CN112950905B - Gas station early warning system and method based on Internet of things - Google Patents
Gas station early warning system and method based on Internet of things Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 24
- 239000000463 material Substances 0.000 claims abstract description 126
- 238000012544 monitoring process Methods 0.000 claims abstract description 35
- 238000001514 detection method Methods 0.000 claims description 12
- 238000012549 training Methods 0.000 claims description 12
- 230000005484 gravity Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 231100000279 safety data Toxicity 0.000 claims description 6
- 238000012795 verification Methods 0.000 claims description 6
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000004880 explosion Methods 0.000 description 2
- 239000007788 liquid Substances 0.000 description 2
- 230000001066 destructive effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000446 fuel Substances 0.000 description 1
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2462—Approximate or statistical queries
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- 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
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
Abstract
The invention discloses an early warning method of a gas station based on the Internet of things, which comprises the steps of identifying the materials of the gas station, wherein an identification time point is generated when the materials enter a gas station distribution place for the first time, and classifying the materials through identification information; the method comprises the steps that materials in a gas station are updated in real time through a material information updating unit, different materials correspond to different safety levels and updating rates, the updating mode is that stock and states of the materials are detected through a fixed sensor and a movable sensor, and the sensing position of the movable sensor is controlled according to material information sent by the material information updating unit; the method comprises the steps of setting a high safety level for materials which possibly have risks or are serious after the risks appear, setting a low safety level for materials which are not easy to appear at risks, setting a rule learning module, acquiring the updating speed of different types of materials by the rule learning module, and distributing the monitoring strategy of the movable sensor through the trained rule learning module.
Description
Technical Field
The invention relates to the technical field of monitoring of the Internet of things, in particular to an early warning system and an early warning method of a gas station based on the Internet of things.
Background
The internet of things is an important component of a new generation of information technology, and as the name suggests, the internet of things is the internet with connected objects, which has two meanings: firstly, the core and the foundation of the internet of things are the internet, and the internet is an extended and expanded network on the basis of the internet; secondly, the user end extends and expands to any object to carry out information exchange and communication, therefore, the definition of the internet of things is as follows: any object is connected with the Internet according to an agreed protocol through information sensing equipment such as radio frequency identification, an infrared sensor, a global positioning system, a laser scanner and the like to carry out information exchange and communication, so that a network for intelligently identifying, positioning, tracking, monitoring and managing the object is realized.
Specifically, in the monitoring and early warning range of the gas station, currently, the monitoring and early warning of the abnormal storage of the finished oil in the gas station is generally performed by a dispatcher according to the real storage data and manual experience of the finished oil in the gas station of the liquid level meter storage monitoring system. Inventory monitoring and early warning patents in other fields generally set a static or dynamic inventory early warning threshold in advance, and when the monitored inventory or the predicted inventory in a given period in the future is lower than the threshold, the early warning is triggered.
To prevent fuel cut-off events, gas stations often set a high inventory level as a warning threshold for safe inventory. However, there are nearly 10 million stations in the country, and the total amount of safety stock that all of these stations add together is extremely costly to store. The existing method for early warning the threshold value of the finished oil stock set statically and only by experience is the same as the stock monitoring and early warning in most other fields, whether the stock level is abnormal or not is judged only according to the real-time read stock, and the influence of scene elements closely related to the stock consumption speed and the stock replenishment process is not considered.
Simultaneously, filling station is because the particularity of itself, and when the storage jar takes place to leak, meets the mars, causes the conflagration, if the suppression measure is untimely, will arouse a series of chain reaction, causes bigger loss, produces the continuity explosion, and it is huge to produce shock wave strength can destroy equipment and factory building in the twinkling of an eye, and the destructive power is extremely strong. The oil tank area environment of an automobile filling station relates to the area with the most oil, the oil belongs to flammable liquid, the probability of fire and explosion accidents is high, and in case of the accidents, the consequences are quite serious, so that the possible leakage problem and the safety problem of the oil station are also needed to be noticed in the process of designing the filling station.
Disclosure of Invention
The present invention is directed to at least solving the problems of the prior art. Therefore, the invention discloses an early warning method of a gas station based on the Internet of things, which comprises the following steps:
step 1, marking the materials of the gas station, wherein a marking time point is formed when the materials enter a gathering and distributing place of the gas station for the first time, and classifying the materials through marking information;
step 2, updating the materials in the gas station in real time through a material information updating unit, wherein different materials correspond to different safety levels and updating rates, and the updating mode is to detect the stock and the state (current state) of the materials through a fixed sensor and a movable sensor, wherein the sensing position of the movable sensor is controlled according to the material information sent by the material information updating unit;
step 3, setting a high safety level for materials which may have risks or have serious risks after the risks and setting a low safety level for materials which are not easy to have risks, setting a rule learning module, acquiring the updating speed of different types of materials by the rule learning module, selecting a material updating state with a preset time length as a training set, taking the real material consumption state as a verification set parameter, and training the rule learning module;
and 4, distributing the monitoring strategy of the movable sensor through the trained rule learning module.
The embodiment of the invention further discloses that the fixed sensor, the movable sensor electronic vision detection unit and the temperature and humidity detection unit compare data with material safety data in real time through an internet of things analysis center to obtain a maximum risk value of material safety and a minimum storage value of material use.
The embodiment of the invention further discloses that the gas station is provided with a data processing center, and the material safety condition and the storage condition of the current gas station are locally analyzed, wherein the local analysis is the change of the monitoring strategy through a preset rule learning model.
The embodiment of the invention further discloses that the monitoring strategy comprises the steps of controlling the mobile sensor to change the monitoring position and making the change of the monitoring center of gravity of the materials which are changing and are possibly changing at the future time.
The embodiment of the invention further discloses that when the data of the analysis center of the internet of things is inconsistent with the locally analyzed prediction data, the prediction data is compared, if the data is within the preset range, the control is not performed according to the locally analyzed prediction data, and if the data exceeds the preset range, an early warning is given out and the locally analyzed data is uploaded.
The invention also provides an early warning system of the gas station based on the Internet of things, which comprises a marking module, wherein the marking module is used for marking the material of the gas station, the marking time point is generated when the material enters the gas station distribution place for the first time, and the material is classified according to the marking information;
the system comprises a material information updating unit, a data processing unit and a data processing unit, wherein the material information updating unit is used for updating materials in a gas station in real time, different materials correspond to different safety levels and updating rates, and the updating mode is that stock and state (current state) of the materials are detected by a fixed sensor and a movable sensor, wherein the sensing position of the movable sensor is controlled according to the material information sent by the material information updating unit;
the safety risk management unit is used for setting a high safety level for materials which possibly have risks or have serious risks after the risks and setting a low safety level for materials which are not easy to generate risks, and is provided with a rule learning module, the rule learning module is used for acquiring the updating speed of different types of materials, selecting the material updating state with a preset time length as a training set, and training the rule learning module by using the real material consumption state as a verification set parameter;
and the monitoring strategy management unit distributes the monitoring strategy of the movable sensor through the trained rule learning module.
The embodiment of the invention further discloses that the fixed sensor, the movable sensor electronic vision detection unit and the temperature and humidity detection unit compare data with material safety data in real time through an internet of things analysis center to obtain a maximum risk value of material safety and a minimum storage value of material use.
The invention further discloses that the early warning system further comprises a data processing center, wherein the data processing center is arranged at the local position of the gas station and is used for locally analyzing the material safety condition and the storage condition of the current gas station, and the local analysis is that the monitoring strategy is changed through a preset rule learning model.
The embodiment of the invention further discloses that the monitoring strategy comprises the steps of controlling the mobile sensor to change the monitoring position and making the change of the monitoring center of gravity of the materials which are changing and are possibly changing at the future time.
The embodiment of the invention further discloses that the information calibration unit compares the predicted data when the data of the analysis center of the internet of things is inconsistent with the predicted data of the local analysis, does not control according to the predicted data of the local analysis if the data of the analysis center of the internet of things is within a preset range, and sends out early warning and uploads the local analysis data if the data of the analysis center of the internet of things is beyond the preset range.
Drawings
The invention will be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. In the drawings, like reference numerals designate corresponding parts throughout the different views.
Fig. 1 is a flowchart of an early warning method of a gas station based on the internet of things according to the present invention.
Detailed Description
Example one
As shown in fig. 1, the embodiment discloses an early warning method for a gas station based on the internet of things, which includes:
step 1, marking the material of the gas station, wherein the marking time point is when the material enters the gas station distribution place for the first time,
classifying the materials through the identification information;
step 2, updating the materials in the gas station in real time through a material information updating unit, wherein different materials correspond to different safety levels and updating rates, and the updating mode is to detect the stock and the state (current state) of the materials through a fixed sensor and a movable sensor, wherein the sensing position of the movable sensor is controlled according to the material information sent by the material information updating unit;
step 3, setting a high safety level for materials which may have risks or have serious risks after the risks and setting a low safety level for materials which are not easy to have risks, setting a rule learning module, acquiring the updating speed of different types of materials by the rule learning module, selecting a material updating state with a preset time length as a training set, taking the real material consumption state as a verification set parameter, and training the rule learning module;
and 4, distributing the monitoring strategy of the movable sensor through the trained rule learning module.
The embodiment of the invention further discloses that the fixed sensor, the movable sensor electronic vision detection unit and the temperature and humidity detection unit compare data with material safety data in real time through an internet of things analysis center to obtain a maximum risk value of material safety and a minimum storage value of material use.
The embodiment of the invention further discloses that the gas station is provided with a data processing center, and the material safety condition and the storage condition of the current gas station are locally analyzed, wherein the local analysis is the change of the monitoring strategy through a preset rule learning model.
The embodiment of the invention further discloses that the monitoring strategy comprises the steps of controlling the mobile sensor to change the monitoring position and making the change of the monitoring center of gravity of the materials which are changing and are possibly changing at the future time.
The embodiment of the invention further discloses that when the data of the analysis center of the internet of things is inconsistent with the locally analyzed prediction data, the prediction data is compared, if the data is within the preset range, the control is not performed according to the locally analyzed prediction data, and if the data exceeds the preset range, an early warning is given out and the locally analyzed data is uploaded.
Example 2
The embodiment describes the inventive concept of the invention from the hardware perspective, and further provides an early warning system of a gas station based on the internet of things, wherein the early warning system comprises a marking module, the gas station supplies are marked through the marking module, a marking time point occurs when the supplies enter a gas station distribution place for the first time, and the supplies are classified through marking information;
the system comprises a material information updating unit, a data processing unit and a data processing unit, wherein the material information updating unit is used for updating materials in a gas station in real time, different materials correspond to different safety levels and updating rates, and the updating mode is that stock and state (current state) of the materials are detected by a fixed sensor and a movable sensor, wherein the sensing position of the movable sensor is controlled according to the material information sent by the material information updating unit;
the safety risk management unit is used for setting a high safety level for materials which possibly have risks or have serious risks after the risks and setting a low safety level for materials which are not easy to generate risks, and is provided with a rule learning module, the rule learning module is used for acquiring the updating speed of different types of materials, selecting the material updating state with a preset time length as a training set, and training the rule learning module by using the real material consumption state as a verification set parameter;
and the monitoring strategy management unit distributes the monitoring strategy of the movable sensor through the trained rule learning module.
The embodiment of the invention further discloses that the fixed sensor, the movable sensor electronic vision detection unit and the temperature and humidity detection unit compare data with material safety data in real time through an internet of things analysis center to obtain a maximum risk value of material safety and a minimum storage value of material use.
The invention further discloses that the early warning system further comprises a data processing center, wherein the data processing center is arranged at the local position of the gas station and is used for locally analyzing the material safety condition and the storage condition of the current gas station, and the local analysis is that the monitoring strategy is changed through a preset rule learning model.
The embodiment of the invention further discloses that the monitoring strategy comprises the steps of controlling the mobile sensor to change the monitoring position and making the change of the monitoring center of gravity of the materials which are changing and are possibly changing at the future time.
The embodiment of the invention further discloses that the information calibration unit compares the predicted data when the data of the analysis center of the internet of things is inconsistent with the predicted data of the local analysis, does not control according to the predicted data of the local analysis if the data of the analysis center of the internet of things is within a preset range, and sends out early warning and uploads the local analysis data if the data of the analysis center of the internet of things is beyond the preset range.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Although the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the invention. It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention. The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (10)
1. The early warning method of the gas station based on the Internet of things is characterized by comprising the following steps:
step 1, marking the materials of the gas station, wherein a marking time point is formed when the materials enter a gathering and distributing place of the gas station for the first time, and classifying the materials through marking information;
step 2, updating the materials in the gas station in real time through a material information updating unit, wherein different materials correspond to different safety levels and updating rates, the updating mode is that stock and states of the materials are detected through a fixed sensor and a movable sensor, the states are current states, and the sensing position of the movable sensor is controlled according to the material information sent by the material information updating unit;
step 3, setting a high safety level for materials which may have risks or have serious risks after the risks and setting a low safety level for materials which are not easy to have risks, setting a rule learning module, acquiring the updating speed of different types of materials by the rule learning module, selecting a material updating state with a preset time length as a training set, taking the real material consumption state as a verification set parameter, and training the rule learning module;
and 4, distributing the monitoring strategy of the movable sensor through the trained rule learning module.
2. The Internet of things-based gas station early warning method as claimed in claim 1, wherein the fixed sensor, the movable sensor electronic vision detection unit and the temperature and humidity detection unit compare data with material safety data in real time through an Internet of things analysis center to obtain a maximum risk value of material safety and a minimum storage value of material use.
3. The internet of things-based gas station early warning method as claimed in claim 2, wherein the gas station is provided with a data processing center for locally analyzing the material safety condition and the storage condition of the current gas station, and the local analysis is to change the monitoring strategy through a preset rule learning model.
4. The method as claimed in claim 3, wherein the monitoring strategy comprises controlling the mobile sensor to change the monitoring position, and making a change of the monitoring center of gravity for the material which is changing and may change in future time.
5. The internet of things-based gas station early warning method as claimed in claim 2, wherein when the data of the internet of things analysis center is inconsistent with the locally analyzed prediction data, the prediction data is compared, if the data is within a preset range, the control is not performed according to the locally analyzed prediction data, and if the data is beyond the preset range, early warning is given out and the locally analyzed data is uploaded.
6. The early warning system of the gas station based on the Internet of things is characterized by comprising a marking module, wherein the marking module is used for marking the materials of the gas station, marking time points occur when the materials enter a gas station distribution place for the first time, and the materials are classified through marking information;
the system comprises a material information updating unit, a data processing unit and a data processing unit, wherein the material information updating unit is used for updating materials in a gas station in real time, different materials correspond to different safety levels and updating rates, the updating mode is that stock and states of the materials are detected through a fixed sensor and a movable sensor, the states are current states, and the sensing position of the movable sensor is controlled according to material information sent by the material information updating unit;
the safety risk management unit is used for setting a high safety level for materials which possibly have risks or have serious risks after the risks and setting a low safety level for materials which are not easy to generate risks, and is provided with a rule learning module, the rule learning module is used for acquiring the updating speed of different types of materials, selecting the material updating state with a preset time length as a training set, and training the rule learning module by using the real material consumption state as a verification set parameter;
and the monitoring strategy management unit distributes the monitoring strategy of the movable sensor through the trained rule learning module.
7. The Internet of things-based gas station early warning system as claimed in claim 6, wherein the fixed sensor, the movable sensor electronic vision detection unit and the temperature and humidity detection unit compare data with material safety data in real time through an Internet of things analysis center to obtain a maximum risk value of material safety and a minimum storage value of material use.
8. The internet-of-things-based gas station early warning system as claimed in claim 7, further comprising a data processing center, wherein the data processing center is disposed locally at the gas station and locally analyzes the material security and storage of the current gas station, and the local analysis is a change of the monitoring policy through a preset rule learning model.
9. The internet of things-based gasoline station early warning system as claimed in claim 8, wherein the monitoring strategy comprises controlling the mobile sensor to implement a change of monitoring location, making a change of monitoring center of gravity for materials that are changing and may change at a future time.
10. The internet of things-based gas station early warning system as claimed in claim 7, wherein the information calibration unit compares the predicted data when the data passing through the internet of things analysis center is inconsistent with the predicted data of the local analysis, does not control according to the predicted data of the local analysis if the data is within a preset range, and sends out an early warning and uploads the local analysis data if the data is beyond the preset range.
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| CN114694420B (en) * | 2022-03-23 | 2024-01-12 | 合肥置顶信息技术有限公司 | Civil aviation weather forecast making and publishing system and method capable of intelligently correcting errors |
| CN115098227B (en) * | 2022-08-24 | 2022-12-27 | 中诚华隆计算机技术有限公司 | Method and device for updating dynamic information of security equipment |
Family Cites Families (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7663502B2 (en) * | 1992-05-05 | 2010-02-16 | Intelligent Technologies International, Inc. | Asset system control arrangement and method |
| AU5050000A (en) * | 1999-05-07 | 2000-11-21 | Safety Adherence Technology (Pty) Ltd | Surveillance system |
| JP2005156541A (en) * | 2003-10-30 | 2005-06-16 | Tsuuden:Kk | Liquid leakage sensor and liquid leakage sensing system |
| EP1600351B1 (en) * | 2004-04-01 | 2007-01-10 | Heuristics GmbH | Method and system for detecting defects and hazardous conditions in passing rail vehicles |
| US8214139B2 (en) * | 2008-01-25 | 2012-07-03 | Garmin Switzerland Gmbh | Position source selection |
| CN101533551B (en) * | 2009-04-03 | 2010-11-24 | 中国安全生产科学研究院 | Gas station security monitoring pre-warning and emergency management system and method thereof |
| KR20150019230A (en) * | 2013-08-13 | 2015-02-25 | 주식회사 에스원 | Method and apparatus for tracking object using multiple camera |
| CN103592423A (en) * | 2013-10-18 | 2014-02-19 | 中国石油化工股份有限公司 | Gas station underground storage gasoline tank blasting simulation device |
| KR101659558B1 (en) * | 2014-08-29 | 2016-09-23 | 중소기업은행 | Monitoring system of gas fuel container |
| CN104994357B (en) * | 2015-07-23 | 2019-08-23 | 杨珊珊 | A kind of the image information update method and more new system of monitoring area |
| CN106501451B (en) * | 2016-10-21 | 2018-10-19 | 中国科学院上海高等研究院 | A kind of disposition optimization method, system and the server of gas sensor |
| EP3662331A4 (en) * | 2017-08-02 | 2021-04-28 | Strong Force Iot Portfolio 2016, LLC | Methods and systems for detection in an industrial internet of things data collection environment with large data sets |
| US10322802B1 (en) * | 2017-09-07 | 2019-06-18 | Amazon Technologies, Inc. | Deployable sensors |
| CN108490889B (en) * | 2018-02-08 | 2019-09-10 | 浙江大学 | The safe big data data source method for generation of intelligent plant and device based on TE model |
| CN108876015B (en) * | 2018-05-22 | 2021-03-26 | 大连理工大学 | Scene-based gas station finished product oil inventory dynamic monitoring and early warning method in Internet of things environment |
| CN109377043A (en) * | 2018-10-17 | 2019-02-22 | 深圳市中电数通智慧安全科技股份有限公司 | A kind of dangerous substances managing and control system |
| CN109738365B (en) * | 2018-12-26 | 2021-10-01 | 天津大学 | Gas leakage source searching method based on TDLAS sensor |
| US20200334524A1 (en) * | 2019-04-17 | 2020-10-22 | Here Global B.V. | Edge learning |
| DE102019217225A1 (en) * | 2019-11-07 | 2021-05-12 | Robert Bosch Gmbh | Method for training a machine learning system for an object recognition device |
| CN111071978B (en) * | 2019-12-23 | 2021-10-15 | 航天科技控股集团股份有限公司 | Oil gun verification information input system and method based on position information |
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