CN115526527A - Risk control method and device based on medical equipment operation and maintenance data - Google Patents
Risk control method and device based on medical equipment operation and maintenance data Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
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- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/40—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades
Abstract
The embodiment of the invention relates to the technical field of medical equipment, and discloses a risk control method based on operation and maintenance data of medical equipment, which comprises the following steps: acquiring equipment operation and maintenance data and an equipment number generated in the operation process of medical equipment; carrying out data processing on the equipment operation and maintenance data to obtain an operation and maintenance detection data set; performing feature screening on the processed operation and maintenance detection data set to obtain a corresponding operation and maintenance data feature set; sending the corresponding operation and maintenance data feature set to an input layer of a fault perception model which is constructed in advance for fault identification so as to determine a fault detection result; and determining whether to perform early warning operation according to the fault detection result. According to the risk control method based on the operation and maintenance data of the medical equipment, disclosed by the embodiment of the invention, the operation and maintenance operations are carried out on the acquired large amount of medical operation and maintenance data, so that the large amount of medical data is reduced into data content more fitting actual faults, and the evaluation on the operation state of the medical equipment is realized by combining a fault perception model.
Description
Technical Field
The invention relates to the technical field of medical equipment, in particular to a risk control method and device based on operation and maintenance data of medical equipment.
Background
At present, various systems need to simultaneously interface a plurality of ports of patients, doctors, departments of hospitals and the like to be used as nerve centers for hospital operation. The stability requirement on each system is extremely high, and once a certain set of system fails, the whole business process is influenced.
In the traditional medical equipment management, more energy is usually spent in an early equipment purchasing place in a hospital side, much attention is not paid to later operation and maintenance, and corresponding abnormality detection cannot be completed in time when equipment abnormality occurs in the follow-up process; due to the limitations of different fields, doctors often have the limitation of unclear description when describing faults, and further the problems that fault judgment cannot be accurately carried out and repair and accessory purchasing cannot be carried out are caused. The main body for reporting the adverse events of the medical instruments in the hospital is medical care personnel, but partial medical care personnel cannot find risk points in the using process of the medical instruments and effectively identify the adverse events in the using process of the medical instruments due to the lack of related knowledge of the adverse events of the medical instruments or professional knowledge of the medical instruments, so that the phenomenon of missing report is caused; meanwhile, some medical workers cannot correctly recognize adverse events and divide the adverse events into medical accidents, so that the overall risk is greatly increased. Therefore, designing a method capable of performing risk management and control on operation and maintenance of medical equipment is a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
Aiming at the defects, the embodiment of the invention discloses a risk control method based on operation and maintenance data of medical equipment, which can quickly sense and identify the condition of the medical equipment, and has strong compatibility and high active early warning efficiency.
The embodiment of the invention discloses a risk control method based on medical equipment operation and maintenance data in a first aspect, which comprises the following steps:
acquiring equipment operation and maintenance data and an equipment number generated in the operation process of medical equipment by a sensor cluster arranged at the medical equipment, performing data association on the equipment operation and maintenance data and the equipment number, and performing fusion, integration, interaction and error correction on the equipment operation and maintenance data by a data fusion algorithm of a Storm cluster;
performing data processing on the equipment operation and maintenance data to obtain an operation and maintenance detection data set; performing feature screening on the processed operation and maintenance detection data set to obtain a corresponding operation and maintenance data feature set;
sending the corresponding operation and maintenance data feature set to an input layer of a fault perception model which is constructed in advance to perform fault identification so as to determine a fault detection result; the fault perception model intelligently perceives the fault types of the medical equipment by introducing machine learning, and continuously and iteratively updates the mapping relation between the fault perception and the active early warning to construct a fault perception model;
and determining whether to perform early warning operation according to the fault detection result.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the performing feature screening on the processed operation and maintenance detection data set to obtain a corresponding operation and maintenance data feature set includes:
processing the operation and maintenance detection data set after being processed to generate rough set information;
performing dimensionality reduction operation on the rough set through a rough set reduction algorithm to generate a fault evaluation rule, wherein the fault evaluation rule comprises a plurality of fault influence indexes;
and screening and matching the rough set information and a pre-configured fault knowledge base based on the fault evaluation rule to generate an operation and maintenance data characteristic set.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the processing the operation and maintenance detection data set to generate rough set information includes:
performing quantization processing on the operation and maintenance detection data set to generate quantized operation and maintenance data;
acquiring fault type information according to the quantitative operation and maintenance data and a pre-configured fault knowledge base;
and generating rough set information by taking the fault index of the quantized operation and maintenance data as a condition attribute and taking fault type information as a decision attribute.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the determining whether to perform an early warning operation according to the fault detection result includes:
inputting the fault detection result into a fault detection formula to compare data so as to determine a current fault parameter, if the fault parameter is greater than a set value, performing alarm operation, and if the fault parameter is less than the set value, not performing alarm operation; the fault detection formula is as follows:
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the risk control method further includes:
receiving department information and equipment types input by a user, wherein the equipment types comprise basic instruments, medical inspection types, operation treatment types, emergency treatment types and medical image types;
and calling a corresponding fault perception model based on the department information and the equipment type to identify the fault.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the risk control method further includes:
when the corresponding medical equipment is not networked, configuring a corresponding intelligent router for the corresponding medical equipment, and sending the equipment operation and maintenance data and the equipment number to the overhaul cloud platform through the intelligent router for operation and maintenance monitoring;
the fault perception model is constructed and completed through the following steps:
acquiring a training sample data set; the training sample data set comprises normal sample data and fault sample data;
inputting the training sample data set into a depth residual error network based on a convolutional encoder for training, and acquiring data distribution characteristics of a normal operation and maintenance state and an abnormal operation and maintenance state;
the method comprises the steps of realizing the autonomous decision-making and iterative updating of the mapping relation between fault perception and active early warning of a test sample by adopting reinforcement learning, and constructing an active early warning mechanism of the medical equipment; and storing data of each updated parameter to realize the construction of the fault perception model.
As an optional implementation manner, in the first aspect of the embodiment of the present invention, the risk control method further includes:
determining a fault type according to the fault detection result; calling historical use information of corresponding equipment based on the equipment number;
determining corresponding maintenance indicators for the fault type and historical usage information; the maintenance indexes comprise a maintenance quality index, a maintenance efficiency index and a maintenance expenditure index;
determining a corresponding maintenance status value according to the maintenance index and a maintenance evaluation formula, wherein the maintenance evaluation formula is as follows:
where k is a maintenance status value, x i To maintain the quality index, y i For maintenance efficiency index, z i Is a maintenance expenditure index; alpha, beta and gamma are respectively the influence factors of the maintenance quality index, the maintenance efficiency index and the maintenance expenditure index;
and sending the maintenance state numerical value to a user side to remind so as to determine whether to carry out maintenance operation.
The second aspect of the embodiments of the present invention discloses a risk control device based on medical equipment operation and maintenance data, including:
a data acquisition module: the system comprises a sensor cluster, a data fusion algorithm and a data interaction algorithm, wherein the sensor cluster is arranged at the medical equipment and is used for acquiring equipment operation and maintenance data and an equipment number generated in the operation process of the medical equipment, performing data association on the equipment operation and maintenance data and the equipment number, and performing fusion, integration, interaction and error correction on the equipment operation and maintenance data through the data fusion algorithm calculated by a Storm cluster;
a data processing module: the device operation and maintenance data processing system is used for carrying out data processing on the device operation and maintenance data to obtain an operation and maintenance detection data set; performing feature screening on the processed operation and maintenance detection data set to obtain a feature vector set of corresponding operation and maintenance data;
a data detection module: the fault detection system is used for sending the corresponding operation and maintenance data feature set to an input layer of a fault perception model which is constructed in advance to carry out fault identification so as to determine a fault detection result; the fault perception model intelligently perceives the fault type of the medical equipment by introducing machine learning, and continuously iteratively updates the mapping relation between the fault perception and the active early warning to construct a fault perception model;
the early warning operation module: and the fault detection module is used for determining whether to perform early warning operation according to the fault detection result.
A third aspect of an embodiment of the present invention discloses an electronic device, including: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program codes stored in the memory for executing the risk control method based on the operation and maintenance data of the medical equipment disclosed by the first aspect of the embodiment of the invention.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is disclosed, which stores a computer program, where the computer program enables a computer to execute the risk control method based on the operation and maintenance data of the medical device disclosed in the first aspect of the embodiments of the present invention.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
according to the risk control method based on the operation and maintenance data of the medical equipment, disclosed by the embodiment of the invention, the large amount of medical data is reduced into data content more fitting actual faults by performing dimension reduction operation on the large amount of acquired medical operation and maintenance data, the evaluation on the operation state of the medical equipment is realized by combining the fault perception model, the autonomous evolution of the fault tolerance of the operation and maintenance state of the medical equipment is realized, and the overall early warning efficiency is greatly improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a risk control method based on medical device operation and maintenance data according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a data dimension reduction operation according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of rough set generation as disclosed in an embodiment of the present invention;
FIG. 4 is a schematic flow chart of a fault-aware model disclosed in an embodiment of the present invention;
FIG. 5 is a schematic flow chart of a maintenance status evaluation disclosed in an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a risk control device based on operation and maintenance data of a medical device according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
It should be noted that the terms "first", "second", "third", "fourth", etc. in the description and claims of the present invention are used for distinguishing different objects, and are not used for describing a specific order. The terms "comprises," "comprising," and "having," and any variations thereof, of embodiments of the present invention are intended to cover non-exclusive inclusions, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In the traditional medical equipment management, a hospital often expends more energy on equipment purchasing in the early stage, does not pay much attention to later-stage operation and maintenance, and cannot timely finish corresponding abnormality detection when equipment abnormality occurs in the later stage; due to the limitations of different fields, doctors often have the limitation of unclear description when describing faults, and further the problems that fault judgment cannot be accurately carried out and repair and accessory purchasing cannot be carried out are caused. The main body for reporting the adverse events of the medical instruments in the hospital is medical personnel, but partial medical personnel cannot find risk points in the using process of the medical instruments and effectively identify the adverse events in the using process of the medical instruments due to the lack of relevant knowledge of the adverse events of the medical instruments or professional knowledge of the medical instruments, so that the phenomenon of missing report is caused; meanwhile, some medical staff cannot correctly know the adverse events and divide the adverse events into medical accidents, so that the overall risk is greatly increased. Based on the above, the embodiment of the invention discloses a risk control method and device based on medical equipment operation and maintenance data, electronic equipment and a storage medium, wherein a large amount of medical data is reduced into data content more fitting actual faults by performing dimension reduction operation on the obtained large amount of medical operation and maintenance data, the evaluation on the operating state of the medical equipment is realized by combining a fault perception model, the autonomous evolution of the fault tolerance of the medical equipment operation and maintenance state is realized, and the overall early warning efficiency is greatly improved.
Example one
Referring to fig. 1, fig. 1 is a schematic flow chart of a risk control method based on medical device operation and maintenance data according to an embodiment of the present invention. The execution main body of the method described in the embodiment of the present invention is an execution main body composed of software or/and hardware, and the execution main body can receive related information in a wired or/and wireless manner and can send a certain instruction. Of course, it may also have certain processing and storage functions. The execution subject may control a plurality of devices, for example, a remote physical server or a cloud server and related software, or may be a local host or a server and related software for performing related operations on a device installed somewhere. In some scenarios, multiple storage devices may also be controlled, which may be co-located with the device or located in a different location. As shown in fig. 1, the risk control method based on the operation and maintenance data of the medical device includes the following steps:
s101: acquiring equipment operation and maintenance data and an equipment number generated in the operation process of medical equipment by a sensor cluster arranged at the medical equipment, performing data association on the equipment operation and maintenance data and the equipment number, and performing fusion, integration, interaction and error correction on the equipment operation and maintenance data by a data fusion algorithm of a Storm cluster;
in the embodiment of the invention, the medical equipment multi-dimensional operation and maintenance state sensing layer adopts a sensor cluster to acquire the operation and maintenance information of the medical equipment and temporarily store and calculate the operation and maintenance information, so as to provide bottom data for fault sensing; the unstructured medical equipment multi-source heterogeneous fault data sample pool construction layer conducts unstructured processing on bottom layer data.
When the specific operation is carried out, a user can carry out unstructured data analysis based on the equipment picture through code scanning maintenance; the equipment fault description input by a user can be acquired to carry out natural language processing on the equipment fault description to obtain data which can be conveniently identified subsequently; when directed to picture data, it may guide the user to mark the failure point; the specific implementation mode is that the picture shooting angle is defined, and the user is defined to carry out the shooting operation according to a specific direction, so that the recognition operation efficiency can be improved.
More preferably, the risk control method further includes:
when the corresponding medical equipment is not networked, the corresponding intelligent router is configured for the corresponding medical equipment, and the equipment operation and maintenance data and the equipment number are sent to the maintenance cloud platform through the intelligent router for operation and maintenance monitoring.
The networking operation of the medical equipment is realized by configuring the intelligent router, so that most of equipment can be overhauled through the cloud platform. For the non-intelligent component, the router is adopted to carry out intelligent networking detection, so that the whole equipment can be more unified, and all medical equipment can realize detection; the intelligent networking can be realized through a webpage end; can be deleted and added
The intelligent networking deployment medical equipment maintenance platform can meet the safety precaution requirements of the system data communication process. The intelligent networking router has private network encryption, data is encrypted by adopting an asymmetric RSA/AES algorithm, and a special safe networking channel is established in a wide area network, so that data acquisition of medical equipment can be completed under the condition that internal information of a hospital network is not involved, and the intelligent networking router has the advantages of high operation speed, high safety and low resource loss, and provides a safe and reliable data transmission link for interconnection of the medical equipment and an overhaul platform.
The scheme of the embodiment of the invention can also carry out IP address management. The IP address management function can help the data center to reasonably plan daily and long-term use of the network address, and network safety is improved.
It scans the table at regular time by the tool to find the state of the IP address in the network segment. The method comprises the following steps: used, unused, managed IP, reserved IP, etc. And classifying the IP address state in real time, presenting the IP address state in a view mode, distinguishing different states according to different colors, counting the states in real time, and ensuring the reasonable use of the network address. Automatic discovery of application topology: the method has the advantages that all the technical stacks and the association relations of the applications are discovered automatically, a user is helped to grasp the whole state of one application and the association application from the macro, and the change trends of the request number, the response time, the errors and the like, and the problems of each layer are located quickly.
S102: performing data processing on the equipment operation and maintenance data to obtain an operation and maintenance detection data set; performing feature screening on the processed operation and maintenance detection data set to obtain a corresponding operation and maintenance data feature set;
more preferably, fig. 2 is a schematic flow chart of a data dimension reduction operation disclosed in the embodiment of the present invention, and as shown in fig. 2, the performing feature screening on the operation and maintenance detection data set to obtain a corresponding operation and maintenance data feature set includes:
s1021: processing the operation and maintenance detection data set after being processed to generate rough set information;
s1022: performing dimensionality reduction operation on the rough set through a rough set reduction algorithm to generate a fault evaluation rule, wherein the fault evaluation rule comprises a plurality of fault influence indexes;
s1023: and screening and matching the rough set information and a pre-configured fault knowledge base based on the fault evaluation rule to generate an operation and maintenance data characteristic set.
More preferably, fig. 3 is a schematic flow chart of rough set generation disclosed in the embodiment of the present invention, and as shown in fig. 3, the processing the operation and maintenance detection data set to generate rough set information includes:
s1021a: performing quantization processing on the operation and maintenance detection data set to generate quantized operation and maintenance data;
s1021b: acquiring fault type information according to the quantitative operation and maintenance data and a pre-configured fault knowledge base;
s1021c: and generating rough set information by taking the fault index of the quantized operation and maintenance data as a condition attribute and taking fault type information as a decision attribute.
In rough set theory, knowledge is considered a classification capability, and the behavior of people is basically the ability to distinguish real or abstract objects. It is assumed that objects (or elements, samples, individuals) within the domain of discourse initially already have the necessary information or knowledge by which they can be classified into different categories. If two objects have the same information, they are indistinguishable, i.e., cannot be separated from each other based on the information already present.
The core of the rough set theory is the equivalence relation, which is usually used to replace classification, and the sample set is divided into equivalence classes according to the equivalence relation. The equivalence relations on the sets and the division on the sets are in one-to-one correspondence and are determined uniquely. In a mathematical sense, equivalence relations on a set and division of the set are equivalent concepts, that is, the division is classification. The basic idea is that from the knowledge base point of view, each equivalence class is called a concept, i.e. a piece of knowledge (rule). I.e. each equivalence class uniquely represents a concept to which different objects belonging to an equivalence class are indistinguishable. The screening operation of the condition attributes and the decision attributes is realized through the mode. In the embodiment of the invention, rough set reduction is carried out on the ultrasonic instrument, fault factors such as power module faults, load current, ultrasonic equipment faults, a display module and the like can be selected as condition attributes, and abnormal display, self-checking alarm and the like can be selected as decision attributes. Determining a relationship between the decision attribute and the condition attribute; and enabling the quantized attributes to be used as input and output of a subsequent fault sensing module.
S103: sending the corresponding operation and maintenance data feature set to an input layer of a fault perception model which is constructed in advance for fault identification so as to determine a fault detection result; the fault perception model intelligently perceives the fault types of the medical equipment by introducing machine learning, and continuously and iteratively updates the mapping relation between the fault perception and the active early warning to construct a fault perception model;
the fault prediction is carried out by obtaining a corresponding perception model through continuous iterative updating.
More preferably, the risk control method further includes:
receiving department information and equipment types input by a user, wherein the equipment types comprise basic instruments, medical inspection types, operation treatment types, emergency treatment types and medical image types;
and calling a corresponding fault perception model based on the department information and the equipment type to identify the fault.
Performing fine management according to different departments and equipment; different department types are determined, and different fault perception models are set for different departments, because different departments have differences in use habits, the result detection is different finally. More refined control and adjustment are realized through the mode.
Fig. 4 is a schematic flow chart of the fault sensing model disclosed in the embodiment of the present invention, and as shown in fig. 4, the fault sensing model is constructed and completed through the following steps:
s1031: acquiring a training sample data set; the training sample data set comprises normal sample data and fault sample data;
s1032: inputting the training sample data set into a depth residual error network based on a convolutional encoder for training, and acquiring data distribution characteristics of a normal operation and maintenance state and an abnormal operation and maintenance state;
s1033: the method comprises the steps of realizing the autonomous decision-making and iterative updating of the mapping relation between fault perception and active early warning of a test sample by adopting reinforcement learning, and constructing an active early warning mechanism of the medical equipment; and storing data of each updated parameter to realize the construction of the fault perception model.
The construction of the corresponding perception model is realized through the steps, the medical equipment generally comprises an electronic circuit, a machine structure, an optical path, a water path, an air path and the like, and the basic diagnosis and treatment functions of the medical equipment are completed through mutual cooperation among the structures. When the structure or function of each module of the medical equipment has a quantitative change fault, the function of the equipment can not be influenced, and when the fault changes from quantitative change to qualitative change or the faults among the modules are accumulated, the basic function of the equipment can be influenced. And detecting each attribute parameter to realize comprehensive prejudgment.
S104: and determining whether to perform early warning operation according to the fault detection result.
More preferably, the determining whether to perform an early warning operation according to the fault detection result includes:
inputting the fault detection result into a fault detection formula to compare data so as to determine a current fault parameter, if the fault parameter is greater than a set value, performing alarm operation, and if the fault parameter is less than the set value, not performing alarm operation; the fault detection formula is as follows:
And the accurate alarm operation is realized through the specific node detection setting.
As an optional implementation manner, in a first aspect of the embodiment of the present invention, fig. 5 is a schematic flow chart of maintenance state evaluation disclosed in the embodiment of the present invention, and as shown in fig. 5, the risk control method further includes:
s106: determining a fault type according to the fault detection result; calling historical use information of corresponding equipment based on the equipment number;
s107: determining a corresponding maintenance index for the fault type and historical usage information; the maintenance indexes comprise a maintenance quality index, a maintenance efficiency index and a maintenance expenditure index;
s108: determining a corresponding maintenance state value according to the maintenance index and a maintenance evaluation formula, wherein the maintenance evaluation formula is as follows:
wherein k is a maintenance status value, x i To maintain the quality index, y i For maintenance efficiency index, z i Is a maintenance expenditure index; alpha, beta and gamma are respectively the influence factors of the maintenance quality index, the maintenance efficiency index and the maintenance expenditure index;
s109: and sending the maintenance state numerical value to a user side to remind so as to determine whether to carry out maintenance operation.
In the actual process, if the specific fault type can be determined, a corresponding maintenance scheme can be determined, and the current maintenance state is determined according to the historical maintenance condition; through the specific steps, more refined maintenance evaluation can be realized, because when one device is seriously aged or the maintenance cost is too high, the evaluation process can be started to determine whether to perform further maintenance operation, because when the equipment is seriously aged, namely the maintenance can be completed, a fault can be expected to occur within a certain time, the existing scheme cannot quantitatively evaluate the condition, and in the actual operation process, the benefit of a hospital can be damaged to a certain extent due to the reduction of clinical satisfaction caused by equipment maintenance; therefore, when the maintenance is carried out, the maintenance quality, the maintenance efficiency and the maintenance expenditure can be integrated to determine whether the maintenance is necessary, when the value is detected to reach the set value, the maintenance cost is determined to be high, the cost is not only the expense cost, but also other costs caused by problems of subsequent equipment, and at this time, the maintenance is not necessary, and whether the equipment is updated can be determined.
Performing data evaluation based on the fault type to determine whether to perform maintenance; the method mainly aims at the maintenance quality, the maintenance efficiency and the maintenance expenditure; the maintenance quality evaluation indexes comprise maintenance success rate, equipment starting rate, equipment rejection rate and clinical satisfaction degree; the maintenance efficiency evaluation index comprises a maintenance period, response time and accessory supply time, and the maintenance expenditure evaluation index is maintenance cost and management cost; more accurate numerical judgment can be realized through the omnibearing data evaluation, the conventional perceptual cognition is improved to rational cognition, and more theoretical numerical bases are provided for subsequent equipment replacement or equipment maintenance; the hospital is helped to save the cost, and the user satisfaction degree is improved.
In specific implementation, the content of multi-dimensional early warning management can be added, and the algorithm capability of automatic learning can be notified according to the basic rules related to early warning, such as: cluster merging, IP merging, etc. aggregates the early warnings related to the early warning at the same time. In addition, through the intelligent analysis to the early warning, the user can avoid the emergence of invalid early warning, early warning storm, and the investigation and the location to the trouble fast promote early warning managerial ability comprehensively. Early warning convergence, effective early warning is identified: and compressing and removing the repeated early warning and the invalid early warning which are generated in a large amount in a short time, and identifying the valid early warning. Early warning aggregation, assisting in locating problems: merging according to the cluster, merging according to the IP, merging according to the network segment, merging according to the abnormal types and merging according to the relation between the host machine and the virtual machine. Through the mode, the efficiency of early warning can be greatly improved, the problem of large-scale early warning caused by power supply at the regional position or other abnormity is avoided, and the early warning can be effectively managed in an all-round mode.
In specific implementation, the scheme of the embodiment of the invention can also carry out data statistics according to different dimensions and assessment indexes, form a maintenance report, grasp the product performance differences of different brands in time and provide decision basis for ideal planning and purchasing research in the future; that is, the comprehensive study and judgment are performed by adding more dimensional data.
According to the risk control method based on the operation and maintenance data of the medical equipment, disclosed by the embodiment of the invention, the operation and maintenance operations are carried out on the acquired large amount of medical operation and maintenance data, so that the large amount of medical data is reduced into data content more fitting actual faults, the evaluation on the operation state of the medical equipment is realized by combining a fault perception model, the autonomous evolution of the fault tolerance of the operation and maintenance state of the medical equipment is realized, and the overall early warning efficiency is greatly improved.
Example two
Referring to fig. 6, fig. 6 is a schematic structural diagram of a risk control device based on operation and maintenance data of medical equipment according to an embodiment of the present invention. As shown in fig. 6, the risk control device based on the operation and maintenance data of the medical equipment may include:
the data acquisition module 21: the system comprises a sensor cluster, a data fusion algorithm and a data interaction algorithm, wherein the sensor cluster is arranged at the medical equipment and is used for acquiring equipment operation and maintenance data and an equipment number generated in the operation process of the medical equipment, performing data association on the equipment operation and maintenance data and the equipment number, and performing fusion, integration, interaction and error correction on the equipment operation and maintenance data through the data fusion algorithm calculated by a Storm cluster;
the data processing module 22: the device operation and maintenance data processing system is used for carrying out data processing on the device operation and maintenance data to obtain an operation and maintenance detection data set; performing feature screening on the processed operation and maintenance detection data set to obtain a feature vector set of corresponding operation and maintenance data;
the data detection module 23: the fault detection system is used for sending the corresponding operation and maintenance data feature set to an input layer of a fault perception model which is constructed in advance to carry out fault identification so as to determine a fault detection result; the fault perception model intelligently perceives the fault type of the medical equipment by introducing machine learning, and continuously iteratively updates the mapping relation between the fault perception and the active early warning to construct a fault perception model;
the early warning operation module 24: and the fault detection module is used for determining whether to perform early warning operation according to the fault detection result.
According to the risk control method based on the operation and maintenance data of the medical equipment, disclosed by the embodiment of the invention, the large amount of medical data is reduced into data content more fitting actual faults by performing dimension reduction operation on the large amount of acquired medical operation and maintenance data, the evaluation on the operation state of the medical equipment is realized by combining the fault perception model, the autonomous evolution of the fault tolerance of the operation and maintenance state of the medical equipment is realized, and the overall early warning efficiency is greatly improved.
EXAMPLE III
Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure. The electronic device may be a computer, a server, or the like, and certainly, may also be an intelligent device such as a mobile phone, a tablet computer, a monitoring terminal, or the like, and an image acquisition device having a processing function. As shown in fig. 7, the electronic device may include:
a memory 510 storing executable program code;
a processor 520 coupled to the memory 510;
the processor 520 calls the executable program code stored in the memory 510 to perform part or all of the steps of the risk control method based on the operation and maintenance data of the medical device in the first embodiment.
The embodiment of the invention discloses a computer-readable storage medium which stores a computer program, wherein the computer program enables a computer to execute part or all of the steps in the risk control method based on the operation and maintenance data of medical equipment in the first embodiment.
The embodiment of the invention also discloses a computer program product, wherein when the computer program product runs on a computer, the computer is enabled to execute part or all of the steps in the risk control method based on the operation and maintenance data of the medical equipment in the first embodiment.
The embodiment of the invention also discloses an application publishing platform, wherein the application publishing platform is used for publishing a computer program product, and when the computer program product runs on a computer, the computer is enabled to execute part or all of the steps in the risk control method based on the operation and maintenance data of the medical equipment in the first embodiment.
In various embodiments of the present invention, it should be understood that the sequence numbers of the processes do not mean the execution sequence necessarily in order, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented as a software functional unit and sold or used as a stand-alone product, may be stored in a computer accessible memory. Based on such understanding, the technical solution of the present invention, which is a part of or contributes to the prior art in essence, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for causing a computer device (which may be a personal computer, a server, a network device, or the like, and may specifically be a processor in the computer device) to execute part or all of the steps of the method according to the embodiments of the present invention.
In the embodiments provided herein, it should be understood that "B corresponding to a" means that B is associated with a from which B can be determined. It should also be understood, however, that determining B from a does not mean determining B from a alone, but may also be determined from a and/or other information.
Those of ordinary skill in the art will appreciate that some or all of the steps of the methods of the embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, including Read-Only Memory (ROM), random Access Memory (RAM), programmable Read-Only Memory (PROM), erasable Programmable Read-Only Memory (EPROM), one-time Programmable Read-Only Memory (OTPROM), electrically Erasable Programmable Read-Only Memory (EEPROM), compact Disc Read-Only (CD-ROM) or other Memory capable of storing data, magnetic tape, or any other medium capable of carrying computer data.
The risk control method, device, electronic device and storage medium based on the operation and maintenance data of the medical device disclosed in the embodiment of the present invention are described in detail above, and specific examples are applied in the text to explain the principle and the implementation of the present invention, and the description of the above embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A risk control method based on medical equipment operation and maintenance data is characterized by comprising the following steps:
acquiring equipment operation and maintenance data and an equipment number generated in the operation process of medical equipment by a sensor cluster arranged at the medical equipment, performing data association on the equipment operation and maintenance data and the equipment number, and performing fusion, integration, interaction and error correction on the equipment operation and maintenance data by a data fusion algorithm of a Storm cluster;
performing data processing on the equipment operation and maintenance data to obtain an operation and maintenance detection data set; performing feature screening on the processed operation and maintenance detection data set to obtain a corresponding operation and maintenance data feature set;
sending the corresponding operation and maintenance data feature set to an input layer of a fault perception model which is constructed in advance to perform fault identification so as to determine a fault detection result; the fault perception model intelligently perceives the fault types of the medical equipment by introducing machine learning, and continuously and iteratively updates the mapping relation between the fault perception and the active early warning to construct a fault perception model;
and determining whether to perform early warning operation according to the fault detection result.
2. The risk control method based on operation and maintenance data of medical equipment according to claim 1, wherein the performing feature screening on the operation and maintenance detection data set to obtain a corresponding operation and maintenance data feature set comprises:
processing the operation and maintenance detection data set after being processed to generate rough set information;
performing dimensionality reduction operation on the rough set through a rough set reduction algorithm to generate a fault evaluation rule, wherein the fault evaluation rule comprises a plurality of fault influence indexes;
and screening and matching the rough set information and a pre-configured fault knowledge base based on the fault evaluation rule to generate an operation and maintenance data characteristic set.
3. The risk control method based on medical device operation and maintenance data of claim 2, wherein the processing the operation and maintenance detection data set to generate rough set information comprises:
performing quantization processing on the operation and maintenance detection data set to generate quantized operation and maintenance data;
acquiring fault type information according to the quantitative operation and maintenance data and a pre-configured fault knowledge base;
and generating rough set information by taking the fault index of the quantized operation and maintenance data as a condition attribute and taking fault type information as a decision attribute.
4. The risk control method based on the operation and maintenance data of the medical equipment according to claim 2, wherein the determining whether to perform an early warning operation according to the fault detection result comprises:
inputting the fault detection result into a fault detection formula to compare data so as to determine a current fault parameter, if the fault parameter is greater than a set value, performing alarm operation, and if the fault parameter is less than the set value, not performing alarm operation; the fault detection formula is as follows:
5. The risk control method based on the operation and maintenance data of the medical equipment according to claim 1, further comprising:
receiving department information and equipment types input by a user, wherein the equipment types comprise basic instruments, medical inspection types, operation treatment types, emergency treatment types and medical image types;
and calling a corresponding fault perception model based on the department information and the equipment type to identify the fault.
6. The risk control method based on medical device operation and maintenance data of claim 1, wherein the risk control method further comprises:
when the corresponding medical equipment is not networked, configuring a corresponding intelligent router for the corresponding medical equipment, and sending the equipment operation and maintenance data and the equipment number to the overhaul cloud platform through the intelligent router for operation and maintenance monitoring;
the fault perception model is constructed and completed through the following steps:
acquiring a training sample data set; the training sample data set comprises normal sample data and fault sample data;
inputting the training sample data set into a depth residual error network based on a convolutional encoder for training, and acquiring data distribution characteristics of a normal operation and maintenance state and an abnormal operation and maintenance state;
the method comprises the steps of realizing the autonomous decision-making and iterative updating of the mapping relation between fault perception and active early warning of a test sample by adopting reinforcement learning, and constructing an active early warning mechanism of the medical equipment; and storing data of each updated parameter to realize the construction of the fault perception model.
7. The risk control method based on the operation and maintenance data of the medical equipment according to claim 1, further comprising:
determining a fault type according to the fault detection result; calling historical use information of corresponding equipment based on the equipment number;
determining corresponding maintenance indicators for the fault type and historical usage information; the maintenance indexes comprise a maintenance quality index, a maintenance efficiency index and a maintenance expenditure index;
determining a corresponding maintenance status value according to the maintenance index and a maintenance evaluation formula, wherein the maintenance evaluation formula is as follows:
where k is a maintenance status value, x i For maintenance of quality index, y i For maintenance efficiency index, z i Is a maintenance expenditure index; alpha, beta and gamma are respectively the influence factors of the maintenance quality index, the maintenance efficiency index and the maintenance expenditure index;
and sending the maintenance state value to a user side for reminding so as to determine whether to perform maintenance operation.
8. A risk control device based on medical equipment operation and maintenance data, characterized by comprising:
a data acquisition module: the system comprises a sensor cluster, a data fusion algorithm and a data interaction algorithm, wherein the sensor cluster is arranged at the medical equipment and is used for acquiring equipment operation and maintenance data and an equipment number generated in the operation process of the medical equipment, performing data association on the equipment operation and maintenance data and the equipment number, and performing fusion, integration, interaction and error correction on the equipment operation and maintenance data through the data fusion algorithm calculated by a Storm cluster;
a data processing module: the device operation and maintenance data processing system is used for carrying out data processing on the device operation and maintenance data to obtain an operation and maintenance detection data set; performing feature screening on the processed operation and maintenance detection data set to obtain a feature vector set of corresponding operation and maintenance data;
a data detection module: the fault detection system comprises a fault sensing model, a fault detection module and a fault detection module, wherein the fault sensing model is used for acquiring a fault detection result; the fault perception model intelligently perceives the fault type of the medical equipment by introducing machine learning, and continuously iteratively updates the mapping relation between the fault perception and the active early warning to construct a fault perception model;
the early warning operation module: and the fault detection module is used for determining whether to perform early warning operation according to the fault detection result.
9. An electronic device, comprising: a memory storing executable program code; a processor coupled with the memory; the processor calls the executable program code stored in the memory for executing the risk control method based on the operation and maintenance data of the medical equipment according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program causes a computer to execute the risk control method based on medical device operation and maintenance data according to any one of claims 1 to 7.
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