CN114690660A - Electric appliance control system based on smart home - Google Patents
Electric appliance control system based on smart home Download PDFInfo
- Publication number
- CN114690660A CN114690660A CN202210488834.6A CN202210488834A CN114690660A CN 114690660 A CN114690660 A CN 114690660A CN 202210488834 A CN202210488834 A CN 202210488834A CN 114690660 A CN114690660 A CN 114690660A
- Authority
- CN
- China
- Prior art keywords
- intelligent
- appliance control
- data
- equipment
- household appliance
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 239000013598 vector Substances 0.000 claims abstract description 41
- 238000000034 method Methods 0.000 claims abstract description 35
- 238000012937 correction Methods 0.000 claims description 39
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 4
- 238000012986 modification Methods 0.000 claims description 4
- 230000004048 modification Effects 0.000 claims description 4
- 230000001502 supplementing effect Effects 0.000 claims description 4
- 230000009469 supplementation Effects 0.000 claims description 3
- 238000004458 analytical method Methods 0.000 description 6
- 230000002159 abnormal effect Effects 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000013501 data transformation Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B15/00—Systems controlled by a computer
- G05B15/02—Systems controlled by a computer electric
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2642—Domotique, domestic, home control, automation, smart house
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Selective Calling Equipment (AREA)
Abstract
The invention discloses an electric appliance control system based on an intelligent home, belonging to the technical field of intelligent home and comprising a data association module, a control module and a server; the data association module associates the intelligent electrical appliance to obtain associated intelligent equipment; the control module is used for controlling the intelligent household appliance, and the specific method comprises the following steps: acquiring associated intelligent equipment, setting a characteristic factor of the associated intelligent equipment, setting an intelligent household appliance control scheme according to the characteristic factor and the corresponding associated intelligent equipment to form an intelligent household appliance control scheme library, and establishing a matching vector space according to the intelligent household appliance control scheme library; the method comprises the steps of obtaining current time, matching and associating intelligent equipment, collecting corresponding characteristic factor data, converting the obtained characteristic factor data into characteristic vectors, inputting the characteristic vectors into a matching vector space for matching, obtaining corresponding intelligent household appliance control scheme numbers, and matching the corresponding intelligent household appliance control schemes from an intelligent household appliance control scheme library according to the obtained intelligent household appliance control scheme numbers.
Description
Technical Field
The invention belongs to the technical field of intelligent home furnishing, and particularly relates to an electric appliance control system based on the intelligent home furnishing.
Background
Along with the development of science and technology and the improvement of living standard of people, the computer, the embedded system and the network communication technology are more and more deeply inserted into the life of people, so that the residence and household appliances of people form a trend towards intelligent development; the intelligent household product integrates an automatic control system and a computer network system to realize intelligent control; however, the linkage of the existing smart home is not good, and personalized experience of a user cannot be realized, so that the invention provides the smart home-based electric appliance control system, which is used for further improving the automation degree of the smart electric appliances in a house based on the smart home, realizing the association control and further improving the comfortable experience of the user.
Disclosure of Invention
In order to solve the problems existing in the scheme, the invention provides an electric appliance control system based on smart home.
The purpose of the invention can be realized by the following technical scheme:
the intelligent household-based electric appliance control system comprises a data association module, a control module and a server;
the data association module is used for associating the intelligent electric appliances to obtain associated intelligent equipment;
the control module is used for controlling the intelligent household appliance, and the specific method comprises the following steps:
acquiring associated intelligent equipment, setting a characteristic factor of the associated intelligent equipment, setting an intelligent household appliance control scheme according to the characteristic factor and the corresponding associated intelligent equipment to form an intelligent household appliance control scheme library, and establishing a matching vector space according to the intelligent household appliance control scheme library;
acquiring current time, matching and associating intelligent equipment, acquiring corresponding characteristic factor data, converting the acquired characteristic factor data into characteristic vectors, inputting the characteristic vectors into a matching vector space for matching, acquiring corresponding intelligent household appliance control scheme numbers, and matching the corresponding intelligent household appliance control schemes from an intelligent household appliance control scheme library according to the acquired intelligent household appliance control scheme numbers; and carrying out intelligent household appliance control according to the obtained intelligent household appliance control scheme.
Further, the working method of the data association module comprises the following steps:
the method comprises the steps of obtaining intelligent electrical appliances in a user home, marking the intelligent electrical appliances as target equipment, obtaining historical use data of the target equipment, analyzing the historical use data to obtain fixed association values among the target equipment, setting a time correction coefficient table, marking every two target equipment as to-be-selected associated equipment and marking the associated equipment as i, wherein i is 1, 2, … … and n, and n is a positive integer; matching the fixed association value of the association equipment to be selected, and marking as GPi; acquiring current time, marking as matching time, inputting the matching time and the to-be-selected associated equipment into a time correction coefficient table for matching, acquiring a corresponding time correction coefficient, and marking as SXi; according to the initial correlation value formula
Calculating an initial correlation value; wherein b1 and b2 are both proportionality coefficients with the value range of 0<b1≤1，0<b2 is less than or equal to 1; and setting an intelligent electrical appliance association threshold value X1, and determining corresponding associated intelligent equipment according to the intelligent electrical appliance association threshold value X1 and the initial association value CGi.
Further, the method for analyzing the historical use data comprises the following steps:
performing historical use data appointed keyword collaborative extraction to obtain single key data, performing single key data conversion to obtain key data coordinates, inputting the obtained key data coordinates into a coordinate space, performing clustering based on a K-means algorithm to obtain corresponding clusters, integrating the single key data belonging to the same cluster into a key data set, analyzing the key data set to obtain a fixed association value between corresponding target devices.
Further, the method for setting the characteristic factors of the associated intelligent equipment comprises the following steps:
and acquiring influence factors influencing the starting of the associated intelligent equipment, integrating the influence factors, marking the influence factors as characteristic factors, and setting the interval range corresponding to the characteristic factors.
Further, the method for establishing the matching vector space according to the intelligent household appliance control scheme library comprises the following steps:
the method comprises the steps of identifying each characteristic factor interval range corresponding to an intelligent household appliance control scheme, dividing vector areas corresponding to the intelligent household appliance control scheme in a vector space according to the identified characteristic factor interval range, setting an identification matching unit, wherein the identification matching unit is used for identifying which vector area an input characteristic vector is located in, and matching the corresponding intelligent household appliance control scheme number according to the identified vector area.
Further, the intelligent device management system further comprises a use correction module, wherein the use correction module is used for performing dynamic correction on the associated intelligent device according to the use data of the user.
Further, the working method using the correction module comprises the following steps:
acquiring a use record of a user on the intelligent electric appliance within a specified time, extracting and combining the use record, and acquiring personalized data; acquiring associated intelligent equipment, identifying differential data between individualized data and the associated intelligent equipment, identifying time attributes of the differential data, setting dynamic correction coefficients of the differential data, supplementing the dynamic correction coefficients according to the associated equipment to be selected, marking the dynamic correction coefficients CYi, and calculating a dynamic associated value according to a dynamic associated value formula DTi ═ lambda × CYi × CGi, wherein lambda is a correction factor, and the value range is 0< lambda is less than or equal to 1; and dynamically adjusting the associated intelligent equipment according to the calculated dynamic association value.
Further, the dynamic correction coefficient CYi for non-differentiated data supplementation is 1.
Compared with the prior art, the invention has the beneficial effects that: the intelligent household appliances in the house are intelligently associated through the mutual matching of the data association module and the control module, so that the linkage control of the intelligent household appliances in the house is realized, the automation degree of the intelligent household appliances in the house is further improved, and the comfortable experience of a user is improved; by arranging the use correction module, the personalized experience of the user is perfected, and dynamic adjustment is performed according to the use process data of the user, so that the user can live more comfortably and comfortably.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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, the smart home-based appliance control system includes a data association module, a usage modification module, a control module, and a server;
the data association module is used for associating the intelligent electric appliance, and the specific method comprises the following steps:
acquiring intelligent electrical appliances such as an intelligent air conditioner, an intelligent electric lamp and the like in the home of a user; marking as target equipment, acquiring historical use data of the target equipment, analyzing the historical use data to obtain a fixed association value between each pair of target equipment, setting a time correction coefficient table, marking each two pairs of target equipment as to-be-selected associated equipment, and marking as i, wherein i is 1, 2, … … and n, and n is a positive integer; matching the fixed correlation values of the correlation devices to be selected, and marking the fixed correlation values as GPi; acquiring current time, marking as matching time, inputting the matching time and the to-be-selected associated equipment into a time correction coefficient table for matching, acquiring a corresponding time correction coefficient, and marking as SXi; according to the initial correlation value formula
Calculating an initial correlation value; wherein b1 and b2 are both proportionality coefficients with the value range of 0<b1≤1，0<b2 is less than or equal to 1; setting intelligenceAn appliance association threshold X1, which is set by the expert group for discussion; and determining corresponding associated intelligent equipment according to the intelligent electrical appliance association threshold value X1 and the initial association value CGi.
And determining corresponding associated intelligent equipment according to the intelligent electrical appliance association threshold value X1 and the initial association value CGi, identifying the association relationship of each associated equipment according to the determined associated equipment, and integrating the integration of multiple associated equipment, namely associating multiple intelligent electrical appliances.
The historical use data of the target device refers to the past use data of the same type of intelligent household appliances, and the use data of the same type of intelligent household appliances can be acquired from the internet or other channels for the newly installed intelligent household appliances.
The method for analyzing the historical use data comprises the following steps:
performing historical use data appointed keyword collaborative extraction to obtain single key data, performing single key data conversion to obtain key data coordinates, inputting the obtained key data coordinates into a coordinate space, performing clustering based on a K-means algorithm to obtain corresponding clusters, integrating the single key data belonging to the same cluster into a key data set, analyzing the key data set to obtain a fixed association value between corresponding target devices.
The step of cooperatively extracting the appointed keywords of the historical use data is to set corresponding keywords according to the types of the intelligent electrical appliances and extract corresponding data in the historical data according to the set keywords; if one historical data comprises data of an air conditioner, an electric door window and the like, the specified keyword collaborative extraction is to extract use related data between the air conditioner and the electric door window, and when the air conditioner runs, the door window is in what state, namely, the data of mutual related influence.
The method for performing single key data transformation comprises the following steps:
identifying single key data, assigning the single key data, performing matching assignment by adopting a mode of establishing an assignment table by an expert group, or performing intelligent assignment by adopting a mode of establishing a neural network model, wherein the part which is not disclosed is common knowledge in the field, so detailed description is not needed, the key data coordinates are integrated after assignment is completed, and in the process, corresponding coordinate templates are arranged, namely, the corresponding data are assigned and filled in corresponding positions in the coordinate templates.
And clustering is carried out based on a K-means algorithm, the specific clustering process is common knowledge in the field, and the K value can be set according to the number of the target devices.
The method for analyzing the key data set comprises the following steps:
acquiring a distribution image corresponding to a cluster, integrating a key data set and the corresponding cluster distribution image into analysis data, acquiring a DNN network based on a CNN network to establish an intelligent model, establishing a training set according to the analysis data, training the intelligent model, marking the intelligent model which is trained successfully as an analysis model, analyzing the analysis data through the analysis model, and acquiring a fixed association value between corresponding target devices.
The setting of the time correction coefficient table is to set corresponding correction coefficients according to the time of two target devices, such as month, specific time of day, and the like, because the association of different target devices in different seasonal times is different, specifically, the time correction coefficient table may be established by the expert group according to the fixed association value between each target device, and the time correction coefficients between the corresponding target devices are matched according to the type of the target devices.
Because different users may have different use habits, and the setting of the associated intelligent device is not very personalized for the users, the dynamic adjustment is needed to be performed according to the use process data of the users on the basis of the associated intelligent device, so that the users live more comfortably and comfortably.
The use correction module is used for dynamically correcting the associated intelligent equipment according to the use data of the user, and the specific method comprises the following steps:
acquiring the use record of the user on the intelligent electric appliance within the set time, wherein the set time is the system set time and can be adjusted by the user; extracting and combining the use records to obtain personalized data; acquiring the associated intelligent equipment, namely adjusting the previously set associated intelligent equipment according to the acquired data; identifying differential data between individualized data and associated intelligent equipment, identifying time attributes of the differential data, namely corresponding time periods, setting dynamic correction coefficients of the differential data, supplementing the dynamic correction coefficients according to the associated equipment to be selected, marking the dynamic correction coefficients CYi, namely the dynamic correction coefficients CYi for non-differential data supplementation are 1, and supplementing the dynamic correction coefficients CYi for the associated equipment to be selected of the non-differential data are 1; calculating a dynamic correlation value according to a dynamic correlation value formula DTi which is lambda multiplied by CYi multiplied by CGi, wherein lambda is a correction factor, and the value range is 0< lambda is less than or equal to 1; and dynamically adjusting the associated intelligent equipment according to the calculated dynamic association value.
The method for extracting and combining the records is the same as that in the data association module and is equivalent to a key data set.
Identifying the difference between the individualized data and the associated intelligent equipment is to correspondingly identify the difference between the individualized data and the associated intelligent equipment, specifically, identifying whether the corresponding intelligent electrical appliance is associated to use or not from the individualized data according to the associated intelligent equipment, if the corresponding intelligent electrical appliance is not associated to use or is associated to a new electrical appliance, integrating the corresponding individualized data and the intelligent electrical appliance which is not associated to use into the difference data, comparing the difference data in a period of time, and if the difference data is not different once, listing the difference data as the difference data, and setting a corresponding threshold value, which is specifically the common knowledge in the field.
The method for setting the dynamic coefficient correction comprises the following steps:
identifying corresponding time attributes and abnormal electrical appliances, wherein the abnormal electrical appliances are not associated or are associated with new electrical appliances; acquiring a corresponding fixed correlation value, integrating the time attribute, the abnormal electrical appliance and the fixed correlation value into dynamic data, and analyzing the dynamic data to obtain a corresponding dynamic correction coefficient; through carrying out intelligent analysis based on deep learning, the specific unpublished is common knowledge in the field.
The control module is used for controlling the intelligent household appliance, and the specific method comprises the following steps:
acquiring associated intelligent equipment, setting a characteristic factor of the associated intelligent equipment, setting an intelligent household appliance control scheme according to the characteristic factor and the corresponding associated intelligent equipment to form an intelligent household appliance control scheme library, and establishing a matching vector space according to the intelligent household appliance control scheme library;
acquiring current time, matching and associating intelligent equipment, acquiring corresponding characteristic factor data, converting the acquired characteristic factor data into characteristic vectors, inputting the characteristic vectors into a matching vector space for matching, acquiring corresponding intelligent household appliance control scheme numbers, and matching the corresponding intelligent household appliance control schemes from an intelligent household appliance control scheme library according to the acquired intelligent household appliance control scheme numbers; and carrying out intelligent household appliance control according to the obtained intelligent household appliance control scheme.
The method for setting the characteristic factors of the associated intelligent equipment comprises the following steps:
the method comprises the steps of obtaining influence factors influencing the starting of the associated intelligent equipment, such as factors of weather, temperature, air quality, visibility and the like, integrating the influence factors, marking the influence factors as characteristic factors, setting an interval range corresponding to the characteristic factors, considering starting of the corresponding intelligent electrical appliance in the interval range, and setting according to specific historical data.
The setting of the intelligent appliance control scheme according to the characteristic factors and the corresponding associated intelligent devices is to set an operation scheme corresponding to the associated intelligent devices according to the corresponding characteristic factors and the associated intelligent devices, and specifically, the setting can be manually performed by disclosing the common knowledge in the field.
The method for establishing the matching vector space according to the intelligent household appliance control scheme library comprises the following steps:
the method comprises the steps of identifying each characteristic factor interval range corresponding to an intelligent household appliance control scheme, dividing a vector area corresponding to the intelligent household appliance control scheme in a vector space according to the identified characteristic factor interval range, setting an identification matching unit, wherein the identification matching unit is used for identifying which vector area an input characteristic vector is located in, and matching the corresponding intelligent household appliance control scheme number according to the identified vector area.
The obtained characteristic factor data are converted into the characteristic vectors, namely the characteristic vectors are converted into numerical values according to the corresponding data, the numerical values are integrated into the characteristic vectors, and in the conversion process, for the numerical data, a corresponding numerical value matching table can be established, for example, different weather corresponds to different numerical values.
The above formulas are all calculated by removing dimensions and taking numerical values thereof, the formula is a formula which is obtained by acquiring a large amount of data and performing software simulation to obtain the closest real situation, and the preset parameters and the preset threshold value in the formula are set by the technical personnel in the field according to the actual situation or obtained by simulating a large amount of data.
The working principle of the invention is as follows: carrying out intelligent electrical appliance association through a data association module to obtain associated intelligent equipment; acquiring associated intelligent equipment, setting a characteristic factor of the associated intelligent equipment, setting an intelligent household appliance control scheme according to the characteristic factor and the corresponding associated intelligent equipment to form an intelligent household appliance control scheme library, and establishing a matching vector space according to the intelligent household appliance control scheme library; acquiring current time, matching and associating intelligent equipment, acquiring corresponding characteristic factor data, converting the acquired characteristic factor data into characteristic vectors, inputting the characteristic vectors into a matching vector space for matching, acquiring corresponding intelligent household appliance control scheme numbers, and matching the corresponding intelligent household appliance control schemes from an intelligent household appliance control scheme library according to the acquired intelligent household appliance control scheme numbers; and carrying out intelligent household appliance control according to the obtained intelligent household appliance control scheme.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the spirit and scope of the present invention.
Claims (8)
1. The intelligent household-based electric appliance control system is characterized by comprising a data association module, a control module and a server;
the data association module is used for associating the intelligent electric appliances to obtain associated intelligent equipment;
the control module is used for controlling the intelligent household appliance, and the specific method comprises the following steps:
acquiring associated intelligent equipment, setting a characteristic factor of the associated intelligent equipment, setting an intelligent household appliance control scheme according to the characteristic factor and the corresponding associated intelligent equipment to form an intelligent household appliance control scheme library, and establishing a matching vector space according to the intelligent household appliance control scheme library;
acquiring current time, matching and associating intelligent equipment, acquiring corresponding characteristic factor data, converting the acquired characteristic factor data into characteristic vectors, inputting the characteristic vectors into a matching vector space for matching, acquiring corresponding intelligent household appliance control scheme numbers, and matching the corresponding intelligent household appliance control schemes from an intelligent household appliance control scheme library according to the acquired intelligent household appliance control scheme numbers; and carrying out intelligent household appliance control according to the obtained intelligent household appliance control scheme.
2. The intelligent household-based electric appliance control system according to claim 1, wherein the working method of the data association module comprises the following steps:
the method comprises the steps of obtaining intelligent electrical appliances in a user home, marking the intelligent electrical appliances as target equipment, obtaining historical use data of the target equipment, analyzing the historical use data to obtain fixed association values among the target equipment, setting a time correction coefficient table, marking every two target equipment as to-be-selected associated equipment and marking the associated equipment as i, wherein i is 1, 2, … … and n, and n is a positive integer; matching the fixed association value of the association equipment to be selected, and marking as GPi; acquiring current time, marking as matching time, inputting the matching time and the to-be-selected associated equipment into a time correction coefficient table for matching, acquiring a corresponding time correction coefficient, and marking as SXi; according to the initial correlation value formula
Calculating an initial correlation value; wherein b1 and b2 are both proportionality coefficients with the value range of 0<b1≤1，0<b2 is less than or equal to 1; and setting an intelligent electrical appliance association threshold value X1, and determining corresponding associated intelligent equipment according to the intelligent electrical appliance association threshold value X1 and the initial association value CGi.
3. The smart home-based appliance control system of claim 2, wherein the method of analyzing historical usage data comprises:
performing historical use data appointed keyword collaborative extraction to obtain single key data, performing single key data conversion to obtain key data coordinates, inputting the obtained key data coordinates into a coordinate space, performing clustering based on a K-means algorithm to obtain corresponding clusters, integrating the single key data belonging to the same cluster into a key data set, analyzing the key data set to obtain a fixed association value between corresponding target devices.
4. The intelligent home-based electric appliance control system according to claim 1, wherein the method for setting the characteristic factors associated with the intelligent devices comprises:
and acquiring influence factors influencing the starting of the associated intelligent equipment, integrating the influence factors, marking the influence factors as characteristic factors, and setting the interval range corresponding to the characteristic factors.
5. The intelligent home-based appliance control system according to claim 1, wherein the method for establishing the matching vector space according to the intelligent appliance control scheme library comprises:
the method comprises the steps of identifying each characteristic factor interval range corresponding to an intelligent household appliance control scheme, dividing a vector area corresponding to the intelligent household appliance control scheme in a vector space according to the identified characteristic factor interval range, setting an identification matching unit, wherein the identification matching unit is used for identifying which vector area an input characteristic vector is located in, and matching the corresponding intelligent household appliance control scheme number according to the identified vector area.
6. The smart home-based appliance control system according to claim 1, further comprising a usage modification module, wherein the usage modification module is configured to perform dynamic modification of the associated smart devices according to usage data of the user.
7. The smart home-based appliance control system according to claim 6, wherein the working method using the correction module comprises:
acquiring a use record of a user on the intelligent electric appliance within a specified time, extracting and combining the use record, and acquiring personalized data; acquiring associated intelligent equipment, identifying differential data between individualized data and the associated intelligent equipment, identifying time attributes of the differential data, setting dynamic correction coefficients of the differential data, supplementing the dynamic correction coefficients according to the associated equipment to be selected, marking the dynamic correction coefficients CYi, and calculating a dynamic associated value according to a dynamic associated value formula DTi (lambda is a correction factor) and a numeric area of 0< lambda is less than or equal to 1 by multiplying the dynamic correction coefficients by CYi multiplied by CGi; and dynamically adjusting the associated intelligent equipment according to the calculated dynamic association value.
8. The smart home-based appliance control system of claim 7, wherein the dynamic correction factor CYi for non-differentiated data supplementation is 1.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210488834.6A CN114690660A (en) | 2022-05-06 | 2022-05-06 | Electric appliance control system based on smart home |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210488834.6A CN114690660A (en) | 2022-05-06 | 2022-05-06 | Electric appliance control system based on smart home |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114690660A true CN114690660A (en) | 2022-07-01 |
Family
ID=82145326
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210488834.6A Pending CN114690660A (en) | 2022-05-06 | 2022-05-06 | Electric appliance control system based on smart home |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114690660A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116068910A (en) * | 2023-04-06 | 2023-05-05 | 江西财经大学 | Intelligent home control method and system based on big data |
| CN117201566A (en) * | 2023-11-01 | 2023-12-08 | 无锡迪富智能电子股份有限公司 | Fusion control method for intelligent home interaction |
-
2022
- 2022-05-06 CN CN202210488834.6A patent/CN114690660A/en active Pending
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116068910A (en) * | 2023-04-06 | 2023-05-05 | 江西财经大学 | Intelligent home control method and system based on big data |
| CN117201566A (en) * | 2023-11-01 | 2023-12-08 | 无锡迪富智能电子股份有限公司 | Fusion control method for intelligent home interaction |
| CN117201566B (en) * | 2023-11-01 | 2024-03-19 | 无锡迪富智能电子股份有限公司 | Fusion control method for intelligent home interaction |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114690660A (en) | Electric appliance control system based on smart home | |
| CN108488987B (en) | Control method of air conditioning apparatus, storage medium, and apparatus | |
| WO2021073462A1 (en) | 10 kv static load model parameter identification method based on similar daily load curves | |
| CN108121211A (en) | Control method, server and the computer readable storage medium of home appliance | |
| CN104471628B (en) | Information processor, information processing method and system | |
| CN115422452B (en) | Smart home control method, device, equipment and storage medium based on big data | |
| CN116881555A (en) | User personalized news pushing system based on AI and big data analysis | |
| CN109698777A (en) | Smart home mode configuration method, device and equipment | |
| CN114884220A (en) | Intelligent power distribution method and system | |
| CN110096793A (en) | A kind of house energy consumption prediction technique based on residential subscribers' activity pattern | |
| Ding et al. | Multiemotional product color design using gray theory and nondominated sorting genetic algorithm‐III | |
| CN117559443A (en) | Ordered power utilization control method for large industrial user cluster under peak load | |
| CN110286600A (en) | The scene setting method and device of smart home operating system | |
| CN116951780A (en) | Intelligent central water heater management method and system | |
| CN110909036A (en) | Functional module recommendation method and device | |
| CN115759664A (en) | User carbon portrait method based on electric energy use behaviors | |
| CN115203617A (en) | Webpage access single-button auxiliary input system based on RPA | |
| CN115238951A (en) | Power load prediction method and device | |
| CN108537407A (en) | A kind of multisystem data integration processing system | |
| CN114676400A (en) | Identity determination method, storage medium and electronic device | |
| CN109754333B (en) | Regional lightning sensitive load excavation method with multi-region aggregation | |
| CN107300857B (en) | Electric energy management system for sensing indoor environment information | |
| CN116821451B (en) | User management system and method based on full life cycle | |
| CN109726461A (en) | A kind of multiplexing electric abnormality judgment method based on user power utilization data | |
| CN117156625B (en) | LED lamp strip adjusting control system and method based on electric data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |