CN111710406B - Remote maintenance method and device for medical equipment and readable storage medium - Google Patents
Remote maintenance method and device for medical equipment and readable storage medium Download PDFInfo
- Publication number
- CN111710406B CN111710406B CN202010527913.4A CN202010527913A CN111710406B CN 111710406 B CN111710406 B CN 111710406B CN 202010527913 A CN202010527913 A CN 202010527913A CN 111710406 B CN111710406 B CN 111710406B
- Authority
- CN
- China
- Prior art keywords
- fault
- diagnosed
- equipment
- medical equipment
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000012423 maintenance Methods 0.000 title claims abstract description 143
- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000012545 processing Methods 0.000 claims abstract description 50
- 238000004891 communication Methods 0.000 claims abstract description 16
- 238000003745 diagnosis Methods 0.000 claims description 93
- 230000008569 process Effects 0.000 description 11
- 239000000243 solution Substances 0.000 description 11
- 230000006870 function Effects 0.000 description 7
- 238000004458 analytical method Methods 0.000 description 6
- 230000008439 repair process Effects 0.000 description 3
- 238000004364 calculation method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000004171 remote diagnosis Methods 0.000 description 2
- 230000000747 cardiac effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000002347 injection Methods 0.000 description 1
- 239000007924 injection Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- 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
- G16H40/60—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 operation of medical equipment or devices
- G16H40/67—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 operation of medical equipment or devices for remote operation
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
Abstract
The invention discloses a remote maintenance method and equipment for medical equipment and a readable storage medium, wherein the remote maintenance method for the medical equipment is applied to a cloud server, and the cloud server is in communication connection with a plurality of medical equipment, and the method comprises the following steps: receiving fault data transmitted by any medical device, and determining the medical device transmitting the fault data as the medical device to be diagnosed; processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed, and generating a fault reason corresponding to the medical equipment to be diagnosed; and determining maintenance information corresponding to the fault cause, and remotely maintaining the medical equipment to be diagnosed according to the maintenance information. The invention realizes the rapid and efficient maintenance of the medical equipment, simplifies the maintenance flow and ensures the maintenance accuracy.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method and an apparatus for remote maintenance of a medical device, and a readable storage medium.
Background
Medical equipment is an indispensable equipment for medical institutions such as hospitals, social health, clinics, and the like, and various kinds of equipment such as a contrast apparatus, a scanner, a respirator, a control pump, an injection pump, and the like. Moreover, the normal operation of the individual medical devices has a crucial effect on the treatment of the disorders.
When each medical device fails, the medical device is usually reported to a manufacturer of the medical device, and the manufacturer assigns maintenance personnel to a medical institution for maintenance; or return the failed medical device to the manufacturer for repair. However, no matter the maintenance personnel are assigned for maintenance or are reversely sent to the manufacturer for maintenance, the whole maintenance period is long, the efficiency is low, and the operation flow is complex.
Therefore, how to repair the medical equipment quickly and efficiently and simplify the repair process is a technical problem to be solved currently.
Disclosure of Invention
The invention mainly aims to provide a remote maintenance method and equipment for medical equipment and a readable storage medium, and aims to solve the technical problems of how to quickly and efficiently maintain the medical equipment and simplify the maintenance flow in the prior art.
In order to achieve the above object, the present invention provides a remote maintenance method of a medical device, the remote maintenance method of the medical device is applied to a cloud server, the cloud server is in communication connection with a plurality of medical devices, and the remote maintenance method of the medical device includes the following steps:
Receiving fault data transmitted by any medical device, and determining the medical device transmitting the fault data as the medical device to be diagnosed;
processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed, and generating a fault reason corresponding to the medical equipment to be diagnosed;
and determining maintenance information corresponding to the fault cause, and remotely maintaining the medical equipment to be diagnosed according to the maintenance information.
Optionally, the step of remotely maintaining the medical device to be diagnosed according to the maintenance information includes:
determining the information type of the maintenance information;
if the information type is a self-repairing type, feeding back the maintenance information to the medical equipment to be diagnosed, and controlling the medical equipment to be diagnosed to operate based on the maintenance information so as to maintain the medical equipment to be diagnosed;
and if the information type is a non-self-repairing type, feeding back the maintenance information to a management terminal corresponding to the medical equipment to be diagnosed so as to enable a holder of the management terminal to maintain the medical equipment to be diagnosed.
Optionally, the step of processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed, and generating the fault cause corresponding to the medical equipment to be diagnosed includes:
Extracting fault codes and fault phenomenon data from the fault data, and judging whether the fault codes and the fault phenomenon data are matched or not;
if the fault code is matched with the fault phenomenon data, searching a historical reason corresponding to the fault code from the historical fault information, and generating the historical reason into a fault reason corresponding to the medical equipment to be diagnosed;
and if the fault code is not matched with the fault phenomenon data, processing the fault data according to the historical fault information to generate a fault reason corresponding to the medical equipment to be diagnosed.
Optionally, the step of processing the fault data according to the historical fault information and generating a fault cause corresponding to the medical device to be diagnosed includes:
reading preset equipment cluster groups generated based on the historical fault information, extracting position attribute tags in the fault data, and determining fault equipment cluster groups corresponding to the position attribute tags in the preset equipment cluster groups;
processing the fault data according to the fault equipment cluster group to obtain a target cluster corresponding to the medical equipment to be diagnosed;
And acquiring a cluster reason corresponding to the target cluster, and generating the cluster reason into a fault reason corresponding to the medical equipment to be diagnosed.
Optionally, the step of processing the fault data according to the fault equipment cluster group to obtain a target cluster corresponding to the medical equipment to be diagnosed includes:
coding each fault characteristic element in the fault data to obtain a fault code;
acquiring cluster center codes of each fault equipment cluster in the fault equipment cluster group, and calculating similarity values between the fault codes and each cluster center code;
and determining a target cluster corresponding to the medical equipment to be diagnosed in each fault equipment cluster according to the magnitude relation between the similarity values.
Optionally, the step of determining the faulty device cluster group corresponding to the location attribute tag in each preset device cluster group includes:
comparing the group position labels of the preset equipment cluster groups with the position attribute labels to determine target group position labels corresponding to the position attribute labels in the group position labels;
searching a target equipment cluster group corresponding to the target group position label in each preset equipment cluster group, and determining the target equipment cluster group as a fault equipment cluster group corresponding to the position attribute label.
Optionally, the step of processing the fault data according to the historical fault information corresponding to the medical device to be diagnosed includes:
judging whether the fault data contains pre-diagnosis data or not, and if so, judging whether the pre-diagnosis data is valid or not;
if the pre-diagnosis data are valid, executing the step of processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed;
if the pre-diagnosis data are invalid, performing pre-diagnosis on the medical equipment to be diagnosed according to the fault data;
and if the fault data does not contain the pre-diagnosis data, returning pre-diagnosis indication information to the medical equipment to be diagnosed.
Optionally, the step of pre-diagnosing the medical device to be diagnosed according to the fault data includes:
generating a pre-diagnosis result of the medical equipment to be diagnosed, and determining the result type of the pre-diagnosis result;
if the result type is a success type, the pre-diagnosis result is issued to the medical equipment to be diagnosed so that the medical equipment to be diagnosed can be remotely maintained based on the pre-diagnosis result;
And if the result type is a failure type, executing the step of processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed.
Further, to achieve the above object, the present invention also provides a remote maintenance device for a medical device, where the remote maintenance device for a medical device includes a memory, a processor, and a remote maintenance program for a medical device stored on the memory and executable on the processor, and the remote maintenance program for a medical device, when executed by the processor, implements the steps of the remote maintenance method for a medical device as described above.
Further, to achieve the above object, the present invention also provides a readable storage medium having stored thereon a remote maintenance program of a medical device, which when executed by a processor, implements the steps of the remote maintenance method of a medical device as described above.
The remote maintenance method of the medical equipment, the equipment and the readable storage medium are applied to a cloud server in communication connection with a plurality of medical equipment, and each medical equipment is internally provided with an Internet of things card capable of collecting fault data. After receiving fault data transmitted by any one medical device, the cloud server determines the medical device as the medical device to be diagnosed which has a fault and needs remote diagnosis and maintenance; then analyzing and processing the received fault data according to the historical fault information corresponding to the medical equipment to be diagnosed in the cloud server to obtain the fault reason of the medical equipment to be diagnosed; and further determining maintenance information corresponding to the fault cause, and remotely maintaining the medical equipment to be diagnosed according to the maintenance information. The historical fault information corresponding to the medical equipment to be diagnosed is faults of the type of the equipment to be diagnosed in the past operation process and solutions of the faults; the method is used as a basis for analysis processing, the current fault data of the medical equipment to be diagnosed are analyzed and processed, the accuracy of analysis processing is ensured, and the remote maintenance of the medical equipment to be diagnosed by obtaining accurate maintenance information is facilitated. Therefore, the medical equipment is maintained rapidly and efficiently, the maintenance flow is simplified, and the maintenance accuracy is ensured.
Drawings
FIG. 1 is a schematic diagram of a device hardware operating environment involved in a remote maintenance device embodiment of a medical device of the present invention;
fig. 2 is a flowchart of a first embodiment of a remote maintenance method of a medical device according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides remote maintenance equipment of medical equipment, and referring to fig. 1, fig. 1 is a schematic structural diagram of equipment hardware operation environment related to an embodiment scheme of the remote maintenance equipment of the medical equipment.
As shown in fig. 1, the remote maintenance device of the medical device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the hardware configuration of the remote maintenance device of the medical device shown in fig. 1 does not constitute a limitation of the remote maintenance device of the medical device, and may include more or fewer components than shown, or may combine certain components, or may be arranged in a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a remote maintenance program of the medical device may be included in the memory 1005 as one type of readable storage medium. The operating system is a program for managing and controlling remote maintenance equipment and software resources of the medical equipment, and supports the operation of a network communication module, a user interface module, the remote maintenance program of the medical equipment and other programs or software; the network communication module is used to manage and control the network interface 1004; the user interface module is used to manage and control the user interface 1003.
In the remote maintenance device hardware structure of the medical device shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; the processor 1001 may call a remote maintenance program of the medical device stored in the memory 1005 and perform the following operations:
Receiving fault data transmitted by any medical device, and determining the medical device transmitting the fault data as the medical device to be diagnosed;
processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed, and generating a fault reason corresponding to the medical equipment to be diagnosed;
and determining maintenance information corresponding to the fault cause, and remotely maintaining the medical equipment to be diagnosed according to the maintenance information.
Further, the step of remotely maintaining the medical device to be diagnosed according to the maintenance information includes:
determining the information type of the maintenance information;
if the information type is a self-repairing type, feeding back the maintenance information to the medical equipment to be diagnosed, and controlling the medical equipment to be diagnosed to operate based on the maintenance information so as to maintain the medical equipment to be diagnosed;
and if the information type is a non-self-repairing type, feeding back the maintenance information to a management terminal corresponding to the medical equipment to be diagnosed so as to enable a holder of the management terminal to maintain the medical equipment to be diagnosed.
Further, the step of processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed, and generating the fault cause corresponding to the medical equipment to be diagnosed includes:
Extracting fault codes and fault phenomenon data from the fault data, and judging whether the fault codes and the fault phenomenon data are matched or not;
if the fault code is matched with the fault phenomenon data, searching a historical reason corresponding to the fault code from the historical fault information, and generating the historical reason into a fault reason corresponding to the medical equipment to be diagnosed;
and if the fault code is not matched with the fault phenomenon data, processing the fault data according to the historical fault information to generate a fault reason corresponding to the medical equipment to be diagnosed.
Further, the step of processing the fault data according to the historical fault information to generate a fault cause corresponding to the medical equipment to be diagnosed includes:
reading preset equipment cluster groups generated based on the historical fault information, extracting position attribute tags in the fault data, and determining fault equipment cluster groups corresponding to the position attribute tags in the preset equipment cluster groups;
processing the fault data according to the fault equipment cluster group to obtain a target cluster corresponding to the medical equipment to be diagnosed;
And acquiring a cluster reason corresponding to the target cluster, and generating the cluster reason into a fault reason corresponding to the medical equipment to be diagnosed.
Further, the step of processing the fault data according to the fault equipment cluster group to obtain a target cluster corresponding to the medical equipment to be diagnosed includes:
coding each fault characteristic element in the fault data to obtain a fault code;
acquiring cluster center codes of each fault equipment cluster in the fault equipment cluster group, and calculating similarity values between the fault codes and each cluster center code;
and determining a target cluster corresponding to the medical equipment to be diagnosed in each fault equipment cluster according to the magnitude relation between the similarity values.
Further, the step of determining the faulty equipment cluster group corresponding to the location attribute tag in each preset equipment cluster group includes:
comparing the group position labels of the preset equipment cluster groups with the position attribute labels to determine target group position labels corresponding to the position attribute labels in the group position labels;
searching a target equipment cluster group corresponding to the target group position label in each preset equipment cluster group, and determining the target equipment cluster group as a fault equipment cluster group corresponding to the position attribute label.
Further, before the step of processing the fault data according to the historical fault information corresponding to the medical device to be diagnosed, the processor 1001 may call a remote maintenance program of the medical device stored in the memory 1005, and perform the following operations:
judging whether the fault data contains pre-diagnosis data or not, and if so, judging whether the pre-diagnosis data is valid or not;
if the pre-diagnosis data are valid, executing the step of processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed;
if the pre-diagnosis data are invalid, performing pre-diagnosis on the medical equipment to be diagnosed according to the fault data;
and if the fault data does not contain the pre-diagnosis data, returning pre-diagnosis indication information to the medical equipment to be diagnosed.
Further, after the step of pre-diagnosing the medical apparatus to be diagnosed according to the fault data, the processor 1001 may call a remote maintenance program of the medical apparatus stored in the memory 1005 and perform the following operations:
generating a pre-diagnosis result of the medical equipment to be diagnosed, and determining the result type of the pre-diagnosis result;
If the result type is a success type, the pre-diagnosis result is issued to the medical equipment to be diagnosed so that the medical equipment to be diagnosed can be remotely maintained based on the pre-diagnosis result;
and if the result type is a failure type, executing the step of processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed.
The specific implementation manner of the remote maintenance device of the medical device of the present invention is substantially the same as the embodiments of the remote maintenance method of the medical device described below, and will not be described herein.
The invention also provides a remote maintenance method of the medical equipment.
Referring to fig. 2, fig. 2 is a flowchart illustrating a first embodiment of a remote maintenance method for a medical device according to the present invention.
Embodiments of the present invention provide embodiments of a method of remote maintenance of medical devices, it being noted that although a logical sequence is illustrated in the flow chart, in some cases the steps illustrated or described may be performed in a different order than that illustrated herein. Specifically, the remote maintenance method of the medical device in the embodiment is applied to a cloud server, where the cloud server is in communication connection with a plurality of medical devices, and the remote maintenance method of the medical device includes:
Step S10, receiving fault data transmitted by any medical equipment, and determining the medical equipment transmitting the fault data as the medical equipment to be diagnosed.
The remote maintenance method of the medical equipment is applied to the cloud server. The cloud server is in communication connection with a plurality of medical devices in a plurality of medical institutions in a plurality of areas, and is suitable for carrying out real-time remote maintenance on each medical device accessed into the cloud server according to initial preset maintenance data of each type of medical device and historical fault maintenance data. Specifically, the internet of things network cards for data acquisition are arranged in each medical device, and the internet of things network cards can be set to have a fault distinguishing function or not. For the internet of things network card with the fault distinguishing function, reference data representing normal operation of each function of the medical equipment are preset, after the real-time operation data of each function are collected, the real-time operation data of each function are compared with the respective reference data by the internet of things network card, and whether the difference of the real-time operation data and the respective reference data is large is determined. If the difference is large, judging that the medical equipment has faults, and further transmitting the real-time operation data representing the faults to a cloud server as fault data for processing. For the internet of things card without the fault distinguishing function, only the real-time operation data of each medical device is collected and transmitted to the cloud server. The cloud server is provided with reference data representing normal operation of each function of the medical equipment in advance, when the real-time operation data transmitted by each medical equipment are received, the real-time operation data are compared with the corresponding reference data, and whether the difference between the real-time operation data and the corresponding reference data is large is judged. If the real-time operation data is larger, judging that the medical equipment has faults, and processing the received real-time operation data as fault data.
Further, after the cloud server receives the fault data transmitted by any one of the medical devices, the medical device transmitting the fault data is used as the medical device to be diagnosed with the fault and needing diagnosis and maintenance. Among the medical devices accessed by the cloud server, a plurality of medical devices may have faults at the same time and need diagnosis and maintenance, and the medical devices are used as medical devices to be diagnosed for diagnosis and maintenance. In addition, the diagnostic maintenance process of each medical device to be diagnosed is similar, so the embodiment is described with respect to performing diagnostic maintenance on one medical device to be diagnosed.
And step S20, processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed, and generating a fault reason corresponding to the medical equipment to be diagnosed.
It is understood that various types of faults are inevitably generated in the previous operation process of other medical equipment of the equipment type of the medical equipment to be diagnosed, and data of the previous diagnosis and maintenance of the various types of faults are used as historical fault information corresponding to the medical equipment to be diagnosed. Analyzing and processing current fault data of the medical equipment to be diagnosed according to the historical fault information, analyzing and comparing the historical fault information with similar types and similar fault performances to obtain fault reasons corresponding to the medical equipment to be diagnosed, and representing the reasons of the current faults of the medical equipment to be diagnosed.
And step S30, determining maintenance information corresponding to the fault cause, and remotely maintaining the medical equipment to be diagnosed according to the maintenance information.
Further, a scheme for releasing the fault caused by the fault reason in the historical fault information is used as maintenance information corresponding to the fault reason, and after the maintenance information is determined, remote maintenance is carried out on the medical equipment to be diagnosed according to the maintenance information. For a medical device to be diagnosed of model a, the fault manifestation thereof comprises a1 and a2; in the historical fault information of the equipment, the fault corresponding to the fault expression a1 is b1, and the scheme for releasing the fault expression a1 is c1; the failure cause corresponding to the failure expression a2 is b2, the solution for releasing the failure expression a2 is c2, the failure cause corresponding to the failure expression a3 is b3, and the solution for releasing the failure expression a3 is c3; therefore, the maintenance information c1 and c2 corresponding to the fault reasons b1 and b2 and the corresponding maintenance information c1 and c2 can be determined, and the medical equipment to be diagnosed is remotely maintained by using the maintenance information c1 and c 2. Specifically, the step of remotely maintaining the medical device to be diagnosed according to the maintenance information includes:
step S31, determining the information type of the maintenance information;
step S32, if the information type is a self-repairing type, feeding back the maintenance information to the medical equipment to be diagnosed, and controlling the operation of the medical equipment to be diagnosed based on the maintenance information so as to maintain the medical equipment to be diagnosed;
And step S33, if the information type is a non-self-repairing type, feeding back the maintenance information to a management terminal corresponding to the medical equipment to be diagnosed so as to enable a holder of the management terminal to maintain the medical equipment to be diagnosed.
Understandably, the faults occurring in the medical devices to be diagnosed are diverse, and the maintenance information formed for relieving the faults is also diverse. The device comprises faults which are relieved by means of equipment restarting, software resetting and the like, and also comprises faults which need to be relieved by manual operation. In the embodiment, faults which can be relieved by self-restarting of equipment, software reset and the like and are independent of manual operation are taken as self-repairing faults; and taking the fault which depends on manual operation release as a non-self-repairing fault. Aiming at self-repairing faults and non-self-repairing faults, the information types of the maintenance information of the self-repairing faults and the non-self-repairing faults are different. The maintenance information of the self-repairing fault is an instruction for indicating equipment to restart or software to reset, and the maintenance information of the non-self-repairing fault is a specific operation step of repairing. Setting the two types of maintenance information as different information types, and continuously determining the information type of the maintenance information after determining the maintenance information corresponding to the fault reason. The information types can be distinguished through different marks, and when the maintenance information carries marks representing self-repairing, the information type is determined to be the self-repairing type; and when the maintenance information carries the identification representing the non-self-repairing, determining the information type as the non-self-repairing type.
Further, after the information type is determined to be the self-repairing type, feeding back maintenance information of the self-repairing type to the medical equipment to be diagnosed, and controlling the operation of the medical equipment to be diagnosed through the maintenance information; namely, the medical equipment to be diagnosed is controlled to restart or the software thereof is reset, and the fault of the medical equipment to be diagnosed is relieved through remote maintenance. It should be noted that, the fault data is derived from the medical equipment to be diagnosed which operates in real time, and represents the current operation state of the medical equipment to be diagnosed, and in the process of controlling the operation of the medical equipment to be diagnosed through the maintenance information, the fault data is controlled in a distinguishing manner according to the type of the medical equipment to be diagnosed. If the medical equipment to be diagnosed is of a type which is restarted or reset by software and does not affect the patient, such as a heart rate monitor, the medical equipment to be diagnosed is controlled to be restarted or reset by software. If the medical equipment to be diagnosed belongs to the type that the restarting or the software resetting affects the patient, such as a cardiac resuscitation instrument, after the diagnosis and treatment of the current word are finished and the operation of the medical equipment to be diagnosed is stopped, the restarting or the software resetting of the medical equipment to be diagnosed is controlled so as to avoid affecting the diagnosis and treatment of the patient.
The remote maintenance method of the medical equipment is applied to a cloud server in communication connection with a plurality of medical equipment, and each medical equipment is internally provided with an Internet of things card capable of collecting fault data. After receiving fault data transmitted by any one medical device, the cloud server determines the medical device as the medical device to be diagnosed which has a fault and needs remote diagnosis and maintenance; then analyzing and processing the received fault data according to the historical fault information corresponding to the medical equipment to be diagnosed in the cloud server to obtain the fault reason of the medical equipment to be diagnosed; and further determining maintenance information corresponding to the fault cause, and remotely maintaining the medical equipment to be diagnosed according to the maintenance information. The historical fault information corresponding to the medical equipment to be diagnosed is faults of the type of the equipment to be diagnosed in the past operation process and solutions of the faults; the method is used as a basis for analysis processing, the current fault data of the medical equipment to be diagnosed are analyzed and processed, the accuracy of analysis processing is ensured, and the remote maintenance of the medical equipment to be diagnosed by obtaining accurate maintenance information is facilitated. Therefore, the medical equipment is maintained rapidly and efficiently, the maintenance flow is simplified, and the maintenance accuracy is ensured.
Further, based on the first embodiment of the remote maintenance method of the medical device of the present invention, a second embodiment of the remote maintenance method of the medical device of the present invention is presented.
The second embodiment of the remote maintenance method of a medical device is different from the first embodiment of the remote maintenance method of a medical device in that the step of processing the fault data according to the historical fault information corresponding to the medical device to be diagnosed and generating the fault cause corresponding to the medical device to be diagnosed includes:
s21, extracting fault codes and fault phenomenon data from the fault data, and judging whether the fault codes and the fault phenomenon data are matched or not;
step S22, if the fault code is matched with the fault phenomenon data, searching a history reason corresponding to the fault code from the history fault information, and generating the history reason into a fault reason corresponding to the medical equipment to be diagnosed;
and step S23, if the fault code is not matched with the fault phenomenon data, processing the fault data according to the historical fault information to generate a fault reason corresponding to the medical equipment to be diagnosed.
It will be appreciated that the failure of the medical device may be an uncertainty, may be exactly the same failure as the other devices of the same type, and may be a new failure with variability. For this, the present embodiment sets a different analysis mechanism to obtain the cause of the failure. Specifically, the fault data includes fault codes and fault phenomenon data obtained through pre-diagnosis. After the medical equipment to be diagnosed generates faults and fault phenomenon data representing fault phenomena are obtained, the fault codes of the current fault phenomenon data are determined through various pre-stored fault phenomenon data and the corresponding fault codes thereof. And then the fault codes and the fault phenomenon data thereof are formed into fault data to be transmitted to the cloud server so as to simply represent the fault phenomenon through the fault codes therein.
Further, considering the pre-stored various fault phenomenon data and the corresponding fault codes, the fault phenomenon data of some faults are inevitably omitted. If a new fault does not occur in a certain type, the pre-stored fault phenomenon data does not contain the fault phenomenon data of the new fault and the fault codes thereof, and the fault codes obtained by pre-diagnosing the fault are not matched with the fault phenomenon data, so that the fault codes cannot represent the fault phenomenon. Therefore, after the cloud server receives the fault data, the fault code and the fault phenomenon data in the fault data are extracted to judge, and whether the fault code and the fault phenomenon data are matched is determined. If the fault codes are matched, the fault code can accurately represent the fault phenomenon, the current fault is the fault which occurs in the past, and the historical fault information contains the historical reasons for causing the fault represented by the fault code. And searching the historical reasons corresponding to the fault codes from the historical fault information to serve as the fault reasons corresponding to the medical equipment to be diagnosed.
Further, if the fault code is not matched with the fault phenomenon data, the fault code cannot accurately represent the fault phenomenon, so that the current fault is a fault with a difference from the previous fault, and each historical reason in the historical fault information and the maintenance information thereof cannot accurately reflect the reason of the current fault. At this time, the fault data is analyzed and processed by combining a large amount of past historical fault data and respective maintenance schemes in the historical fault information, so as to obtain the fault reason of the current fault corresponding to the medical equipment to be diagnosed. Specifically, the step of processing the fault data according to the historical fault information to generate a fault cause corresponding to the medical equipment to be diagnosed includes:
step S231, reading preset equipment cluster groups generated based on the historical fault information, extracting position attribute tags in the fault data, and determining fault equipment cluster groups corresponding to the position attribute tags in the preset equipment cluster groups;
understandably, different geographic locations affect the operation of the medical devices, and the cloud server classifies the various medical devices according to the different geographic locations. For each type of medical equipment, if the geographical position of each medical equipment has little influence on operation, the medical equipment is classified into one type, and if the geographical position of each medical equipment has great influence on operation, the medical equipment is classified into different types. And clustering according to respective fault data aiming at each type of medical equipment to obtain a plurality of equipment clusters. In each type of medical equipment, the fault data similarly represent each medical equipment of the same type of faults to be clustered into an equipment cluster; the fault data greatly differ to characterize the clustering of medical devices of different types of faults into different device clusters. The plurality of device clusters formed by each type of medical device form a preset device cluster group of each type of medical device. And the cloud server reads the preset equipment cluster group which is generated based on the historical fault information and characterizes the attribution of various medical equipment in different geographic positions.
Further, the geographic position of the medical equipment to be diagnosed, which is currently in fault, has correlation with the fault reason, so that the position attribute label representing the geographic position of the medical equipment to be diagnosed in the fault data is extracted, and the fault equipment cluster group corresponding to the position attribute label in each preset equipment cluster group is determined. The geographical position of each medical device of the fault device cluster group is generated and has similarity with the geographical position of the medical device to be diagnosed. And analyzing the fault reasons of the current medical equipment to be diagnosed according to the historical fault information of the medical equipment with the similarity of the geographic positions, and eliminating the influence of the geographic positions on the fault reasons.
Specifically, the step of determining the faulty equipment cluster group corresponding to the location attribute tag in each preset equipment cluster group includes:
step S2311, comparing the group position labels of the preset device cluster groups with the position attribute labels to determine target group position labels corresponding to the position attribute labels in the group position labels;
step S2312, searching for a target device cluster group corresponding to the target group position tag in the preset device cluster groups, and determining the target device cluster group as a faulty device cluster group corresponding to the position attribute tag.
Further, each preset device cluster group is formed by each medical device located in different geographic positions and carries a group position tag representing the geographic position of each medical device. The cloud server reads the group position labels of each preset equipment cluster group, compares the group position labels with the position attribute labels lifted from the fault data, and determines the group position label to which the position attribute label belongs. Each group of position labels is characterized by a longitude and latitude coordinate range, and when the longitude and latitude coordinates characterized by the position attribute labels exist in the longitude and latitude coordinate range of a certain group of position labels, the position attribute labels are described to belong to the group of position labels. And taking the home group position label as a target group position label corresponding to the position attribute label in the group position labels. And searching the preset equipment cluster group with the target group position label, wherein the searched preset equipment cluster group is the target equipment cluster group corresponding to the target group position label in each preset equipment cluster group. And determining the target equipment cluster group as a fault equipment cluster group corresponding to the position attribute label so as to analyze the fault reason through the fault equipment clusters corresponding to the fault data in the fault equipment cluster group.
Step S232, processing the fault data according to the fault equipment cluster group to obtain a target cluster corresponding to the medical equipment to be diagnosed;
further, the fault device cluster group includes a plurality of fault device clusters, which represent different fault types, and one fault device cluster corresponds to one similar fault type. After determining the fault equipment cluster group with geographical position similarity with the medical equipment to be diagnosed, the fault equipment cluster with similarity with the medical equipment to be diagnosed in fault type is also required to be found out from the fault equipment cluster group. Specifically, the type analysis processing is performed on the fault data to determine a target cluster corresponding to the medical equipment to be diagnosed in the fault equipment cluster group, wherein the target cluster is similar to the medical equipment to be diagnosed in the fault type. The step of processing the fault data to obtain the target cluster corresponding to the medical equipment to be diagnosed comprises the following steps:
step S2321, each fault characteristic element in the fault data is encoded, and a fault code is obtained;
step S2322, obtaining cluster center codes of each fault device cluster in the fault device cluster group, and calculating similarity values between the fault codes and the cluster center codes;
Step S2323, determining a target cluster corresponding to the medical device to be diagnosed in each faulty device cluster according to the magnitude relation between the similarity values.
Further, each fault characteristic element which represents the fault phenomenon characteristic in the fault data is encoded to obtain a fault code. The coding mode is preset according to requirements, such as simhash coding, and each fault characteristic element is converted into binary data with preset bit numbers; the preset number of bits is set according to the requirement, such as 64 bits or 128 bits. Meanwhile, each fault device cluster forming the fault device cluster group is provided with a cluster center code, wherein the cluster center code is a mean value of cluster element codes in the fault device cluster, and the cluster elements are formed by each fault data clustered to the fault device cluster. Obtaining cluster center codes of each fault equipment cluster, and carrying out similarity calculation on the fault codes and each cluster center code respectively to obtain similarity values between the fault codes and each cluster center code. The similarity calculation may be to calculate a cosine distance or a euclidean distance, which is not limited thereto.
Further, the similarity values are compared to obtain a magnitude relation between the similarity values, and the maximum similarity value is determined from the magnitude relation. The maximum similarity value characterizes a fault equipment cluster with the highest similarity with fault data, so that a cluster center code for generating the maximum similarity value is searched, the fault equipment cluster with the cluster center code is determined by the searched cluster center code, namely a target cluster corresponding to the medical equipment to be diagnosed, and the fault equipment cluster with the similarity with the medical equipment to be diagnosed in fault type is characterized.
Step S233, acquiring a cluster reason corresponding to the target cluster, and generating the cluster reason as a failure reason corresponding to the medical device to be diagnosed.
Further, the target cluster is a set of the same type of similarity historical faults, and comprises fault data of each time of historical faults and fault reasons of each time of historical faults. And acquiring a cluster reason corresponding to the target cluster after determining the target cluster, and forming the cluster reason into a fault reason corresponding to the medical equipment to be diagnosed according to the fault data of the medical equipment to be diagnosed. Searching fault data which is the most similar to the fault data of the medical equipment to be diagnosed from the fault data of each time of historical faults, and further forming fault reasons corresponding to the most similar fault data in the cluster reasons into fault reasons corresponding to the medical equipment to be diagnosed. The method is convenient for forming maintenance information through fault reasons, and carrying out remote maintenance on the medical equipment to be diagnosed.
In the implementation, for the situation that the fault code and the fault phenomenon data are matched, the historical reasons corresponding to the fault code are directly searched and used as the fault reasons, and the fault reasons can be rapidly determined and maintained. And for the situation that the fault codes and the fault phenomenon data are not matched, determining a target cluster which is similar to the medical equipment to be diagnosed in the position in the ground and the fault type from each preset equipment cluster group obtained by clustering according to the historical fault information, and obtaining the fault cause of the medical equipment to be diagnosed according to the cluster cause of the target cluster, so that the accuracy of the determined fault cause is ensured, and the accuracy of remote maintenance is improved.
Further, based on the first or second embodiment of the remote maintenance method of the medical device of the present invention, a third embodiment of the remote maintenance method of the medical device of the present invention is presented.
The third embodiment of the remote maintenance method for a medical device is different from the first or second embodiment of the remote maintenance method for a medical device in that the step of processing the fault data according to the historical fault information corresponding to the medical device to be diagnosed includes, before:
step S40, judging whether the fault data contain pre-diagnosis data or not, and judging whether the pre-diagnosis data are valid or not if the fault data contain the pre-diagnosis data;
step S50, if the pre-diagnosis data are valid, executing a step of processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed;
step S60, if the pre-diagnosis data are invalid, performing pre-diagnosis on the medical equipment to be diagnosed according to the fault data;
and step S70, if the fault data does not contain the pre-diagnosis data, returning the pre-diagnosis instruction information to the medical equipment to be diagnosed.
In this embodiment, the cloud server determines whether the medical device to be diagnosed performs pre-diagnosis on the fault, and for simple and common faults, the medical device to be diagnosed automatically solves the problem through pre-diagnosis, so that on one hand, the efficiency of fault diagnosis is improved, and on the other hand, the fault processing pressure of the cloud server is reduced. Specifically, the pre-diagnosis forms a fault code and fault phenomenon data in the fault data, the fault code and the fault phenomenon data are used as the pre-diagnosis data, and whether the medical equipment to be diagnosed performs pre-diagnosis on the fault is judged by judging whether the fault data contain the pre-diagnosis data. If the cloud server comprises pre-diagnosis data, the representation performs pre-diagnosis on the fault, the medical equipment to be diagnosed determines that the medical equipment cannot solve the fault by itself through the pre-diagnosis, and fault data comprising the pre-diagnosis data are generated and transmitted to the cloud server for processing. If the failure data are judged not to contain the pre-diagnosis data, the fact that the to-be-diagnosed medical equipment does not perform pre-diagnosis on the failure is indicated, and the cloud server returns pre-diagnosis indication information to the to-be-diagnosed medical equipment so as to indicate the to-be-diagnosed medical equipment to perform pre-diagnosis.
Further, for the case that the fault data includes pre-diagnosis data, the cloud server further determines validity of the pre-diagnosis data, wherein the validity of the pre-diagnosis data is characterized by correlation between the generated fault code and the fault phenomenon data. And setting a correlation threshold, and if the correlation between the two is larger than the correlation threshold, indicating that the fault code is generated based on the fault phenomenon data, judging that the pre-diagnosis data is valid. And otherwise, judging that the fault code is not generated based on the fault phenomenon data, and judging that the pre-diagnosis data is invalid.
Further, if the pre-diagnosis data are judged to be valid, the fault data are analyzed and processed according to the historical fault information corresponding to the medical equipment to be diagnosed, and the fault reasons corresponding to the medical equipment to be diagnosed are generated. When the fault data contains the fault code in the pre-diagnosis data and the fault phenomenon data are completely matched, the historical reasons corresponding to the fault code are searched from the historical fault information and are used as the fault reasons corresponding to the medical equipment to be diagnosed. When the correlation between the fault code and the fault phenomenon data is larger than the correlation threshold value but is not completely matched, analyzing similar geographic positions and similar fault types of the fault data according to the historical fault information to obtain fault reasons corresponding to the medical equipment to be diagnosed.
Further, if the correlation between the determined fault code and the fault phenomenon data is low, the pre-diagnosis data is invalid, and the cloud server performs pre-diagnosis on the medical equipment according to the fault data. Analyzing a fault code corresponding to the fault phenomenon data, and judging whether the fault can be automatically solved by the medical equipment to be diagnosed or not through the fault code; and then determining whether the fault is solved by the cloud server or the medical equipment to be diagnosed.
Still further, the step of pre-diagnosing the medical device to be diagnosed according to the fault data includes:
step a, generating a pre-diagnosis result of the medical equipment to be diagnosed, and determining the result type of the pre-diagnosis result;
step b, if the result type is a success type, the pre-diagnosis result is issued to the medical equipment to be diagnosed so that the medical equipment to be diagnosed can be remotely maintained based on the pre-diagnosis result;
and c, if the result type is a failure type, executing the step of processing the failure data according to the historical failure information corresponding to the medical equipment to be diagnosed.
And the cloud server analyzes the fault code corresponding to the fault phenomenon data, and after judging the solution of the fault represented by the fault code, a pre-diagnosis result of the medical equipment to be diagnosed is formed. And the pre-diagnosis result carries a solution identifier for representing the solution so as to represent the solution of the fault by the cloud server or the self-solution of the medical equipment to be diagnosed. Identifying the variability of the characterized solution as a variability of the pre-diagnosis result type; the method comprises the steps of enabling a user to be in self-solution by the medical equipment to be in anger, enabling the result type corresponding to the pre-diagnosis result to be a success type, enabling the result type corresponding to the pre-diagnosis result to be a failure type by the cloud server.
Further, after the pre-diagnosis result of the medical device to be diagnosed is generated, the type of the result is determined according to the scheme identification therein. If the result type is a success type, the pre-diagnosis result is issued to the medical equipment to be diagnosed, so that the medical equipment to be diagnosed can automatically solve the faults according to the pre-diagnosis result, and the remote maintenance of the medical equipment is realized. If the result type is the failure type, the cloud server analyzes and processes the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed, searches the fault reason and solves the fault.
According to the embodiment, by setting the pre-diagnosis mechanism, whether the medical equipment to be diagnosed is subjected to pre-diagnosis and the effectiveness of the pre-diagnosis are judged, so that the medical equipment to be diagnosed is ensured to be subjected to effective pre-diagnosis before fault data are transmitted to the cloud server, and the fault diagnosis of the medical equipment to be diagnosed comprises three progressive stages. The first stage is a pre-diagnosis process of the medical equipment to be diagnosed, the second stage is a process of judging that the fault code is matched with the fault phenomenon data in the cloud server diagnosis process and searching the historical reasons, and the third stage is a process of judging that the fault code is not matched with the fault phenomenon data in the cloud server diagnosis process and analyzing the fault reasons according to the historical fault information. If the pre-diagnosis stage can solve the fault, the medical equipment to be diagnosed automatically solves the fault, if the fault is not solved, the second stage is started, and if the fault code is not matched with the fault phenomenon data in the second stage, the third stage is started. In this way, the efficiency and accuracy of fault resolution are improved by means of layer-by-layer resolution.
In addition, the embodiment of the invention also provides a readable storage medium.
The readable storage medium has stored thereon a remote maintenance program for a medical device, which when executed by a processor implements the steps of the remote maintenance method for a medical device as described above.
The specific implementation manner of the readable storage medium of the present invention may be substantially the same as that of the foregoing embodiments of the remote maintenance method of the medical device, and will not be described herein.
While the embodiments of the present invention have been described above with reference to the drawings, the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many modifications may be made thereto by those of ordinary skill in the art without departing from the spirit of the present invention and the scope of the appended claims, which are to be accorded the full scope of the present invention as defined by the following description and drawings, or by any equivalent structures or equivalent flow changes, or by direct or indirect application to other relevant technical fields.
Claims (9)
1. The remote maintenance method of the medical equipment is characterized by being applied to a cloud server, wherein the cloud server is in communication connection with a plurality of medical equipment, and the remote maintenance method of the medical equipment comprises the following steps of:
Receiving fault data transmitted by any medical device, and determining the medical device transmitting the fault data as the medical device to be diagnosed;
processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed, and generating a fault reason corresponding to the medical equipment to be diagnosed;
the step of processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed, and generating the fault cause corresponding to the medical equipment to be diagnosed comprises the following steps:
extracting fault codes and fault phenomenon data from the fault data, and judging whether the fault codes and the fault phenomenon data are matched or not, wherein the fault data further comprise a position attribute tag;
if the fault code is not matched with the fault phenomenon data, processing the fault data according to the historical fault information to generate a fault reason corresponding to the medical equipment to be diagnosed;
determining maintenance information corresponding to the fault cause, and remotely maintaining the medical equipment to be diagnosed according to the maintenance information;
the step of processing the fault data according to the historical fault information and generating the fault cause corresponding to the medical equipment to be diagnosed comprises the following steps:
Reading preset equipment cluster groups generated based on the historical fault information, extracting position attribute tags in the fault data, and determining fault equipment cluster groups corresponding to the position attribute tags in the preset equipment cluster groups;
processing the fault data according to the fault equipment cluster group to obtain a target cluster corresponding to the medical equipment to be diagnosed; and acquiring a cluster reason corresponding to the target cluster, and generating the cluster reason into a fault reason corresponding to the medical equipment to be diagnosed.
2. The remote maintenance method of a medical device according to claim 1, wherein the step of remotely maintaining the medical device to be diagnosed according to the maintenance information includes:
determining the information type of the maintenance information;
if the information type is a self-repairing type, feeding back the maintenance information to the medical equipment to be diagnosed, and controlling the medical equipment to be diagnosed to operate based on the maintenance information so as to maintain the medical equipment to be diagnosed;
and if the information type is a non-self-repairing type, feeding back the maintenance information to a management terminal corresponding to the medical equipment to be diagnosed so as to enable a holder of the management terminal to maintain the medical equipment to be diagnosed.
3. The remote maintenance method of a medical device according to claim 1, wherein the step of processing the fault data according to the history fault information corresponding to the medical device to be diagnosed, and generating the cause of the fault corresponding to the medical device to be diagnosed comprises:
if the fault code is matched with the fault phenomenon data, searching a historical reason corresponding to the fault code from the historical fault information, and generating the historical reason into a fault reason corresponding to the medical equipment to be diagnosed.
4. The remote maintenance method of a medical device according to claim 1, wherein the step of processing the fault data according to the fault device cluster group to obtain a target cluster corresponding to the medical device to be diagnosed comprises:
coding each fault characteristic element in the fault data to obtain a fault code;
acquiring cluster center codes of each fault equipment cluster in the fault equipment cluster group, and calculating similarity values between the fault codes and each cluster center code;
and determining a target cluster corresponding to the medical equipment to be diagnosed in each fault equipment cluster according to the magnitude relation between the similarity values.
5. The remote maintenance method of a medical device according to claim 1, wherein the step of determining a faulty device cluster group corresponding to the location attribute tag in each of the preset device cluster groups includes:
comparing the group position labels of the preset equipment cluster groups with the position attribute labels to determine target group position labels corresponding to the position attribute labels in the group position labels;
searching a target equipment cluster group corresponding to the target group position label in each preset equipment cluster group, and determining the target equipment cluster group as a fault equipment cluster group corresponding to the position attribute label.
6. The remote maintenance method of a medical device according to any one of claims 1 to 5, wherein the step of processing the fault data based on the historical fault information corresponding to the medical device to be diagnosed includes, before:
judging whether the fault data contains pre-diagnosis data or not, and if so, judging whether the pre-diagnosis data is valid or not;
if the pre-diagnosis data is valid, executing the step of processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed:
If the pre-diagnosis data are invalid, performing pre-diagnosis on the medical equipment to be diagnosed according to the fault data;
and if the fault data does not contain the pre-diagnosis data, returning pre-diagnosis indication information to the medical equipment to be diagnosed.
7. The remote maintenance method of a medical device according to claim 6, wherein the step of pre-diagnosing the medical device to be diagnosed based on the fault data includes:
generating a pre-diagnosis result of the medical equipment to be diagnosed, and determining the result type of the pre-diagnosis result;
if the result type is a success type, the pre-diagnosis result is issued to the medical equipment to be diagnosed so that the medical equipment to be diagnosed can be remotely maintained based on the pre-diagnosis result;
and if the result type is a failure type, executing the step of processing the fault data according to the historical fault information corresponding to the medical equipment to be diagnosed.
8. A remote maintenance device for a medical device, characterized in that the remote maintenance device for a medical device comprises a memory, a processor and a remote maintenance program for a medical device stored on the memory and executable on the processor, which remote maintenance program for a medical device, when executed by the processor, implements the steps of the remote maintenance method for a medical device according to any one of claims 1-7.
9. A readable storage medium, wherein a remote maintenance program of a medical device is stored on the readable storage medium, which when executed by a processor, implements the steps of the remote maintenance method of a medical device according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010527913.4A CN111710406B (en) | 2020-06-10 | 2020-06-10 | Remote maintenance method and device for medical equipment and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010527913.4A CN111710406B (en) | 2020-06-10 | 2020-06-10 | Remote maintenance method and device for medical equipment and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111710406A CN111710406A (en) | 2020-09-25 |
| CN111710406B true CN111710406B (en) | 2024-03-01 |
Family
ID=72540853
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010527913.4A Active CN111710406B (en) | 2020-06-10 | 2020-06-10 | Remote maintenance method and device for medical equipment and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111710406B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112637880B (en) * | 2020-12-23 | 2022-11-15 | 北京东土拓明科技有限公司 | Problematic cell processing method and device |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108257659A (en) * | 2017-12-01 | 2018-07-06 | 深圳市新产业生物医学工程股份有限公司 | Fault handling method, fault treating apparatus and electric terminal |
| CN108566405A (en) * | 2018-01-05 | 2018-09-21 | 深圳市宝润科技有限公司 | Method for monitoring state, device and the storage medium of Medical Devices |
| CN109977146A (en) * | 2019-05-07 | 2019-07-05 | 山东省科学院激光研究所 | Method for diagnosing faults, device and electronic equipment |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9826011B2 (en) * | 2014-07-31 | 2017-11-21 | Istreamplanet Co. | Method and system for coordinating stream processing at a video streaming platform |
-
2020
- 2020-06-10 CN CN202010527913.4A patent/CN111710406B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108257659A (en) * | 2017-12-01 | 2018-07-06 | 深圳市新产业生物医学工程股份有限公司 | Fault handling method, fault treating apparatus and electric terminal |
| CN108566405A (en) * | 2018-01-05 | 2018-09-21 | 深圳市宝润科技有限公司 | Method for monitoring state, device and the storage medium of Medical Devices |
| CN109977146A (en) * | 2019-05-07 | 2019-07-05 | 山东省科学院激光研究所 | Method for diagnosing faults, device and electronic equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111710406A (en) | 2020-09-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2017101864A4 (en) | Method, device, server and storage apparatus of reviewing SQL | |
| WO2021012637A1 (en) | Intelligent follow-up method and system based on radiographic image report, device, and storage medium | |
| US9542543B2 (en) | Biometric authentication device, biometric authentication method and computer readable, non-transitory medium | |
| EP3258397A1 (en) | Text address processing method and apparatus | |
| CN107909330B (en) | Workflow data processing method and device, storage medium and computer equipment | |
| EP3396558B1 (en) | Method for user identifier processing, terminal and nonvolatile computer readable storage medium thereof | |
| CN108287823B (en) | Message data processing method and device, computer equipment and storage medium | |
| CN113360722B (en) | Fault root cause positioning method and system based on multidimensional data map | |
| CN111985241A (en) | Medical information query method, device, electronic equipment and medium | |
| CN111710406B (en) | Remote maintenance method and device for medical equipment and readable storage medium | |
| CN115512823A (en) | Equipment exception handling method and device, exception handling system and equipment | |
| CN112073407A (en) | System, method and storage medium for real-time judgment of abnormal equipment in high-concurrency service | |
| CN114996288A (en) | Data comparison method and device, computer storage medium and electronic equipment | |
| CN113918526A (en) | Log processing method and device, computer equipment and storage medium | |
| CN109445768A (en) | Database script generation method, device, computer equipment and storage medium | |
| CN112596723B (en) | Database script generation method, device, equipment and medium | |
| CN104657248A (en) | Java thread stack analysis method and system | |
| CN111158973B (en) | Web application dynamic evolution monitoring method | |
| CN111739619A (en) | Medical resource sharing method and device and readable storage medium | |
| CN110390424B (en) | Method, device, equipment and medium for estimating service life of test object | |
| CN111078870A (en) | Evaluation data processing method, evaluation data processing device, evaluation data processing medium, and computer device | |
| CN111353874A (en) | Intelligent service system of bank outlets | |
| CN109032897A (en) | Data dispatching method, host and solid state hard disk | |
| CN112231039B (en) | Work order information statistical method, device, computer equipment and storage medium | |
| CN113138906A (en) | Call chain data acquisition method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |