CN109065135B

CN109065135B - Hospital logistics equipment full-life-cycle management cloud platform, method and system

Info

Publication number: CN109065135B
Application number: CN201810950351.7A
Authority: CN
Inventors: 刘利达
Original assignee: Individual
Current assignee: Individual
Priority date: 2018-08-20
Filing date: 2018-08-20
Publication date: 2020-03-10
Anticipated expiration: 2038-08-20
Also published as: CN109065135A

Abstract

The invention discloses a hospital logistics equipment full life cycle management cloud platform, a user terminal and a system, wherein the cloud platform comprises: the equipment management module is used for managing equipment information, sensor information arranged on the equipment and in the surrounding environment of the equipment, full-view camera information and the association among the information; the data monitoring module is used for receiving real-time equipment operation data and equipment surrounding environment safety factor data sent by the equipment and the sensor and storing the data to the monitoring data management module; monitoring the running state of equipment according to the received data, when the equipment is monitored to have data abnormality, sending an image acquisition command to a full-view camera corresponding to the equipment, receiving current image data of the equipment, and storing the abnormal data and the image data to an abnormal data management module. The invention realizes the comprehensive monitoring of the operation state of the hospital logistics equipment, reduces the burden of logistics workers on equipment maintenance and ensures the safe operation of instruments.

Description

Hospital logistics equipment full-life-cycle management cloud platform, method and system

Technical Field

The invention belongs to the field of full-life-cycle management of equipment, and particularly relates to a full-life-cycle management cloud platform, method and system for hospital logistics equipment.

Background

Hospitals belong to intensive personnel places, the buildings are dense, the equipment is centralized, the flow of flammable and explosive articles such as pressure vessels, chemical reagents, bedding paper and the like is large for many people, the number of vulnerable groups is large, and in case of accidents such as fire disasters and the like, casualties and property losses are huge. Therefore, it is important to securely manage the devices.

However, the current equipment life cycle management mostly adopts a ledger management mode, and can record the status data of equipment such as warehousing-in and warehousing-out time information, commissioning time, equipment maintenance condition, equipment transfer, scrapping disposal and the like, thereby realizing the management of the equipment life cycle status. However, the conventional equipment maintenance mainly includes regular inspection and after-repair, and the updating of the ledger data is actively recorded into the system after the equipment is manually inspected or repaired. If the standing book mode is adopted, omission of events easily occurs, whether data abnormity occurs in the time without detection, whether hidden dangers possibly causing data abnormity exist around the equipment or not cannot be known, and only the troubleshooting can be performed after the fault occurs.

Although there are currently solutions that incorporate real-time operational data into a device full lifecycle management system. However, the storage of the standing book information and the real-time operation data of the equipment cannot realize uniform management, and no association is established between the data; moreover, although the ledger information records data for equipment maintenance and repair, the management of these is distributed, and a large amount of data cannot be effectively used.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention provides a hospital logistics equipment full-life-cycle management cloud platform, a user terminal and a system.

In order to achieve the purpose, the invention adopts the following technical scheme:

a hospital logistics equipment full lifecycle management cloud platform, comprising:

the equipment management module is used for managing equipment information, sensor information arranged on the equipment and in the surrounding environment of the equipment, full-view camera information and the association among the information;

the data monitoring module is used for receiving real-time equipment operation data and equipment surrounding environment safety factor data sent by the equipment and the sensor and storing the data to the monitoring data management module; monitoring the running state of equipment according to the received data, when the equipment is monitored to have data abnormality, sending an image acquisition command to a full-view camera corresponding to the equipment, receiving current image data of the equipment, and storing the abnormal data and the image data to an abnormal data management module.

Further, when the data abnormality of the equipment is monitored, alarm information is generated, sent to the user terminal and stored in the abnormal data management module.

Further, when data abnormality of the equipment is monitored, a work order is generated according to the equipment information and the abnormal data, sent to the user terminal and stored in the work order management module; and receiving and storing the data abnormal reason fed back by the user terminal.

Further, the generating the work order comprises:

acquiring historical work order data, and establishing a data abnormal reason prediction model;

taking the abnormal data as input, and obtaining possible reasons and corresponding probabilities based on the data abnormal reason prediction model;

and writing abnormal data, equipment and/or sensor information of the abnormal data, and the possible alarm reasons and the probability into a preset work order template to generate a work order.

And further, the system also comprises a data analysis module which is used for predicting equipment faults, evaluating equipment manufacturers and predicting the state of similar equipment based on the full life cycle historical data.

Further, the device failure prediction comprises:

acquiring historical work order information, and screening a work order related to equipment faults according to work order processing information;

acquiring corresponding monitoring data according to the work order;

establishing an equipment fault prediction model for each equipment based on the monitoring data and the equipment fault information;

and the data monitoring module is used for predicting the equipment state based on the model.

Further, the equipment manufacturer evaluation comprises:

acquiring historical work order data, and screening a work order related to equipment faults according to the reasons of data abnormality;

acquiring corresponding monitoring data according to the work order;

analyzing whether the equipment fault is an external environment factor or an equipment self factor based on the monitoring data and the equipment fault information, and acquiring a data record of the fault caused by the equipment self factor;

extracting corresponding equipment information, including manufacturer information and equipment commissioning time;

and evaluating the equipment of different manufacturers according to the commissioning time, the failure occurrence frequency and the failure occurrence type.

Further, the device state prediction includes:

acquiring full life cycle data of the same equipment of the same manufacturer;

acquiring the ambient temperature and humidity of the equipment, and drawing a trend graph of the temperature and the humidity along with time;

clustering equipment based on a temperature and humidity trend graph to obtain a plurality of equipment clusters similar to the surrounding environment;

for each cluster, extracting fault data and fault time of equipment in the cluster from the historical work order, and analyzing the relation between each fault type and commissioning time;

the method comprises the steps of clustering equipment to be monitored based on surrounding environment data to obtain a cluster to which the equipment belongs, and predicting the state of the equipment based on the relation between the fault type corresponding to the cluster and commissioning time.

One or more embodiments provide a hospital logistics equipment full-life-cycle management user terminal, which establishes a communication connection with the cloud platform, and includes:

the data query module is used for acquiring real-time operation data of the specified equipment from the cloud platform;

the alarm management module is used for receiving alarm information sent by the cloud platform;

and the work order management module is used for receiving the work orders sent by the cloud platform and receiving the data exception reasons input by the user and sending the data exception reasons to the cloud platform.

One or more embodiments provide a hospital logistics equipment full life cycle management system, which includes the cloud platform and the user terminal.

The invention has the advantages of

1. The hospital logistics equipment full life cycle management system is based on the traditional standing book mode management equipment information, and also receives real-time operation data and peripheral environment data of equipment, generates a work order when the data are abnormal, and stores the work order and the work order processing condition, so that closed-loop management of multi-dimensional data such as the equipment standing book data, the real-time operation data of the equipment, alarm data, the work order data and the like is realized, and effective data support is provided for subsequent big data analysis.

2. The method is based on the stored equipment operation data and the historical work order data, and by analyzing the relation between the equipment fault and factors such as a manufacturer and operation time, the evaluation of the equipment is more objective, a data basis is provided for the equipment operation abnormity early warning and the equipment health state evaluation, and data support is provided for follow-up purchase.

3. The method makes full use of the full life cycle data, establishes various management auxiliary methods such as equipment fault prediction, equipment manufacturer evaluation, equipment state prediction and the like, predicts the fault in advance, ensures that logistics workers are prepared in advance, contacts related manufacturers or third-party maintenance personnel in advance, avoids processing delay caused by low professional skill level of the logistics workers, reduces the maintenance cost of the equipment, prolongs the service life of the equipment, and ensures safe and stable operation of the hospital logistics equipment.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.

Fig. 1 is a functional framework diagram of the hospital logistics equipment full life cycle management system of the present disclosure.

Detailed Description

It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.

The embodiments and features of the embodiments in the present application may be combined with each other without conflict.

Example one

The embodiment discloses hospital logistics equipment full life cycle management cloud platform includes:

the device information management module manages basic information (device number, factory information, model, starting time, installation position and the like) of the device and intelligent monitoring terminal information associated with the device, and comprises: the device comprises sensor identification information installed on the device, and identification information of a sensor for monitoring the surrounding environment of the device and a full-view camera.

When the data abnormality of the equipment is monitored, alarm information and a work order are generated and sent to the user terminal, and are stored in the abnormal data management module and the work order management module respectively.

In one or more embodiments, the alarm may be sent to the hospital logistics manager and/or the third party maintenance staff by one or more of voice, text and image, and simultaneously send the corresponding work order.

In one or more embodiments of the present disclosure, the cloud platform stores a correspondence between related person information and an alarm information level, and a correspondence between project information and third-party maintenance person information. The related personnel information comprises basic personnel information, belonged items (electric, elevator, boiler, gas, secondary water supply, sewage treatment, medical treatment and the like) and jobs, the alarm level corresponding to the jobs is preset, and when the alarm information is generated, the alarm information is sent to related personnel of corresponding items and levels: and determining a worker receiving the alarm information according to the equipment information, the alarm level and the worker information, and sending the alarm information to a user terminal of the worker.

The alarm is classified according to the emergency situation of the event, and a primary early warning threshold, a secondary alarm threshold and a tertiary serious alarm threshold which are preset and respectively correspond to the real-time operation data of the equipment and the safety factor data of the surrounding environment of the equipment are stored. Respectively comparing real-time equipment operation data and peripheral environment safety factor data which are received in real time and sent by an intelligent monitoring terminal with a stored highest threshold value corresponding to the real-time equipment operation data and the peripheral environment safety factor data, if the real-time equipment operation data and the peripheral environment safety factor data exceed the range of the highest threshold value, judging that the data are abnormal, and generating alarm information of a corresponding level; otherwise, the comparison is carried out with the stored corresponding lower grade threshold value until the comparison with all grade threshold values is completed.

And if the alarm is not cancelled in a period of time after the first-level alarm information or the second-level alarm information is sent and the data is continuously abnormal, generating the alarm information of a higher level.

As a substitute scheme for grading, establishing a relational database according to different user experience values, wherein the relational database stores alarm reasons, real-time equipment operation data, safety factor data of the surrounding environment of the equipment and the incidence relation between the alarm reasons and the alarm levels; and respectively comparing the real-time equipment operation data and the peripheral environment safety factor data which are sent by the intelligent monitoring terminal and received in real time with the relational database to generate alarm information of corresponding levels.

As another alternative scheme of grading, a historical information database is established according to historical data, historical alarm information and work order processing information of the intelligent monitoring terminal, relationships among the historical data of the intelligent monitoring terminal and between the historical data and the historical alarm information are analyzed by adopting machine learning, a relationship database is established, and an association relationship between an alarm reason and an alarm grade is preset; and respectively comparing the real-time equipment operation data and the peripheral environment safety factor data which are sent by the intelligent monitoring terminal and received in real time with the relational database to generate alarm information of corresponding levels.

For low-level alarms, only the alarms are sent to logistics management personnel, and for higher-level alarms, the alarms need to be sent to third-party maintenance personnel at the same time.

And the maintenance personnel reach the site for processing according to the alarm information, fill the reason of the data abnormity into the work order and send the work order to the cloud platform. And the data monitoring module receives the data abnormity reason fed back by the user terminal and stores the data abnormity reason to the work order management module. When the work order is completed, the alarm is automatically cancelled.

Wherein, intelligent monitoring terminal includes:

sensor devices including, but not limited to, current sensors, voltage sensors, residual current sensors, temperature sensors, humidity sensors;

and the full-view camera is used for acquiring the current images of the equipment and the surrounding environment where the data are abnormal.

In one or more embodiments, the apparatus comprises:

electrical equipment including, but not limited to, high and low voltage distribution cabinets, transformers, distribution boxes, cables, bridges, cable ducts, floor power wells, etc.;

elevator equipment including, but not limited to, haulers, reduction boxes, transformers, reactors, control cabinets, etc.;

the central air conditioner comprises central air conditioner internal unit equipment, central air conditioner external unit equipment and equipment for supporting normal operation of the central air conditioner;

medical oxygen devices, including but not limited to oxygen cylinders;

boiler equipment including, but not limited to, economizers;

sewage treatment equipment including but not limited to fans, lift pumps, reflux pumps, and conditioning tanks;

and the secondary water supply equipment comprises, but is not limited to, a high-area water pump, a low-area water pump, a control cabinet, a frequency converter and a water tank.

Correspondingly, the device real-time operation data received by the cloud platform comprises:

real-time current data of the electrical equipment, real-time voltage data of the equipment, residual current data of the equipment and temperature data of cables inside the equipment;

the method comprises the following steps of noise of an elevator dragging machine, temperature of the dragging machine, oil level of a reduction gearbox, temperature of a transformer, temperature of a reactor, temperature of a machine room and temperature of a control cabinet.

The temperature of cooling water inlet of central air-conditioning equipment, the temperature difference of chilled water inlet and outlet, the pressure difference of chilled water inlet and outlet, the small temperature difference of a condenser, the degree of superheat of exhaust, overhigh condensation saturation temperature, overlarge condensation pressure, exhaust temperature, evaporation pressure, oil filter pressure, oil temperature, oil filter screen pressure difference and external heat preservation temperature;

the outlet pressure of the oxygen tank body, the inlet pressure of the busbar, the temperature in the tank body, the liquid level and the outlet pressure of the decompression threshold;

the boiler system comprises an economizer outlet water temperature, an economizer inlet water temperature, an economizer smoke outlet temperature, a condenser inlet water temperature, a condenser outlet water temperature and (condensation smoke outlet) smoke exhaust temperature;

the current water level of the boiler, the conversion value of a (probe) liquid level switch, the pressure of the boiler, the running current of the boiler, the humidity of a boiler control cabinet and the temperature of the boiler control cabinet;

the secondary water supply equipment comprises a high-area water pump single-phase voltage, a high-area water pump single-phase current, a low-area water pump single-phase voltage, a low-area water pump single-phase current, a high-area water pump water pumping pressure (21 layers), a low-area water pump water pumping pressure (11 layers), a control cabinet temperature, a control cabinet humidity, a frequency converter state, a water source state and a water tank water level;

the sewage treatment equipment comprises a sewage treatment device, a lifting pump 1, a lifting pump 2, a reflux pump, a regulating tank and the like, wherein the sewage treatment device comprises a pool liquid level, an aerobic pool liquid level, an anaerobic pool liquid level, a lifting pump water outlet pressure and a fan pipeline pressure which need a water pump to provide power.

The monitoring data management module is used for managing the installation position and real-time operation data of the sensor; preferably, weather data (temperature, humidity, rainfall, wind speed, etc.) is also acquired.

The abnormal data management module is used for managing abnormal data and image data shot by the full-view camera when data abnormality occurs;

and the work order management module is used for managing work order information and comprises work order generation time, work order content, work order processing information, work order completion time and a completion person.

In one or more embodiments, the generating the work order comprises:

establishing an alarm reason prediction model based on historical work order data;

taking the abnormal data as input, and calculating possible alarm reasons and probability based on the alarm reason predictive model;

and writing abnormal data, a sensor and an occurrence position of the abnormal data, and the possible alarm reason and probability into a preset work order template to generate a work order.

The cloud platform further comprises a data analysis module comprising one or more of the following sub-modules:

an equipment failure prediction submodule configured to:

acquiring corresponding monitoring data according to the work order, wherein the monitoring data comprises operation data of the equipment, sensor data installed on the equipment and sensor data for detecting the surrounding environment;

and establishing an equipment fault prediction model for each equipment based on the monitoring data and the equipment fault information.

A device vendor evaluation sub-module configured to:

analyzing whether the equipment fault is an external environment factor or an equipment self factor based on the monitoring data and the equipment fault information; acquiring data records of faults caused by self factors of equipment;

A device state prediction sub-module configured to:

acquiring full life cycle data of similar equipment, preferably, acquiring the similar equipment of the same manufacturer;

acquiring ambient environment data of the devices, including ambient temperature and humidity, and drawing a trend graph of the temperature and the humidity along with time; clustering equipment based on a temperature and humidity trend graph to obtain a plurality of equipment clusters similar to the surrounding environment;

On the basis, the cloud platform also stores coping strategies corresponding to various fault types, and when a certain fault is predicted to occur, the coping strategies are pushed to the back office staff, so that the back office staff can prepare in advance. For example, when a failure is predicted in which a component may age, a logistical personnel is notified to prepare the component in advance, or the manufacturer is contacted to purchase the component in advance.

For example, a distribution box is selected as an analysis object, distribution boxes produced by all hospital manufacturers A are obtained, distribution boxes with similar surrounding environments are extracted as samples to be regularly mined, so that interference of other factors on equipment is avoided, and based on fault data of the whole life cycle of the distribution boxes with similar surrounding environments, the faults of similar equipment when the commissioning time reaches are analyzed, and the service life of the equipment is about to end when the commissioning time reaches. Through foreseeing the equipment state based on big data, the logistics personnel can contact relevant producer or third party dimension guarantor in advance and carry out the purchase in advance of equipment part or overhaul in advance, because of the maintenance delay that logistics personnel professional knowledge leads to inadequately when having avoided the trouble to take place, has guaranteed the steady operation of hospital logistics equipment.

Example two

The embodiment aims to provide the user terminal which establishes communication connection with the cloud platform. The method comprises the following steps:

the data query module is used for acquiring real-time operation data of the equipment from the cloud platform;

EXAMPLE III

The purpose of this embodiment is to provide a hospital logistics equipment full life cycle management, including the cloud platform and the user terminal in the above embodiments.

The invention has the advantages of

As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.

Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.

Claims

1. The utility model provides a hospital logistics equipment full life cycle management cloud platform which characterized in that includes:

the data monitoring module is used for receiving real-time equipment operation data and equipment surrounding environment safety factor data sent by the equipment and the sensor and storing the data to the monitoring data management module; monitoring the running state of equipment according to the received data, when the equipment is monitored to have data abnormality, sending an image acquisition command to a full-view camera corresponding to the equipment, receiving current image data of the equipment, and storing the abnormal data and the image data to an abnormal data management module;

when the data abnormality of the equipment is monitored, generating a work order according to the equipment information and the abnormal data, sending the work order to the user terminal, and storing the work order to the work order management module; receiving and storing the data abnormal reason fed back by the user terminal;

the system also comprises a data analysis module, a data analysis module and a data analysis module, wherein the data analysis module is used for predicting equipment faults, evaluating equipment manufacturers and predicting the state of similar equipment on the basis of the historical data of the full life cycle;

the device fault prediction comprises:

acquiring corresponding monitoring data according to the work order;

establishing an equipment fault prediction model based on the monitoring data and the equipment fault information;

the data monitoring module is used for predicting the equipment state based on the model;

the device state prediction comprises:

acquiring full life cycle data of the same equipment of the same manufacturer;

2. The hospital logistics equipment full life cycle management cloud platform of claim 1, wherein when it is monitored that data abnormality occurs in the equipment, alarm information is further generated, sent to the user terminal, and stored in the abnormal data management module.

3. The hospital logistics equipment full lifecycle management cloud platform of claim 1, wherein the generating of the work order comprises:

and writing the abnormal data, the equipment and/or sensor information where the abnormal data occurs, and the possible reasons and the probability into a preset work order template to generate a work order.

4. The hospital logistics equipment full lifecycle management cloud platform of claim 1, wherein the equipment manufacturer evaluation comprises:

acquiring corresponding monitoring data according to the work order;

5. A hospital logistics equipment full life cycle management user terminal, which is connected with the cloud platform of any one of claims 1-4 in a communication manner, and comprises:

6. A hospital logistics equipment full lifecycle management system, comprising the cloud platform of any of claims 1-4 and the user terminal of claim 5.