CN113110202A - Household power control system based on Internet of things - Google Patents
Household power control system based on Internet of things Download PDFInfo
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- CN113110202A CN113110202A CN202110460169.5A CN202110460169A CN113110202A CN 113110202 A CN113110202 A CN 113110202A CN 202110460169 A CN202110460169 A CN 202110460169A CN 113110202 A CN113110202 A CN 113110202A
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
The invention discloses a household power control system based on the Internet of things, which comprises a monitoring module, a controller, a data analysis module, an alarm module, a problem classification module and a maintenance management module, wherein the monitoring module is used for monitoring the household power control system; the data analysis module is used for acquiring the electricity utilization information of the household appliance and carrying out early warning analysis processing; the problem classification module is used for recording, reporting and uploading the fault problems of the early warning electric appliance by a user, and the controller carries out similarity matching on the fault problems and the fault problems stored in the problem library; acquiring the fault category of the fault problem; the maintenance management module is used for receiving the maintenance instruction and distributing corresponding maintenance personnel for maintenance; according to the invention, the power consumption of the household appliance and the equipment temperature can be monitored and analyzed in real time, so that a user can be reminded of maintaining in time, the power utilization safety is improved, and the implicit consumption of electric energy is reduced; meanwhile, the invention can reasonably select corresponding maintenance personnel for maintenance according to the maintenance and distribution value, thereby improving the maintenance efficiency.
Description
Technical Field
The invention relates to the technical field of electric power safety, in particular to a household electric power control system based on the Internet of things.
Background
Since the second industrial revolution, electricity plays an increasingly important role in the life and production process of people, and at present, although many new energy sources are used for generating electricity such as wind power, water conservancy, solar energy and biochemical electricity generation, electricity is still a relatively deficient living resource for people. In the life of people, the continuous utilization of electric appliances brings about the consumption of a large amount of electric energy, especially some invisible energy consumption, which is often ignored by people, for example, the consumption of the electric energy is aggravated due to aging or some small faults after the electric appliances are used, according to incomplete statistics, a standard household is continuously electrified for use under the condition that the household electric appliances are not replaced in time after aging or some small faults are not maintained, compared with the normal household electric appliances, 720 degrees of electric energy consumption per year is generated, and under the large background that energy is increasingly tense nowadays, how to effectively manage the invisible energy consumption becomes an increasingly prominent problem.
The existing household power control system cannot perform system analysis on the service condition of the household appliance and analyze the power consumption and the temperature of the household appliance through real-time monitoring, so that a user is timely reminded to maintain and analyze fault problems in advance and reasonably select maintenance personnel to maintain according to the fault problems.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a household power control system based on the Internet of things.
The purpose of the invention can be realized by the following technical scheme: a household power control system based on the Internet of things comprises a monitoring module, a controller, a data analysis module, an alarm module, a problem classification module, a selection module and a maintenance management module;
the monitoring module is used for monitoring the power consumption information of the household appliance in real time and sending the power consumption information to the controller, the data analysis module is used for acquiring the power consumption information of the household appliance and carrying out early warning analysis processing, and the specific analysis processing steps are as follows:
the method comprises the following steps: acquiring real-time power utilization information of the household appliance; marking the hourly power consumption of the household appliance as unit power consumption Di; comparing the unit electricity usage Di with a unit electricity threshold;
if the unit electricity consumption Di is larger than the unit electricity consumption threshold, marking the unit electricity consumption as the unit electricity consumption; further analysis is carried out on the unit electricity consumption, and the method specifically comprises the following steps:
s11: marking the moment when the electricity consumption of the influencing unit appears as the electricity influence starting moment;
calculating the time difference between the electric quantity influence starting time and the power-on starting time of the household appliance to obtain electric quantity influence buffer duration, and marking the electric quantity influence buffer duration as H1;
s12: setting a plurality of electric quantity buffer duration coefficients and marking as Kc; c is 1, 2, …, 15; and K1 > K2 > … > K15; each electric quantity buffer duration coefficient Kc corresponds to a preset electric quantity influence buffer duration range; the concrete expression is as follows: the preset electric quantity influence buffer duration range corresponding to K1 is (0, K1), the preset electric quantity influence buffer duration range corresponding to K2 is (K1, K2), …, the preset electric quantity influence buffer duration range corresponding to K15 is (K14, K15; wherein K1 is more than 0 and K2 is more than … is more than K15;
when H1 belongs to (Kc-1, Kc), presetting an electric quantity buffer duration coefficient corresponding to the electric quantity influence buffer duration range as Kc;
s13: obtaining an influence value HC corresponding to the electric quantity influence buffer duration by using a formula HC of H1 multiplied by Kc;
comparing the impact value HC with an impact threshold; if the influence value HC is larger than the influence threshold value, generating an early warning signal;
step two: acquiring the equipment temperature after the household appliance starts to operate, and acquiring a curve graph of the equipment temperature along with the change of time; analyzing the temperature of the equipment to obtain an early warning value of the household appliance;
if the early warning value is larger than the corresponding early warning threshold value, generating an early warning signal;
step three: marking the corresponding household appliance as an early warning appliance; the data analysis module sends the early warning signal and the early warning equipment to the controller; the controller drives the control alarm module to send out an alarm after receiving the early warning signal and the early warning equipment; and controlling the early warning equipment to be powered off.
Further, the monitoring module comprises an electric quantity monitoring unit and a temperature monitoring unit; the electric quantity monitoring unit is used for monitoring the electric quantity of the household appliance in real time; the temperature monitoring unit is used for monitoring the temperature of the household appliance in real time; the electricity consumption information includes a power-on start time of the home appliance, an amount of electricity used per hour, and a temperature of the home appliance.
Further, the device temperature is analyzed in the second step, and the specific analysis steps are as follows:
s21: comparing the equipment temperature of the household appliance with the corresponding temperature threshold, and marking the temperature as an influence temperature when the equipment temperature is greater than the corresponding temperature threshold;
s22: marking the moment when the influence temperature appears as the temperature influence starting moment;
calculating the time difference between the temperature influence starting time and the power-on starting time of the household appliance to obtain the temperature influence buffering time length, and marking the temperature influence buffering time length as H2;
comparing the temperature impact buffer duration H2 to a temperature buffer duration threshold;
if H2 is less than the temperature buffer duration threshold, generating an early warning signal;
s23: if H2 is not less than the temperature buffer duration threshold, calculating the difference between the influence temperature and the corresponding temperature threshold to obtain an over-temperature value;
comparing the overtemperature value with an overtemperature threshold value;
if the overtemperature value is larger than the overtemperature threshold value; generating an early warning signal;
if the overtemperature value is less than or equal to the overtemperature threshold value; integrating the overtemperature value with time to obtain overtemperature integral parameters of each time period of the household appliance, and marking the overtemperature integral parameters as DC;
calculating the time difference between the temperature influence starting moment and the current time of the system to obtain the temperature influence duration, and marking the temperature influence duration as WT; calculating the time difference between the electrifying starting time of the household appliance and the current time of the system to obtain the working duration, and marking the working duration as GT; wherein GT is WT + H2;
obtaining an early warning value DF of the household appliance by using a formula DF of (DC × a1)/(GT × a2) -H2 × a3, wherein a1, a2 and a3 are coefficient factors;
s24: setting a plurality of early warning threshold values, and marking the early warning threshold values as Rm; m is 1, 2, …, 10; r1 < R2 < … … < R10; each early warning threshold value Rm corresponds to a preset working time length range; the concrete expression is as follows: the preset working time length range corresponding to R1 is (0, R1), the preset working time length range corresponding to R2 is (R1, R2], the preset working time length range corresponding to … and R10 is (R9, R10), wherein R1 is greater than 0, R2 is greater than … is greater than R10;
when GT belongs to (ym-1, ym), presetting an early warning threshold value Rm corresponding to the working time length range;
s25: and if DF is larger than the corresponding early warning threshold value Rm, generating an early warning signal.
Furthermore, the problem classification module is used for recording, reporting and uploading the fault problem of the early warning electric appliance by a user, and the controller performs similarity matching on the fault problem and the fault problem stored in the problem database; acquiring the fault category of the fault problem; the method specifically comprises the following steps:
VV 1: marking the reported and uploaded fault problem as a target problem; marking the fault problems stored in the problem library as reference problems;
extracting keywords from the target problem and the reference problem respectively, matching the keywords of the target problem with the keywords of the reference problem to obtain keyword contact ratio, and marking the keyword contact ratio as CH;
VV 2: marking the reference problem with the keyword contact ratio CH being more than 90% as a similar problem;
counting the times of selecting similar problems within thirty days before the current time of the system, and marking the times as the selecting frequency P1;
obtaining a matching value HY of a similar problem by using a formula HY ═ CH × b1+ P1 × b 2; wherein b1 and b2 are coefficient factors;
sequencing and displaying the similar problems according to the size of the matching value HY;
VV 3: a user selects corresponding similar problems through a selection module; meanwhile, the selected times of the similar problem are increased by one; marking the selected similar problems as selected problems, and considering that the selected problems are consistent with the fault categories of the target problems;
the problem classification module is used for transmitting the early warning electric appliance, the fault problem and the corresponding fault category to the controller; the controller is used for fusing the early warning electric appliance, the fault problem and the corresponding fault category to form a maintenance instruction and sending the maintenance instruction to the maintenance management module.
Furthermore, the maintenance management module is used for receiving a maintenance instruction and distributing corresponding maintenance personnel for maintenance; the method comprises the following specific steps:
GG 1: collecting maintenance records of a maintenance worker in about three months; the maintenance record comprises the maintenance of the electric appliance, the maintenance duration, the maintenance amount and the fault category;
GG 2: matching the early warning electrical appliance in the maintenance instruction with the maintenance electrical appliance in the maintenance record, and marking the maintenance personnel of the early warning electrical appliance in the maintenance instruction in the maintenance record as the primary selection personnel;
acquiring a maintenance record of the primary selection personnel for the early warning electric appliance and marking the maintenance record as a reference maintenance record;
GG 3: further analysis of the reference service record was performed: the method specifically comprises the following steps:
marking the fault category in the maintenance instruction as a target fault category; counting the maintenance times of the primary selection personnel for the target fault category and marking as C1;
summing the maintenance time lengths of the primary selection personnel for the target fault category, taking the average value to obtain the average maintenance time length, and marking the average maintenance time length as CT;
summing the maintenance amounts of the primary selection personnel aiming at the target fault category, taking the average value to obtain an average maintenance amount, and marking the average maintenance amount as CE;
obtaining a maintenance value WP of the primary candidate by using a formula WP of (C1 × b3)/(CT × b4+ CE × b5), wherein b3, b4 and b5 are coefficient factors;
GG 4: marking the initially selected person with the maximum dimension and join value WP as a selected person;
and the maintenance management module is used for sending the maintenance instruction to the mobile phone terminal of the selected person.
Further, the further analysis of the unit power consumption is further carried out in the step one, and the method specifically comprises the following steps:
v11: calculating the difference between the unit electricity consumption and the unit electricity threshold to obtain an excess value; comparing the excess value with an excess threshold value, if the excess value is larger than the excess threshold value; generating an early warning signal;
v12: if the excess value is less than or equal to the excess threshold value; integrating the excess value with time to obtain the excess electric energy value of each time period of the household appliance, and marking the value as DE;
marking the time period duration corresponding to the excess electric energy value as an excess duration; and marked as DT;
v13: setting a plurality of excess electric energy thresholds and marking the thresholds as Yg; g is 1, 2, …, 20; y1 < Y2 < … … < Y20; each excess electric energy threshold value Yg corresponds to a preset excess duration range; the concrete expression is as follows: the preset excess time length range corresponding to Y1 is (0, Y1), the preset excess time length range corresponding to Y2 is (Y1, Y2], …, the preset excess time length range corresponding to Y20 is (Y19, Y20), wherein Y1 is more than 0, Y2 is more than … is more than Y20;
when DT belongs to (Yg, Yg +1], presetting an excess electric energy threshold corresponding to an excess duration range as Yg;
v14: and if DE is larger than the excess electric energy threshold Yg, generating an early warning signal.
The invention has the beneficial effects that:
1. the data analysis module is used for acquiring the electricity utilization information of the household appliance and carrying out early warning analysis processing, and if the unit electricity consumption Di is larger than the unit electricity consumption threshold, the unit electricity consumption is marked as the unit electricity consumption which is influenced; calculating the time difference between the electric quantity influence starting time and the power-on starting time of the household appliance to obtain electric quantity influence buffer duration; obtaining an influence value corresponding to the electric quantity influence buffering duration; if the influence value HC is larger than the influence threshold value, generating an early warning signal; obtaining a curve graph of the temperature of the equipment along with the change of time; analyzing the temperature of the equipment; acquiring an early warning value DF of the household appliance; if DF is larger than the corresponding early warning threshold value Rm, an early warning signal is generated; the system can carry out system analysis on the service condition of the household appliance, and the power consumption of the household appliance and the equipment temperature are monitored and analyzed in real time, so that a user is reminded of maintaining in time, the power utilization safety is improved, and the implicit consumption of electric energy is reduced;
2. the problem classification module is used for recording and reporting fault problems of the early warning electric appliance by a user, and matching the fault problems with the similarity of the fault problems stored in a problem library by the controller; acquiring the fault category of the fault problem; the maintenance management module is used for receiving the maintenance instruction and distributing corresponding maintenance personnel for maintenance; collecting maintenance records of a maintenance worker in about three months; matching the early warning electrical appliance in the maintenance instruction with the maintenance electrical appliance in the maintenance record, and marking the maintenance personnel of the early warning electrical appliance in the maintenance instruction in the maintenance record as the primary selection personnel; obtaining a maintenance and allocation value of the initially selected person, and marking the initially selected person with the maximum maintenance and allocation value WP as the selected person; the maintenance management module is used for sending the maintenance instruction to the mobile phone terminal of the selected person, so that the selected person can know the problems of the early warning electric appliance and the faults in advance, and meanwhile, the corresponding maintenance person can be reasonably selected according to the maintenance and allocation value to perform maintenance, and the maintenance efficiency is improved.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the system of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, a home power control system based on the internet of things includes a monitoring module, a controller, a data analysis module, an alarm module, a problem classification module, a selection module and a maintenance management module;
the monitoring module is used for monitoring the electricity utilization information of the household appliance in real time and sending the electricity utilization information to the controller, and comprises an electric quantity monitoring unit and a temperature monitoring unit; the electric quantity monitoring unit is used for monitoring the electric quantity of the household appliance in real time; the temperature monitoring unit is used for monitoring the temperature of the household appliance in real time;
the data analysis module is used for acquiring the power utilization information of the household appliance and carrying out early warning analysis processing, and the specific analysis processing steps are as follows:
the method comprises the following steps: acquiring real-time power utilization information of the household appliance; the electricity utilization information comprises the power-on starting time of the household appliance, the electricity consumption per hour and the temperature of the household appliance;
marking the hourly power consumption of the household appliance as unit power consumption Di; comparing the unit electricity usage Di with a unit electricity threshold;
if the unit electricity consumption Di is larger than the unit electricity consumption threshold, marking the unit electricity consumption as the unit electricity consumption; further analysis is carried out on the unit electricity consumption, and the method specifically comprises the following steps:
s11: marking the moment when the electricity consumption of the influencing unit appears as the electricity influence starting moment;
calculating the time difference between the electric quantity influence starting time and the power-on starting time of the household appliance to obtain electric quantity influence buffer duration, and marking the electric quantity influence buffer duration as H1;
s12: setting a plurality of electric quantity buffer duration coefficients and marking as Kc; c is 1, 2, …, 15; and K1 > K2 > … > K15; each electric quantity buffer duration coefficient Kc corresponds to a preset electric quantity influence buffer duration range; the concrete expression is as follows: the preset electric quantity influence buffer duration range corresponding to K1 is (0, K1), the preset electric quantity influence buffer duration range corresponding to K2 is (K1, K2), …, the preset electric quantity influence buffer duration range corresponding to K15 is (K14, K15; wherein K1 is more than 0 and K2 is more than … is more than K15;
when H1 belongs to (Kc-1, Kc), presetting an electric quantity buffer duration coefficient corresponding to the electric quantity influence buffer duration range as Kc;
s13: obtaining an influence value HC corresponding to the electric quantity influence buffer duration by using a formula HC of H1 multiplied by Kc;
comparing the impact value HC with an impact threshold; if the influence value HC is larger than the influence threshold value, generating an early warning signal;
step two: acquiring the equipment temperature after the household appliance starts to operate, and acquiring a curve graph of the equipment temperature along with the change of time; analyzing the temperature of the equipment; the specific analysis steps are as follows:
s21: comparing the equipment temperature of the household appliance with the corresponding temperature threshold, and marking the temperature as an influence temperature when the equipment temperature is greater than the corresponding temperature threshold;
s22: marking the moment when the influence temperature appears as the temperature influence starting moment;
calculating the time difference between the temperature influence starting time and the power-on starting time of the household appliance to obtain the temperature influence buffering time length, and marking the temperature influence buffering time length as H2;
comparing the temperature impact buffer duration H2 to a temperature buffer duration threshold;
if H2 is less than the temperature buffer duration threshold, generating an early warning signal;
s23: if H2 is not less than the temperature buffer duration threshold, calculating the difference between the influence temperature and the corresponding temperature threshold to obtain an over-temperature value;
comparing the overtemperature value with an overtemperature threshold value;
if the overtemperature value is larger than the overtemperature threshold value; generating an early warning signal;
if the overtemperature value is less than or equal to the overtemperature threshold value; integrating the overtemperature value with time to obtain overtemperature integral parameters of each time period of the household appliance, and marking the overtemperature integral parameters as DC;
calculating the time difference between the temperature influence starting moment and the current time of the system to obtain the temperature influence duration, and marking the temperature influence duration as WT; calculating the time difference between the electrifying starting time of the household appliance and the current time of the system to obtain the working duration, and marking the working duration as GT; wherein GT is WT + H2;
obtaining an early warning value DF of the household appliance by using a formula DF (DC × a1)/(GT × a2) -H2 × a3, wherein a1, a2 and a3 are coefficient factors, for example, a1 takes 0.35, a2 takes 0.51 and a3 takes 0.77;
s24: setting a plurality of early warning threshold values, and marking the early warning threshold values as Rm; m is 1, 2, …, 10; r1 < R2 < … … < R10; each early warning threshold value Rm corresponds to a preset working time length range; the concrete expression is as follows: the preset working time length range corresponding to R1 is (0, R1), the preset working time length range corresponding to R2 is (R1, R2], the preset working time length range corresponding to … and R10 is (R9, R10), wherein R1 is greater than 0, R2 is greater than … is greater than R10;
when GT belongs to (ym-1, ym), presetting an early warning threshold value Rm corresponding to the working time length range;
s25: if DF is larger than the corresponding early warning threshold value Rm, an early warning signal is generated;
step three: marking the corresponding household appliance as an early warning appliance; the data analysis module sends the early warning signal and the early warning equipment to the controller; the controller drives the control alarm module to send out an alarm after receiving the early warning signal and the early warning equipment; and controlling the early warning equipment to shut down;
the problem classification module is used for recording and reporting fault problems of the early warning electric appliance by a user, and matching the fault problems with the similarity of the fault problems stored in the problem library by the controller; acquiring the fault category of the fault problem; the method specifically comprises the following steps:
VV 1: marking the reported and uploaded fault problem as a target problem; marking the fault problems stored in the problem library as reference problems;
extracting keywords from the target problem and the reference problem respectively, matching the keywords of the target problem with the keywords of the reference problem to obtain keyword contact ratio, and marking the keyword contact ratio as CH; for example: keywords of the target question are "A, B, C"; the keywords of the reference question are "B, C, D", where the keywords of the target question are three, and two of the keywords are identical to the keywords of the reference question, CH 2/3-66.67%;
VV 2: marking the reference problem with the keyword contact ratio CH being more than 90% as a similar problem;
counting the times of selecting similar problems within thirty days before the current time of the system, and marking the times as the selecting frequency P1;
obtaining a matching value HY of a similar problem by using a formula HY ═ CH × b1+ P1 × b 2; wherein b1 and b2 are both coefficient factors, for example, b1 takes 0.66, b2 takes 0.71;
sequencing and displaying the similar problems according to the size of the matching value HY;
VV 3: a user selects corresponding similar problems through a selection module; meanwhile, the selected times of the similar problem are increased by one; marking the selected similar problems as selected problems, and considering that the selected problems are consistent with the fault categories of the target problems;
the problem classification module is used for transmitting the early warning electric appliance, the fault problem and the corresponding fault category to the controller; the controller is used for fusing the early warning electric appliance, the fault problem and the corresponding fault category to form a maintenance instruction and sending the maintenance instruction to the maintenance management module; the maintenance management module is used for receiving the maintenance instruction and distributing corresponding maintenance personnel for maintenance; the method comprises the following specific steps:
GG 1: collecting maintenance records of a maintenance worker in about three months; the maintenance record comprises the maintenance of the electric appliance, the maintenance duration, the maintenance amount and the fault category;
GG 2: matching the early warning electrical appliance in the maintenance instruction with the maintenance electrical appliance in the maintenance record, and marking the maintenance personnel of the early warning electrical appliance in the maintenance instruction in the maintenance record as the primary selection personnel;
acquiring a maintenance record of the primary selection personnel for the early warning electric appliance and marking the maintenance record as a reference maintenance record;
GG 3: further analysis of the reference service record was performed: the method specifically comprises the following steps:
marking the fault category in the maintenance instruction as a target fault category; counting the maintenance times of the primary selection personnel for the target fault category and marking as C1;
summing the maintenance time lengths of the primary selection personnel for the target fault category, taking the average value to obtain the average maintenance time length, and marking the average maintenance time length as CT;
summing the maintenance amounts of the primary selection personnel aiming at the target fault category, taking the average value to obtain an average maintenance amount, and marking the average maintenance amount as CE;
obtaining a maintenance value WP of the primary candidate by using a formula WP of (C1 × b3)/(CT × b4+ CE × b5), wherein b3, b4 and b5 are coefficient factors; for example, b3 takes the value 2.01; b4 takes the value of 1.11, b5 takes the value of 0.58;
GG 4: marking the initially selected person with the maximum dimension and join value WP as a selected person;
the maintenance management module is used for sending a maintenance instruction to a mobile phone terminal of a selected person, so that the selected person can know problems of the early warning electric appliance and faults in advance, and meanwhile, the corresponding maintenance person can be reasonably selected according to the maintenance and allocation value for maintenance, and the maintenance efficiency is improved;
in another embodiment of the invention: further analyzing the unit power consumption in the first step, specifically comprising:
v11: calculating the difference between the unit electricity consumption and the unit electricity threshold to obtain an excess value; comparing the excess value with an excess threshold value, if the excess value is larger than the excess threshold value; generating an early warning signal;
v12: if the excess value is less than or equal to the excess threshold value; integrating the excess value with time to obtain the excess electric energy value of each time period of the household appliance, and marking the value as DE;
marking the time period duration corresponding to the excess electric energy value as an excess duration; and marked as DT;
v13: setting a plurality of excess electric energy thresholds and marking the thresholds as Yg; g is 1, 2, …, 20; y1 < Y2 < … … < Y20; each excess electric energy threshold value Yg corresponds to a preset excess duration range; the concrete expression is as follows: the preset excess time length range corresponding to Y1 is (0, Y1), the preset excess time length range corresponding to Y2 is (Y1, Y2], …, the preset excess time length range corresponding to Y20 is (Y19, Y20), wherein Y1 is more than 0, Y2 is more than … is more than Y20;
when DT belongs to (Yg, Yg +1], presetting an excess electric energy threshold corresponding to an excess duration range as Yg;
v14: and if DE is larger than the excess electric energy threshold Yg, generating an early warning signal.
The working principle of the invention is as follows:
when the household power control system works, a monitoring module is used for monitoring power utilization information of household appliances in real time and sending the power utilization information to a controller; the data analysis module is used for acquiring the electricity utilization information of the household appliance, carrying out early warning analysis processing, and marking the unit electricity consumption as the unit electricity consumption influenced if the unit electricity consumption Di is larger than the unit electricity consumption threshold; calculating the time difference between the electric quantity influence starting time and the power-on starting time of the household appliance to obtain electric quantity influence buffer duration; obtaining an influence value corresponding to the electric quantity influence buffering duration; if the influence value HC is larger than the influence threshold value, generating an early warning signal; obtaining a curve graph of the temperature of the equipment along with the change of time; analyzing the temperature of the equipment; acquiring an early warning value DF of the household appliance; if DF is larger than the corresponding early warning threshold value Rm, an early warning signal is generated; the system can carry out system analysis on the service condition of the household appliance, and the power consumption of the household appliance and the equipment temperature are monitored and analyzed in real time, so that a user is reminded of maintaining in time, the power utilization safety is improved, and the implicit consumption of electric energy is reduced;
the problem classification module is used for recording and reporting fault problems of the early warning electric appliance by a user, and matching the fault problems with the similarity of the fault problems stored in the problem library by the controller; acquiring the fault category of the fault problem; the controller is used for fusing the early warning electric appliance, the fault problem and the corresponding fault category to form a maintenance instruction and sending the maintenance instruction to the maintenance management module; the maintenance management module is used for receiving the maintenance instruction and distributing corresponding maintenance personnel for maintenance; collecting maintenance records of a maintenance worker in about three months; matching the early warning electrical appliance in the maintenance instruction with the maintenance electrical appliance in the maintenance record, and marking the maintenance personnel of the early warning electrical appliance in the maintenance instruction in the maintenance record as the primary selection personnel; obtaining a maintenance and allocation value of the initially selected person, and marking the initially selected person with the maximum maintenance and allocation value WP as the selected person; the maintenance management module is used for sending the maintenance instruction to the mobile phone terminal of the selected person, so that the selected person can know the problems of the early warning electric appliance and the faults in advance, and meanwhile, the corresponding maintenance person can be reasonably selected according to the maintenance and allocation value to perform maintenance, and the maintenance efficiency is improved.
The formula and the coefficient factor are both obtained by acquiring a large amount of data to perform software simulation and performing parameter setting processing by corresponding experts, and the formula and the coefficient factor which are consistent with a real result are obtained.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. A household power control system based on the Internet of things is characterized by comprising a monitoring module, a controller, a data analysis module, an alarm module, a problem classification module, a selection module and a maintenance management module;
the monitoring module is used for monitoring the power consumption information of the household appliance in real time and sending the power consumption information to the controller, the data analysis module is used for acquiring the power consumption information of the household appliance and carrying out early warning analysis processing, and the specific analysis processing steps are as follows:
the method comprises the following steps: acquiring real-time power utilization information of the household appliance; marking the hourly power consumption of the household appliance as unit power consumption Di; comparing the unit electricity usage Di with a unit electricity threshold;
if the unit electricity consumption Di is larger than the unit electricity consumption threshold, marking the unit electricity consumption as the unit electricity consumption; further analysis is carried out on the unit electricity consumption, and the method specifically comprises the following steps:
s11: marking the moment when the electricity consumption of the influencing unit appears as the electricity influence starting moment;
calculating the time difference between the electric quantity influence starting time and the power-on starting time of the household appliance to obtain electric quantity influence buffer duration, and marking the electric quantity influence buffer duration as H1;
s12: setting a plurality of electric quantity buffer duration coefficients and marking as Kc; c is 1, 2, …, 15; and K1 > K2 > … > K15; each electric quantity buffer duration coefficient Kc corresponds to a preset electric quantity influence buffer duration range; the concrete expression is as follows: the preset electric quantity influence buffer duration range corresponding to K1 is (0, K1), the preset electric quantity influence buffer duration range corresponding to K2 is (K1, K2), …, the preset electric quantity influence buffer duration range corresponding to K15 is (K14, K15; wherein K1 is more than 0 and K2 is more than … is more than K15;
when H1 belongs to (Kc-1, Kc), presetting an electric quantity buffer duration coefficient corresponding to the electric quantity influence buffer duration range as Kc;
s13: obtaining an influence value HC corresponding to the electric quantity influence buffer duration by using a formula HC of H1 multiplied by Kc;
comparing the impact value HC with an impact threshold; if the influence value HC is larger than the influence threshold value, generating an early warning signal;
step two: acquiring the equipment temperature after the household appliance starts to operate, and acquiring a curve graph of the equipment temperature along with the change of time; analyzing the temperature of the equipment to obtain an early warning value of the household appliance;
if the early warning value is larger than the corresponding early warning threshold value, generating an early warning signal;
step three: marking the corresponding household appliance as an early warning appliance; the data analysis module sends the early warning signal and the early warning equipment to the controller; the controller drives the control alarm module to send out an alarm after receiving the early warning signal and the early warning equipment; and controlling the early warning equipment to be powered off.
2. The Internet of things-based home power control system of claim 1, wherein the monitoring module comprises a power monitoring unit and a temperature monitoring unit; the electric quantity monitoring unit is used for monitoring the electric quantity of the household appliance in real time; the temperature monitoring unit is used for monitoring the temperature of the household appliance in real time; the electricity consumption information includes a power-on start time of the home appliance, an amount of electricity used per hour, and a temperature of the home appliance.
3. The internet of things-based home power control system of claim 1, wherein in the second step, the device temperature is analyzed, and the specific analysis steps are as follows:
s21: comparing the equipment temperature of the household appliance with the corresponding temperature threshold, and marking the temperature as an influence temperature when the equipment temperature is greater than the corresponding temperature threshold;
s22: marking the moment when the influence temperature appears as the temperature influence starting moment;
calculating the time difference between the temperature influence starting time and the power-on starting time of the household appliance to obtain the temperature influence buffering time length, and marking the temperature influence buffering time length as H2;
comparing the temperature impact buffer duration H2 to a temperature buffer duration threshold;
if H2 is less than the temperature buffer duration threshold, generating an early warning signal;
s23: if H2 is not less than the temperature buffer duration threshold, calculating the difference between the influence temperature and the corresponding temperature threshold to obtain an over-temperature value;
comparing the overtemperature value with an overtemperature threshold value;
if the overtemperature value is larger than the overtemperature threshold value; generating an early warning signal;
if the overtemperature value is less than or equal to the overtemperature threshold value; integrating the overtemperature value with time to obtain overtemperature integral parameters of each time period of the household appliance, and marking the overtemperature integral parameters as DC;
calculating the time difference between the temperature influence starting moment and the current time of the system to obtain the temperature influence duration, and marking the temperature influence duration as WT; calculating the time difference between the electrifying starting time of the household appliance and the current time of the system to obtain the working duration, and marking the working duration as GT; wherein GT is WT + H2;
obtaining an early warning value DF of the household appliance by using a formula DF of (DC × a1)/(GT × a2) -H2 × a3, wherein a1, a2 and a3 are coefficient factors;
s24: setting a plurality of early warning threshold values, and marking the early warning threshold values as Rm; m is 1, 2, …, 10; r1 < R2 < … … < R10; each early warning threshold value Rm corresponds to a preset working time length range; the concrete expression is as follows: the preset working time length range corresponding to R1 is (0, R1), the preset working time length range corresponding to R2 is (R1, R2], the preset working time length range corresponding to … and R10 is (R9, R10), wherein R1 is greater than 0, R2 is greater than … is greater than R10;
when GT belongs to (ym-1, ym), presetting an early warning threshold value Rm corresponding to the working time length range;
s25: and if DF is larger than the corresponding early warning threshold value Rm, generating an early warning signal.
4. The Internet of things-based home power control system of claim 1, wherein the problem classification module is used for recording, reporting and uploading fault problems of the early warning electrical appliance by a user, and the controller performs similarity matching between the fault problems and fault problems stored in a problem database; acquiring the fault category of the fault problem; the method specifically comprises the following steps:
VV 1: marking the reported and uploaded fault problem as a target problem; marking the fault problems stored in the problem library as reference problems;
extracting keywords from the target problem and the reference problem respectively, matching the keywords of the target problem with the keywords of the reference problem to obtain keyword contact ratio, and marking the keyword contact ratio as CH;
VV 2: marking the reference problem with the keyword contact ratio CH being more than 90% as a similar problem;
counting the times of selecting similar problems within thirty days before the current time of the system, and marking the times as the selecting frequency P1;
obtaining a matching value HY of a similar problem by using a formula HY ═ CH × b1+ P1 × b 2; wherein b1 and b2 are coefficient factors;
sequencing and displaying the similar problems according to the size of the matching value HY;
VV 3: a user selects corresponding similar problems through a selection module; meanwhile, the selected times of the similar problem are increased by one; marking the selected similar problems as selected problems, and considering that the selected problems are consistent with the fault categories of the target problems;
the problem classification module is used for transmitting the early warning electric appliance, the fault problem and the corresponding fault category to the controller; the controller is used for fusing the early warning electric appliance, the fault problem and the corresponding fault category to form a maintenance instruction and sending the maintenance instruction to the maintenance management module.
5. The Internet of things-based home power control system of claim 1, wherein the maintenance management module is configured to receive a maintenance instruction and assign a corresponding maintenance person to perform maintenance; the method comprises the following specific steps:
GG 1: collecting maintenance records of a maintenance worker in about three months; the maintenance record comprises the maintenance of the electric appliance, the maintenance duration, the maintenance amount and the fault category;
GG 2: matching the early warning electrical appliance in the maintenance instruction with the maintenance electrical appliance in the maintenance record, and marking the maintenance personnel of the early warning electrical appliance in the maintenance instruction in the maintenance record as the primary selection personnel;
acquiring a maintenance record of the primary selection personnel for the early warning electric appliance and marking the maintenance record as a reference maintenance record;
GG 3: further analysis of the reference service record was performed: the method specifically comprises the following steps:
marking the fault category in the maintenance instruction as a target fault category; counting the maintenance times of the primary selection personnel for the target fault category and marking as C1;
summing the maintenance time lengths of the primary selection personnel for the target fault category, taking the average value to obtain the average maintenance time length, and marking the average maintenance time length as CT;
summing the maintenance amounts of the primary selection personnel aiming at the target fault category, taking the average value to obtain an average maintenance amount, and marking the average maintenance amount as CE;
obtaining a maintenance value WP of the primary candidate by using a formula WP of (C1 × b3)/(CT × b4+ CE × b5), wherein b3, b4 and b5 are coefficient factors;
GG 4: marking the initially selected person with the maximum dimension and join value WP as a selected person;
and the maintenance management module is used for sending the maintenance instruction to the mobile phone terminal of the selected person.
6. The internet of things-based home power control system of claim 1, wherein the further analysis of the influence on the unit power consumption in the step one further comprises:
v11: calculating the difference between the unit electricity consumption and the unit electricity threshold to obtain an excess value; comparing the excess value with an excess threshold value, if the excess value is larger than the excess threshold value; generating an early warning signal;
v12: if the excess value is less than or equal to the excess threshold value; integrating the excess value with time to obtain the excess electric energy value of each time period of the household appliance, and marking the value as DE;
marking the time period duration corresponding to the excess electric energy value as an excess duration; and marked as DT;
v13: setting a plurality of excess electric energy thresholds and marking the thresholds as Yg; g is 1, 2, …, 20; y1 < Y2 < … … < Y20; each excess electric energy threshold value Yg corresponds to a preset excess duration range; the concrete expression is as follows: the preset excess time length range corresponding to Y1 is (0, Y1), the preset excess time length range corresponding to Y2 is (Y1, Y2], …, the preset excess time length range corresponding to Y20 is (Y19, Y20), wherein Y1 is more than 0, Y2 is more than … is more than Y20;
when DT belongs to (Yg, Yg +1], presetting an excess electric energy threshold corresponding to an excess duration range as Yg;
v14: and if DE is larger than the excess electric energy threshold Yg, generating an early warning signal.
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| CN114035466A (en) * | 2021-11-05 | 2022-02-11 | 肇庆高峰机械科技有限公司 | Control system of duplex position magnetic sheet arrangement machine |
| CN114345556A (en) * | 2022-01-06 | 2022-04-15 | 深圳市福斯托精密电子设备有限公司 | Electric pulse control system and method |
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| CN116011795A (en) * | 2023-03-27 | 2023-04-25 | 国网山东省电力公司烟台供电公司 | Distributed power supply group regulation group control management system based on data analysis |
| CN117097572A (en) * | 2023-10-19 | 2023-11-21 | 吉林省东启铭网络科技有限公司 | Household Internet of things terminal and operation method thereof |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114035466A (en) * | 2021-11-05 | 2022-02-11 | 肇庆高峰机械科技有限公司 | Control system of duplex position magnetic sheet arrangement machine |
| CN114345556A (en) * | 2022-01-06 | 2022-04-15 | 深圳市福斯托精密电子设备有限公司 | Electric pulse control system and method |
| CN114345556B (en) * | 2022-01-06 | 2024-03-12 | 深圳市福斯托精密电子设备有限公司 | Electric pulse control system and method |
| CN114362176A (en) * | 2022-03-10 | 2022-04-15 | 浙江浙能能源服务有限公司 | Stabilizing system for square cabin nucleic acid laboratory hybrid power supply |
| CN116011795A (en) * | 2023-03-27 | 2023-04-25 | 国网山东省电力公司烟台供电公司 | Distributed power supply group regulation group control management system based on data analysis |
| CN116011795B (en) * | 2023-03-27 | 2023-08-01 | 国网山东省电力公司烟台供电公司 | Distributed power supply group regulation group control management system based on data analysis |
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