CN113485147B - Intelligent home control method and system based on big data analysis - Google Patents

Intelligent home control method and system based on big data analysis Download PDF

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CN113485147B
CN113485147B CN202110883382.7A CN202110883382A CN113485147B CN 113485147 B CN113485147 B CN 113485147B CN 202110883382 A CN202110883382 A CN 202110883382A CN 113485147 B CN113485147 B CN 113485147B
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preset
feature
item
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CN113485147A (en
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李超
褚富强
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Nupt Institute Of Big Data Research At Yancheng
Nanjing University of Posts and Telecommunications
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Nupt Institute Of Big Data Research At Yancheng
Nanjing University of Posts and Telecommunications
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    • 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]

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Abstract

The invention provides an intelligent home control method and system based on big data analysis, wherein the method comprises the following steps: step S1: acquiring a device set used by a user, wherein the device set comprises: a plurality of first devices; step S2: determining a first demand parameter corresponding to the first device based on a preset demand parameter library, wherein the first demand parameter comprises: a first user parameter and/or a first house parameter; step S3: acquiring big data, and extracting an optimal control scheme corresponding to first equipment from the big data based on a first demand parameter; step S4: and controlling the corresponding first equipment based on the optimal control scheme. According to the intelligent home control method and system based on big data analysis, when the user starts the intelligent home equipment, the optimal control scheme of the intelligent home equipment is determined based on the big data, the intelligent home equipment is directly controlled, the working mode does not need to be set by the user, and the intelligent home control method and system are more intelligent.

Description

Intelligent home control method and system based on big data analysis
Technical Field
The invention relates to the technical field of big data analysis, in particular to an intelligent home control method and system based on big data analysis.
Background
At present, most of various devices of smart homes need users to set working modes by themselves, and are still not intelligent enough, so a solution is urgently needed.
Disclosure of Invention
One of the purposes of the invention is to provide an intelligent home control method and system based on big data analysis, when a user starts the intelligent home equipment, the optimal control scheme of the intelligent home equipment is determined based on the big data, the intelligent home equipment is directly controlled, the user does not need to set a working mode by himself, and the intelligent home control method and system are more intelligent.
The embodiment of the invention provides an intelligent home control method based on big data analysis, which comprises the following steps:
step S1: acquiring a device set used by a user, wherein the device set comprises: a plurality of first devices;
step S2: determining a first demand parameter corresponding to the first device based on a preset demand parameter library, wherein the first demand parameter comprises: a first user parameter and/or a first house parameter;
step S3: acquiring big data, and extracting an optimal control scheme corresponding to first equipment from the big data based on a first demand parameter;
step S4: and controlling the corresponding first equipment based on the optimal control scheme.
Preferably, in step S3, the acquiring big data includes:
acquiring a preset node set, wherein the node set comprises: a plurality of nodes;
acquiring target data through a node, wherein the target data comprises: second demand parameters of other users, the second demand parameters including: a second user parameter, a second house parameter, and usage records of a plurality of second devices;
and integrating the target data acquired by each node to acquire big data.
Preferably, in step S3, the extracting the optimal control scheme corresponding to the first device from the big data includes:
performing feature extraction on the first demand parameters to obtain a plurality of first features;
performing feature extraction on the second demand parameters to obtain a plurality of second features;
matching the first feature with a second feature having a second feature type that is the same as the first feature type based on the first feature type of the first feature;
obtaining a matching result, wherein the matching result comprises: a third feature type and a matching value common to the first feature and the second feature to be matched;
determining a contribution value corresponding to the third feature type and the matching value together based on a preset contribution value comparison library, and associating the contribution value with a second feature matched in the matching result;
extracting other users meeting a preset first condition from the other users, and taking the other users as target users;
establishing a first time axis, and expanding a plurality of first sub-records related to first equipment corresponding to a first requirement parameter in the use record of a target user on the first time axis to obtain a plurality of first record items;
extracting a first record item which meets a preset second condition from the first record items, and taking the first record item as a second record item;
extracting a first record item which meets a preset third condition from the first record items, and taking the first record item as a third record item;
extracting a first control scheme from the second record item, and giving a first weight to the first control scheme;
extracting a second control scheme from the third record item, and giving a second weight to the second control scheme;
acquiring a preset extraction model, inputting a first control scheme with a first weight and a second control scheme with a second weight into the extraction model, and acquiring an optimal control scheme;
wherein the first condition comprises: the contribution values of the second characteristics in the second demand parameters of other users are all larger than or equal to a preset contribution value threshold;
the second condition includes: other first record items do not exist in a first time range preset after the first record items on the time axis;
the third condition includes: the first difference degree between other first record items appearing in a first time range preset after the first record item on the time axis and the first record item is less than or equal to a preset first difference degree threshold value;
the first weight is greater than the second weight.
Preferably, the smart home control method based on big data analysis further includes:
step S5: carrying out adaptive adjustment on the optimal control scheme of each first device;
the adaptive adjustment of the optimal control scheme of each first device includes:
summarizing all the optimal control schemes of the first equipment to obtain a first scheme set;
determining an important value corresponding to the first equipment based on a preset important value library;
taking the first equipment corresponding to the maximum value of the important value in the first equipment as second equipment, and taking the rest first equipment as third equipment;
respectively acquiring a first scheme of second equipment and a second scheme of third equipment in a first scheme set;
establishing a conflict comparison library, and determining a first conflict item and a first conflict value between the first scheme and the second scheme based on the conflict comparison library;
determining an adjusting scheme corresponding to the second scheme, the first conflict item and the first conflict value together based on a preset adjusting scheme library;
the corresponding second scheme is adjusted based on the adjustment scheme.
Preferably, the creating of the conflict comparison library includes:
acquiring a preset second scheme set, and randomly selecting a scheme combination from the second scheme set, wherein the scheme combination comprises the following steps: a third aspect and a fourth aspect;
attempting to determine a second conflict item between the third scenario and the fourth scenario based on a preset library of conflict items;
if the determination is successful, respectively determining fourth equipment corresponding to the third scheme and fifth equipment corresponding to the fourth scheme;
establishing a second time axis, and expanding a plurality of second sub-records related to fourth equipment in the use records of other users on the second time axis to obtain a plurality of fourth record items;
establishing a third time axis, and expanding a plurality of third sub-records related to the fifth equipment in the use records of other users on the second time axis to obtain a plurality of fifth record items;
extracting features of the third scheme to obtain a plurality of third features;
performing feature extraction on the fourth record item to obtain a plurality of fourth features;
matching the third characteristic with the fourth characteristic, and if the matching is in accordance with the first characteristic, determining that the fourth characteristic to be matched corresponds to the first position of the fourth record item on the second time axis;
determining a second position on a third time axis corresponding to the first position;
acquiring a fifth recording item in a preset second time range before and/or after a second position on a third time axis, and taking the fifth recording item as a sixth recording item;
performing feature extraction on the fourth scheme to obtain a plurality of fifth features;
performing feature extraction on the sixth record item to obtain a plurality of sixth features;
matching the fifth feature with the sixth feature, and if the matching is in accordance with the fifth feature, determining a third position of the sixth record item corresponding to the matched sixth feature on a third time axis;
if the third position is in front of the second position, acquiring a fourth record item which finally appears in a preset third time range after the first position on the second time axis, and taking the fourth record item as a seventh record item;
acquiring a second difference degree between the seventh record item and the fourth record item at the first position;
if the second difference degree is larger than or equal to a preset difference degree threshold value, counting for one time;
if the third position is at the second position, acquiring a fifth record item which finally appears in a preset third time range after the third position on a third time axis, and taking the fifth record item as an eighth record item;
acquiring a third difference degree between the eighth record item and a sixth record item at a third position;
if the third difference degree is larger than or equal to the difference degree threshold value, counting for one time;
summarizing counting results to obtain a counting sum;
determining a count and a corresponding second conflict value based on a preset conflict value library;
taking the third scheme, the fourth scheme, the second conflict item and the second conflict value as a group of control groups;
acquiring a preset blank database, and storing a comparison group into the blank database;
and when all the comparison groups are stored in the blank database, taking the blank database as a conflict comparison database to finish the establishment.
The embodiment of the invention provides an intelligent home control system based on big data analysis, which comprises:
an obtaining module, configured to obtain a device set that is being used by a user, where the device set includes: a plurality of first devices;
the determining module is configured to determine a first demand parameter corresponding to the first device based on a preset demand parameter library, where the first demand parameter includes: a first user parameter and/or a first house parameter;
the extraction module is used for acquiring the big data and extracting the optimal control scheme corresponding to the first equipment from the big data based on the first demand parameter;
and the control module is used for controlling the corresponding first equipment based on the optimal control scheme.
Preferably, the extraction module performs the following operations:
acquiring a preset node set, wherein the node set comprises: a plurality of nodes;
acquiring target data through a node, wherein the target data comprises: second demand parameters of other users, the second demand parameters including: a second user parameter, a second house parameter, and usage records of a plurality of second devices;
and integrating the target data acquired by each node to acquire big data.
Preferably, the extraction module performs the following operations:
performing feature extraction on the first demand parameters to obtain a plurality of first features;
performing feature extraction on the second demand parameters to obtain a plurality of second features;
matching the first feature with a second feature having a second feature type that is the same as the first feature type based on the first feature type of the first feature;
obtaining a matching result, wherein the matching result comprises: a third feature type and a matching value common to the first feature and the second feature to be matched;
determining a contribution value corresponding to the third feature type and the matching value together based on a preset contribution value comparison library, and associating the contribution value with a second feature matched in the matching result;
extracting other users meeting a preset first condition from the other users, and taking the other users as target users;
establishing a first time axis, and expanding a plurality of first sub-records related to first equipment corresponding to a first requirement parameter in the use record of a target user on the first time axis to obtain a plurality of first record items;
extracting a first record item which meets a preset second condition from the first record items, and taking the first record item as a second record item;
extracting a first record item which meets a preset third condition from the first record items, and taking the first record item as a third record item;
extracting a first control scheme from the second record item, and giving a first weight to the first control scheme;
extracting a second control scheme from the third record item, and giving a second weight to the second control scheme;
acquiring a preset extraction model, inputting a first control scheme with a first weight and a second control scheme with a second weight into the extraction model, and acquiring an optimal control scheme;
wherein the first condition comprises: the contribution values of the second characteristics in the second demand parameters of other users are all larger than or equal to a preset contribution value threshold;
the second condition includes: other first record items do not exist in a first time range preset after the first record items on the time axis;
the third condition includes: the first difference degree between other first record items appearing in a first time range preset after the first record item on the time axis and the first record item is less than or equal to a preset first difference degree threshold value;
the first weight is greater than the second weight.
Preferably, the smart home control system based on big data analysis further includes:
the adjusting module is used for adaptively adjusting the optimal control scheme of each first device;
the adjustment module performs the following operations:
summarizing all the optimal control schemes of the first equipment to obtain a first scheme set;
determining an important value corresponding to the first equipment based on a preset important value library;
taking the first equipment corresponding to the maximum value of the important value in the first equipment as second equipment, and taking the rest first equipment as third equipment;
respectively acquiring a first scheme of second equipment and a second scheme of third equipment in a first scheme set;
establishing a conflict comparison library, and determining a first conflict item and a first conflict value between the first scheme and the second scheme based on the conflict comparison library;
determining an adjusting scheme corresponding to the second scheme, the first conflict item and the first conflict value together based on a preset adjusting scheme library;
the corresponding second scheme is adjusted based on the adjustment scheme.
Preferably, the adjusting module performs the following operations:
acquiring a preset second scheme set, and randomly selecting a scheme combination from the second scheme set, wherein the scheme combination comprises the following steps: a third aspect and a fourth aspect;
attempting to determine a second conflict item between the third scenario and the fourth scenario based on a preset library of conflict items;
if the determination is successful, respectively determining fourth equipment corresponding to the third scheme and fifth equipment corresponding to the fourth scheme;
establishing a second time axis, and expanding a plurality of second sub-records related to fourth equipment in the use records of other users on the second time axis to obtain a plurality of fourth record items;
establishing a third time axis, and expanding a plurality of third sub-records related to the fifth equipment in the use records of other users on the second time axis to obtain a plurality of fifth record items;
extracting features of the third scheme to obtain a plurality of third features;
performing feature extraction on the fourth record item to obtain a plurality of fourth features;
matching the third characteristic with the fourth characteristic, and if the matching is in accordance with the first characteristic, determining that the fourth characteristic to be matched corresponds to the first position of the fourth record item on the second time axis;
determining a second position on a third time axis corresponding to the first position;
acquiring a fifth recording item in a preset second time range before and/or after a second position on a third time axis, and taking the fifth recording item as a sixth recording item;
performing feature extraction on the fourth scheme to obtain a plurality of fifth features;
performing feature extraction on the sixth record item to obtain a plurality of sixth features;
matching the fifth feature with the sixth feature, and if the matching is in accordance with the fifth feature, determining a third position of the sixth record item corresponding to the matched sixth feature on a third time axis;
if the third position is in front of the second position, acquiring a fourth record item which finally appears in a preset third time range after the first position on the second time axis, and taking the fourth record item as a seventh record item;
acquiring a second difference degree between the seventh record item and the fourth record item at the first position;
if the second difference degree is larger than or equal to a preset difference degree threshold value, counting for one time;
if the third position is at the second position, acquiring a fifth record item which finally appears in a preset third time range after the third position on a third time axis, and taking the fifth record item as an eighth record item;
acquiring a third difference degree between the eighth record item and a sixth record item at a third position;
if the third difference degree is larger than or equal to the difference degree threshold value, counting for one time;
summarizing counting results to obtain a counting sum;
determining a count and a corresponding second conflict value based on a preset conflict value library;
taking the third scheme, the fourth scheme, the second conflict item and the second conflict value as a group of control groups;
acquiring a preset blank database, and storing a comparison group into the blank database;
and when all the comparison groups are stored in the blank database, taking the blank database as a conflict comparison database to finish the establishment.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of an intelligent home control method based on big data analysis in an embodiment of the present invention;
fig. 2 is a schematic diagram of an intelligent home control system based on big data analysis in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides an intelligent home control method based on big data analysis, which comprises the following steps of:
step S1: acquiring a device set used by a user, wherein the device set comprises: a plurality of first devices;
step S2: determining a first demand parameter corresponding to the first device based on a preset demand parameter library, wherein the first demand parameter comprises: a first user parameter and/or a first house parameter;
step S3: acquiring big data, and extracting an optimal control scheme corresponding to first equipment from the big data based on a first demand parameter;
step S4: and controlling the corresponding first equipment based on the optimal control scheme.
The working principle and the beneficial effects of the technical scheme are as follows:
obtain a set of devices being used by a user [ a plurality of first devices, for example: intelligence air conditioner, intelligent stereo set, intelligence machine of sweeping floor etc. based on predetermined demand parameter storehouse [ a database, the demand parameter that every equipment corresponds is stored in, confirms the first demand parameter that first equipment corresponds, and first demand parameter includes: first user parameters [ user-set family member age, preferences, etc. ] and/or first house parameters [ room area, etc. ], such as: when the intelligent air conditioner is controlled, the user parameters and the house parameters need to be based, and when the intelligent sweeper is controlled, only the house area needs to be determined; acquiring big data, and extracting an optimal control scheme from the big data; controlling the corresponding first device based on the optimal control scheme; meanwhile, the user can also manually intervene to adjust the control scheme;
according to the embodiment of the invention, when the user starts the intelligent household equipment, the optimal control scheme of the intelligent household equipment is determined based on the big data, the intelligent household equipment is directly controlled, the working mode does not need to be set by the user, and the intelligent household equipment is more intelligent.
The embodiment of the invention provides an intelligent home control method based on big data analysis, and in the step S3, big data is obtained, wherein the method comprises the following steps:
acquiring a preset node set, wherein the node set comprises: a plurality of nodes;
acquiring target data through a node, wherein the target data comprises: second demand parameters of other users, the second demand parameters including: a second user parameter, a second house parameter, and usage records of a plurality of second devices;
and integrating the target data acquired by each node to acquire big data.
The working principle and the beneficial effects of the technical scheme are as follows:
acquiring target data through nodes in a preset node set [ containing a plurality of nodes, each node corresponding to one other user ], wherein the target data contains second user parameters [ for example: family membership information, preferences, etc.), second house parameters [ house area, etc.), and usage records of a plurality of second devices [ smart home devices of other users [ for example: what kind of working mode is set, etc.; before the product is put into use or in the initial stage, experiments of different user parameters and different house parameters can be carried out in the background, and a large amount of target data is generated.
The embodiment of the invention provides an intelligent home control method based on big data analysis, and in step S3, an optimal control scheme corresponding to first equipment is extracted from big data, and the method comprises the following steps:
performing feature extraction on the first demand parameters to obtain a plurality of first features;
performing feature extraction on the second demand parameters to obtain a plurality of second features;
matching the first feature with a second feature having a second feature type that is the same as the first feature type based on the first feature type of the first feature;
obtaining a matching result, wherein the matching result comprises: a third feature type and a matching value common to the first feature and the second feature to be matched;
determining a contribution value corresponding to the third feature type and the matching value together based on a preset contribution value comparison library, and associating the contribution value with a second feature matched in the matching result;
extracting other users meeting a preset first condition from the other users, and taking the other users as target users;
establishing a first time axis, and expanding a plurality of first sub-records related to first equipment corresponding to a first requirement parameter in the use record of a target user on the first time axis to obtain a plurality of first record items;
extracting a first record item which meets a preset second condition from the first record items, and taking the first record item as a second record item;
extracting a first record item which meets a preset third condition from the first record items, and taking the first record item as a third record item;
extracting a first control scheme from the second record item, and giving a first weight to the first control scheme;
extracting a second control scheme from the third record item, and giving a second weight to the second control scheme;
acquiring a preset extraction model, inputting a first control scheme with a first weight and a second control scheme with a second weight into the extraction model, and acquiring an optimal control scheme;
wherein the first condition comprises: the contribution values of the second characteristics in the second demand parameters of other users are all larger than or equal to a preset contribution value threshold;
the second condition includes: other first record items do not exist in a first time range preset after the first record items on the time axis;
the third condition includes: the first difference degree between other first record items appearing in a first time range preset after the first record item on the time axis and the first record item is less than or equal to a preset first difference degree threshold value;
the first weight is greater than the second weight.
The working principle and the beneficial effects of the technical scheme are as follows:
if the optimal working scheme of the first device is extracted from big data consisting of a plurality of target data, the optimal working scheme is ensured to meet the actual use requirement of a user, namely, the second requirement parameter in the target data is sufficiently met with the first requirement parameter of the user, and then the use record in the corresponding target data can be referred;
therefore, feature extraction is respectively carried out on the first requirement parameter and the second requirement parameter to obtain a plurality of first features and second features; matching the first feature with the second feature (matching with the feature type) to obtain a matching result, wherein the matching result comprises a third feature type with the same first feature and the same second feature and a matching value between the third feature type and the third feature type; comparing a database (a database) with preset contribution values, wherein the database stores the contribution values corresponding to different feature types and different matching values, and background personnel continuously count and update the contribution values, wherein the larger the contribution value is, the larger the reference value of the corresponding use record is if the corresponding feature of the feature type is matched is, and the third feature type and the corresponding contribution value of the matching value are determined; if other users meet a preset first condition, the use records of the other users have reference values, and the other users are screened out as target users; expanding a plurality of sub-records related to the corresponding first equipment in the usage record of the target user on a first time axis [ record generation time can be expanded when corresponding to a time node on the time axis ], and obtaining a plurality of first record items; when a user generates a one-time usage record [ e.g.: setting a working mode, if the condition is satisfied, and no new use record can be generated soon after the use record, meeting a second condition; when the user generates a use record, if the user is not satisfied at all, slightly adjusting (the first difference between the previous and the next use records is small), and explaining that the control scheme in the use record which is generated at the beginning is still, the third condition is satisfied; extracting a second record item and a third record item, then extracting a first control scheme and a second control scheme, reasonably giving different weights, inputting a preset extraction model (preset), learning a large number of manual extraction records by using a machine learning algorithm to generate a model, and during extraction, emphasizing on a control scheme with large weight, judging popularity and the like of the control scheme) and extracting an optimal control scheme;
the preset contribution value threshold specifically includes: for example, 97; presetting a first time range: for example, 4 minutes; the preset first difference threshold specifically includes: for example, 2;
the embodiment of the invention extracts the optimal control scheme of the first equipment from the big data, is convenient and quick, and has applicability under the trend that the big data tends to be popular; when the use records of other users are referred, the first demand parameter and the second demand parameter are matched, so that the reasonability is achieved, and the accuracy of extracting the optimal control scheme is improved; setting a contribution value comparison library to conveniently determine the reference value of the feature type; capturing the actual scene of the normal user if the normal user is satisfied or more satisfied with the current control scheme, setting a second condition and a third condition, and quickly extracting the first control scheme and the second control scheme, so that the method is ingenious and convenient; different weights are given to the first control scheme and the second control scheme, and the rationality is improved; based on the extraction model, an optimal control scheme is extracted, and the working efficiency of the system is improved.
The embodiment of the invention provides an intelligent home control method based on big data analysis, which further comprises the following steps:
step S5: carrying out adaptive adjustment on the optimal control scheme of each first device;
the adaptive adjustment of the optimal control scheme of each first device includes:
summarizing all the optimal control schemes of the first equipment to obtain a first scheme set;
determining an important value corresponding to the first equipment based on a preset important value library;
taking the first equipment corresponding to the maximum value of the important value in the first equipment as second equipment, and taking the rest first equipment as third equipment;
respectively acquiring a first scheme of second equipment and a second scheme of third equipment in a first scheme set;
establishing a conflict comparison library, and determining a first conflict item and a first conflict value between the first scheme and the second scheme based on the conflict comparison library;
determining an adjusting scheme corresponding to the second scheme, the first conflict item and the first conflict value together based on a preset adjusting scheme library;
the corresponding second scheme is adjusted based on the adjustment scheme.
The working principle and the beneficial effects of the technical scheme are as follows:
determining an important value of first equipment based on a preset important value library (a database in which the important value of each household intelligent equipment is stored); taking the first equipment with the largest importance value as second equipment, and taking the rest first equipment as third equipment; determining a first scheme of a second device and a second scheme of a third device; determining a first conflict item and a first conflict value between the first scheme and the second scheme based on the conflict comparison library; based on a preset adjustment scheme library (a database) in which adjustment schemes corresponding to different schemes, conflict items and conflict values are stored, adjusting the corresponding second scheme based on the adjustment schemes, and controlling the corresponding intelligent household equipment by using the adjusted second scheme; the intelligent home equipment with the maximum importance can be ensured to operate according to the optimal control scheme, and the other equipment is correspondingly adjusted without conflict with the intelligent home equipment; after the adaptive adjustment is intervened, the optimal control scheme is not extracted to control the corresponding first equipment until no conflict exists;
for example: the optimal control scheme of the intelligent air conditioner is based on outdoor temperature regulation, mainly takes room temperature regulation and indoor temperature proper maintenance and mainly takes silence as the main control scheme; the intelligent sound box is in an active mode, and the sound is large and dynamic; the conflict items of the two are as follows: multiple devices emit greater volume, which may cause discomfort to the user; the conflict value is: 80; the importance value of the room temperature regulation is large because the importance of the room temperature regulation is high; the control scheme of the intelligent sound box is adjusted, the volume is reduced or the sound box is turned off, and the comfort level of the use environment is guaranteed;
when the optimal control schemes are determined by the plurality of first devices, considering the special scene that the user may conflict in actual use, determining conflict items and conflict values, then determining an adjustment scheme, and performing adaptive adjustment on the corresponding second scheme, so as to ensure that the optimal control schemes of the first devices do not conflict and give the user the optimal experience feeling; meanwhile, an important library is arranged, so that the importance of the first equipment can be distinguished conveniently and rapidly.
The embodiment of the invention provides an intelligent home control method based on big data analysis, which is used for establishing a conflict comparison library and comprises the following steps:
acquiring a preset second scheme set, and randomly selecting a scheme combination from the second scheme set, wherein the scheme combination comprises the following steps: a third aspect and a fourth aspect;
attempting to determine a second conflict item between the third scenario and the fourth scenario based on a preset library of conflict items;
if the determination is successful, respectively determining fourth equipment corresponding to the third scheme and fifth equipment corresponding to the fourth scheme;
establishing a second time axis, and expanding a plurality of second sub-records related to fourth equipment in the use records of other users on the second time axis to obtain a plurality of fourth record items;
establishing a third time axis, and expanding a plurality of third sub-records related to the fifth equipment in the use records of other users on the second time axis to obtain a plurality of fifth record items;
extracting features of the third scheme to obtain a plurality of third features;
performing feature extraction on the fourth record item to obtain a plurality of fourth features;
matching the third characteristic with the fourth characteristic, and if the matching is in accordance with the first characteristic, determining that the fourth characteristic to be matched corresponds to the first position of the fourth record item on the second time axis;
determining a second position on a third time axis corresponding to the first position;
acquiring a fifth recording item in a preset second time range before and/or after a second position on a third time axis, and taking the fifth recording item as a sixth recording item;
performing feature extraction on the fourth scheme to obtain a plurality of fifth features;
performing feature extraction on the sixth record item to obtain a plurality of sixth features;
matching the fifth feature with the sixth feature, and if the matching is in accordance with the fifth feature, determining a third position of the sixth record item corresponding to the matched sixth feature on a third time axis;
if the third position is in front of the second position, acquiring a fourth record item which finally appears in a preset third time range after the first position on the second time axis, and taking the fourth record item as a seventh record item;
acquiring a second difference degree between the seventh record item and the fourth record item at the first position;
if the second difference degree is larger than or equal to a preset difference degree threshold value, counting for one time;
if the third position is at the second position, acquiring a fifth record item which finally appears in a preset third time range after the third position on a third time axis, and taking the fifth record item as an eighth record item;
acquiring a third difference degree between the eighth record item and a sixth record item at a third position;
if the third difference degree is larger than or equal to the difference degree threshold value, counting for one time;
summarizing counting results to obtain a counting sum;
determining a count and a corresponding second conflict value based on a preset conflict value library;
taking the third scheme, the fourth scheme, the second conflict item and the second conflict value as a group of control groups;
acquiring a preset blank database, and storing a comparison group into the blank database;
and when all the comparison groups are stored in the blank database, taking the blank database as a conflict comparison database to finish the establishment.
The working principle and the beneficial effects of the technical scheme are as follows:
selecting a third scheme and a fourth scheme from a preset second scheme set (different control schemes of different household intelligent devices); determining fourth equipment actually controlled by the third scheme and fifth equipment actually controlled by the fourth scheme; establishing a second time axis and a third time axis; expanding a second sub-record related to a fourth device in the use records of other users on a second time axis to obtain a plurality of fourth record items; expanding a third sub-record related to a fifth device in the use records of other users on a third time axis to obtain a plurality of fifth record items; if the fourth record item is matched with the third scheme, determining a first position of the corresponding fourth record item on a second time axis; the second time axis is the same as the third time axis, and a second position corresponding to the first position on the third time axis is determined; if the second location is preceded and/or a second time range is preset [ e.g.: the fifth record item appearing in 2 minutes is matched with the fourth scheme, which indicates that the user adjusts the control scheme of the fifth device in a short time after setting the control scheme of the fourth device, or the user adjusts the control scheme of the fourth device in a short time after setting the control scheme of the fifth device, and the control schemes of the fifth device and the fourth device conflict with each other; the more the matching times are, the more the adjustment times are, and the larger the count sum is; determining a count and a corresponding second conflict value based on a preset conflict value library (a database) in which each count and a corresponding conflict value are stored; determining a second conflict item based on a preset conflict item library (a database) in which conflict items between two different schemes are stored; determining a comparison group, and storing the comparison library into a blank database; before or at the initial stage of product input, conflict experiments of different schemes can be carried out at the background, a large number of use records are generated, and after the experimenters find conflicts, the control schemes of the conflict schemes are adjusted; when the conflict comparison library is established, the embodiment of the invention analyzes the use record again, accurately captures the adjustment operation (adjustment in a short time) generated by the user if the user feels that the control schemes conflict with each other, and improves the analysis efficiency.
An embodiment of the present invention provides an intelligent home control system based on big data analysis, as shown in fig. 2, including:
an obtaining module 1, configured to obtain a device set that is being used by a user, where the device set includes: a plurality of first devices;
the determining module 2 is configured to determine, based on a preset demand parameter library, a first demand parameter corresponding to the first device, where the first demand parameter includes: a first user parameter and/or a first house parameter;
the extraction module 3 is used for acquiring the big data and extracting the optimal control scheme corresponding to the first equipment from the big data based on the first demand parameter;
and the control module 4 is used for controlling the corresponding first equipment based on the optimal control scheme.
The working principle and the beneficial effects of the technical scheme are as follows:
obtain a set of devices being used by a user [ a plurality of first devices, for example: intelligence air conditioner, intelligent stereo set, intelligence machine of sweeping floor etc. based on predetermined demand parameter storehouse [ a database, the demand parameter that every equipment corresponds is stored in, confirms the first demand parameter that first equipment corresponds, and first demand parameter includes: first user parameters [ user-set family member age, preferences, etc. ] and/or first house parameters [ room area, etc. ], such as: when the intelligent air conditioner is controlled, the user parameters and the house parameters need to be based, and when the intelligent sweeper is controlled, only the house area needs to be determined; acquiring big data, and extracting an optimal control scheme from the big data; controlling the corresponding first device based on the optimal control scheme; meanwhile, the user can also manually intervene to adjust the control scheme;
according to the embodiment of the invention, when the user starts the intelligent household equipment, the optimal control scheme of the intelligent household equipment is determined based on the big data, the intelligent household equipment is directly controlled, the working mode does not need to be set by the user, and the intelligent household equipment is more intelligent.
The embodiment of the invention provides an intelligent home control system based on big data analysis, and an extraction module 3 executes the following operations:
acquiring a preset node set, wherein the node set comprises: a plurality of nodes;
acquiring target data through a node, wherein the target data comprises: second demand parameters of other users, the second demand parameters including: a second user parameter, a second house parameter, and usage records of a plurality of second devices;
and integrating the target data acquired by each node to acquire big data.
The working principle and the beneficial effects of the technical scheme are as follows:
acquiring target data through nodes in a preset node set [ containing a plurality of nodes, each node corresponding to one other user ], wherein the target data contains second user parameters [ for example: family membership information, preferences, etc.), second house parameters [ house area, etc.), and usage records of a plurality of second devices [ smart home devices of other users [ for example: what kind of working mode is set, etc.; before the product is put into use or in the initial stage, experiments of different user parameters and different house parameters can be carried out in the background, and a large amount of target data is generated.
The embodiment of the invention provides an intelligent home control system based on big data analysis, and an extraction module 3 executes the following operations:
performing feature extraction on the first demand parameters to obtain a plurality of first features;
performing feature extraction on the second demand parameters to obtain a plurality of second features;
matching the first feature with a second feature having a second feature type that is the same as the first feature type based on the first feature type of the first feature;
obtaining a matching result, wherein the matching result comprises: a third feature type and a matching value common to the first feature and the second feature to be matched;
determining a contribution value corresponding to the third feature type and the matching value together based on a preset contribution value comparison library, and associating the contribution value with a second feature matched in the matching result;
extracting other users meeting a preset first condition from the other users, and taking the other users as target users;
establishing a first time axis, and expanding a plurality of first sub-records related to first equipment corresponding to a first requirement parameter in the use record of a target user on the first time axis to obtain a plurality of first record items;
extracting a first record item which meets a preset second condition from the first record items, and taking the first record item as a second record item;
extracting a first record item which meets a preset third condition from the first record items, and taking the first record item as a third record item;
extracting a first control scheme from the second record item, and giving a first weight to the first control scheme;
extracting a second control scheme from the third record item, and giving a second weight to the second control scheme;
acquiring a preset extraction model, inputting a first control scheme with a first weight and a second control scheme with a second weight into the extraction model, and acquiring an optimal control scheme;
wherein the first condition comprises: the contribution values of the second characteristics in the second demand parameters of other users are all larger than or equal to a preset contribution value threshold;
the second condition includes: other first record items do not exist in a first time range preset after the first record items on the time axis;
the third condition includes: the first difference degree between other first record items appearing in a first time range preset after the first record item on the time axis and the first record item is less than or equal to a preset first difference degree threshold value;
the first weight is greater than the second weight.
The working principle and the beneficial effects of the technical scheme are as follows:
if the optimal working scheme of the first device is extracted from big data consisting of a plurality of target data, the optimal working scheme is ensured to meet the actual use requirement of a user, namely, the second requirement parameter in the target data is sufficiently met with the first requirement parameter of the user, and then the use record in the corresponding target data can be referred;
therefore, feature extraction is respectively carried out on the first requirement parameter and the second requirement parameter to obtain a plurality of first features and second features; matching the first feature with the second feature (matching with the feature type) to obtain a matching result, wherein the matching result comprises a third feature type with the same first feature and the same second feature and a matching value between the third feature type and the third feature type; comparing a database (a database) with preset contribution values, wherein the database stores the contribution values corresponding to different feature types and different matching values, and background personnel continuously count and update the contribution values, wherein the larger the contribution value is, the larger the reference value of the corresponding use record is if the corresponding feature of the feature type is matched is, and the third feature type and the corresponding contribution value of the matching value are determined; if other users meet a preset first condition, the use records of the other users have reference values, and the other users are screened out as target users; expanding a plurality of sub-records related to the corresponding first equipment in the usage record of the target user on a first time axis [ record generation time can be expanded when corresponding to a time node on the time axis ], and obtaining a plurality of first record items; when a user generates a one-time usage record [ e.g.: setting a working mode, if the condition is satisfied, and no new use record can be generated soon after the use record, meeting a second condition; when the user generates a use record, if the user is not satisfied at all, slightly adjusting (the first difference between the previous and the next use records is small), and explaining that the control scheme in the use record which is generated at the beginning is still, the third condition is satisfied; extracting a second record item and a third record item, then extracting a first control scheme and a second control scheme, reasonably giving different weights, inputting a preset extraction model (preset), learning a large number of manual extraction records by using a machine learning algorithm to generate a model, and during extraction, emphasizing on a control scheme with large weight, judging popularity and the like of the control scheme) and extracting an optimal control scheme;
the preset contribution value threshold specifically includes: for example, 97; presetting a first time range: for example, 4 minutes; the preset first difference threshold specifically includes: for example, 2;
the embodiment of the invention extracts the optimal control scheme of the first equipment from the big data, is convenient and quick, and has applicability under the trend that the big data tends to be popular; when the use records of other users are referred, the first demand parameter and the second demand parameter are matched, so that the reasonability is achieved, and the accuracy of extracting the optimal control scheme is improved; setting a contribution value comparison library to conveniently determine the reference value of the feature type; capturing the actual scene of the normal user if the normal user is satisfied or more satisfied with the current control scheme, setting a second condition and a third condition, and quickly extracting the first control scheme and the second control scheme, so that the method is ingenious and convenient; different weights are given to the first control scheme and the second control scheme, and the rationality is improved; based on the extraction model, an optimal control scheme is extracted, and the working efficiency of the system is improved.
The embodiment of the invention provides an intelligent home control system based on big data analysis, which further comprises:
the adjusting module is used for adaptively adjusting the optimal control scheme of each first device;
the adjustment module performs the following operations:
summarizing all the optimal control schemes of the first equipment to obtain a first scheme set;
determining an important value corresponding to the first equipment based on a preset important value library;
taking the first equipment corresponding to the maximum value of the important value in the first equipment as second equipment, and taking the rest first equipment as third equipment;
respectively acquiring a first scheme of second equipment and a second scheme of third equipment in a first scheme set;
establishing a conflict comparison library, and determining a first conflict item and a first conflict value between the first scheme and the second scheme based on the conflict comparison library;
determining an adjusting scheme corresponding to the second scheme, the first conflict item and the first conflict value together based on a preset adjusting scheme library;
the corresponding second scheme is adjusted based on the adjustment scheme.
The working principle and the beneficial effects of the technical scheme are as follows:
determining an important value of first equipment based on a preset important value library (a database in which the important value of each household intelligent equipment is stored); taking the first equipment with the largest importance value as second equipment, and taking the rest first equipment as third equipment; determining a first scheme of a second device and a second scheme of a third device; determining a first conflict item and a first conflict value between the first scheme and the second scheme based on the conflict comparison library; based on a preset adjustment scheme library (a database) in which adjustment schemes corresponding to different schemes, conflict items and conflict values are stored, adjusting the corresponding second scheme based on the adjustment schemes, and controlling the corresponding intelligent household equipment by using the adjusted second scheme; the intelligent home equipment with the maximum importance can be ensured to operate according to the optimal control scheme, and the other equipment is correspondingly adjusted without conflict with the intelligent home equipment; after the adaptive adjustment is intervened, the optimal control scheme is not extracted to control the corresponding first equipment until no conflict exists;
for example: the optimal control scheme of the intelligent air conditioner is based on outdoor temperature regulation, mainly takes room temperature regulation and indoor temperature proper maintenance and mainly takes silence as the main control scheme; the intelligent sound box is in an active mode, and the sound is large and dynamic; the conflict items of the two are as follows: multiple devices emit greater volume, which may cause discomfort to the user; the conflict value is: 80; the importance value of the room temperature regulation is large because the importance of the room temperature regulation is high; the control scheme of the intelligent sound box is adjusted, the volume is reduced or the sound box is turned off, and the comfort level of the use environment is guaranteed;
when the optimal control schemes are determined by the plurality of first devices, considering the special scene that the user may conflict in actual use, determining conflict items and conflict values, then determining an adjustment scheme, and performing adaptive adjustment on the corresponding second scheme, so as to ensure that the optimal control schemes of the first devices do not conflict and give the user the optimal experience feeling; meanwhile, an important library is arranged, so that the importance of the first equipment can be distinguished conveniently and rapidly.
The embodiment of the invention provides an intelligent home control system based on big data analysis, and an adjusting module executes the following operations:
acquiring a preset second scheme set, and randomly selecting a scheme combination from the second scheme set, wherein the scheme combination comprises the following steps: a third aspect and a fourth aspect;
attempting to determine a second conflict item between the third scenario and the fourth scenario based on a preset library of conflict items;
if the determination is successful, respectively determining fourth equipment corresponding to the third scheme and fifth equipment corresponding to the fourth scheme;
establishing a second time axis, and expanding a plurality of second sub-records related to fourth equipment in the use records of other users on the second time axis to obtain a plurality of fourth record items;
establishing a third time axis, and expanding a plurality of third sub-records related to the fifth equipment in the use records of other users on the second time axis to obtain a plurality of fifth record items;
extracting features of the third scheme to obtain a plurality of third features;
performing feature extraction on the fourth record item to obtain a plurality of fourth features;
matching the third characteristic with the fourth characteristic, and if the matching is in accordance with the first characteristic, determining that the fourth characteristic to be matched corresponds to the first position of the fourth record item on the second time axis;
determining a second position on a third time axis corresponding to the first position;
acquiring a fifth recording item in a preset second time range before and/or after a second position on a third time axis, and taking the fifth recording item as a sixth recording item;
performing feature extraction on the fourth scheme to obtain a plurality of fifth features;
performing feature extraction on the sixth record item to obtain a plurality of sixth features;
matching the fifth feature with the sixth feature, and if the matching is in accordance with the fifth feature, determining a third position of the sixth record item corresponding to the matched sixth feature on a third time axis;
if the third position is in front of the second position, acquiring a fourth record item which finally appears in a preset third time range after the first position on the second time axis, and taking the fourth record item as a seventh record item;
acquiring a second difference degree between the seventh record item and the fourth record item at the first position;
if the second difference degree is larger than or equal to a preset difference degree threshold value, counting for one time;
if the third position is at the second position, acquiring a fifth record item which finally appears in a preset third time range after the third position on a third time axis, and taking the fifth record item as an eighth record item;
acquiring a third difference degree between the eighth record item and a sixth record item at a third position;
if the third difference degree is larger than or equal to the difference degree threshold value, counting for one time;
summarizing counting results to obtain a counting sum;
determining a count and a corresponding second conflict value based on a preset conflict value library;
taking the third scheme, the fourth scheme, the second conflict item and the second conflict value as a group of control groups;
acquiring a preset blank database, and storing a comparison group into the blank database;
and when all the comparison groups are stored in the blank database, taking the blank database as a conflict comparison database to finish the establishment.
The working principle and the beneficial effects of the technical scheme are as follows:
selecting a third scheme and a fourth scheme from a preset second scheme set (different control schemes of different household intelligent devices); determining fourth equipment actually controlled by the third scheme and fifth equipment actually controlled by the fourth scheme; establishing a second time axis and a third time axis; expanding a second sub-record related to a fourth device in the use records of other users on a second time axis to obtain a plurality of fourth record items; expanding a third sub-record related to a fifth device in the use records of other users on a third time axis to obtain a plurality of fifth record items; if the fourth record item is matched with the third scheme, determining a first position of the corresponding fourth record item on a second time axis; the second time axis is the same as the third time axis, and a second position corresponding to the first position on the third time axis is determined; if the second location is preceded and/or a second time range is preset [ e.g.: the fifth record item appearing in 2 minutes is matched with the fourth scheme, which indicates that the user adjusts the control scheme of the fifth device in a short time after setting the control scheme of the fourth device, or the user adjusts the control scheme of the fourth device in a short time after setting the control scheme of the fifth device, and the control schemes of the fifth device and the fourth device conflict with each other; the more the matching times are, the more the adjustment times are, and the larger the count sum is; determining a count and a corresponding second conflict value based on a preset conflict value library (a database) in which each count and a corresponding conflict value are stored; determining a second conflict item based on a preset conflict item library (a database) in which conflict items between two different schemes are stored; determining a comparison group, and storing the comparison library into a blank database; before or at the initial stage of product input, conflict experiments of different schemes can be carried out at the background, a large number of use records are generated, and after the experimenters find conflicts, the control schemes of the conflict schemes are adjusted; when the conflict comparison library is established, the embodiment of the invention analyzes the use record again, accurately captures the adjustment operation (adjustment in a short time) generated by the user if the user feels that the control schemes conflict with each other, and improves the analysis efficiency.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (6)

1. The intelligent home control method based on big data analysis is characterized by comprising the following steps:
step S1: obtaining a device set being used by a user, the device set comprising: a plurality of first devices;
step S2: determining a first demand parameter corresponding to the first device based on a preset demand parameter library, where the first demand parameter includes: a first user parameter and/or a first house parameter;
step S3: acquiring big data, and extracting an optimal control scheme corresponding to the first equipment from the big data based on the first demand parameter;
step S4: controlling the corresponding first equipment based on the optimal control scheme;
in step S3, acquiring big data includes:
acquiring a preset node set, wherein the node set comprises: a plurality of nodes;
obtaining, by the node, target data, the target data comprising: second demand parameters of other users, the second demand parameters including: a second user parameter, a second house parameter, and usage records of a plurality of second devices;
integrating the target data acquired by each node to acquire big data;
in step S3, extracting an optimal control scheme corresponding to the first device from the big data includes:
performing feature extraction on the first requirement parameter to obtain a plurality of first features;
performing feature extraction on the second demand parameters to obtain a plurality of second features;
matching the first feature and the second feature having a second feature type that is the same as the first feature type based on a first feature type of the first feature;
obtaining a matching result, wherein the matching result comprises: a third feature type and a matching value common to the first feature and the second feature to be matched;
determining a contribution value corresponding to the third feature type and the matching value together based on a preset contribution value comparison library, and associating the contribution value with the second feature matched in the matching result;
extracting other users meeting a preset first condition from the other users, and taking the other users as target users;
establishing a first time axis, and expanding a plurality of first sub-records related to the first device corresponding to the first demand parameter in the usage record of the target user on the first time axis to obtain a plurality of first record items;
extracting the first record items meeting a preset second condition from the first record items, and taking the first record items as second record items;
extracting the first record items meeting a preset third condition from the first record items, and taking the first record items as third record items;
extracting a first control scheme from the second record item, and giving a first weight to the first control scheme;
extracting a second control scheme from the third record item, and giving a second weight to the second control scheme;
acquiring a preset extraction model, inputting the first control scheme with the first weight and the second control scheme with the second weight into the extraction model, and acquiring an optimal control scheme;
wherein the first condition comprises: the contribution values of the second features in the second demand parameters of the other users are all larger than or equal to a preset contribution value threshold;
the second condition includes: other first record items do not exist in a first time range preset after the first record items on the time axis;
the third condition includes: a first difference degree between other first record items appearing in a first time range preset after the first record item on the time axis and the first record item is less than or equal to a preset first difference degree threshold value;
the first weight is greater than the second weight.
2. The smart home control method based on big data analysis according to claim 1, further comprising:
step S5: adaptively adjusting the optimal control scheme of each of the first devices;
wherein adaptively adjusting the optimal control scheme for each of the first devices comprises:
summarizing the optimal control schemes of all the first equipment to obtain a first scheme set;
determining an important value corresponding to the first equipment based on a preset important value library;
taking the first equipment corresponding to the maximum value of the important value in the first equipment as second equipment, and taking the rest first equipment as third equipment;
respectively acquiring a first scheme of the second device and a second scheme of the third device in the first scheme set;
establishing a conflict comparison library, and determining a first conflict item and a first conflict value between the first scheme and the second scheme based on the conflict comparison library;
determining an adjusting scheme corresponding to the second scheme, the first conflict item and the first conflict value together based on a preset adjusting scheme library;
adjusting the corresponding second scheme based on the adjustment scheme.
3. The smart home control method based on big data analysis as claimed in claim 2, wherein establishing a conflict comparison library comprises:
acquiring a preset second scheme set, and randomly selecting a scheme combination from the second scheme set, wherein the scheme combination comprises: a third aspect and a fourth aspect;
attempting to determine a second conflicting item between the third scenario and the fourth scenario based on a preset library of conflicting items;
if the determination is successful, respectively determining fourth equipment corresponding to the third scheme and fifth equipment corresponding to the fourth scheme;
establishing a second time axis, and expanding a plurality of second sub-records related to the fourth device in the usage records of the other users on the second time axis to obtain a plurality of fourth record items;
establishing a third time axis, and expanding a plurality of third sub-records related to the fifth device in the usage records of the other users on the second time axis to obtain a plurality of fifth record items;
extracting features of the third scheme to obtain a plurality of third features;
performing feature extraction on the fourth record item to obtain a plurality of fourth features;
matching the third feature with a fourth feature, and if the matching is in accordance with the third feature, determining that the fourth feature to be matched corresponds to a first position of the fourth record item on the second time axis;
determining a second position on the third time axis corresponding to the first position;
acquiring the fifth record item within a preset second time range before and/or after the second position on the third time axis, and taking the fifth record item as a sixth record item;
performing feature extraction on the fourth scheme to obtain a plurality of fifth features;
performing feature extraction on the sixth record item to obtain a plurality of sixth features;
matching the fifth feature with the sixth feature, and if the matching is in accordance with the sixth feature, determining that the sixth feature for matching corresponds to a third position of the sixth recording item on the third time axis;
if the third position is in front of the second position, acquiring the fourth record item which appears last in a preset third time range after the first position on the second time axis, and taking the fourth record item as a seventh record item;
acquiring a second difference degree between the seventh record item and the fourth record item at the first position;
if the second difference degree is larger than or equal to a preset difference degree threshold value, counting for the first time;
if the third position is behind the second position, acquiring the fifth record item which appears last in a preset third time range after the third position on the third time axis, and taking the fifth record item as an eighth record item;
acquiring a third difference degree between the eighth record item and the sixth record item at the third position;
if the third difference degree is larger than or equal to the difference degree threshold value, counting for one time;
summarizing counting results to obtain a counting sum;
determining the count and a corresponding second conflict value based on a preset conflict value library;
using the third scheme, the fourth scheme, the second conflict item and the second conflict value as a group of control groups;
acquiring a preset blank database, and storing the control group into the blank database;
and when all the comparison groups are stored in the blank database, taking the blank database as a conflict comparison database to finish the establishment.
4. The utility model provides an intelligent house control system based on big data analysis which characterized in that includes:
an obtaining module, configured to obtain a device set being used by a user, where the device set includes: a plurality of first devices;
a determining module, configured to determine, based on a preset demand parameter library, a first demand parameter corresponding to the first device, where the first demand parameter includes: a first user parameter and/or a first house parameter;
the extraction module is used for acquiring big data and extracting an optimal control scheme corresponding to the first equipment from the big data based on the first requirement parameter;
the control module is used for controlling the corresponding first equipment based on the optimal control scheme;
the extraction module performs the following operations:
acquiring a preset node set, wherein the node set comprises: a plurality of nodes;
obtaining, by the node, target data, the target data comprising: second demand parameters of other users, the second demand parameters including: a second user parameter, a second house parameter, and usage records of a plurality of second devices;
integrating the target data acquired by each node to acquire big data;
the extraction module performs the following operations:
performing feature extraction on the first requirement parameter to obtain a plurality of first features;
performing feature extraction on the second demand parameters to obtain a plurality of second features;
matching the first feature and the second feature having a second feature type that is the same as the first feature type based on a first feature type of the first feature;
obtaining a matching result, wherein the matching result comprises: a third feature type and a matching value common to the first feature and the second feature to be matched;
determining a contribution value corresponding to the third feature type and the matching value together based on a preset contribution value comparison library, and associating the contribution value with the second feature matched in the matching result;
extracting other users meeting a preset first condition from the other users, and taking the other users as target users;
establishing a first time axis, and expanding a plurality of first sub-records related to the first device corresponding to the first demand parameter in the usage record of the target user on the first time axis to obtain a plurality of first record items;
extracting the first record items meeting a preset second condition from the first record items, and taking the first record items as second record items;
extracting the first record items meeting a preset third condition from the first record items, and taking the first record items as third record items;
extracting a first control scheme from the second record item, and giving a first weight to the first control scheme;
extracting a second control scheme from the third record item, and giving a second weight to the second control scheme;
acquiring a preset extraction model, inputting the first control scheme with the first weight and the second control scheme with the second weight into the extraction model, and acquiring an optimal control scheme;
wherein the first condition comprises: the contribution values of the second features in the second demand parameters of the other users are all larger than or equal to a preset contribution value threshold;
the second condition includes: other first record items do not exist in a first time range preset after the first record items on the time axis;
the third condition includes: a first difference degree between other first record items appearing in a first time range preset after the first record item on the time axis and the first record item is less than or equal to a preset first difference degree threshold value;
the first weight is greater than the second weight.
5. The smart home control system based on big data analysis as claimed in claim 4, further comprising:
the adjusting module is used for adaptively adjusting the optimal control scheme of each first device;
the adjustment module performs the following operations:
summarizing the optimal control schemes of all the first equipment to obtain a first scheme set;
determining an important value corresponding to the first equipment based on a preset important value library;
taking the first equipment corresponding to the maximum value of the important value in the first equipment as second equipment, and taking the rest first equipment as third equipment;
respectively acquiring a first scheme of the second device and a second scheme of the third device in the first scheme set;
establishing a conflict comparison library, and determining a first conflict item and a first conflict value between the first scheme and the second scheme based on the conflict comparison library;
determining an adjusting scheme corresponding to the second scheme, the first conflict item and the first conflict value together based on a preset adjusting scheme library;
adjusting the corresponding second scheme based on the adjustment scheme.
6. The smart home control system based on big data analysis as claimed in claim 5, wherein the adjusting module performs the following operations:
acquiring a preset second scheme set, and randomly selecting a scheme combination from the second scheme set, wherein the scheme combination comprises: a third aspect and a fourth aspect;
attempting to determine a second conflicting item between the third scenario and the fourth scenario based on a preset library of conflicting items;
if the determination is successful, respectively determining fourth equipment corresponding to the third scheme and fifth equipment corresponding to the fourth scheme;
establishing a second time axis, and expanding a plurality of second sub-records related to the fourth device in the usage records of the other users on the second time axis to obtain a plurality of fourth record items;
establishing a third time axis, and expanding a plurality of third sub-records related to the fifth device in the usage records of the other users on the second time axis to obtain a plurality of fifth record items;
extracting features of the third scheme to obtain a plurality of third features;
performing feature extraction on the fourth record item to obtain a plurality of fourth features;
matching the third feature with a fourth feature, and if the matching is in accordance with the third feature, determining that the fourth feature to be matched corresponds to a first position of the fourth record item on the second time axis;
determining a second position on the third time axis corresponding to the first position;
acquiring the fifth record item within a preset second time range before and/or after the second position on the third time axis, and taking the fifth record item as a sixth record item;
performing feature extraction on the fourth scheme to obtain a plurality of fifth features;
performing feature extraction on the sixth record item to obtain a plurality of sixth features;
matching the fifth feature with the sixth feature, and if the matching is in accordance with the sixth feature, determining that the sixth feature for matching corresponds to a third position of the sixth recording item on the third time axis;
if the third position is in front of the second position, acquiring the fourth record item which appears last in a preset third time range after the first position on the second time axis, and taking the fourth record item as a seventh record item;
acquiring a second difference degree between the seventh record item and the fourth record item at the first position;
if the second difference degree is larger than or equal to a preset difference degree threshold value, counting for the first time;
if the third position is behind the second position, acquiring the fifth record item which appears last in a preset third time range after the third position on the third time axis, and taking the fifth record item as an eighth record item;
acquiring a third difference degree between the eighth record item and the sixth record item at the third position;
if the third difference degree is larger than or equal to the difference degree threshold value, counting for one time;
summarizing counting results to obtain a counting sum;
determining the count and a corresponding second conflict value based on a preset conflict value library;
using the third scheme, the fourth scheme, the second conflict item and the second conflict value as a group of control groups;
acquiring a preset blank database, and storing the control group into the blank database;
and when all the comparison groups are stored in the blank database, taking the blank database as a conflict comparison database to finish the establishment.
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