CN113793051A - Early warning scheme determination method - Google Patents
Early warning scheme determination method Download PDFInfo
- Publication number
- CN113793051A CN113793051A CN202111114815.9A CN202111114815A CN113793051A CN 113793051 A CN113793051 A CN 113793051A CN 202111114815 A CN202111114815 A CN 202111114815A CN 113793051 A CN113793051 A CN 113793051A
- Authority
- CN
- China
- Prior art keywords
- data
- aipark
- platform
- early warning
- emergency management
- 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
- 238000000034 method Methods 0.000 title claims abstract description 44
- 238000009825 accumulation Methods 0.000 claims abstract description 19
- 238000013473 artificial intelligence Methods 0.000 claims abstract description 19
- 238000004088 simulation Methods 0.000 claims abstract description 10
- 238000013135 deep learning Methods 0.000 claims description 6
- 230000008447 perception Effects 0.000 abstract description 3
- 230000000007 visual effect Effects 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
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
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0633—Workflow analysis
-
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a method for determining an early warning scheme, which comprises the following working steps: the front-end intelligent device collects data, the AIpark platform analyzes the data based on big data accumulation and artificial intelligence, determines a pre-warning plan, learns unknown conditions, optimizes emergency management processes and carries out simulation on the emergency management processes. According to the invention, data collected by the front-end equipment is transmitted and collected, comprehensive data perception with high frequency, high space-time precision and multiple dimensions is realized, the AIpark platform can organize inquiry and backtrack in a organized and structured manner for visual analysis, the AIpark platform analyzes the collected data based on large data accumulation and artificial intelligence, when the data can be executed, the most reasonable early warning plan is transmitted to the front-end intelligent equipment through the AIpark platform, and meanwhile, the front-end intelligent equipment sends out an early warning signal, so that an active early warning scheme is realized.
Description
Technical Field
The invention relates to the technical field of early warning schemes, in particular to a method for determining an early warning scheme.
Background
The early warning mechanism is a system for issuing warning in advance, an early warning system consisting of a mechanism, a system, a network, measures and the like for providing warning in time is used for realizing advanced feedback of information, a foundation is laid for arranging and preventing risks in time, in the process of formulating the early warning mechanism, identification and analysis are fully carried out theoretically, particularly in the process of managing crisis, the crisis management theory tells people that an emergency does not necessarily evolve into a crisis event, if prevention is in place, many things can not happen, so in the process of formulating the early warning mechanism, scientificity is fully followed, and when the early warning scheme is used, a scientific early warning scheme determining mechanism is needed for determining the correct use of the early warning scheme.
The scheme determination method in the prior art has the defects that:
1. the method for determining the scheme in the prior art cannot realize comprehensive data perception with high frequency, high space-time precision and multiple dimensions, organize inquiry and backtracking orderly and structurally, perform visual analysis and solve the problem;
2. the scheme determining method in the prior art cannot learn unknown conditions, cannot perform simulation on emergency management processes, and compiles the most reasonable early warning plan for each emergency.
Disclosure of Invention
The invention aims to provide a method for determining an early warning scheme, so as to solve the problems in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions, and a method for determining an early warning scheme includes the following working steps:
(1) the front-end intelligent equipment collects data;
(2) the AIpark platform analyzes data based on big data accumulation and artificial intelligence to determine an early warning plan;
(3) learning unknown conditions, optimizing emergency management processes,
(4) and carrying out simulation on the emergency management process.
Preferably, in the step (1), data is collected through a front-end intelligent device, and the collected data is uploaded to an AIpark platform.
Preferably, in the step (2), the collected data is analyzed by the AIpark platform based on big data accumulation and artificial intelligence, and when the data is not executable, the front-end intelligent device does not perform any operation.
Preferably, in the step (2), the acquired data is analyzed based on big data accumulation and artificial intelligence through the AIpark platform, when the data is executable, the target early warning plan is transmitted to the front-end intelligent device through the AIpark platform, and meanwhile, the front-end intelligent device sends out an early warning signal.
Preferably, in the step (2), the collected data is analyzed by the AIpark platform based on big data accumulation and artificial intelligence, and when the data is executable but no early warning plan exists, the analyzed data is transmitted to the AIpark platform.
Preferably, in the step (3), the AIpark platform performs deep learning on the analyzed data, and the data related to the emergency management process is subjected to deep learning.
Preferably, in the step (4), the optimized emergency management process is subjected to simulation by the AIpark platform, a target early warning plan is compiled for each emergency, and the optimized emergency management process is uploaded to the cloud.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, data collected by the front-end equipment is transmitted and collected, comprehensive data perception with high frequency, high space-time precision and multiple dimensions is realized, the AIpark platform can organize inquiry and backtrack in a organized and structured manner for visual analysis, the AIpark platform analyzes the collected data based on large data accumulation and artificial intelligence, when the data can be executed, a target early warning plan is transmitted to the front-end intelligent equipment through the AIpark platform, and meanwhile, the front-end intelligent equipment sends out an early warning signal, so that an active early warning scheme is realized.
2. According to the invention, the AIpark platform analyzes the acquired data based on big data accumulation and artificial intelligence, when the data is executable but no early warning plan exists, the analyzed data is transmitted to the AIpark platform, the AIpark platform continuously and deeply learns the analyzed data and unknown conditions to optimize the emergency management process, the AIpark platform carries out simulation on the optimized emergency management process, the most reasonable early warning plan is compiled for each emergency condition, and the optimized emergency management process is uploaded to the cloud.
Drawings
Fig. 1 is a schematic structural diagram of a work flow chart of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "front", "rear", "both ends", "one end", "the other end", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it is to be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "connected," and the like are to be construed broadly, such as "connected," which may be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Referring to fig. 1, an embodiment of the present invention provides a method for determining an early warning scheme, which includes the following steps: (1) the method comprises the steps that (1) data are collected by front-end intelligent equipment, (2) an AIpark platform analyzes the data based on big data accumulation and artificial intelligence to determine an early warning plan, (3) unknown conditions are learned, and an emergency management process is optimized, and (4) simulation is carried out on the emergency management process; in the step (1), data are collected through a front-end intelligent device, and the collected data are uploaded to an AIpark platform; in the step (2), the acquired data is analyzed through the AIpark platform based on big data accumulation and artificial intelligence, and when the data is unexecutable, the front-end intelligent equipment does not execute any operation; in the step (2), the acquired data are analyzed based on big data accumulation and artificial intelligence through the AIpark platform, when the data can be executed, the target early warning plan is transmitted to the front-end intelligent device through the AIpark platform, and meanwhile, the front-end intelligent device sends out an early warning signal; in the step (2), the acquired data is analyzed through the AIpark platform based on big data accumulation and artificial intelligence, and when the data is executable but no early warning plan exists, the analyzed data is transmitted to the AIpark platform; in the step (3), the analyzed data is subjected to deep learning on the data related to the emergency management process through the AIpark platform; and (4) performing simulation on the optimized emergency management process through the AIpark platform, compiling a target early warning plan for each emergency, and uploading the optimized emergency management process to the cloud.
Example 1:
analyzing the collected data based on big data accumulation and artificial intelligence through an AIpark platform, and when the data is unexecutable;
(1) the front-end intelligent equipment does not execute any operation
Example 2:
analyzing the collected data based on big data accumulation and artificial intelligence through an AIpark platform, and when the data is executable;
(1) transmitting the most reasonable early warning plan to the front-end intelligent equipment through the AIpark platform;
(2) simultaneously, the front-end intelligent equipment sends out early warning signals
Example 3:
analyzing the collected data based on big data accumulation and artificial intelligence through an AIpark platform, and when the data is executable but no early warning plan exists;
(1) transmitting the analyzed data to an AIpark platform;
(2) performing deep learning on the analyzed data through an AIpark platform, wherein the deep learning is performed on the data related to the emergency management process;
(3) and performing simulation on the optimized emergency management flow through the AIpark platform, compiling a target early warning plan for each emergency, and uploading the optimized emergency management flow to the cloud.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (7)
1. A method for determining an early warning scheme is characterized by comprising the following steps: the method comprises the following working steps:
(1) the front-end intelligent equipment collects data;
(2) the AIpark platform analyzes data based on big data accumulation and artificial intelligence to determine an early warning plan;
(3) learning unknown conditions, optimizing emergency management processes,
(4) and carrying out simulation on the emergency management process.
2. The warning scheme determination method of claim 1, wherein: in the step (1), data are acquired through the front-end intelligent equipment, and the data acquired by the front-end equipment are transmitted and acquired.
3. The warning scheme determination method of claim 1, wherein: in the step (2), the acquired data is analyzed based on big data accumulation and artificial intelligence through the AIpark platform, and when the data is not executable, the front-end intelligent device does not execute any operation.
4. The warning scheme determination method of claim 1, wherein: in the step (2), the acquired data are analyzed through the AIpark platform based on big data accumulation and artificial intelligence, when the data can be executed, the target early warning plan is transmitted to the front-end intelligent device through the AIpark platform, and meanwhile, the front-end intelligent device sends out an early warning signal.
5. The warning scheme determination method of claim 1, wherein: in the step (2), the acquired data are analyzed through the AIpark platform based on big data accumulation and artificial intelligence, and when the data are executable but no early warning plan exists, the analyzed data are transmitted to the AIpark platform.
6. The warning scheme determination method of claim 1, wherein: in the step (3), the analyzed data is subjected to deep learning on the data related to the emergency management process through the AIpark platform.
7. The warning scheme determination method of claim 1, wherein: and (4) performing simulation on the optimized emergency management process through the AIpark platform, compiling a target early warning plan for each emergency, and uploading the optimized emergency management process to the cloud.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111114815.9A CN113793051A (en) | 2021-09-23 | 2021-09-23 | Early warning scheme determination method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111114815.9A CN113793051A (en) | 2021-09-23 | 2021-09-23 | Early warning scheme determination method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113793051A true CN113793051A (en) | 2021-12-14 |
Family
ID=79184139
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111114815.9A Pending CN113793051A (en) | 2021-09-23 | 2021-09-23 | Early warning scheme determination method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113793051A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100049485A1 (en) * | 2008-08-20 | 2010-02-25 | International Business Machines Corporation | System and method for analyzing effectiveness of distributing emergency supplies in the event of disasters |
| CN103700054A (en) * | 2013-12-10 | 2014-04-02 | 中国地质大学武汉 | Sudden-onset geological disaster emergency plan digitization system |
| CN108805441A (en) * | 2018-06-06 | 2018-11-13 | 广西桂冠电力股份有限公司 | power emergency command system |
| CN111626636A (en) * | 2020-06-05 | 2020-09-04 | 成都格理特电子技术有限公司 | Industrial safety emergency management platform based on big data analysis technology |
| CN112541656A (en) * | 2020-11-11 | 2021-03-23 | 同方股份有限公司 | Intelligent security integrated platform with risk potential prediction capability |
-
2021
- 2021-09-23 CN CN202111114815.9A patent/CN113793051A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100049485A1 (en) * | 2008-08-20 | 2010-02-25 | International Business Machines Corporation | System and method for analyzing effectiveness of distributing emergency supplies in the event of disasters |
| CN103700054A (en) * | 2013-12-10 | 2014-04-02 | 中国地质大学武汉 | Sudden-onset geological disaster emergency plan digitization system |
| CN108805441A (en) * | 2018-06-06 | 2018-11-13 | 广西桂冠电力股份有限公司 | power emergency command system |
| CN111626636A (en) * | 2020-06-05 | 2020-09-04 | 成都格理特电子技术有限公司 | Industrial safety emergency management platform based on big data analysis technology |
| CN112541656A (en) * | 2020-11-11 | 2021-03-23 | 同方股份有限公司 | Intelligent security integrated platform with risk potential prediction capability |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN116796907A (en) | Water environment dynamic monitoring system and method based on Internet of things | |
| CN109032099A (en) | Engineering machinery assemble production line online awareness system | |
| CN109490713A (en) | A kind of method and system moving inspection and interactive diagnosis for cable run | |
| CN110046834B (en) | Workplace harmful gas health risk quantitative evaluation system and evaluation method thereof | |
| CN113112635B (en) | Conventional inspection system for intelligent equipment | |
| CN116185757B (en) | Intelligent monitoring system for energy consumption of machine room | |
| CN115499521A (en) | Multi-channel multi-interface protocol digital acquisition control system for bottom layer equipment | |
| CN116720324A (en) | Traction substation key equipment fault early warning method and system based on prediction model | |
| CN117236930A (en) | 5G and artificial intelligence based highway bridge comprehensive monitoring system | |
| CN112988843B (en) | SMT chip mounter fault management and diagnosis system based on SQL Server database | |
| CN201017232Y (en) | Industry process non-linearity failure diagnosis device based on fisher | |
| CN113065798A (en) | Safety production risk early warning method and system | |
| CN116054416B (en) | Intelligent monitoring operation and maintenance management system based on Internet of things | |
| CN113793051A (en) | Early warning scheme determination method | |
| CN101714190A (en) | Role diagnostic system for bridge structural state | |
| CN115239071A (en) | Industrial production environment risk assessment system based on big data | |
| CN113052499B (en) | Power infrastructure dumb resource management method, device, terminal and medium | |
| CN111082987B (en) | Ubiquitous power Internet of things-oriented wiring network operation and maintenance system and method | |
| CN115470657A (en) | Boiler operation management system based on electric automation | |
| CN115438093A (en) | Power communication equipment fault judgment method and detection system | |
| CN114584585A (en) | Industrial equipment self-diagnosis system and method based on Internet of things | |
| CN113593069A (en) | Intelligent inspection system for mathematics twin | |
| CN117768013B (en) | Intelligent operation and maintenance system of satellite ground station link | |
| CN114756026B (en) | Inspection control system for experimental environment safety inspection | |
| CN114520543B (en) | Operation and maintenance system for secondary equipment |
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 |