, serving as the associated environmental state of the operation event. The operation event, the window period device parameter subset, the parameter deviation of each data point, the device parameter baseline, and the associated environmental state are encapsulated to form a composite event record. All composite event records corresponding to operation events constitute the composite event record set.

4. The intelligent operation and maintenance management method for medical equipment according to claim 3, characterized in that, The step of comparing each equipment parameter data point after the operation timestamp in the subset of equipment parameters during the window period with the corresponding equipment parameter baseline, and calculating the parameter deviation of each data point, includes: For each data point in the window period device parameter subset whose timestamp is after the operation timestamp, determine the device parameter type to which the data point belongs; obtain the central trend value in the device parameter baseline calculated for the device parameter type; subtract the central trend value from the data point's value to obtain the initial difference of the data point; perform a calculation on the initial difference with a preset normalized reference value to obtain a standardized deviation metric value, which is used as the parameter deviation of the data point; wherein, the central trend value is the mean or median in the device parameter baseline, and the normalized reference value is the standard deviation in the device parameter baseline, the absolute value of the central trend value, or a fixed physical unit constant.

5. The intelligent operation and maintenance management method for medical equipment according to claim 1, characterized in that, The step of matching the composite event record set with a preset rule base to generate a list of operation-related anomaly records and a list of equipment fault anomaly records includes: Each composite event record in the composite event record set is logically matched against rule entries in a pre-set rule base. Each rule entry contains a combination of matching conditions for operational characteristics, device response parameter deviations, and associated environmental states within the composite event record. When a composite event record satisfies all matching conditions of a rule entry, it is assigned the root cause classification identifier corresponding to that rule entry. All composite event records assigned root cause classification identifiers pointing to operational problems are aggregated to generate the operation-related anomaly record list. All composite event records assigned root cause classification identifiers pointing to device malfunctions are aggregated to generate the device malfunction anomaly record list.

6. The intelligent operation and maintenance management method for medical equipment according to claim 5, characterized in that, The process of aggregating all composite event records assigned root cause classification identifiers pointing to device malfunctions to generate the device malfunction anomaly record list includes: Iterate through all compound event records that have been assigned root cause classification labels; From the preset rule base, obtain all root cause classification identifiers defined as pointing to equipment failures, forming a set of equipment failure root cause identifiers; compare the root cause classification identifiers contained in the currently traversed composite event records with the set of equipment failure root cause identifiers; if the root cause classification identifier of the current composite event record exists in the set of equipment failure root cause identifiers, then add the composite event record to a temporary set; after completing the traversal of all composite event records, output the temporary set as the list of equipment failure exception records.

7. The intelligent operation and maintenance management method for medical equipment according to claim 1, characterized in that, The steps of generating user prompts based on the list of operation-related anomaly records, generating maintenance instructions based on the list of device malfunction anomaly records, and updating the operation and maintenance status table based on the list of operation-related anomaly records and the list of device malfunction anomaly records include: For each record in the operation-related anomaly record list, a user prompt message containing specific operation guidance is generated based on the root cause classification identifier and operator identifier contained in the record; for each record in the equipment failure anomaly record list, a maintenance work order containing a fault description and maintenance suggestions is generated based on the root cause classification identifier and abnormal equipment parameter information contained in the record; statistics are performed on the operation-related anomaly record list and the equipment failure anomaly record list respectively to obtain the number of operation-related anomalies and the number of equipment failure anomalies; based on the number of operation-related anomalies and the number of equipment failure anomalies, the anomaly statistics field associated with the corresponding medical equipment in the operation and maintenance status table is updated.

8. A medical equipment intelligent operation and maintenance management system, characterized in that, include: The acquisition module is used to acquire the equipment parameter sequence of medical devices, the user operation event sequence, and the environmental monitoring data sequence. The extraction module is used to extract corresponding window period equipment parameters from the equipment parameter sequence based on the occurrence time of each operation event in the operation event sequence, and extract corresponding window period environmental parameters from the environmental monitoring data sequence to generate a composite event record set; the matching module is used to match the composite event record set with a preset rule base to generate an operation-related abnormal record list and an equipment fault abnormal record list. The generation module is used to generate user prompt instructions based on the operation-related anomaly record list, generate maintenance instructions based on the equipment fault anomaly record list, and update the operation and maintenance status table based on the operation-related anomaly record list and the equipment fault anomaly record list.

9. A terminal device, characterized in that, The terminal device includes a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein when the computer program is executed by the processor, it implements the intelligent operation and maintenance management method for medical devices as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, it implements the intelligent operation and maintenance management method for medical devices as described in any one of claims 1 to 7.

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