CN112305943A - Safety early warning method and system based on intelligent multi-meter integrated platform - Google Patents

Safety early warning method and system based on intelligent multi-meter integrated platform Download PDF

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Publication number
CN112305943A
CN112305943A CN202011034731.XA CN202011034731A CN112305943A CN 112305943 A CN112305943 A CN 112305943A CN 202011034731 A CN202011034731 A CN 202011034731A CN 112305943 A CN112305943 A CN 112305943A
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China
Prior art keywords
meters
user
data
time
real
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Inventor
赵玉莲
严婷
李光海
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Shenzhen Mintai Intelligent Technology Co ltd
Shenzhen Topband Software Technology Co ltd
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Shenzhen Mintai Intelligent Technology Co ltd
Shenzhen Topband Software Technology Co ltd
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Priority to CN202011034731.XA priority Critical patent/CN112305943A/en
Publication of CN112305943A publication Critical patent/CN112305943A/en
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00Programme-control systems
    • G05B19/02Programme-control systems electric
    • G05B19/04Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01DMEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
    • G01D18/00Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00

Abstract

The invention relates to a safety early warning method and a system based on an intelligent multi-table-in-one platform, which comprises the following steps: acquiring real-time data of a plurality of meters; comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging whether the real-time data of the plurality of meters is abnormal or not according to the comparison result; if yes, outputting early warning information and/or outputting a closing control signal. According to the invention, the use data of the life of the user is acquired, the behavior habit curve of the user is generated based on the use data, whether the user is abnormal or not is accurately judged based on the behavior habit curve, and the user is timely warned when the user is abnormal, so that the life and property safety of the user is ensured. On the other hand, the system can also inform relevant departments of maintenance in time, so that more convenience is brought to the life of the user.

Description

Safety early warning method and system based on intelligent multi-meter integrated platform
Technical Field
The invention relates to the technical field of intelligent interconnection, in particular to a safety early warning method and system based on an intelligent multi-table-in-one platform.
Background
Along with the improvement of the life quality of people, people have higher requirements on the safety, comfort and intelligent degree of the living environment. The safety of gas, water and electricity is more and more paid attention by people.
However, the existing system for alarming for abnormal gas, water and electricity consumption or leakage is complex, high in manufacturing cost, high in debugging difficulty and high in management, operation and maintenance cost, so that the system is difficult to popularize and poor in practicability.
Disclosure of Invention
The invention aims to solve the technical problem of providing a safety early warning method and system based on an intelligent multi-table-in-one platform aiming at the defects in the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: a safety early warning method based on an intelligent multi-table-in-one platform is constructed, and comprises the following steps:
acquiring real-time data of a plurality of meters;
comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging whether the real-time data of the plurality of meters is abnormal or not according to the comparison result;
if yes, outputting early warning information and/or outputting a closing control signal.
Preferably, the acquiring real-time data of the plurality of gauges includes:
acquiring a plurality of table historical data;
and processing the historical data of the plurality of tables to obtain the behavior habit curve of the user.
Preferably, the plurality of table history data includes: the historical data of a plurality of tables in spring working days, the historical data of a plurality of tables in summer holidays, the historical data of a plurality of tables in summer working days, the historical data of a plurality of tables in summer holidays, the historical data of a plurality of tables in autumn working days, the historical data of a plurality of tables in autumn holidays, the historical data of a plurality of tables in winter working days, and the historical data of a plurality of tables in winter holidays;
the processing the historical data of the plurality of tables to obtain the user behavior habit curve comprises the following steps:
respectively processing the historical data of the plurality of meters on spring working days and the historical data of the plurality of meters on summer holidays to obtain behavior habit curves of the users on spring working days and behavior habit curves of the users on spring holidays;
respectively processing the historical data of the plurality of meters on summer workdays and the historical data of the plurality of meters on summer festivals and holidays to obtain behavior habit curves of the user on summer workdays and behavior habit curves of the user on summer festivals and holidays;
respectively processing historical data of the plurality of meters on the autumn workdays and historical data of the plurality of meters on the autumn holidays to obtain behavior habit curves of the users on the autumn workdays and behavior habit curves of the users on the autumn holidays;
and respectively processing the historical data of the plurality of meters on the working days in winter and the historical data of the plurality of meters on holidays in winter to obtain the behavior habit curve of the user on the working days in winter and the behavior habit curve of the user on the holidays in winter.
Preferably, the comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and determining whether the plurality of meters are abnormal according to the comparison result includes:
and comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging that the real-time data is abnormal if the difference between the real-time data and the behavior habit curve of the user is larger than a preset range.
Preferably, the method further comprises:
if the judgment is abnormal, judging whether the use data of the plurality of meters meet the conditions according to the real-time data of the plurality of meters;
if yes, judging that the user normally uses the system;
if not, the judgment is abnormal.
Preferably, whether the usage data of the plurality of tables satisfies a condition includes:
if two or more tables in the plurality of tables have usage data, the condition is satisfied; if one of the tables has usage data, the condition is satisfied.
Preferably, the method further comprises:
if the comparison result is abnormal, acquiring abnormal time;
comparing the abnormal time with preset time;
and if the abnormal time is greater than the preset time, outputting a closing control signal and outputting early warning information.
Preferably, the method further comprises:
if the judgment result is abnormal, outputting notification information and acquiring abnormal time;
judging whether returned normal use information is received or not;
if yes, no action is executed;
if not, judging whether the abnormal time is greater than the preset time or not;
and if the abnormal time is greater than the preset time, outputting a closing control signal and outputting early warning information.
Preferably, the method further comprises:
after outputting a closing control signal, judging whether a request for opening a valve is received;
if yes, a control signal for opening the valve is output.
The invention also provides a safety early warning system based on the intelligent multi-table-in-one platform, which comprises: the system comprises a plurality of meters, an intelligent multi-meter integrated platform and a terminal;
the plurality of meters are used for acquiring data in real time and outputting a plurality of meters real-time data;
the terminal receives early warning information and notification information output by the intelligent multi-meter integrated platform and receives a valve opening request input by a user;
the intelligent multi-meter integrated platform is communicated with the meters and the terminal and is used for:
acquiring real-time data of a plurality of meters;
comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging whether the real-time data of the plurality of meters is abnormal or not according to the comparison result;
if yes, outputting early warning information and/or outputting a closing control signal.
The safety early warning method and the system based on the intelligent multi-meter integrated platform have the following beneficial effects that: the method comprises the following steps: acquiring real-time data of a plurality of meters; comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging whether the real-time data of the plurality of meters is abnormal or not according to the comparison result; if yes, outputting early warning information and/or outputting a closing control signal. According to the invention, the use data of the life of the user is acquired, the behavior habit curve of the user is generated based on the use data, whether the user is abnormal or not is accurately judged based on the behavior habit curve, and the user is timely warned when the user is abnormal, so that the life and property safety of the user is ensured. On the other hand, the system can also inform relevant departments of maintenance in time, so that more convenience is brought to the life of the user.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a schematic flow chart of a security early warning method based on an intelligent multi-expression platform according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a summer working day behavior curve provided by an embodiment of the invention;
FIG. 3 is a schematic diagram of a behavior curve of a holiday in summer according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a behavior curve of a season working day provided by an embodiment of the present invention;
FIG. 5 is a diagram illustrating behavior curves of holidays in summer according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of a security early warning system based on an intelligent multi-expression platform according to an embodiment of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
In order to solve the problems of complex warning system, high manufacturing cost, high debugging difficulty and high operation and maintenance cost of abnormal gas, electricity and water consumption or leakage at present, the invention provides a safety early warning method and a safety early warning system based on an intelligent multi-meter integrated platform, which can generate a corresponding user behavior habit curve according to the daily water, electricity and gas consumption data of a user, thereby accurately judging whether the abnormal condition exists based on the behavior habit curve, timely warning and informing the user when the abnormal condition occurs, and ensuring the safety of lives and properties of the user. On the other hand, the system can also inform relevant departments of maintenance in time, so that more convenience is brought to the life of the user. Furthermore, the invention provides a safety alarm based on the intelligent multi-meter-in-one platform, does not need to maintain an additional system, can realize real-time monitoring and abnormal alarm of each using device only by utilizing the existing multi-meter-in-one platform, has low cost, simple operation and low operation and maintenance cost, can support remote control, and can greatly save manpower and material resources.
Referring to fig. 1, fig. 1 is a safety pre-warning method based on an intelligent multi-expression platform according to an embodiment of the present invention.
As shown in fig. 1, the method may include:
and step S101, acquiring real-time data of a plurality of tables.
In some embodiments, the plurality of meters includes, but is not limited to, electricity meters, water meters, gas meters, heating meters, and the like.
The plurality of meter real-time data include, but are not limited to, electric meter real-time data, water meter real-time data, gas meter real-time data, heating meter real-time data, and the like.
Further, before obtaining the plurality of real-time data of the meter, the method comprises: acquiring a plurality of table historical data; and processing the historical data of the plurality of tables to obtain a user behavior habit curve.
In some embodiments, the plurality of table history data comprises: the historical data of a plurality of tables in spring working days, the historical data of a plurality of tables in summer holidays, the historical data of a plurality of tables in summer working days, the historical data of a plurality of tables in summer holidays, the historical data of a plurality of tables in autumn working days, the historical data of a plurality of tables in autumn holidays, the historical data of a plurality of tables in winter working days, and the historical data of a plurality of tables in winter holidays.
Wherein, processing a plurality of table historical data, and obtaining a user behavior habit curve comprises: and respectively processing the historical data of the plurality of meters on spring working days and the historical data of the plurality of meters on summer holidays to obtain behavior habit curves of the users on the spring working days and behavior habit curves of the users on the spring holidays. And respectively processing the historical data of the plurality of meters on the summer working days and the historical data of the plurality of meters on the summer holidays to obtain the behavior habit curve of the user on the summer working days and the behavior habit curve of the user on the summer holidays. And respectively processing the historical data of the plurality of meters on the autumn workdays and the historical data of the plurality of meters on the autumn holidays to obtain the behavior habit curve of the user on the autumn workdays and the behavior habit curve of the user on the autumn holidays. And respectively processing the historical data of the plurality of meters on the working days in winter and the historical data of the plurality of meters on holidays in winter to obtain the behavior habit curve of the user on the working days in winter and the behavior habit curve of the user on the holidays in winter.
In some embodiments, the user behavior habit curves are as shown in fig. 2-5. Fig. 2 is a schematic diagram of a behavior curve of a summer working day according to an embodiment of the present invention; FIG. 3 is a schematic diagram of a behavior curve of a holiday in summer according to an embodiment of the present invention; FIG. 4 is a schematic diagram of a behavior curve of a season working day provided by an embodiment of the present invention; fig. 5 is a schematic diagram of a behavior curve of a summer holiday according to an embodiment of the present invention.
As shown in fig. 2 to 5, the behavior habit curves of the user are generated according to the historical data of the user life, so that the usage habits of the user in various time periods (such as different seasons, different time dates (working days or holidays), and even 24 hours a day (including but not limited to the time of three meals a day, several times of sleeping, several times of getting up, etc.) can be inferred according to the behavior habit curves of the user.
And S102, comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging whether the data are abnormal or not according to the comparison result.
In some embodiments, comparing the real-time data of the plurality of tables with the behavior habit curve of the user, and determining whether the behavior habit curve is abnormal according to the comparison result includes: and comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging that the real-time data is abnormal if the difference between the real-time data and the behavior habit curve of the user is larger than a preset range.
Specifically, by acquiring a plurality of meter real-time data and comparing the plurality of meter real-time data with the corresponding user behavior habit curves respectively, if the difference between the real-time data and the user behavior habit curves is larger than a preset range, the abnormality can be judged. If the dosage suddenly rises, or the difference between the dosage and the curve is obviously larger than the normal value, the dosage can be judged to be abnormal.
Further, in some embodiments, in order to further determine whether the abnormality is true after determining that the abnormality is true, the present invention further includes: judging whether the use data of the plurality of meters meet the conditions or not according to the real-time data of the plurality of meters; if yes, judging that the user normally uses the system; if not, the judgment is abnormal. Wherein whether the usage data of the plurality of tables satisfies a condition includes: if two or more tables in the plurality of tables have the use data, the condition is satisfied; if one of the tables has usage data, the condition is satisfied.
Specifically, after the abnormal condition is determined, the real-time data of the plurality of meters is further compared to determine whether all the meters have the use condition, if only one meter has the use condition, the abnormal condition (such as leakage) can be determined, and if two or more meters in all the meters have the use condition at the same time, the normal use of the user is determined. For example, the current sleeping state of the user can be judged according to the behavior habit curve and the time period of the user, if the water meter real-time data show that the consumption of the electric meter is suddenly increased at the moment, the abnormality of the water meter can be preliminarily judged, at the moment, whether the electric meter, the gas meter, the heating meter and the like have real-time use data or not can be further judged, if one or two of the electric meter, the gas meter, the heating meter and the like have the real-time use data (such as the electric meter or both the electric meter and the gas have the use data), the user is judged to get up and normally use, and therefore.
And S103, if yes, outputting early warning information and/or outputting a closing control signal.
Further, in some embodiments, if the comparison result is determined to be abnormal, acquiring abnormal time; comparing the abnormal time with preset time; and if the abnormal time is greater than the preset time, outputting a closing control signal and outputting early warning information. Specifically, by acquiring the abnormal time and comparing the abnormal time with the preset time, when the abnormal time is greater than the preset time, the closing control signal is output, and the early warning information is output, so that the condition of false alarm can be further avoided, false closing can be avoided, and the accuracy is improved.
Further, in some other embodiments, if the comparison result is judged to be abnormal, outputting notification information and acquiring abnormal time; judging whether returned normal use information is received or not; if yes, no action is executed; if not, judging whether the abnormal time is greater than the preset time or not; and if the abnormal time is greater than the preset time, outputting a closing control signal and outputting early warning information. Specifically, when the user is judged to be abnormal according to the comparison result, notification information is output to notify the user, whether the user is using the system can be inquired in a manner of notifying the user, if the user returns normal use information (namely, the returned normal use information is received), the user is judged to be normally using the system, and a closing control signal is not output; if the normal use information returned by the user is not received after the preset time, the abnormal use information is judged to be abnormal, and at the moment, a closing control signal is output and early warning information is output. Can be when appearing unusually through the output control signal that closes, control corresponding valve promptly and close, avoid the abnormal conditions to take place continuously, reduce the risk, improve safety, can also effectively avoid the energy waste simultaneously.
Further, in some embodiments, the safety precaution method may further include: after outputting a closing control signal, judging whether a request for opening a valve is received; if yes, a control signal for opening the valve is output.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an alternative embodiment of the embodiments of the present invention.
As shown in fig. 6, the safety precaution system based on the intelligent multi-expression platform is provided. The safety early warning system can be used for realizing the safety early warning method based on the intelligent multi-table-in-one platform disclosed by the embodiment of the invention. Wherein, this safety precaution system based on platform is unified to many tables of intelligence can include: a plurality of meters, a platform and a terminal are unified to many tables of intelligence.
Specifically, the plurality of meters are used for collecting data in real time and outputting a plurality of meters real-time data. In some embodiments, the plurality of meters may include, but are not limited to, electricity meters, water meters, gas meters, heating meters, and the like. The electric meter, the water meter, the gas meter and the heating meter are respectively used for acquiring real-time electricity consumption, real-time water consumption, real-time gas consumption and real-time heating consumption. Accordingly, the plurality of real-time data includes: electric quantity real-time data, water quantity real-time data, gas quantity real-time data and heating gas quantity real-time data.
In some embodiments, the intelligent multi-representation platform is in communication with a plurality of representations and terminals for: acquiring real-time data of a plurality of meters; comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging whether the real-time data of the plurality of meters is abnormal or not according to the comparison result; if yes, outputting early warning information and/or outputting a closing control signal.
Specifically, as shown in fig. 6, the intelligent multi-meter-in-one platform is in communication connection with each meter (including but not limited to a water meter, an electric meter, a gas meter, a heating meter, or other meters), receives real-time data collected by each meter in real time, outputs early warning information to a terminal to notify a user when the intelligent multi-meter-in-one platform monitors abnormal use according to comparison between the real-time data and a user behavior habit curve, and outputs a closing control signal to a corresponding device valve to close the valve after the maintenance is completed (such as receiving maintenance completion information or opening a valve request), and outputs an opening control signal to a corresponding valve to control the opening of the valve.
In some embodiments, the early warning information output by the intelligent multi-expression platform can be notified to the user through the terminal. Or in other embodiments, the early warning information output by the intelligent multi-expression platform can also inform the user in a short message or telephone mode so as to inform the user to check the field condition in time and effectively prevent the abnormal condition from occurring or continuing.
In some embodiments, the terminal receives the early warning information and the notification information output by the intelligent multi-expression platform and receives a valve opening request input by a user.
Specifically, the terminal can receive and display the early warning information output by the intelligent multi-meter integrated platform, so that a user can check and master the real-time situation in time and remind the user to check the field situation, and the terminal can report whether the user timely receives and reads the early warning information or completes maintenance application information to the intelligent multi-meter integrated platform, so that information interaction between the intelligent multi-meter integrated platform and the user is completed.
Further, the terminal receives a valve opening request input by a user and sends the valve opening request input by the user to the intelligent multi-meter integration platform so as to inform the intelligent multi-meter integration platform to open the corresponding valve.
Further, as shown in fig. 6, in some embodiments, the user may also request for the opening of the valve after the completion of the maintenance by dialing the platform phone.
The intelligent multi-meter integrated platform is connected and communicated with the electric meter, the water meter, the gas meter, the heating meter and the like on the basis of the intelligent multi-meter integrated platform, the aspects of life of people are related, the life use habits (behavior habits) of users are obtained through the platform, the life work and rest rules of the users are deduced, the behavior habit curves of the users are generated, when abnormal use occurs, accurate judgment can be carried out, the users are informed in time, corresponding valves are closed, safety problems are avoided, potential safety hazards are eliminated, and the life and property safety of the users are guaranteed. On the other hand, the intelligent multi-meter integrated platform can also communicate with related maintenance departments, and the related maintenance departments are informed in time when abnormity occurs, so that more convenience is brought to the life of a user.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and are intended to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the scope of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should be covered by the claims of the present invention.

Claims (10)

1. A safety early warning method based on an intelligent multi-table-in-one platform is characterized by comprising the following steps:
acquiring real-time data of a plurality of meters;
comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging whether the real-time data of the plurality of meters is abnormal or not according to the comparison result;
if yes, outputting early warning information and/or outputting a closing control signal.
2. The intelligent multi-representation platform-based safety early warning method according to claim 1, wherein the obtaining of the real-time data of the plurality of representations comprises:
acquiring a plurality of table historical data;
and processing the historical data of the plurality of tables to obtain the behavior habit curve of the user.
3. The intelligent multi-expression platform-based safety precaution method according to claim 2, wherein the plurality of tables have historical data comprising: the historical data of a plurality of tables in spring working days, the historical data of a plurality of tables in summer holidays, the historical data of a plurality of tables in summer working days, the historical data of a plurality of tables in summer holidays, the historical data of a plurality of tables in autumn working days, the historical data of a plurality of tables in autumn holidays, the historical data of a plurality of tables in winter working days, and the historical data of a plurality of tables in winter holidays;
the processing the historical data of the plurality of tables to obtain the user behavior habit curve comprises the following steps:
respectively processing the historical data of the plurality of meters on spring working days and the historical data of the plurality of meters on summer holidays to obtain behavior habit curves of the users on spring working days and behavior habit curves of the users on spring holidays;
respectively processing the historical data of the plurality of meters on summer workdays and the historical data of the plurality of meters on summer festivals and holidays to obtain behavior habit curves of the user on summer workdays and behavior habit curves of the user on summer festivals and holidays;
respectively processing historical data of the plurality of meters on the autumn workdays and historical data of the plurality of meters on the autumn holidays to obtain behavior habit curves of the users on the autumn workdays and behavior habit curves of the users on the autumn holidays;
and respectively processing the historical data of the plurality of meters on the working days in winter and the historical data of the plurality of meters on holidays in winter to obtain the behavior habit curve of the user on the working days in winter and the behavior habit curve of the user on the holidays in winter.
4. The safety pre-warning method based on the intelligent multi-meter-in-one platform as claimed in claim 3, wherein the comparing the real-time data of the plurality of meters with the behavior habit curve of the user and judging whether the plurality of meters are abnormal according to the comparison result comprises:
and comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging that the real-time data is abnormal if the difference between the real-time data and the behavior habit curve of the user is larger than a preset range.
5. The intelligent multi-expression-in-one platform-based safety pre-warning method according to claim 4, further comprising:
if the judgment is abnormal, judging whether the use data of the plurality of meters meet the conditions according to the real-time data of the plurality of meters;
if yes, judging that the user normally uses the system;
if not, the judgment is abnormal.
6. The intelligent multi-expression-in-one platform-based safety precaution method according to claim 5, wherein whether the usage data of the plurality of the expressions meets the condition comprises:
if two or more tables in the plurality of tables have usage data, the condition is satisfied; if one of the tables has usage data, the condition is satisfied.
7. The intelligent multi-expression-in-one platform-based safety pre-warning method according to claim 1, further comprising:
if the comparison result is abnormal, acquiring abnormal time;
comparing the abnormal time with preset time;
and if the abnormal time is greater than the preset time, outputting a closing control signal and outputting early warning information.
8. The intelligent multi-expression-in-one platform-based safety pre-warning method according to claim 1, further comprising:
if the judgment result is abnormal, outputting notification information and acquiring abnormal time;
judging whether returned normal use information is received or not;
if yes, no action is executed;
if not, judging whether the abnormal time is greater than the preset time or not;
and if the abnormal time is greater than the preset time, outputting a closing control signal and outputting early warning information.
9. The intelligent multi-expression-in-one platform-based safety pre-warning method according to claim 1, further comprising:
after outputting a closing control signal, judging whether a request for opening a valve is received;
if yes, a control signal for opening the valve is output.
10. The utility model provides a safety precaution system based on platform is unified to many tables of intelligence which characterized in that includes: the system comprises a plurality of meters, an intelligent multi-meter integrated platform and a terminal;
the plurality of meters are used for acquiring data in real time and outputting a plurality of meters real-time data;
the terminal receives early warning information and notification information output by the intelligent multi-meter integrated platform and receives a valve opening request input by a user;
the intelligent multi-meter integrated platform is communicated with the meters and the terminal and is used for:
acquiring real-time data of a plurality of meters;
comparing the real-time data of the plurality of meters with the behavior habit curve of the user, and judging whether the real-time data of the plurality of meters is abnormal or not according to the comparison result;
if yes, outputting early warning information and/or outputting a closing control signal.
CN202011034731.XA 2020-09-27 2020-09-27 Safety early warning method and system based on intelligent multi-meter integrated platform Pending CN112305943A (en)

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