CN112181006A - Environment intelligent processing method and system based on big data and storage medium - Google Patents
Environment intelligent processing method and system based on big data and storage medium Download PDFInfo
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- 238000003672 processing method Methods 0.000 title claims abstract description 14
- 238000013507 mapping Methods 0.000 claims abstract description 49
- 230000004044 response Effects 0.000 claims abstract description 38
- 238000006243 chemical reaction Methods 0.000 claims abstract description 20
- 239000007921 spray Substances 0.000 claims abstract description 14
- 238000000034 method Methods 0.000 claims abstract description 13
- 238000012545 processing Methods 0.000 claims abstract description 10
- 230000007613 environmental effect Effects 0.000 claims description 133
- 238000005507 spraying Methods 0.000 claims description 24
- 230000008859 change Effects 0.000 claims description 15
- 230000000694 effects Effects 0.000 claims description 9
- 230000004927 fusion Effects 0.000 claims description 9
- 238000012216 screening Methods 0.000 claims description 9
- 238000004590 computer program Methods 0.000 claims description 7
- 230000003238 somatosensory effect Effects 0.000 claims description 4
- 238000009529 body temperature measurement Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000004891 communication Methods 0.000 description 4
- 239000003595 mist Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D22/00—Control of humidity
- G05D22/02—Control of humidity characterised by the use of electric means
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F6/00—Air-humidification, e.g. cooling by humidification
- F24F6/12—Air-humidification, e.g. cooling by humidification by forming water dispersions in the air
Abstract
The invention relates to the technical field of data and environment processing, in particular to an environment intelligent processing method, system and storage medium based on big data; the method comprises the steps of firstly carrying out data signal conversion on an obtained current environment signal to obtain a processed multidimensional data set, then generating a plurality of groups of environment data sets of different types based on the multidimensional data set, respectively detecting environment characteristics in the plurality of groups of environment data sets of different types to obtain environment characteristic information, then mapping the environment characteristic information in the plurality of groups of environment data sets of different types to the multidimensional data set according to classification weights between the plurality of groups of environment data sets of different types and the multidimensional data set to obtain a plurality of groups of environment response information, and finally identifying the plurality of groups of environment response information according to information correlation degrees among the environment characteristic information to obtain personnel distribution position information corresponding to the current environment so as to control the humidifying spray direction and the humidifying spray force. The invention can realize the self-adaptive state adjustment of the humidifier according to the specific humidification environment.
Description
Technical Field
The invention relates to the technical field of data and environment processing, in particular to an environment intelligent processing method and system based on big data and a storage medium.
Background
With the development of economy and the improvement of the living standard of people, the requirements of people on the quality and health of life are higher and higher. For families in dry areas, the humidifier is an essential small household appliance, can effectively improve the dry environment, and improves the quality of life of people.
However, when a conventional humidifier is operated, the conventional humidifier is usually operated based on a preset operation mode, and it is difficult to perform adaptive state adjustment according to a specific humidification environment.
Disclosure of Invention
In order to solve the technical problems in the related art, the invention provides an environment intelligent processing method, an environment intelligent processing system and a storage medium based on big data.
In a first aspect, a big data-based environment intelligent processing method is provided, which is applied to a microcontroller integrated in a humidifier, and the method includes:
acquiring a current environment signal, and performing data signal conversion on the current environment signal to convert the current environment signal into current environment data to obtain a processed multidimensional data set; wherein the current environmental signal is acquired by a sensor integrated within the humidifier;
generating a plurality of sets of different categories of environmental data sets based on the multi-dimensional data sets; respectively detecting the environmental characteristics in the plurality of groups of environmental data sets of different categories to obtain environmental characteristic information in the plurality of groups of environmental data sets of different categories;
mapping the environmental characteristic information in the plurality of groups of environment data sets of different categories to the multi-dimensional data set according to the classification weight between the plurality of groups of environment data sets of different categories and the multi-dimensional data set to obtain a plurality of groups of environmental response information;
identifying the multiple groups of environment response information according to the information correlation degree among the environment characteristic information to obtain personnel distribution position information corresponding to the current environment; and controlling the working state of a humidifying assembly in the humidifier according to the personnel distribution position information so as to adjust the humidifying and spraying direction and the humidifying and spraying strength of the humidifier.
In an optional embodiment, identifying the multiple sets of environment response information according to the information correlation between the environment feature information to obtain the personnel distribution location information corresponding to the current environment includes:
if the ratio of the influence coefficient of any two pieces of environmental characteristic information in the environmental characteristic information to the somatosensory temperature identification coefficient of one piece of environmental characteristic information in the any two pieces of environmental characteristic information exceeds a first set threshold, generating an environmental association weight of the any two pieces of environmental characteristic information, and taking the environmental association weight of the any two pieces of environmental characteristic information as the information correlation degree of the any two pieces of environmental characteristic information;
if the feature dimensions of any two pieces of environment feature information in the environment feature information are the same, and the ratio between the influence coefficient of any two pieces of environment feature information and the somatosensory temperature identification coefficient of one piece of environment feature information in any two pieces of environment feature information exceeds a second set threshold, taking the environment association weight of any two pieces of environment feature information as the information correlation degree of any two pieces of environment feature information; wherein the first set threshold is greater than the second set threshold;
respectively mapping the information correlation degrees to each group of environment response information to obtain environment mapping weights of the information correlation degrees in each group of environment response information, and selecting a target environment mapping weight consistent with a preset identification weight from the environment mapping weights; the preset identification weight is used for representing the weight for identifying the body sensing temperature of the person;
and determining the personnel distribution position information corresponding to the current environment based on the environment response information corresponding to the target environment mapping weight.
In an optional embodiment, the data signal conversion of the current environmental signal includes:
constructing a sensing signal coding list;
and performing data signal conversion on the current environment signal according to the sensing signal coding list.
In an alternative embodiment, constructing the sensing signal coding list includes:
detecting signal amplitude fluctuation characteristics in the current environment signal;
selecting target signal amplitude fluctuation characteristics which meet the environment signal fluctuation range condition from the detected signal amplitude fluctuation characteristics;
and determining signal distortion distribution corresponding to the target signal amplitude fluctuation characteristics, and constructing the signal coding list according to a signal spectrogram corresponding to the signal distortion distribution.
In an optional embodiment, controlling the working state of the humidification assembly in the humidifier according to the information of the personnel distribution position to adjust the humidification mist spray direction and the humidification mist spray force of the humidifier includes:
determining calling probabilities of a plurality of track components to be calibrated for identifying the activity track of the target person and fusion weights among different track components according to the acquired temperature distribution information and image coding information for recording the personnel distribution position information; wherein, each image coding information is an image frame description information of the moving track of the target person, and each temperature distribution information is an infrared temperature measurement description information of the target person;
calibrating the plurality of track components based on the determined calling probabilities of the plurality of track components and the fusion weight among different track components, so that the calling probability of the calibrated track components is greater than the set probability and the fusion weight among the calibrated track components is less than the set weight;
aiming at any expected position change information corresponding to the personnel distribution position information, judging whether the expected position change information is the activity track of the target personnel according to the matching rate of the expected position change information under each track component in the calibrated track components;
if the expected position change information is determined to be the activity track of the target person, a control instruction for adjusting the humidifying and spraying direction and the humidifying and spraying strength of the humidifier is generated according to the expected position change information and is issued to the humidifying assembly, so that the humidifying assembly adjusts the working state.
In an alternative embodiment, generating a plurality of sets of different categories of environmental data sets based on the multi-dimensional data set includes:
determining an environment category identification in a data dimension profile of the multi-dimensional dataset, wherein the environment category identification comprises a set of environment tags of a same environment dataset of the multi-dimensional dataset;
processing the environment category identification through a structure description value in list structure information of a preset environment category list, and determining a first category orientation matched with the environment category identification;
based on the first class direction, determining a second class direction matched with the environment class identification through a structural parameter in list structure information in the preset environment class list;
and screening the environment category identification through the environment classification logic information of the preset environment category list based on the second category direction matched with the environment category identification to output a screening result, and sequentially grouping the multi-dimensional data sets according to the screening result to obtain a plurality of groups of environment data sets of different categories.
In an optional embodiment, the detecting the environmental features in the multiple sets of environmental data sets of different categories respectively to obtain the environmental feature information in the multiple sets of environmental data sets of different categories includes:
aiming at each group of environment data sets, constructing a first data distribution queue corresponding to environment index data of the environment data sets and a second data distribution queue corresponding to environment influence factors of the environment data sets; the first data distribution queue and the second data distribution queue respectively comprise a plurality of queue units with different queue priorities;
extracting initial unit data of the environment index data in any queue unit of the first data distribution queue, and determining a queue unit with the minimum queue priority in the second data distribution queue as a target queue unit;
mapping the initial unit data to the target queue unit according to correlation coefficients among a plurality of groups of environment data sets of different types, obtaining mapping unit data in the target queue unit, and generating a feature matching list between the environment index data and the environment influence factor based on a mapping path between the initial unit data and the mapping unit data;
and acquiring unit data to be processed in the target queue unit by taking the mapping unit data as a reference, mapping the unit data to be processed to the queue unit where the initial unit data is located according to the characteristic matching sequence corresponding to the characteristic matching list, obtaining unit characteristic data corresponding to the unit data to be processed in the queue unit where the initial unit data is located, and determining the environmental characteristic information of the environmental index data according to the unit characteristic data.
In an optional embodiment, mapping the environmental feature information in the multiple groups of different types of environmental data sets to the multidimensional data set according to the classification weights between the multiple groups of different types of environmental data sets and the multidimensional data set, so as to obtain multiple groups of environmental response information, where the method includes:
determining the classification weight based on the environmental characteristic information of the plurality of sets of different categories of environmental data sets and the data update frequency of the multi-dimensional data set;
and sequentially mapping the environmental characteristic information in the plurality of groups of environment data sets of different types to the multi-dimensional data set according to the classification priority of the classification weight in the plurality of groups of environment data sets of different types to obtain a plurality of groups of environment response information.
In a second aspect, a big data based environment intelligent processing system is provided, which is applied to a microcontroller integrated in a humidifier, and the system includes:
the signal conversion module is used for acquiring a current environment signal, and performing data signal conversion on the current environment signal so as to convert the current environment signal into current environment data to obtain a processed multidimensional data set; wherein the current environmental signal is acquired by a sensor integrated within the humidifier;
the characteristic detection module is used for generating a plurality of groups of environment data sets of different categories based on the multi-dimensional data sets; respectively detecting the environmental characteristics in the plurality of groups of environmental data sets of different categories to obtain environmental characteristic information in the plurality of groups of environmental data sets of different categories;
the information mapping module is used for mapping the environmental characteristic information in the plurality of groups of environment data sets of different categories to the multi-dimensional data set according to the classification weight between the plurality of groups of environment data sets of different categories and the multi-dimensional data set to obtain a plurality of groups of environmental response information;
the humidifying adjustment module is used for identifying the multiple groups of environment response information according to the information correlation degree among the environment characteristic information to obtain personnel distribution position information corresponding to the current environment; and controlling the working state of a humidifying assembly in the humidifier according to the personnel distribution position information so as to adjust the humidifying and spraying direction and the humidifying and spraying strength of the humidifier.
In a third aspect, a storage medium is provided for a microcontroller, on which a computer program is stored, which computer program, when running in the microcontroller, implements the method described above.
The technical scheme provided by the embodiment of the invention can have the following beneficial effects.
Firstly, the acquired current environment signal is subjected to data signal conversion to obtain a processed multidimensional data set, secondly, generating a plurality of groups of environment data sets of different categories based on the multi-dimensional data set and respectively detecting environment characteristics in the plurality of groups of environment data sets of different categories to obtain environment characteristic information, then according to the classification weight between the environmental data sets of a plurality of groups of different categories and the multi-dimensional data set, mapping the environmental characteristic information in the environmental data sets of the plurality of groups of different categories to the multi-dimensional data set to obtain a plurality of groups of environmental response information, finally identifying the plurality of groups of environmental response information according to the information correlation degree among the environmental characteristic information to obtain the personnel distribution position information corresponding to the current environment, the working state of the humidifying component in the humidifier is controlled according to the personnel distribution position information so as to adjust the humidifying and spraying direction and the humidifying and spraying force of the humidifier. Thus, the adaptive state adjustment of the humidifier can be realized according to a specific humidification environment.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic diagram of the communication connections of the structural components of a humidifier according to the present invention;
FIG. 2 is a flow diagram illustrating a big data based environment intelligence processing method in accordance with an exemplary embodiment;
FIG. 3 is a block diagram illustrating a big data based ambient intelligence processing system in accordance with an exemplary embodiment;
fig. 4 is a diagram illustrating a hardware configuration of a microcontroller according to an exemplary embodiment.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of systems and methods consistent with certain aspects of the present application, as detailed in the appended claims.
In order to solve the technical problem, embodiments of the present invention provide an environment intelligent processing method, system and storage medium based on big data, which can actively identify and analyze a specific humidification environment, thereby implementing adaptive adjustment of humidification spray direction and humidification spray intensity of a humidifier.
First, a communication diagram of the structural components of the humidifier 100 shown in fig. 1 is described, wherein the humidifier 100 has a microcontroller 110, a sensor 120, and a humidification assembly 130 integrated therein. Wherein the microcontroller 110 is in communication with the sensor 120 and the humidifying assembly 130, respectively. On the basis of fig. 1, please refer to fig. 2, which is a schematic flow chart of an environment intelligent processing method based on big data, which may be applied to a microcontroller integrated in a humidifier, and specifically may include the contents described in steps S210 to S240 below.
Step S210, obtaining a current environment signal, and performing data signal conversion on the current environment signal to convert the current environment signal into current environment data, so as to obtain a processed multidimensional data set.
In step S210, the current environmental signal is acquired by a sensor integrated within the humidifier.
Step S220, generating a plurality of groups of environment data sets of different categories based on the multi-dimensional data sets; and respectively detecting the environmental characteristics in the plurality of groups of environmental data sets of different categories to obtain environmental characteristic information in the plurality of groups of environmental data sets of different categories.
Step S230, mapping the environmental characteristic information in the multiple groups of different types of environmental data sets to the multidimensional data set according to the classification weights between the multiple groups of different types of environmental data sets and the multidimensional data set, so as to obtain multiple groups of environmental response information.
Step S240, identifying the multiple groups of environment response information according to the information correlation degree among the environment characteristic information to obtain personnel distribution position information corresponding to the current environment; and controlling the working state of a humidifying assembly in the humidifier according to the personnel distribution position information so as to adjust the humidifying and spraying direction and the humidifying and spraying strength of the humidifier.
It can be understood that, based on the above steps S210 to S240, the acquired current environmental signal is first subjected to data signal conversion to obtain a processed multi-dimensional dataset, secondly, generating a plurality of groups of environment data sets of different categories based on the multi-dimensional data set and respectively detecting environment characteristics in the plurality of groups of environment data sets of different categories to obtain environment characteristic information, then according to the classification weight between the environmental data sets of a plurality of groups of different categories and the multi-dimensional data set, mapping the environmental characteristic information in the environmental data sets of the plurality of groups of different categories to the multi-dimensional data set to obtain a plurality of groups of environmental response information, finally identifying the plurality of groups of environmental response information according to the information correlation degree among the environmental characteristic information to obtain the personnel distribution position information corresponding to the current environment, the working state of the humidifying component in the humidifier is controlled according to the personnel distribution position information so as to adjust the humidifying and spraying direction and the humidifying and spraying force of the humidifier. Thus, the adaptive state adjustment of the humidifier can be realized according to a specific humidification environment.
In an alternative embodiment, in order to accurately determine the person distribution position information to achieve the accuracy of the adaptive status adjustment of the humidifier, the multiple sets of environmental response information are identified in step S240 according to the information correlation between the environmental characteristic information, so as to obtain the person distribution position information corresponding to the current environment, which may be exemplarily implemented as described in steps S2411 to S2414 below.
Step S2411, if a ratio between an influence coefficient of any two pieces of environmental characteristic information in the environmental characteristic information and a sensible temperature identification coefficient of one piece of environmental characteristic information in the any two pieces of environmental characteristic information exceeds a first set threshold, generating an environmental association weight of the any two pieces of environmental characteristic information, and taking the environmental association weight of the any two pieces of environmental characteristic information as an information correlation degree of the any two pieces of environmental characteristic information.
Step S2412, if the feature dimensions of any two pieces of environment feature information in the environment feature information are the same, and the ratio between the influence coefficient of any two pieces of environment feature information and the sensible temperature identification coefficient of one piece of environment feature information in any two pieces of environment feature information exceeds a second set threshold, taking the environment association weight of any two pieces of environment feature information as the information correlation degree of any two pieces of environment feature information; wherein the first set threshold is greater than the second set threshold.
Step S2413, respectively mapping the information correlation degrees to each group of environment response information to obtain environment mapping weights of the information correlation degrees in each group of environment response information, and selecting a target environment mapping weight consistent with a preset identification weight from the environment mapping weights; the preset identification weight is used for representing the weight for identifying the body sensing temperature of the person.
Step S2414, determining personnel distribution position information corresponding to the current environment based on the environment response information corresponding to the target environment mapping weight.
In this way, by applying the steps S2411 to S2414, the person distribution position information can be accurately determined to achieve the accuracy of the adaptive state adjustment of the humidifier.
Further, in order to ensure that no signal distortion occurs during the sensing signal conversion, the data signal conversion of the current environment signal described in step S210 includes: constructing a sensing signal coding list; and performing data signal conversion on the current environment signal according to the sensing signal coding list. Therefore, signal distortion can be avoided when the sensing signal is converted.
Further, constructing a sensing signal coding list specifically includes: detecting signal amplitude fluctuation characteristics in the current environment signal; selecting target signal amplitude fluctuation characteristics which meet the environment signal fluctuation range condition from the detected signal amplitude fluctuation characteristics; and determining signal distortion distribution corresponding to the target signal amplitude fluctuation characteristics, and constructing the signal coding list according to a signal spectrogram corresponding to the signal distortion distribution.
In one possible implementation, in order to achieve precise and timely adjustment of the humidification spray direction and humidification spray intensity of the humidifier, the step S240 of controlling the operating state of the humidification assembly in the humidifier according to the staff distribution location information to adjust the humidification spray direction and humidification spray intensity of the humidifier may include the following steps S2421 to S2424.
Step S2421, determining calling probabilities of a plurality of track components to be calibrated for identifying the activity track of the target person and fusion weights among different track components according to the acquired temperature distribution information and image coding information for recording the personnel distribution position information; each image coding information is an image frame description information of the moving track of the target person, and each temperature distribution information is an infrared temperature measurement description information of the target person.
Step S2422, calibrating the plurality of track components based on the determined calling probabilities of the plurality of track components and the fusion weight among different track components, so that the calling probability of the calibrated track components is greater than the set probability, and the fusion weight among the calibrated track components is smaller than the set weight.
Step S2423, aiming at any expected position change information corresponding to the personnel distribution position information, judging whether the expected position change information is the activity track of the target personnel according to the matching rate of the expected position change information under each track component in the calibrated track components.
Step S2424, if the expected position change information is determined to be the activity track of the target person, generating a control instruction for adjusting the humidifying spraying direction and the humidifying spraying strength of the humidifier according to the expected position change information, and sending the control instruction to the humidifying assembly so that the humidifying assembly can adjust the working state.
In specific implementation, the humidification spray direction and the humidification spray strength of the humidifier can be accurately and timely adjusted by applying the steps S2421 to S2424.
Optionally, in order to ensure the accuracy and comprehensiveness of the environment data set, in step S220, based on the multi-dimensional data set, multiple sets of environment data sets of different categories are generated, which may specifically include the contents described in steps S2211 to S2214 below.
Step S2211, determining an environment category identification in a data dimension distribution map of the multi-dimensional dataset, wherein the environment category identification comprises a set of environment tags of a same environment dataset of the multi-dimensional dataset.
Step S2212, the environment category identification is processed through the structural description value in the list structural information of the preset environment category list, and a first category direction matched with the environment category identification is determined.
Step S2213, based on the first category pointing, determining a second category pointing matched with the environment category identifier through a structural parameter in the list structure information in the preset environment category list.
And step S2214, based on the second category direction matched with the environment category identification, screening the environment category identification through the environment classification logic information of the preset environment category list to output a screening result, and sequentially grouping the multi-dimensional data sets according to the screening result to obtain a plurality of groups of environment data sets of different categories.
In this way, the accuracy and comprehensiveness of the environment data set can be ensured through the steps S2211 to S2214.
Further, in order to accurately obtain the environmental feature information, the detecting of the environmental features in the multiple sets of environmental data sets of different categories to obtain the environmental feature information in the multiple sets of environmental data sets of different categories, which is described in step S220, may specifically include the following steps S2221 to S2224.
Step S2221, aiming at each group of environment data sets, a first data distribution queue corresponding to environment index data of the environment data sets and a second data distribution queue corresponding to environment influence factors of the environment data sets are constructed; the first data distribution queue and the second data distribution queue respectively comprise a plurality of queue units with different queue priorities.
Step S2222, extract initial unit data of any queue unit of the environment index data in the first data distribution queue, and determine a queue unit having the smallest queue priority in the second data distribution queue as a target queue unit.
Step S2223, the initial unit data are mapped to the target queue unit according to the correlation coefficient between a plurality of groups of environment data sets of different types, mapping unit data are obtained in the target queue unit, and a feature matching list between the environment index data and the environment influence factor is generated based on the mapping path between the initial unit data and the mapping unit data.
Step S2224, the mapping unit data is used as a reference to obtain the unit data to be processed in the target queue unit, the unit data to be processed is mapped to the queue unit where the initial unit data is located according to the characteristic matching sequence corresponding to the characteristic matching list, the unit characteristic data corresponding to the unit data to be processed is obtained in the queue unit where the initial unit data is located, and the environment characteristic information of the environment index data is determined according to the unit characteristic data.
Thus, based on the contents described in the above steps S2221 to S2224, the environmental characteristic information can be accurately acquired.
In one possible implementation manner, the mapping the environmental feature information in the multiple sets of environment data sets of different categories to the multidimensional data set according to the classification weights between the multiple sets of environment data sets of different categories and the multidimensional data set described in step S230 to obtain multiple sets of environmental response information includes: determining the classification weight based on the environmental characteristic information of the plurality of sets of different categories of environmental data sets and the data update frequency of the multi-dimensional data set; and sequentially mapping the environmental characteristic information in the plurality of groups of environment data sets of different types to the multi-dimensional data set according to the classification priority of the classification weight in the plurality of groups of environment data sets of different types to obtain a plurality of groups of environment response information.
Based on the same inventive concept, please refer to fig. 3 in combination, which shows a big data based environment intelligent processing system 300 applied to a microcontroller integrated in a humidifier, the system includes:
a signal conversion module 310, configured to obtain a current environment signal, perform data signal conversion on the current environment signal, so as to convert the current environment signal into current environment data, and obtain a processed multidimensional data set; wherein the current environmental signal is acquired by a sensor integrated within the humidifier;
a feature detection module 320 configured to generate a plurality of sets of different categories of environment datasets based on the multi-dimensional dataset; respectively detecting the environmental characteristics in the plurality of groups of environmental data sets of different categories to obtain environmental characteristic information in the plurality of groups of environmental data sets of different categories;
an information mapping module 330, configured to map, according to the classification weights between the multiple groups of environment data sets of different categories and the multidimensional data set, environment feature information in the multiple groups of environment data sets of different categories to the multidimensional data set, so as to obtain multiple groups of environment response information;
the humidification adjusting module 340 is configured to identify the multiple sets of environment response information according to the information correlation between the environment characteristic information, so as to obtain the personnel distribution position information corresponding to the current environment; and controlling the working state of a humidifying assembly in the humidifier according to the personnel distribution position information so as to adjust the humidifying and spraying direction and the humidifying and spraying strength of the humidifier.
Further, referring to fig. 4 in conjunction, a microcontroller 110 is shown, including a processor 111 and a memory 112 in communication with each other. Wherein the processor 111 retrieves the computer program from the memory 112 and operates to perform the method shown in fig. 2.
Further, a storage medium is provided, which is applied to a microcontroller, and a computer program is stored thereon, and when the computer program runs in the microcontroller, the method is implemented.
In summary, the method, system and storage medium for intelligent processing of environment based on big data disclosed by the present invention firstly performs data signal conversion on the acquired current environment signal to obtain a processed multidimensional data set, secondly, generating a plurality of groups of environment data sets of different categories based on the multi-dimensional data set and respectively detecting environment characteristics in the plurality of groups of environment data sets of different categories to obtain environment characteristic information, then according to the classification weight between the environmental data sets of a plurality of groups of different categories and the multi-dimensional data set, mapping the environmental characteristic information in the environmental data sets of the plurality of groups of different categories to the multi-dimensional data set to obtain a plurality of groups of environmental response information, finally identifying the plurality of groups of environmental response information according to the information correlation degree among the environmental characteristic information to obtain the personnel distribution position information corresponding to the current environment, so as to control the working state of the humidifying component in the humidifier to adjust the humidifying and spraying direction and the humidifying and spraying strength of the humidifier. Therefore, the self-adaptive state adjustment of the humidifier can be realized according to the specific humidification environment.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof.
Claims (10)
1. An intelligent environment processing method based on big data is applied to a microcontroller integrated in a humidifier, and the method comprises the following steps:
acquiring a current environment signal, and performing data signal conversion on the current environment signal to convert the current environment signal into current environment data to obtain a processed multidimensional data set; wherein the current environmental signal is acquired by a sensor integrated within the humidifier;
generating a plurality of sets of different categories of environmental data sets based on the multi-dimensional data sets; respectively detecting the environmental characteristics in the plurality of groups of environmental data sets of different categories to obtain environmental characteristic information in the plurality of groups of environmental data sets of different categories;
mapping the environmental characteristic information in the plurality of groups of environment data sets of different categories to the multi-dimensional data set according to the classification weight between the plurality of groups of environment data sets of different categories and the multi-dimensional data set to obtain a plurality of groups of environmental response information;
identifying the multiple groups of environment response information according to the information correlation degree among the environment characteristic information to obtain personnel distribution position information corresponding to the current environment; and controlling the working state of a humidifying assembly in the humidifier according to the personnel distribution position information so as to adjust the humidifying and spraying direction and the humidifying and spraying strength of the humidifier.
2. The method of claim 1, wherein identifying the plurality of sets of environmental response information according to the information correlation among the environmental characteristic information to obtain the personnel distribution position information corresponding to the current environment comprises:
if the ratio of the influence coefficient of any two pieces of environmental characteristic information in the environmental characteristic information to the somatosensory temperature identification coefficient of one piece of environmental characteristic information in the any two pieces of environmental characteristic information exceeds a first set threshold, generating an environmental association weight of the any two pieces of environmental characteristic information, and taking the environmental association weight of the any two pieces of environmental characteristic information as the information correlation degree of the any two pieces of environmental characteristic information;
if the feature dimensions of any two pieces of environment feature information in the environment feature information are the same, and the ratio between the influence coefficient of any two pieces of environment feature information and the somatosensory temperature identification coefficient of one piece of environment feature information in any two pieces of environment feature information exceeds a second set threshold, taking the environment association weight of any two pieces of environment feature information as the information correlation degree of any two pieces of environment feature information; wherein the first set threshold is greater than the second set threshold;
respectively mapping the information correlation degrees to each group of environment response information to obtain environment mapping weights of the information correlation degrees in each group of environment response information, and selecting a target environment mapping weight consistent with a preset identification weight from the environment mapping weights; the preset identification weight is used for representing the weight for identifying the body sensing temperature of the person;
and determining the personnel distribution position information corresponding to the current environment based on the environment response information corresponding to the target environment mapping weight.
3. The big data based environment intelligent processing method according to claim 1, wherein performing data signal conversion on the current environment signal comprises:
constructing a sensing signal coding list;
and performing data signal conversion on the current environment signal according to the sensing signal coding list.
4. The big data based environment intelligent processing method according to claim 3, wherein constructing the sensing signal coding list comprises:
detecting signal amplitude fluctuation characteristics in the current environment signal;
selecting target signal amplitude fluctuation characteristics which meet the environment signal fluctuation range condition from the detected signal amplitude fluctuation characteristics;
and determining signal distortion distribution corresponding to the target signal amplitude fluctuation characteristics, and constructing the signal coding list according to a signal spectrogram corresponding to the signal distortion distribution.
5. The big data based environment intelligent processing method according to any one of claims 1 to 4, wherein controlling the working state of a humidifying component in the humidifier according to the personnel distribution position information to adjust the humidifying spray direction and the humidifying spray strength of the humidifier comprises:
determining calling probabilities of a plurality of track components to be calibrated for identifying the activity track of the target person and fusion weights among different track components according to the acquired temperature distribution information and image coding information for recording the personnel distribution position information; wherein, each image coding information is an image frame description information of the moving track of the target person, and each temperature distribution information is an infrared temperature measurement description information of the target person;
calibrating the plurality of track components based on the determined calling probabilities of the plurality of track components and the fusion weight among different track components, so that the calling probability of the calibrated track components is greater than the set probability and the fusion weight among the calibrated track components is less than the set weight;
aiming at any expected position change information corresponding to the personnel distribution position information, judging whether the expected position change information is the activity track of the target personnel according to the matching rate of the expected position change information under each track component in the calibrated track components;
if the expected position change information is determined to be the activity track of the target person, a control instruction for adjusting the humidifying and spraying direction and the humidifying and spraying strength of the humidifier is generated according to the expected position change information and is issued to the humidifying assembly, so that the humidifying assembly adjusts the working state.
6. The big data based environment intelligent processing method according to claim 1, wherein generating a plurality of different categories of environment datasets based on the multi-dimensional datasets comprises:
determining an environment category identification in a data dimension profile of the multi-dimensional dataset, wherein the environment category identification comprises a set of environment tags of a same environment dataset of the multi-dimensional dataset;
processing the environment category identification through a structure description value in list structure information of a preset environment category list, and determining a first category orientation matched with the environment category identification;
based on the first class direction, determining a second class direction matched with the environment class identification through a structural parameter in list structure information in the preset environment class list;
and screening the environment category identification through the environment classification logic information of the preset environment category list based on the second category direction matched with the environment category identification to output a screening result, and sequentially grouping the multi-dimensional data sets according to the screening result to obtain a plurality of groups of environment data sets of different categories.
7. The method according to claim 1, wherein the detecting environmental characteristics in the plurality of sets of environmental data sets of different categories respectively to obtain environmental characteristic information in the plurality of sets of environmental data sets of different categories comprises:
aiming at each group of environment data sets, constructing a first data distribution queue corresponding to environment index data of the environment data sets and a second data distribution queue corresponding to environment influence factors of the environment data sets; the first data distribution queue and the second data distribution queue respectively comprise a plurality of queue units with different queue priorities;
extracting initial unit data of the environment index data in any queue unit of the first data distribution queue, and determining a queue unit with the minimum queue priority in the second data distribution queue as a target queue unit;
mapping the initial unit data to the target queue unit according to correlation coefficients among a plurality of groups of environment data sets of different types, obtaining mapping unit data in the target queue unit, and generating a feature matching list between the environment index data and the environment influence factor based on a mapping path between the initial unit data and the mapping unit data;
and acquiring unit data to be processed in the target queue unit by taking the mapping unit data as a reference, mapping the unit data to be processed to the queue unit where the initial unit data is located according to the characteristic matching sequence corresponding to the characteristic matching list, obtaining unit characteristic data corresponding to the unit data to be processed in the queue unit where the initial unit data is located, and determining the environmental characteristic information of the environmental index data according to the unit characteristic data.
8. The method according to claim 7, wherein mapping the environmental feature information in the plurality of different categories of environmental data sets into the multi-dimensional data set according to the classification weight between the plurality of different categories of environmental data sets and the multi-dimensional data set to obtain a plurality of sets of environmental response information comprises:
determining the classification weight based on the environmental characteristic information of the plurality of sets of different categories of environmental data sets and the data update frequency of the multi-dimensional data set;
and sequentially mapping the environmental characteristic information in the plurality of groups of environment data sets of different types to the multi-dimensional data set according to the classification priority of the classification weight in the plurality of groups of environment data sets of different types to obtain a plurality of groups of environment response information.
9. An intelligent environment processing system based on big data, which is applied to a microcontroller integrated in a humidifier, and comprises:
the signal conversion module is used for acquiring a current environment signal, and performing data signal conversion on the current environment signal so as to convert the current environment signal into current environment data to obtain a processed multidimensional data set; wherein the current environmental signal is acquired by a sensor integrated within the humidifier;
the characteristic detection module is used for generating a plurality of groups of environment data sets of different categories based on the multi-dimensional data sets; respectively detecting the environmental characteristics in the plurality of groups of environmental data sets of different categories to obtain environmental characteristic information in the plurality of groups of environmental data sets of different categories;
the information mapping module is used for mapping the environmental characteristic information in the plurality of groups of environment data sets of different categories to the multi-dimensional data set according to the classification weight between the plurality of groups of environment data sets of different categories and the multi-dimensional data set to obtain a plurality of groups of environmental response information;
the humidifying adjustment module is used for identifying the multiple groups of environment response information according to the information correlation degree among the environment characteristic information to obtain personnel distribution position information corresponding to the current environment; and controlling the working state of a humidifying assembly in the humidifier according to the personnel distribution position information so as to adjust the humidifying and spraying direction and the humidifying and spraying strength of the humidifier.
10. A storage medium for a microcontroller, characterized in that a computer program is stored thereon, which computer program, when running in the microcontroller, implements the method of any one of claims 1-8.
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