CN112396242A - Self-learning intelligent laboratory operation platform and control method thereof - Google Patents
Self-learning intelligent laboratory operation platform and control method thereof Download PDFInfo
- Publication number
- CN112396242A CN112396242A CN202011365058.8A CN202011365058A CN112396242A CN 112396242 A CN112396242 A CN 112396242A CN 202011365058 A CN202011365058 A CN 202011365058A CN 112396242 A CN112396242 A CN 112396242A
- Authority
- CN
- China
- Prior art keywords
- task
- laboratory
- equipment
- module
- personnel
- 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.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Educational Administration (AREA)
- Automatic Analysis And Handling Materials Therefor (AREA)
Abstract
The invention discloses a self-learning intelligent laboratory operation platform, which comprises a task receiving module, a task processing module and a task processing module, wherein the task receiving module is used for receiving task information to be executed; the task allocation module is used for allocating task information; the laboratory staff allocation module is used for allocating laboratory staff; and the laboratory equipment distribution module is used for distributing the laboratory equipment. The invention can improve the defects of the prior art and improve the operation efficiency of a laboratory.
Description
Technical Field
The invention relates to the technical field of laboratory management and operation, in particular to a self-learning intelligent laboratory operation platform and a control method thereof.
Background
When a laboratory carries a large amount of experimental tasks, how to improve the utilization rate of the laboratory as much as possible is a very important task in the laboratory management operation process. In the prior art, the management operation of the laboratory is usually performed through a previous shift. However, the operation process of the laboratory is designed for the experimenter, and also relates to various aspects such as experimental equipment, experimental tasks and the like, and the prior art cannot carry out targeted overall operation on the laboratory.
Disclosure of Invention
The invention aims to provide a self-learning intelligent laboratory operation platform and a control method thereof, which can overcome the defects of the prior art and improve the operation efficiency of a laboratory.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows.
A self-learning intelligent laboratory operations platform, comprising:
the task receiving module is used for receiving task information to be executed;
the task allocation module is used for allocating task information;
the laboratory staff allocation module is used for allocating laboratory staff;
and the laboratory equipment distribution module is used for distributing the laboratory equipment.
A control method of the self-learning intelligent laboratory operation platform comprises the following steps:
A. the laboratory personnel allocation module sends the real-time distributable personnel number to the task allocation module, and the laboratory equipment allocation module sends the real-time distributable equipment number to the task allocation module; the task receiving module sends the received task information to be executed to the task distribution module;
B. the task allocation module calculates the number of executable tasks according to the real-time personnel information and the equipment information and then allocates the task information;
C. the task allocation module predicts the change information of future personnel and equipment according to the result of task allocation, sends the personnel change information to the laboratory personnel allocation module, and sends the equipment change information to the laboratory equipment allocation module.
Preferably, in step a, the laboratory staff allocating module establishes an equipment operation capability list of the allocable staff, the laboratory equipment allocating module establishes an experiment range list of the allocable equipment, and the task receiving module establishes a priority list of the task information.
Preferably, in step a, among the task information of the same priority, the task information that can be executed in parallel is selected, and a parallel association data set between the selected task information is established.
Preferably, in the step B, the task information is firstly grouped according to the priority of the task information from high to low, then the task information group with high priority is preferentially allocated with personnel, and finally equipment is allocated according to the personnel allocation condition; in the task information of the same group, the same person synchronization processing task information which can be executed in parallel is assigned.
Preferably, in step C, the laboratory equipment allocation module is used as the highest priority, and laboratory personnel are allocated in advance on the premise of ensuring that the equipment keeps the highest start-up rate.
Adopt the beneficial effect that above-mentioned technical scheme brought to lie in: the invention improves the matching precision among all elements and reduces the time waste by reasonably distributing tasks, personnel and equipment. By establishing the priority of the tasks and the parallel associated data sets, the potential of the existing resources can be mined to the greatest extent, and the operation efficiency of a laboratory is further improved.
Drawings
FIG. 1 is a block diagram of one embodiment of the present invention.
In the figure: 1. a task receiving module; 2. a task allocation module; 3. a laboratory staff allocation module; 4. a laboratory equipment assignment module.
Detailed Description
The standard parts used in the invention can be purchased from the market, the special-shaped parts can be customized according to the description and the description of the attached drawings, and the specific connection mode of each part adopts the conventional means of mature bolts, rivets, welding, sticking and the like in the prior art, and the detailed description is not repeated.
Referring to fig. 1, one embodiment of the present invention includes,
the task receiving module 1 is used for receiving task information to be executed;
the task allocation module 2 is used for allocating task information;
the laboratory staff allocation module 3 is used for allocating laboratory staff;
and the laboratory equipment distribution module 4 is used for distributing the laboratory equipment.
A control method of the self-learning intelligent laboratory operation platform comprises the following steps:
A. the laboratory personnel allocation module 3 sends the real-time distributable personnel number to the task allocation module 2, and the laboratory equipment allocation module 4 sends the real-time distributable equipment number to the task allocation module 2; the task receiving module 1 sends the received task information to be executed to the task distributing module 2;
B. the task allocation module 2 calculates the number of executable tasks according to the real-time personnel information and the equipment information, and then allocates the task information;
C. the task allocation module 2 predicts the change information of future personnel and equipment according to the result of task allocation, sends the personnel change information to the laboratory personnel allocation module 3, and sends the equipment change information to the laboratory equipment allocation module 4.
In the step A, the laboratory staff distributing module 3 establishes an equipment operation capacity list of distributable staff, the laboratory equipment distributing module 4 establishes an experiment range list of the distributable equipment, and the task receiving module 1 establishes a priority list of task information.
In the step A, task information capable of being executed in parallel is selected from the task information with the same priority, and a parallel association data set between the selected task information is established.
In the step B, firstly, the task information is grouped according to the priority of the task information from high to low, then the task information group with high priority is preferentially distributed with personnel, and finally equipment is distributed according to the personnel distribution condition; in the task information of the same group, the same person synchronization processing task information which can be executed in parallel is assigned.
In the step C, the laboratory equipment distribution module 4 is used as the highest priority, and laboratory personnel are pre-distributed on the premise of ensuring that the equipment keeps the highest start-up rate.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, are merely for convenience of description of the present invention, and do not indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (6)
1. The utility model provides a self-learning type wisdom laboratory operation platform which characterized in that includes:
the task receiving module (1) is used for receiving task information to be executed;
the task allocation module (2) is used for allocating task information;
the laboratory staff allocation module (3) is used for allocating laboratory staff;
a laboratory equipment distribution module (4) for distributing laboratory equipment.
2. The method for controlling a self-learning intelligent laboratory operation platform as recited in claim 1, comprising the steps of:
A. the laboratory personnel allocation module (3) sends the real-time distributable personnel number to the task allocation module (2), and the laboratory equipment allocation module (4) sends the real-time distributable equipment number to the task allocation module (2); the task receiving module (1) sends the received task information to be executed to the task distributing module (2);
B. the task allocation module (2) calculates the number of executable tasks according to the real-time personnel information and the equipment information, and then allocates the task information;
C. the task allocation module (2) predicts the change information of future personnel and equipment according to the result of task allocation, sends the personnel change information to the laboratory personnel allocation module (3), and sends the equipment change information to the laboratory equipment allocation module (4).
3. The method of claim 2, wherein the method comprises: in the step A, a laboratory personnel distribution module (3) establishes an equipment operation capacity list of distributable personnel, a laboratory equipment distribution module (4) establishes an experiment range list of the distributable equipment, and a task receiving module (1) establishes a priority list of task information.
4. The method of claim 3, wherein the method comprises: in the step A, task information capable of being executed in parallel is selected from the task information with the same priority, and a parallel association data set between the selected task information is established.
5. The method of claim 4, wherein the method comprises: in the step B, firstly, the task information is grouped according to the priority of the task information from high to low, then the task information group with high priority is preferentially distributed with personnel, and finally equipment is distributed according to the personnel distribution condition; in the task information of the same group, the same person synchronization processing task information which can be executed in parallel is assigned.
6. The method of claim 5, wherein the method comprises: and step C, the laboratory equipment distribution module (4) is used as the highest priority, and laboratory personnel are pre-distributed on the premise of ensuring that the equipment keeps the highest start-up rate.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011365058.8A CN112396242A (en) | 2020-11-27 | 2020-11-27 | Self-learning intelligent laboratory operation platform and control method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011365058.8A CN112396242A (en) | 2020-11-27 | 2020-11-27 | Self-learning intelligent laboratory operation platform and control method thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112396242A true CN112396242A (en) | 2021-02-23 |
Family
ID=74605470
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011365058.8A Pending CN112396242A (en) | 2020-11-27 | 2020-11-27 | Self-learning intelligent laboratory operation platform and control method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112396242A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101834890A (en) * | 2010-04-02 | 2010-09-15 | 浪潮(北京)电子信息产业有限公司 | Intelligent management system and method of distributed system |
| CN106327087A (en) * | 2016-08-26 | 2017-01-11 | 隆鑫通用动力股份有限公司 | Test task assignment method |
| CN107909263A (en) * | 2017-11-14 | 2018-04-13 | 江苏金智教育信息股份有限公司 | A kind of colleges and universities examine business row's test method and device |
| CN108769233A (en) * | 2018-06-07 | 2018-11-06 | 福建江夏学院 | A kind of resource optimal distribution method based on desktop cloud |
| CN110163474A (en) * | 2019-04-12 | 2019-08-23 | 平安普惠企业管理有限公司 | A kind of method and apparatus of task distribution |
| CN111461517A (en) * | 2020-03-27 | 2020-07-28 | 机械工业仪器仪表综合技术经济研究所 | Intelligent information system for planning laboratory workflow |
| CN111684534A (en) * | 2017-11-22 | 2020-09-18 | 皇家飞利浦有限公司 | Apparatus, system and method for optimizing pathology workflow |
| CN111985855A (en) * | 2020-09-11 | 2020-11-24 | 武汉空心科技有限公司 | Multi-role task distribution system and distribution method based on Internet platform |
-
2020
- 2020-11-27 CN CN202011365058.8A patent/CN112396242A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101834890A (en) * | 2010-04-02 | 2010-09-15 | 浪潮(北京)电子信息产业有限公司 | Intelligent management system and method of distributed system |
| CN106327087A (en) * | 2016-08-26 | 2017-01-11 | 隆鑫通用动力股份有限公司 | Test task assignment method |
| CN107909263A (en) * | 2017-11-14 | 2018-04-13 | 江苏金智教育信息股份有限公司 | A kind of colleges and universities examine business row's test method and device |
| CN111684534A (en) * | 2017-11-22 | 2020-09-18 | 皇家飞利浦有限公司 | Apparatus, system and method for optimizing pathology workflow |
| CN108769233A (en) * | 2018-06-07 | 2018-11-06 | 福建江夏学院 | A kind of resource optimal distribution method based on desktop cloud |
| CN110163474A (en) * | 2019-04-12 | 2019-08-23 | 平安普惠企业管理有限公司 | A kind of method and apparatus of task distribution |
| CN111461517A (en) * | 2020-03-27 | 2020-07-28 | 机械工业仪器仪表综合技术经济研究所 | Intelligent information system for planning laboratory workflow |
| CN111985855A (en) * | 2020-09-11 | 2020-11-24 | 武汉空心科技有限公司 | Multi-role task distribution system and distribution method based on Internet platform |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102033536B (en) | Scheduling, organization and cooperation system and method for multi-robot system | |
| CN102074978B (en) | Charging and replacing power station, charging and replacing control method and system and operation monitoring system | |
| CN111669213B (en) | Dynamic management and control system and management and control method for satellite communication resources | |
| CN104583886A (en) | Production line management method and production line management system | |
| CN104320854B (en) | Resource regulating method and device | |
| CN103945549B (en) | Baseband processing resource allocation system under C-RAN architecture | |
| CN110210789A (en) | Resource distribution dispatching method, the device, equipment of power grid test business | |
| CN109890085A (en) | One kind point priority machine type communication random access backoff parameter determines method | |
| CN110276975A (en) | A kind of automatic driving vehicle shunts dispatching method and system | |
| CN112184528A (en) | Big data intelligent platform in wisdom garden | |
| CN108055701A (en) | A kind of resource regulating method and base station | |
| CN102510403B (en) | Receive and the cluster distributed system and method for real-time analysis for vehicle data | |
| CN105517179A (en) | Wireless resource scheduling method and scheduler | |
| CN105592551A (en) | Channel allocation method and device | |
| CN112396242A (en) | Self-learning intelligent laboratory operation platform and control method thereof | |
| CN106802825A (en) | A kind of dynamic task scheduling method and system based on real-time system | |
| CN103702320A (en) | Method and device for allocating C-RNTIs (cell radio network temporary identifiers) | |
| CN103458520A (en) | Allocation method and device of uplink frequency domain resources | |
| CN110011888A (en) | A kind of modular CAN network load factor optimization method and device | |
| CN116131304A (en) | Power distribution energy storage management system for novel power system | |
| CN101431799B (en) | Method for reducing resource allocation spending | |
| CN114675963A (en) | Multi-task processing method based on equipment priority in photovoltaic 5G base station system | |
| CN102547988B (en) | Distributing method and distributing device for temporary block flow | |
| CN116866966A (en) | Method for downloading base station version, network management server, base station, equipment and medium | |
| CN103906197A (en) | Decision-making method for multi-radio access selection of cognitive radio network |
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 |