CN112255923B - Electric equipment control method, device, server and medium - Google Patents
Electric equipment control method, device, server and medium Download PDFInfo
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- CN112255923B CN112255923B CN202011090233.7A CN202011090233A CN112255923B CN 112255923 B CN112255923 B CN 112255923B CN 202011090233 A CN202011090233 A CN 202011090233A CN 112255923 B CN112255923 B CN 112255923B
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- 238000000034 method Methods 0.000 title claims abstract description 35
- 239000013598 vector Substances 0.000 claims description 36
- 238000012549 training Methods 0.000 claims description 12
- 238000003860 storage Methods 0.000 claims description 11
- 238000012545 processing Methods 0.000 claims description 9
- 238000010801 machine learning Methods 0.000 claims description 6
- 238000013507 mapping Methods 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 239000002699 waste material Substances 0.000 abstract description 7
- 238000009826 distribution Methods 0.000 abstract description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000004378 air conditioning Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000005611 electricity Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004140 cleaning Methods 0.000 description 1
- 238000002790 cross-validation Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 230000008707 rearrangement Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000035807 sensation Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
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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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24147—Distances to closest patterns, e.g. nearest neighbour classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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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
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/26—Pc applications
- G05B2219/2639—Energy management, use maximum of cheap power, keep peak load low
Abstract
The invention discloses an electric equipment control method, an electric equipment control device, a server and a medium. The method comprises the following steps: determining the number of people entering and exiting in the current period of the first area; inputting the number of people entering and exiting to a prediction model to obtain a first number of people range of the first area in a future period and/or a second number of people range of the second area in the future period; and controlling the electric equipment in the first area according to the first people number range, and controlling the electric equipment in the second area according to the second people number range, so that reasonable distribution of electric energy sources is realized, and the problem of energy source waste is avoided.
Description
Technical Field
Embodiments of the present invention relate to data processing technologies, and in particular, to a method, an apparatus, a server, and a medium for controlling an electrical device.
Background
The energy drives the running of cities, the higher the modern cities are, the higher the dependence on the energy is, the higher the energy consumption is, the larger the sustainable development influence on the human society is, as well known, the electricity is the most basic energy, and the progress of the whole human society is more needed, so that the reasonable electricity utilization is of great importance.
Currently, regarding the control of intelligent devices in a campus, a calendar needs to be preset, the on and off time of the intelligent devices is set, and the intelligent devices in the campus are turned on or off at regular time according to the calendar.
However, the method for controlling the intelligent device to be turned on or off at fixed time through the schedule in advance cannot flexibly adjust the intelligent device according to the external environment or emergency, and the problems of unreasonable use of electric energy, energy waste and poor comfort level of users exist.
Disclosure of Invention
The invention provides a control method, a device, a server and a medium for electric equipment, which are used for realizing reasonable distribution of electric energy sources and avoiding the problem of energy waste.
In a first aspect, an embodiment of the present invention provides a method for controlling an electrical device, including:
determining the number of people entering and exiting in the current period of the first area;
inputting the number of people entering and exiting to obtain a first number range of people in a future period of the first area and/or a second number range of people in the future period of the second area;
controlling the electric devices in the first area according to the first person number range and/or controlling the electric devices in the second area according to the second person number range.
In a second aspect, an embodiment of the present invention further provides an electrical device control apparatus, including:
the acquisition module is used for determining the number of people entering and exiting the first area in the current period;
the prediction module is used for inputting the number of people entering and exiting into a prediction model to obtain a first number range of people in a future period in the first area and/or a second number range of people in the future period in the second area;
and the processing module is used for controlling the electric equipment in the first area according to the first person number range and/or controlling the electric equipment in the second area according to the second person number range.
In a third aspect, an embodiment of the present invention further provides a server, where the server includes:
one or more processors;
storage means for storing one or more programs,
the program, when executed by the processor, causes the processor to implement the electrical device control method as provided by any embodiment of the present invention.
In a fourth aspect, an embodiment of the present invention further provides a storage medium having stored thereon a computer program which, when executed by a processor, implements an electrical device control method as provided by any embodiment of the present invention.
According to the invention, the number of people entering and exiting the office building in the current period is determined, the number of people entering and exiting the office building is input into the prediction model, the first number of people range of the office building in the future period is obtained, the second number of people range of the office building in the future period is predicted, the electric equipment of the office building is controlled according to the first number of people range, the second number of people range is used for controlling the electric equipment of the canteen, the problem of energy waste is solved, and reasonable distribution of electric energy sources is realized.
Drawings
FIG. 1 is a flowchart of a method for controlling an electrical device according to a first embodiment of the present invention;
FIG. 2 is a flowchart of a predictive model training process according to an embodiment of the invention;
fig. 3 is a flowchart of a method for controlling an electrical device according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electrical device control apparatus according to a third embodiment of the present invention;
fig. 5 is a schematic diagram of a server structure according to a fourth embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting thereof. It should be further noted that, for convenience of description, only some, but not all of the structures related to the present invention are shown in the drawings.
Example 1
Fig. 1 is a flowchart of an electrical equipment control method according to an embodiment of the present invention, where the embodiment is applicable to a case of controlling electrical equipment according to the number of people in a building, and the method may be executed by an electrical equipment control device, and specifically includes the following steps:
s110, determining the number of people entering and exiting the first area in the current period.
The first area may be an office building in a park, and the number of people entering and exiting refers to the number of people entering the office building and the number of people exiting the corresponding office building.
Specifically, the current time is counted, the number of people entering the campus, the number of people exiting the campus, the number of people entering each building in the campus, the number of people exiting the corresponding building, for example, the current time is 10 points, the number of people entering the office building a at 9 to 10 am is 70, the number of people exiting the office building a at 9 to 10 am is 30, and the number of people entering the campus entrance guard at the corresponding time is 300 and the number of people exiting the campus entrance guard is 30.
S120, inputting the number of people entering and exiting to a prediction model to obtain a first number of people range of the first area in a future period and/or a second number of people range of the second area in the future period.
Wherein the second area may be a building, such as a canteen, associated with the first area. The population ranges may be a population range corresponding to a peak stage, a population range corresponding to a plateau stage, and a population range corresponding to a valley stage.
Specifically, the number of people entering and exiting the office building in the current time period is input into a prediction model, the number of people range of the office building in the future time period and the number of people ranges of other buildings associated with the office building in the future time period are obtained, and therefore the stage of the office building in the future time period and the stage of the other buildings associated with the office building in the future time period are obtained.
Illustratively, the number of people entering and exiting the office building A on the same day in the time period from 9 to 10 am is input into the prediction model, the range of the number of people in the office building A on the same day in the time period from 10 to 11 am is predicted, and the range of the number of people in the canteen in the time period from 10 to 11 am is predicted.
In one embodiment, the number of people entering office building a from 9 a.m. to 10 a.m. on a day is 200, the number of people exiting office building a is 50, the number of people entering and exiting office building a is input into the predictive model, the range of people predicted to get office building a in the same day from 10 a.m. to 11 a.m. is 120 to 160, and the range of people predicted to get canteen in the same day from 10 a.m. to 11 a.m. is 50 to 80.
S130, controlling the electric equipment in the first area according to the first person number range and/or controlling the electric equipment in the second area according to the second person number range.
Specifically, the number range of people in the future period of the office building and the number range of people in the future period of the canteen are predicted by inputting the number of people entering and exiting the present period of the office building into the prediction model, the electric equipment of the office building is controlled according to the number range of people in the future period of the office building, and the electric equipment of the canteen is controlled according to the number range of people in the future period of the canteen.
By way of example, when the number of persons predicted to be in the range of 120 to 160 for office building a at 10 to 11 points and the number of persons predicted to be in the range of 50 to 80 for canteen in the 10 to 11 point time period, then the air conditioning output is increased in controlling the electric devices for office building a and canteen at 10 to 11 points, for example, office building a increases in the 10 to 11 point time period, and the air conditioning output of canteen is correspondingly reduced.
According to the prediction model training flow chart provided by the embodiment of the invention, but not limited to the prediction model training flow chart, as shown in fig. 2, model training 1 is firstly carried out, data are preprocessed by collecting historical data of people entering and exiting each building and people entering and exiting each building, namely data are subjected to data cleaning, dimension reduction or data feature screening, then a regression algorithm is adopted for training a model to obtain a prediction model 2, data to be detected are input and collected in the prediction model 2, a feature vector 4 is extracted to obtain a prediction result 3, namely the number of people of each building, real-time data are added into a training set, and the model is updated.
According to the technical scheme, the number of people entering and exiting the office building in the current period is determined, the number of people entering and exiting the office building is input into the prediction model, the first person number range of the office building in the future period and the second person number range of the office building in the future period are obtained, the electric equipment of the office building is controlled according to the first person number range, the electric equipment of the canteen is controlled by the second person number range, the problem of energy waste is solved, and the reasonable electric energy distribution effect is achieved.
Example two
Fig. 3 is a flowchart of a method for controlling an electrical device according to a second embodiment of the present invention, where the method is embodied on the basis of the foregoing embodiment, and specifically includes the following steps:
s210, obtaining the prediction model through machine learning based on the number of people entering and exiting in the history period of the first area and the number of people entering and exiting in the history period of the second area associated with the first area.
Specifically, according to the number of people entering and exiting from the office building A in the historical time period and the number of people entering and exiting from the dining room associated with the office building A in the historical time period, the number of people entering and exiting from the office building A in the historical time period is subjected to machine learning to obtain a prediction model, and the historical data is classified and marked according to the number of people range, wherein the marks comprise peaks, flat sections and valleys.
By way of example, the history data of the number of people entering and exiting the office building A is obtained by a machine learning method, the range of the number of people corresponding to the peak, the flat section and the valley of the office building is obtained, and the history data is marked. For example, the office building a corresponds to a range of 140 to 180 persons in the peak stage, 100 to 140 persons in the flat stage, 30 to 100 persons in the valley stage, the history data in the office building a is marked according to the number range division, the range of 140 to 180 persons in a certain period is marked as a peak, the range of 100 to 140 persons in a certain period is marked as a flat stage, and the range of 3 to 100 persons in a certain period is marked as a valley.
Preferably, a first feature vector corresponding to the number of people in and out in the current period and a second feature vector corresponding to the number of people in and out in the historical period are determined according to a set format;
the set format is a feature vector of a certain building, the feature vector comprises month, date, time period, personnel entering quantity and personnel leaving quantity, the first feature vector is the number of people entering and exiting a certain building in the current period, and the second feature vector is the number of people entering and exiting a certain building in the historical period.
A first KNN distance between the first feature vector and the second feature vector is determined based on a KNN (K-Nearest Neighbor) algorithm, and a third population range of the first region in a future period and/or a fourth population range of the second region in the future period is determined according to the first KNN distance.
Specifically, a KNN algorithm is adopted, a K value is preset according to experience, the KNN distance between the characteristic vector of the current time period and the characteristic vector of the historical time period of the office building is calculated, and the number range of people of the office building in the future time period and the number range of people of a canteen associated with the office building in the future time period are predicted according to the KNN distance.
And determining the accuracy of the prediction model according to the first person number range and the third person number range and/or the second person number range and the fourth person number range.
And optimizing the K value of the KNN algorithm according to the accuracy of the prediction model so as to update the prediction model in an iterative manner based on the optimized K value.
Specifically, the K value is preset based on experience, the accuracy of the number range of the office building and the canteen in the future period is predicted, the K value is optimized by adopting a cross-validation method, and the prediction model is updated in an iterative mode according to real-time data.
On the basis of the above technical solution, preferably, the iteratively updating the prediction model based on the optimized K value includes: determining a second KNN distance between the first feature vector and the second feature vector; based on the optimized K value, determining a fifth population range of the first area in a future period and/or a sixth population range of the second area in the future period according to the second KNN distance; iteratively updating the prediction model by taking the number of people entering and exiting the first area in the current period, the fifth number of people range of the first area in the future period and/or the sixth number of people range of the second area in the future period as new sample data.
On the basis of the above technical solution, preferably, the determining, based on the optimized K value, a fifth population range of the first area in a future period and/or a sixth population range of the second area in the future period according to the second KNN distance includes: arranging the second eigenvectors from small to large according to the second KNN distance, and selecting the optimized K second eigenvectors; and determining the fifth population range and/or the sixth population range according to the K second feature vectors. This has the advantage that the prediction model is updated in real time, so that the prediction result is more accurate.
S220, inputting the number of people entering and exiting into a prediction model, and obtaining a first number of people range of the first area in a future period and/or a second number of people range of the second area in the future period.
The number of people entering and exiting is the number of people entering and exiting in the current period of the first area.
Specifically, the number of people entering or exiting the office building in the current time period is input into a prediction model, and the number range of people of the office building and the dining hall associated with the office building in the future time period is obtained, namely, whether the number of people of the office building in the future time period belongs to a peak, or belongs to a flat section or a valley, and whether the number of people of the dining hall in the future time period belongs to a peak, or belongs to a flat section or a valley is judged.
S230, controlling the electric equipment in the first area according to the first person number range and/or controlling the electric equipment in the second area according to the second person number range.
Optionally, controlling the electrical device in the first area according to the first person number range includes:
determining target electric equipment operation parameters matched with the first people number range and the external environment of the current period according to a predetermined people number range and a mapping relation between the external environment and the electric equipment operation parameters;
and controlling the electric equipment in the first area based on the target electric equipment operation parameters.
The external environment can be natural brightness or ambient temperature; the electric device may be at least one of an electric lamp, an electric fan, and an air conditioner, and the target electric device may be an electric lamp, an electric fan, or an air conditioner.
Specifically, the number of people in and out of the office building at present is input into a prediction model through a preset mapping relation among the number of people range, the external environment and the operation parameters of the electric equipment, the number of people range of the future time period of the office building and the number of people range of the future time period of the canteen associated with the office building are obtained, and the office building and the canteen in the future time period are determined to belong to the peak, flat section and valley stage according to the number of people ranges corresponding to the office building and the canteen. And controlling the operation parameters of the electric equipment in the future period by combining the operation parameters of the electric equipment matched with the current external environment and the current environment parameters.
In one embodiment, the number of people entering and exiting the office building A from 9 am to 10 am is input into a prediction model, the number of people ranging from 11 am to 12 am is predicted to be 50-100, the range of the number of people belonging to the valley stage of a canteen associated with the office building A from 11 am to 12 am is 140-180, the number of people belonging to a peak, the predicted average heating sensation index (PMV) of the air conditioning equipment is adjusted according to the running parameters of the air conditioning equipment in the external current environment, the output of the canteen air conditioner and the fan is gradually increased, and the output of the air conditioner and the fan of the office building A is reduced.
In one embodiment, the number of people entering and exiting each building and park entrance guard from 8 to 9 am is input into a prediction model, the number range from 9 to 10 am of each building A is predicted, the number range from 9 to 10 am of a public building area is predicted, and the parameters of the lighting equipment in the public building area from 9 to 10 am are controlled by combining the lighting brightness of the current public building area in the external environment.
The specific adjustments are shown in the table below.
According to the technical scheme, a real-time data real-time optimization model is added through a historical data training model, the number of entrance guard persons entering and exiting in a park in the current period, the number of entrance guard persons entering and exiting in an office building and the number of canteen persons entering and exiting in the office building are input into a prediction model, the stage of the office building in the future period is predicted, the stage of the canteen in the future period is predicted, and the stage of a public area of a building in the future period is predicted, and the electric equipment of the office building, the canteen and the public area are controlled according to the corresponding stages respectively, so that the problem of energy waste is solved, and the reasonable electric energy distribution effect is achieved.
Example III
Fig. 4 is a schematic structural diagram of an electrical device control apparatus according to a third embodiment of the present invention, where the present invention provides an electrical device control apparatus, including:
an acquisition module 310, configured to determine the number of people entering and exiting the first area in the current period;
a prediction module 320, configured to input the number of people entering and exiting to a prediction model, to obtain a first number of people range of the first area in a future period and/or a second number of people range of the second area in the future period;
the processing module 330 is configured to control the electric device in the first area according to the first person number range and/or control the electric device in the second area according to the second person number range.
According to the technical scheme, the number of people entering and exiting the office building in the current period is determined through the acquisition module, the number of people entering and exiting the office building is input into the prediction model through the prediction module, the number range of people of the office building in the future period and the number range of people of the canteen in the future period are obtained through prediction, the processing module is used for controlling the electric equipment of the office building and the canteen according to the number range of people respectively, the problem of energy waste is solved, and the reasonable electric energy distribution effect is achieved.
Optionally, the processing module 330 is further configured to control the electrical device in the first area according to the first person number range, including:
determining target electric equipment operation parameters matched with the first person number range and the external environment of the current period according to a predetermined person number range and a mapping relation between the external environment and the electric equipment operation parameters;
the electrical devices of the first zone are controlled based on the target electrical device operating parameters.
Optionally, the processing module is further configured to provide an external environment including natural light brightness and/or ambient temperature;
the electrical device comprises at least one of: electric lamps, electric fans and air conditioners.
Optionally, the apparatus further comprises: the training module 340 is configured to obtain the prediction model through machine learning based on the number of people in and out of the first area in the history period and the number of people in and out of the second area associated with the first area in the history period.
Optionally, the training module 340 is further configured to determine a first feature vector corresponding to the number of people entering and exiting in the current period and a second feature vector corresponding to the number of people entering and exiting in the historical period according to the set format;
determining a first KNN distance between the first feature vector and the second feature vector based on a KNN algorithm, and determining a third population range of the first area in a future period and/or a fourth population range of the second area in the future period according to the first KNN distance;
determining the accuracy of the prediction model according to the first person number range and the third person number range and/or the second person number range and the fourth person number range;
and optimizing the K value of the KNN algorithm according to the accuracy of the prediction model so as to update the prediction model in an iterative manner based on the optimized K value.
Optionally, the training module 340 is further configured to iteratively update the prediction model based on the optimized K value, including:
determining a second KNN distance between the first feature vector and the second feature vector;
based on the optimized K value, determining a fifth population range of the first area in a future period and/or a sixth population range of the second area in the future period according to the second KNN distance;
and iteratively updating the prediction model by taking the number of people entering and exiting the first area in the current period, the fifth number of people range of the first area in the future period and/or the sixth number of people range of the second area in the future period as new sample data.
Optionally, the training module 340 is further configured to determine, based on the optimized K value, a fifth population range of the first area in the future period and/or a sixth population range of the second area in the future period according to the second KNN distance, including:
arranging the second eigenvectors from small to large according to the second KNN distance, and selecting K optimized second eigenvectors;
and determining a fifth population range and/or a sixth population range according to the K second feature vectors.
The product can execute the method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example IV
Fig. 5 is a schematic diagram of a server according to a fourth embodiment of the present invention, and as shown in fig. 5, the server includes a processor 410, a memory 420, an input device 430 and an output device 440; the number of processors 410 in the server may be one or more, one processor 410 being taken as an example in the diagram C; the processor 410, memory 420, input device 430, and output device 440 in the server may be connected by a bus or other means, for example in fig. 5.
The memory 71 is a computer readable storage medium, and may be used to store software programs, computer executable programs, and modules, such as program instructions/modules corresponding to the electrical device control method in the embodiment of the present invention (e.g., the acquisition module 310, the prediction module 320, and the processing module 330 in the electrical device control apparatus). The processor 410 executes various functional applications of the server and data processing, i.e., implements the above-described electrical device control methods, by running software programs, instructions, and modules stored in the memory 420.
Memory 420 may include primarily a program storage area and a data storage area, wherein the program storage area may store an operating system, at least one application program required for functionality; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 420 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, memory 420 may further include memory remotely located with respect to processor 410, which may be connected to a server via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 430 may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the server. The output 440 may include a display device such as a display screen.
Example five
A fifth embodiment of the present invention also provides a storage medium having stored thereon a computer program for executing an electric device control method when executed by a processor, the method comprising:
determining the number of people entering and exiting in the current period of the first area;
inputting the number of people entering and exiting to a prediction model to obtain a first number of people range of the first area in a future period and/or a second number of people range of the second area in the future period;
controlling the electric devices in the first area according to the first person number range and/or controlling the electric devices in the second area according to the second person number range. Of course, the computer-executable program of the storage medium provided by the embodiment of the present invention is not limited to the method operations described above, and may also perform related operations in the electrical device control method provided by any embodiment of the present invention.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., and include several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments of the present invention.
It should be noted that, in the above-mentioned embodiments of the search apparatus, each unit and module included are only divided according to the functional logic, but not limited to the above-mentioned division, as long as the corresponding functions can be implemented; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the present invention.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (8)
1. An electrical device control method, comprising:
determining the number of people entering and exiting in the current period of the first area;
inputting the number of people entering and exiting to a prediction model to obtain a first number of people range of the first area in a future period and/or a second number of people range of the second area in the future period;
controlling the electric equipment in the first area according to the first person number range and/or controlling the electric equipment in the second area according to the second person number range;
obtaining the prediction model through machine learning based on the number of people entering and exiting the first area in a history period and the number of people entering and exiting the second area associated with the first area in the history period;
determining a first feature vector corresponding to the number of people in and out of the current period and a second feature vector corresponding to the number of people in and out of the historical period according to a set format;
determining a first KNN distance between the first feature vector and the second feature vector based on a KNN algorithm, and determining a third population range of the first area in a future period and/or a fourth population range of the second area in the future period according to the first KNN distance;
determining the accuracy of the prediction model according to the first person number range and the third person number range and/or the second person number range and the fourth person number range;
optimizing the K value of the KNN algorithm according to the accuracy of the prediction model so as to update the prediction model in an iteration mode based on the optimized K value;
the set format refers to a feature vector of a certain building, and the feature vector comprises month, date, time period, personnel entering quantity and personnel leaving quantity.
2. The method of claim 1, wherein controlling the electrical devices of the first area according to the first population range comprises:
determining target electric equipment operation parameters matched with the first people number range and the external environment of the current period according to a predetermined people number range and a mapping relation between the external environment and the electric equipment operation parameters;
and controlling the electric equipment in the first area based on the target electric equipment operation parameters.
3. The method of claim 2, wherein the external environment comprises natural light brightness and/or ambient temperature;
the electrical device comprises at least one of: electric lamps, electric fans and air conditioners.
4. The method of claim 1, wherein iteratively updating the predictive model based on the optimized K-value comprises:
determining a second KNN distance between the first feature vector and the second feature vector;
based on the optimized K value, determining a fifth population range of the first area in a future period and/or a sixth population range of the second area in the future period according to the second KNN distance;
iteratively updating the prediction model by taking the number of people entering and exiting the first area in the current period, the fifth number of people range of the first area in the future period and/or the sixth number of people range of the second area in the future period as new sample data.
5. The method according to claim 4, wherein determining a fifth range of people for the future period of time for the first zone and/or a sixth range of people for the future period of time for the second zone based on the optimized K-value, according to the second KNN distance, comprises:
arranging the second eigenvectors from small to large according to the second KNN distance, and selecting the optimized K second eigenvectors;
and determining the fifth population range and/or the sixth population range according to the K second feature vectors.
6. An electrical device control apparatus, comprising:
the acquisition module is used for determining the number of people entering and exiting the first area in the current period;
the prediction module is used for inputting the number of people entering and exiting into a prediction model to obtain a first number range of people in a future period in the first area and/or a second number range of people in the future period in the second area;
the processing module is used for controlling the electric equipment in the first area according to the first person number range and/or controlling the electric equipment in the second area according to the second person number range;
the electric equipment control device also comprises a training module,
the method comprises the steps of obtaining a prediction model through machine learning based on the number of people entering and exiting in a history period of a first area and the number of people entering and exiting in a history period of a second area associated with the first area;
determining a first feature vector corresponding to the number of people entering and exiting in the current period and a second feature vector corresponding to the number of people entering and exiting in the historical period according to the set format;
determining a first KNN distance between the first feature vector and the second feature vector based on a KNN algorithm, and determining a third population range of the first area in a future period and/or a fourth population range of the second area in the future period according to the first KNN distance;
determining the accuracy of the prediction model according to the first person number range and the third person number range and/or the second person number range and the fourth person number range;
optimizing the K value of the KNN algorithm according to the accuracy of the prediction model so as to update the prediction model in an iterative manner based on the optimized K value;
the set format refers to a feature vector of a certain building, and the feature vector comprises month, date, time period, personnel entering quantity and personnel leaving quantity.
7. A server, the server comprising:
one or more processors;
storage means for storing one or more programs,
the program, when executed by the processor, causes the processor to implement the electrical device control method as claimed in any one of claims 1 to 5.
8. A storage medium having stored thereon a computer program, which when executed by a processor implements the electrical device control method according to any one of claims 1-5.
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