CN115681604A - Intelligent valve remote management system and method based on Internet of things - Google Patents
Intelligent valve remote management system and method based on Internet of things Download PDFInfo
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Abstract
The invention relates to the technical field of valve management. The intelligent valve remote management system comprises a data acquisition module, a data transmission module, a data analysis module and a valve control module; the data acquisition module is used for acquiring actual water consumption variation and historical data of the water consumption variation of a user in three time periods of morning, noon and evening, and acquiring a time point of a water valve in an opening state, the number of external personnel and a time period and a time length of retention of the external personnel; the data collected by the data transmission module is transmitted to a database for storage through encryption; the data analysis module analyzes the actual water use variation of the user; the valve control module is used for carrying out alarm prompt on the mobile terminal equipment and controlling the water valve to be closed; according to the invention, the actual water consumption condition of the user is monitored through the prediction model, the conditions that the water valve is not closed tightly and has a fault can be detected, and the real-time alarm is given.
Description
Technical Field
The invention relates to the technical field of valve management, in particular to an intelligent valve remote management system and method based on the Internet of things.
Background
Water resource protection has a close relationship with sustainable development, and today, one of the main fields of water resource protection zombie sustainable development is building a resource-saving society. The water resource is increasingly scarce in the world, and all countries also strengthen the protection of the water resource and improve the water utilization efficiency. The household scientific water consumption is also a global big problem, the main functions of the household water valve are to control the on-off of the water outlet of the water pipe and control the water flow of the water outlet of the water pipe, and when the water valve is not maintained and repaired for a long time, the valve made of metal materials can corrode under the spontaneous interaction with the surrounding environment. If the water valve is corroded, the water leakage condition of the water valve can occur, and the water valve can leak water all the time before a user does not detect the water leakage of the water valve; when a user goes out to rush to forget to close the water valve or the water valve is not tightly closed, the user not only needs to bear the extra cost of actual water consumption, but also wastes water resources.
Disclosure of Invention
The invention aims to provide an intelligent valve remote management system and method based on the Internet of things, and aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: an intelligent valve remote management method based on the Internet of things comprises the following specific steps:
s1-1, acquiring historical data of water consumption variation of a user in three time periods in the morning, the noon and the evening by utilizing big data, and establishing a prediction model of the water consumption variation of the three time periods in the morning, the noon and the evening according to the acquired historical data of the water consumption variation of the three time periods;
s1-2, monitoring the water consumption variation of the three time periods in the morning, the noon and the evening through a prediction model of the water consumption variation of the three time periods in the morning, the noon and the evening, and carrying out fault detection on the water valve when detecting that the difference between the actual water consumption variation of a user at the three moments in the morning, the noon and the evening and the prediction model of the water consumption variation exceeds a set threshold value;
s1-3, when the water valve is detected to be in fault, the valve management system sends an alarm prompt to the mobile terminal equipment at the first time to remind a user to search related personnel for checking and maintaining the water valve;
s1-4, when detecting that the water valve has no fault, analyzing the behavior of a user; whether a user needs water is analyzed, when the user does not need water, the water valve is detected to be in an opening state, the valve management system sends a message that the water valve is not closed to the mobile terminal device, and the water valve is closed.
Further, the specific method for collecting historical data of water consumption variation of the user in three time periods of morning, noon and evening in S1-1 to establish a prediction model of water consumption variation is as follows:
s2-1, collecting historical data of water consumption variation of the user in three time periods of morning, noon and evening, and setting the data of the water consumption variation of the user in the three time periods of morning, noon and evening as sample points (x) 1i ，y 1i )、(x 2i ，y 2i )、(x 3i ，y 3i ) I =1, 2, 3, n, n is a constant; wherein x is 1i Represents the time period during which the ith user uses water in the morning, y 1i Representing the amount of change in water usage by the ith user during the morning hours; wherein x is 2i Represents the time period of the ith user's midday water usage, y 2i Representing the amount of change in water usage by the ith user during the midday time period; x is a radical of a fluorine atom 3i Represents the water usage period at night for the ith user, y 3i Represents the amount of change in water usage by the user at the ith time during the night period; wherein y is 2i <y 1i <y 3i ；
S2-2, generating a fitting curve according to the collected historical data, and setting the fitting curve of three time periods of morning, noon and evening as y 1 ＝k 1 x 1 +b 1 、y 2 ＝k 2 x 2 +b 2 、y 3 ＝k 3 x 3 +b 3 (ii) a Let the fitting values of morning, noon and evening be
Wherein
The predicted value of the water consumption variation amount of the ith user in the morning water consumption time period,
as the ith userThe predicted value of the water usage variation in the water usage time period at noon,
a predicted value of water consumption variation for the ith user in the night water consumption time period;
the definition condition of the known sample points and the fitting curve is
Can obtain
Where k is the curve coefficient, b is the error term,
is a curve coefficient predicted value of the morning water consumption time period and the water consumption variation,
the error term prediction value is the morning water consumption time period and the water consumption variation curve;
is a curve coefficient predicted value of the water consumption time period and the water consumption variation,
the error term prediction value is the water consumption time period at noon and the water consumption variation curve;
a curve system of night water consumption time period and water consumption variationThe number of the predicted values is counted,
the error term prediction value of the water consumption time period at night and the water consumption variation curve is obtained;
s2-3, setting
Need to find k 1 And b 1 So that P is 1 Minimum where P 1 Is the sum of the squares of the residuals; is provided with
Need to find k 2 And b 2 So that P is 2 Minimum where P 2 Is the sum of the squares of the residuals; is provided with
Need to find k 3 And b 3 So that P 3 Minimum of, wherein P 3 Is the sum of the squares of the residuals;
s2-4, calculating
The same can be obtained
S2-5, calculating to obtain a fitting curve of three time periods of morning, noon and evening
Further, in the step S1-2, water usage variation amounts in three time periods, namely, in the morning, in the noon, in the evening, are monitored, and when it is detected that a difference between an actual water usage variation amount of a user at three times in the morning, in the evening and a prediction model of the water usage variation amount exceeds a set threshold, a water valve closing condition is detected; the specific method comprises the following steps:
s3-1, setting the number m of the external personnel in the house where the user is located, wherein the external personnel are located in the houseThe retention time period and the retention time length are d and t respectively; the water consumption variation of the alien substance is Y = eta 1 tm+η 2 tm+η 3 tm + δ, when d ∈ (24]Time, eta 2 ＝η 3 =0; when d ∈ (10]Time, eta 1 ＝η 3 =0; when d ∈ (16]Time, eta 1 ＝η 2 =0; eta of 2 <η 1 <η 3 ，η 2 +η 1 +η 3 =1; the number of the external personnel is the number of visitors collected through the unit electronic door, the external personnel informs a user of the visitors through the input of the house number plate through the unit electronic door, the time of the visitors is obtained when the user opens the electronic door through the control end of the unit electronic door, the face data of the external personnel at the moment is collected when the external personnel are allowed to enter the building, the leaving time of the external personnel is collected through the corridor camera according to face recognition, and the time period of residence and the stay time of the external personnel in the building are generated according to the visiting time and the leaving time of the external personnel in the database.
S3-2, comparing the prediction model of the water consumption variation with the actual water consumption of the morning water consumption time period of the user when y is 1q -y 1(i+1) >When Y is in the specification; jumping to S3-5;
s3-3, comparing the prediction model of the water use variation with the actual water use of the user in the noon water use time period when y 2q -y 2(i+1) >When Y is in the specification; skipping to S3-5;
s3-4, comparing the prediction model of the water use variation with the actual water use of the user in the noon water use time period when y 3q -y 3(i+1) >When Y is in the specification; transferring to S3-5;
and S3-5, detecting that the difference between the actual water use variation of the user at three moments in the morning, the noon and the evening and a prediction model of the water use variation exceeds a set threshold value, and performing fault detection on the water valve.
Further, in S1-4, a specific method for analyzing whether there is a water demand of a user when the water valve fails is as follows: and generating a time point of the water valve in the opening state as a set A according to the historical data, wherein when the actual time point of the opening state of the water valve does not belong to the set A, the user forgets to close the water valve.
An intelligent valve remote management system based on the Internet of things comprises a data acquisition module, a data transmission module, a data analysis module and a valve control module;
the data acquisition module is used for acquiring actual water consumption variation and historical data of the water consumption variation of a user in three time periods of morning, noon and evening, and acquiring a time point of a water valve in an opening state, the number of external personnel, and a time period and a time length during which the external personnel stay;
the data transmission module is used for acquiring historical data of actual water consumption variation of the user in three time periods of morning, noon and evening, the number of the external personnel, the detention time of the external personnel, the opening time of a water valve and the water consumption variation of the user in three time periods of morning, noon and evening and transmitting the historical data to a database for storage through encryption;
the data analysis module establishes a prediction model through historical data of water consumption variation of the user in three time periods of morning, noon and evening to analyze the water consumption variation of the user in the next three time periods of morning, noon and evening;
the valve control module is used for carrying out alarm prompt on the mobile terminal equipment and controlling the water valve to be closed;
the output end of the data acquisition module is connected with the input end of the data transmission module, the output end of the data transmission module is connected with the input end of the data analysis module, and the output end of the data analysis module is connected with the input end of the valve control module.
Further, the data acquisition module comprises an actual water consumption variation acquisition unit for three time periods of morning, noon and evening, a historical data acquisition unit for water consumption variation of three time periods of morning, noon and evening, a number of external personnel acquisition unit, a residence time acquisition unit for external personnel and a water valve start time acquisition unit; the actual water consumption variation acquisition unit of the user in the morning, the noon and the evening acquires data of actual water consumption variations of the user in the morning, the noon and the evening; the historical data acquisition unit for the water consumption variation of the three time periods in the morning, the noon and the evening acquires historical data of the water consumption variation of the user in the three time periods in the morning, the noon and the evening; the number of the external personnel acquisition unit is used for acquiring the number of the external personnel in the house where the user is located; the external person residence time acquisition unit is used for acquiring the residence time period and the residence time length of external persons in the house; the water valve opening time acquisition unit is used for acquiring the time point when the water valve is in an opening state.
Further, the data transmission module comprises a data encryption unit and a data transmission unit; the data encryption unit is used for encrypting the collected actual water consumption variation of the user in three time periods in the morning, at noon and at night, the historical data of the water consumption variation of the user in the three time periods in the morning, at noon and at night, the number of the external personnel, the detention time period and the data of the time point when the water valve is in an open state; the data transmission unit transmits the encrypted data to a database for storage.
Further, the data analysis module comprises a data storage unit, a water valve fault detection unit and a water consumption variation prediction unit; the data storage unit is used for storing the collected actual water use variation of the user in three time periods in the morning, at the noon and at the evening, historical data of the water use variation of the user in the three time periods in the morning, at the noon and at the evening, the number of external personnel, the detention time period and the time point when the water valve is in an open state; the water valve fault detection unit is used for detecting the fault of the water valve when the difference between the actual water consumption variation of a user and the predicted value of the book consumption variation exceeds a set threshold value; the water consumption variation prediction unit is used for establishing a prediction model of water consumption variation according to collected historical data of water consumption variation of the user in three time periods of morning, noon and evening.
Further, the valve control module comprises a mobile terminal alarm unit and a water valve control unit; the mobile terminal alarm unit is used for alarming the mobile terminal when a water valve is in failure or is forgotten to be closed or is not tightly closed; the water valve control unit controls the water valve to be closed when the water valve is in an open state under the condition that a user does not need water.
Compared with the prior art, the invention has the following beneficial effects: according to the method, a prediction model of the water use variation is established through historical data of the water use variation of big data collection water, whether the water valve is in failure or not is analyzed through comparison of the prediction model with the actual water use variation, an alarm prompt is sent to the mobile terminal equipment at the first time, and a user is reminded of searching related personnel to maintain and repair the water valve at the first time; according to the invention, the time point of the water valve in the opening state is analyzed through historical data, the state of the water valve is judged according to the time point of the water valve in the opening state, and the water valve is controlled, so that the waste of water resources is reduced.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of an intelligent valve remote management system based on the internet of things.
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.
Referring to fig. 1, the present invention provides a technical solution: an intelligent valve remote management method based on the Internet of things comprises the following specific steps:
s1-1, acquiring historical data of water consumption variation of a user in three time periods in the morning, the noon and the evening by utilizing big data, and establishing a prediction model of the water consumption variation of the three time periods in the morning, the noon and the evening according to the acquired historical data of the water consumption variation of the three time periods;
s1-2, monitoring the water consumption variation of the three time periods in the morning, the noon and the evening through a prediction model of the water consumption variation of the three time periods in the morning, the noon and the evening, and carrying out fault detection on the water valve when detecting that the difference between the actual water consumption variation of a user at the three moments in the morning, the noon and the evening and the prediction model of the water consumption variation exceeds a set threshold value;
s1-3, when the water valve is detected to be in fault, the valve management system sends an alarm prompt to the mobile terminal equipment at the first time to remind a user to search related personnel for checking and maintaining the water valve;
s1-4, when detecting that the water valve has no fault, analyzing the behavior of a user; and analyzing whether the user has a water demand, detecting that the water valve is in an open state when the user does not have the water demand, sending a message that the water valve is not closed to the mobile terminal equipment by the valve management system, and closing the water valve.
Further, the specific method for collecting historical data of water consumption variation of the user in three time periods of morning, noon and evening in S1-1 to establish a prediction model of water consumption variation is as follows:
s2-1, collecting historical data of water consumption variation of a user in three time periods of morning, noon and evening, and setting the data of the water consumption variation of the user in the three time periods of morning, noon and evening as sample points (x) 1i ，y 1i )、(x 2i ，y 2i )、(x 3i ，y 3i ) I =1, 2, 3, n, n is a constant; wherein x 1i Represents the time period during which the ith user uses water in the morning, y 1i Representing the amount of change in water usage by the ith user during the morning hours; wherein x 2i Represents the time period of the ith user's midday water usage, y 2i Representing the amount of change in water usage by the ith user during the midday time period; x is the number of 3i Represents the water usage period at night for the ith user, y 3i Represents the amount of change in water usage by the user at the ith time during the night period; wherein y is 2i <y 1i <y 3i ；
S2-2, generating a fitting curve according to the collected historical data, and setting the fitting curve of three time periods of morning, noon and evening as y 1 ＝k 1 x 1 +b 1 、y 2 ＝k 2 x 2 +b 2 、y 3 ＝k 3 x 3 +b 3 (ii) a Let the fitting values of morning, noon and evening be
Wherein
The predicted value of the water consumption variation amount of the ith user in the morning water consumption time period,
the predicted value of the water use variation of the ith user in the noon water use period is obtained,
a predicted value of water consumption variation for the ith user in the night water consumption time period;
the definition condition of the known sample points and the fitting curve is
Can obtain
Where k is the curve coefficient, b is the error term,
is a curve coefficient predicted value of the morning water consumption time period and the water consumption variation,
the error term prediction value is the morning water consumption time period and the water consumption variation curve;
is a curve coefficient predicted value of the water consumption time period and the water consumption change amount at noon,
the predicted value of the error term of the water consumption time period and the water consumption variation curve at noon is obtained;
is a curve coefficient predicted value of the night water use time period and the water use variation,
the error term prediction value of the water consumption time period at night and the water consumption variation curve is obtained;
s2-3, setting
Need to find k 1 And b 1 So that P is 1 Minimum where P 1 Is the sum of the squares of the residuals; is provided with
Need to find k 2 And b 2 So that P is 2 Minimum where P 2 Is the sum of the squares of the residuals; is provided with
Need to find k 3 And b 3 So that P is 3 Minimum where P 3 Is the sum of the squares of the residuals;
s2-4, calculating
The same can be obtained
S2-5, calculating to obtain a fitting curve of three time periods of morning, noon and evening
Further, in the step S1-2, the water usage variation amount in three time periods, namely, the morning, the noon and the evening is monitored, and when it is detected that the difference between the actual water usage variation amount of the user at the three moments in the morning, the noon and the evening and the prediction model of the water usage variation amount exceeds a set threshold, the closing condition of the water valve is detected; the specific method comprises the following steps:
s3-1, setting the number m of external personnel in a house where a user is located, wherein the residence time period and the residence time length of the external personnel in the house are d and t respectively; the water consumption variation of the alien substance is Y = eta 1 tm+η 2 tm+η 3 tm + δ, when d ∈ (00-10) 2 ＝η 3 =0; when d ∈ (10 1 ＝η 3 =0; when d ∈ (16 1 ＝η 2 =0; eta of 2 <η 1 <η 3 ，η 2 +η 1 +η 3 ＝1；
S3-2, comparing the prediction model of the water consumption variation with the actual water consumption of the morning water consumption time period of the user when y is 1q -y 1(i+1) >When Y is in the specification; jumping to S3-5;
s3-3, comparing the prediction model of the water use variation with the actual water use of the user in the noon water use time period when y 2q -y 2(i+1) >When Y is in the specification; jumping to S3-5;
s3-4, comparing the prediction model of the water use variation with the actual water use of the user in the noon water use time period when y 3q -y 3(i+1) >When Y is in the specification; transferring to S3-5;
and S3-5, detecting that the difference between the actual water use variation of the user at three moments in the morning, the noon and the evening and the prediction model of the water use variation exceeds a set threshold value, and carrying out fault detection on the water valve.
Further, in S1-4, a specific method for analyzing whether there is a water demand of a user when the water valve fails is as follows: and generating a time point of the water valve in the opening state as a set A according to the historical data, wherein when the actual time point of the opening state of the water valve does not belong to the set A, the user forgets to close the water valve.
An intelligent valve remote management system based on the Internet of things comprises a data acquisition module, a data transmission module, a data analysis module and a valve control module;
the data acquisition module is used for acquiring actual water consumption variation and historical data of the water consumption variation of a user in three time periods of morning, noon and evening, and acquiring a time point of a water valve in an opening state, the number of external personnel, and a time period and a time length during which the external personnel stay;
the data transmission module is used for acquiring historical data of actual water consumption variation of the user in three time periods of morning, noon and evening, the number of the external personnel, the detention time of the external personnel, the opening time of a water valve and the water consumption variation of the user in three time periods of morning, noon and evening and transmitting the historical data to a database for storage through encryption;
the data analysis module establishes a prediction model through historical data of water consumption variation of the user in three time periods of morning, noon and evening to analyze the water consumption variation of the user in the next three time periods of morning, noon and evening;
the valve control module is used for carrying out alarm prompt on the mobile terminal equipment and controlling the water valve to be closed;
the output end of the data acquisition module is connected with the input end of the data transmission module, the output end of the data transmission module is connected with the input end of the data analysis module, and the output end of the data analysis module is connected with the input end of the valve control module.
Furthermore, the data acquisition module comprises an actual water consumption variation acquisition unit for three time periods of morning, noon and evening, a historical data acquisition unit for water consumption variation of three time periods of morning, noon and evening, a number of outsiders acquisition unit, a detention time acquisition unit for outsiders and a water valve starting time acquisition unit; the actual water consumption variation acquisition unit of the user in the morning, the noon and the evening acquires data of actual water consumption variations of the user in the morning, the noon and the evening; the historical data acquisition unit for the water consumption variation of the three time periods in the morning, the noon and the evening acquires historical data of the water consumption variation of the user in the three time periods in the morning, the noon and the evening; the external person number acquisition unit is used for acquiring the number of external persons in the house where the user is located; the external person residence time acquisition unit is used for acquiring the residence time period and the residence time length of external persons in the house; the water valve opening time acquisition unit is used for acquiring the time point when the water valve is in an opening state.
Further, the data transmission module comprises a data encryption unit and a data transmission unit; the data encryption unit is used for encrypting the collected actual water consumption variation of the user in three time periods in the morning, at noon and at night, the historical data of the water consumption variation of the user in the three time periods in the morning, at noon and at night, the number of the external personnel, the detention time period and the data of the time point when the water valve is in an open state; the data transmission unit transmits the encrypted data to a database for storage.
Further, the data analysis module comprises a data storage unit, a water valve fault detection unit and a water consumption variation prediction unit; the data storage unit is used for storing the collected actual water use variation of the user in three time periods in the morning, at the noon and at the evening, historical data of the water use variation of the user in the three time periods in the morning, at the noon and at the evening, the number of external personnel, the detention time period and the time point when the water valve is in an open state; the water valve fault detection unit is used for detecting the fault of the water valve when the difference between the actual water consumption variation of a user and the predicted value of the book consumption variation exceeds a set threshold value; the water consumption variation prediction unit is used for establishing a prediction model of water consumption variation according to collected historical data of water consumption variation of the user in three time periods of morning, noon and evening.
Further, the valve control module comprises a mobile terminal alarm unit and a water valve control unit; the mobile terminal alarm unit is used for alarming the mobile terminal when a water valve is in failure or is forgotten to be closed or is not tightly closed; the water valve control unit controls the water valve to be closed when the water valve is in an open state under the condition that a user does not need water.
In this embodiment:
the historical data of the water consumption variation of the user in three time periods in the morning, the noon and the evening is collected, and the water consumption variation of the water consumption time period in the morning is set as follows: (1, 12), (2, 13), (3, 18), (4, 14), (5, 12), (6, 15);
according to the formula
Can obtain y 1q ＝0.229x 1(i+1) +13.2 forecast the water usage change of the seventh morning water usage period to be (7, 14.8)
Example 1:
monitoring the water meter to obtain that the actual water consumption variation of the user in the seventh morning water consumption time period is 25;
setting the number of the external personnel in the house where the user is located to be 3, wherein the residence time period and the residence time length of the external personnel in the house are respectively 12; the water consumption variation of the alien substance is Y = eta 2 12+η 3 18+ δ, said δ being an error term; eta of 2 <η 1 <η 3 ，η 2 +η 1 +η 3 =1; the water change amount of the foreign person is Y =0.3 + 12+0.6 + 18+5=16.1;
25-14.8 were woven 16.1 over a range of normal water usage variations.
Example 2:
monitoring the water meter to obtain the actual water consumption variable quantity of 25 of the user in the seventh morning water consumption time period;
the number of the outside people of the house where the user is located is set to be 0,
25-14.8>0, comparing the actual water consumption with a policy, and detecting the water valve fault, wherein when the water valve is detected to be not in fault, the time point when the actual water valve is in an open state is (9; generating, from historical data, a set a = (8.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (9)
1. An intelligent valve remote management method based on the Internet of things is characterized by comprising the following steps: the intelligent valve remote management method comprises the following specific steps:
s1-1, acquiring historical data of water consumption variation of a user in three time periods in the morning, the noon and the evening by utilizing big data, and establishing a prediction model of the water consumption variation of the three time periods in the morning, the noon and the evening according to the acquired historical data of the water consumption variation of the three time periods;
s1-2, monitoring the water consumption variation of the three time periods in the morning, the noon and the evening through a prediction model of the water consumption variation of the three time periods in the morning, the noon and the evening, and carrying out fault detection on the water valve when detecting that the difference between the actual water consumption variation of a user at the three moments in the morning, the noon and the evening and the prediction model of the water consumption variation exceeds a set threshold value;
s1-3, when the water valve is detected to be in fault, the valve management system sends an alarm prompt to the mobile terminal equipment for the first time, and reminds a user to search related personnel to check and maintain the water valve;
s1-4, when the water valve is detected to be not in fault, analyzing the behavior of a user; whether a user needs water is analyzed, when the user does not need water, the water valve is detected to be in an opening state, the valve management system sends a message that the water valve is not closed to the mobile terminal device, and the water valve is closed.
2. The intelligent valve remote management method based on the Internet of things of claim 1, wherein: the specific method for acquiring historical data of water consumption variation of a user in three time periods of morning, noon and evening in the S1-1 to establish a prediction model of the water consumption variation is as follows:
s2-1, collecting historical data of water consumption variation of a user in three time periods of morning, noon and evening, and setting the data of the water consumption variation of the user in the three time periods of morning, noon and evening as sample points (x) 1i ，y 1i )、(x 2i ，y 2i )、(x 3i ，y 3i ) I =1, 2, 3, n, n is a constant; wherein x is 1i Represents the time period of the ith user's morning water use, y 1i Representing the amount of change in water usage by the ith user during the morning hours; wherein x 2i Represents the time period of the ith user's midday water usage, y 2i Representing the amount of change in water usage by the ith user during the midday time period; x is the number of 3i Represents the water usage period at night for the ith user, y 3i Representing the amount of change in water usage by the ith user during the night period; wherein y is 2i <y 1i <y 3i ；
S2-2, generating a fitting curve according to the collected historical data, and setting three of morning, noon and eveningThe fitted curve for each time segment is y 1 ＝k 1 x 1 +b 1 、y 2 ＝k 2 x 2 +b 2 、y 3 ＝k 3 x 3 +b 3 (ii) a Let the fitting values in the morning, at noon and at night be respectively
Wherein
The predicted value of the water consumption variation amount of the ith user in the morning water consumption time period,
the predicted value of the water use variation of the ith user in the noon water use period is obtained,
a predicted value of water consumption variation for the ith user in the night water consumption time period;
the definition condition of the known sample points and the fitting curve is
Can obtain
Where k is the curve coefficient, b is the error term,
is a curve coefficient predicted value of the morning water consumption time period and the water consumption change,
the error term prediction value is the water consumption time period in the morning and the water consumption variation curve;
is a curve coefficient predicted value of the water consumption time period and the water consumption variation,
the predicted value of the error term of the water consumption time period and the water consumption variation curve at noon is obtained;
is a curve coefficient predicted value of the night water use time period and the water use variation,
the error term prediction value of the water consumption time period at night and the water consumption variation curve is obtained;
s2-3, setting
Need to find k 1 And b 1 So that P is 1 Minimum of, wherein P 1 Is the sum of the squares of the residuals; is provided with
Need to find k 2 And b 2 So that P is 2 Minimum of, wherein P 2 Is the sum of the squares of the residuals; is provided with
Need to find k 3 And b 3 So that P is 3 Minimum where P 3 Is the sum of the squares of the residuals;
s2-4, obtaining a fitting curve of three time periods of morning, noon and evening through calculation
3. The intelligent valve remote management method based on the Internet of things as claimed in claim 2, wherein: monitoring the water use variation of the three time periods of morning, noon and evening in the S1-2, and detecting the closing condition of the water valve when detecting that the difference between the actual water use variation of the user at the three moments of morning, noon and evening and a prediction model of the water use variation exceeds a set threshold; the specific method comprises the following steps:
s3-1, setting the number m of external personnel in a house where a user is located, and setting the time period and the time length of the external personnel staying in the house as d and t respectively; for use by foreign personsWater variable quantity of Y = eta 1 tm+η 2 tm+η 3 tm + δ, when d ∈ (24-00-10]Time, eta 2 ＝η 3 =0; when d ∈ (10]Time, eta 1 ＝η 3 =0; when d e (16]Time, eta 1 ＝η 2 =0; eta of 2 <η 1 <η 3 ，η 2 +η 1 +η 3 ＝1；
S3-2, comparing the prediction model of the water consumption variation with the actual water consumption of the morning water consumption time period of the user when y is 1q -y 1(i+1) >When Y is in the specification; skipping to S3-5;
s3-3, comparing the prediction model of the water use variable quantity with the actual water use quantity of the user in the noon water use time period when y 2q -y 2(i+1) >When Y is in the specification; jumping to S3-5;
s3-4, comparing the prediction model of the water use variation with the actual water use of the user in the noon water use time period when y 3q -y 3(i+1) >When Y is in the specification; turning to S3-5;
and S3-5, detecting that the difference between the actual water use variation of the user at three moments in the morning, the noon and the evening and a prediction model of the water use variation exceeds a set threshold value, and performing fault detection on the water valve.
4. The intelligent valve remote management method based on the Internet of things of claim 3, wherein: the specific method for analyzing whether the water demand of the user exists or not under the condition that the water valve is not in fault in the S1-4 is as follows: and generating a set A as the time point of the water valve in the opening state according to the historical data, wherein when the actual time point of the water valve in the opening state does not belong to the set A, the user forgets to close the water valve.
5. An intelligent valve remote management system based on the internet of things, applied to the intelligent valve remote management method based on the internet of things of any one of claims 1 to 4, is characterized in that: the intelligent valve remote management system comprises a data acquisition module, a data transmission module, a data analysis module and a valve control module;
the data acquisition module is used for acquiring actual water consumption variation and historical data of the water consumption variation of a user in three time periods of morning, noon and evening, and acquiring a time point of a water valve in an opening state, the number of external personnel, and a time period and a time length during which the external personnel stay;
the data transmission module is used for acquiring historical data of actual water consumption variation of the user in three time periods of morning, noon and evening, the number of the external personnel, the detention time of the external personnel, the opening time of a water valve and the water consumption variation of the user in three time periods of morning, noon and evening and transmitting the historical data to a database for storage through encryption;
the data analysis module establishes a prediction model through historical data of water consumption variation of the user in three time periods of morning, noon and evening to analyze the water consumption variation of the user in the next three time periods of morning, noon and evening;
the valve control module is used for carrying out alarm prompt on the mobile terminal equipment and controlling the water valve to be closed;
the output end of the data acquisition module is connected with the input end of the data transmission module, the output end of the data transmission module is connected with the input end of the data analysis module, and the output end of the data analysis module is connected with the input end of the valve control module.
6. The intelligent valve remote management system based on the internet of things according to claim 5, wherein: the data acquisition module comprises an actual water consumption variation acquisition unit for three time periods of morning, noon and evening, a historical data acquisition unit for water consumption variation of three time periods of morning, noon and evening, a number of outsiders acquisition unit, a detention time acquisition unit for outsiders and a water valve starting time acquisition unit; the actual water consumption variation acquisition unit of the user in the three time periods of morning, noon and evening acquires data of actual water consumption variation of the user in the three time periods of morning, noon and evening of the day; the historical data acquisition unit for the water consumption variation of the three time periods in the morning, the noon and the evening acquires historical data of the water consumption variation of the user in the three time periods in the morning, the noon and the evening; the number of the external personnel acquisition unit is used for acquiring the number of the external personnel in the house where the user is located; the external person residence time acquisition unit is used for acquiring the residence time period and the residence time length of external persons in the house; the water valve opening time acquisition unit is used for acquiring the time point when the water valve is in an opening state.
7. The intelligent valve remote management system based on the internet of things according to claim 5, wherein: the data transmission module comprises a data encryption unit and a data transmission unit; the data encryption unit is used for encrypting the collected actual water consumption variation of the user in three time periods in the morning, at noon and at night, the historical data of the water consumption variation of the user in the three time periods in the morning, at noon and at night, the number of the external personnel, the detention time period and the data of the time point when the water valve is in an open state; the data transmission unit transmits the encrypted data to a database for storage.
8. The intelligent valve remote management system based on the internet of things according to claim 5, wherein: the data analysis module comprises a data storage unit, a water valve fault detection unit and a water consumption variable quantity prediction unit; the data storage unit is used for storing the collected actual water use variation of the user in three time periods in the morning, at noon and at night, historical data of the water use variation of the user in the three time periods in the morning, at noon and at night, the number of external personnel, the detention time period and the data of a time point when the water valve is in an open state; the water valve fault detection unit is used for detecting the fault of the water valve when the difference between the actual water consumption variation of a user and the predicted value of the book consumption variation exceeds a set threshold value; the water consumption variation prediction unit is used for establishing a prediction model of water consumption variation according to collected historical data of the water consumption variation of the user in three time periods of morning, noon and evening.
9. The intelligent valve remote management system based on the internet of things according to claim 5, wherein: the valve control module comprises a mobile terminal alarm unit and a water valve control unit; the mobile terminal alarm unit is used for alarming the mobile terminal when a water valve is in failure or is forgotten to be closed or is not tightly closed; the water valve control unit controls the water valve to be closed when the water valve is in an open state under the condition that a user does not need water.
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| CN117847305A (en) * | 2024-03-08 | 2024-04-09 | 山东开创电气有限公司 | Mining valve control protection system based on embedded computer |
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| CN117847305A (en) * | 2024-03-08 | 2024-04-09 | 山东开创电气有限公司 | Mining valve control protection system based on embedded computer |
| CN117847305B (en) * | 2024-03-08 | 2024-05-14 | 山东开创电气有限公司 | Mining valve control protection system based on embedded computer |
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